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Multivariable Sensors for Ubiquitous Monitoring of Gases in the Era of Internet of Things and Industrial Internet
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Multivariable Sensors for Ubiquitous Monitoring of Gases in the Era of Internet of Things and Industrial Internet
物联网和工业互联网时代用于无处不在的气体监测的多变量传感器

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GE Global Research, Niskayuna, New York 12309, United States
Cite this: Chem. Rev. 2016, 116, 19, 11877–11923
Publication Date (Web):September 7, 2016
https://doi.org/10.1021/acs.chemrev.6b00187
Copyright © 2016 American Chemical Society
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Abstract 抽象

Modern gas monitoring scenarios for medical diagnostics, environmental surveillance, industrial safety, and other applications demand new sensing capabilities. This Review provides analysis of development of new generation of gas sensors based on the multivariable response principles. Design criteria of these individual sensors involve a sensing material with multiresponse mechanisms to different gases and a multivariable transducer with independent outputs to recognize these different gas responses. These new sensors quantify individual components in mixtures, reject interferences, and offer more stable response over sensor arrays. Such performance is attractive when selectivity advantages of classic gas chromatography, ion mobility, and mass spectrometry instruments are canceled by requirements for no consumables, low power, low cost, and unobtrusive form factors for Internet of Things, Industrial Internet, and other applications. This Review is concluded with a perspective for future needs in fundamental and applied aspects of gas sensing and with the 2025 roadmap for ubiquitous gas monitoring.
用于医疗诊断、环境监测、工业安全和其他应用的现代气体监测场景需要新的传感功能。本文综述了基于多变量响应原理的新一代气体传感器的发展。这些传感器的设计标准包括具有对不同气体的多响应机制的传感材料,以及具有独立输出的多变量传感器,以识别这些不同的气体响应。这些新型传感器可量化混合物中的单个组件,抑制干扰,并提供比传感器阵列更稳定的响应。当传统气相色谱、离子淌度和质谱仪器的选择性优势被物联网、工业互联网和其他应用对无耗材、低功耗、低成本和不显眼的外形的要求所抵消时,这种性能就很有吸引力。本综述总结了气体传感基础和应用方面的未来需求,以及 2025 年无处不在的气体监测路线图。

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1 Introduction 1 引言

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Monitoring of gas-phase chemicals such as gases and vapors using portable instruments has been traditionally important in numerous applications including industrial and home safety, environmental surveillance, process monitoring, homeland security, and others. (1-3) To meet diverse detection needs, developed complementary technologies include direct spectroscopy, gas chromatography, mass spectrometry, ion mobility spectrometry, and chemical sensors.
传统上,使用便携式仪器监测气相化学品(如气体和蒸汽)在许多应用中都很重要,包括工业和家庭安全、环境监测、过程监测、国土安全等。(1-3) 为了满足多样化的检测需求,开发了直接光谱、气相色谱、质谱、离子淌度谱和化学传感器等互补技术。
Sensors for monitoring of “gases” (such as any gas-phase chemicals) where a sensing material is applied onto a suitable physical transducer (4, 5) have a suite of attractive operational advantages over other portable instruments. These advantages include tunable sensitivity, continuous real-time determination of the concentrations of specific sample constituents, small power consumption, operation without consumables, and unobtrusive form factors. Unfortunately, existing sensors also have several performance limitations such as high cross-sensitivity and poor selectivity to various gases, (6) inability to preserve detection accuracy in the presence of unknown interferences, and sensor drift, especially noticeable in outdoor applications and in detection of low analyte levels. As a result, these limitations often can revoke advantages of sensors in their intended practical applications. Thus, field uses of gas sensors are often most successful when their poor selectivity is not important, concentrations of measured gases are high enough to make the sensor drift unnoticed, or frequent recalibration is acceptable. (7, 8)
用于监测“气体”(如任何气相化学品)的传感器,将传感材料施加到合适的物理传感器(4,5)上,与其他便携式仪器相比,具有一系列有吸引力的操作优势。这些优点包括可调灵敏度、连续实时测定特定样品成分的浓度、功耗小、无需耗材即可操作以及不显眼的外形尺寸。遗憾的是,现有的传感器也存在一些性能限制,例如交叉灵敏度高,对各种气体的选择性差,(6)在存在未知干扰的情况下无法保持检测精度,以及传感器漂移,在户外应用和低分析物水平检测中尤为明显。因此,这些局限性往往会削弱传感器在其预期实际应用中的优势。因此,当气体传感器的选择性差并不重要,被测气体的浓度高到足以使传感器漂移不被注意,或者频繁重新校准是可以接受的时,气体传感器的现场使用通常是最成功的。(7, 8)
Given the tremendous interest of the chemical research community in improving selectivity of sensor systems (50 000+ publications on “selectivity of gas or vapor sensors”), this Review provides a critical analysis of the recent progress in the development of a new generation of gas sensors based on the multivariable response principles to overcome the insufficient gas-selectivity limitation of existing sensors.
鉴于化学研究界对提高传感器系统选择性的极大兴趣(50 000+篇关于“气体或蒸汽传感器的选择性”的出版物),本综述对基于多变量响应原理的新一代气体传感器开发的最新进展进行了批判性分析,以克服现有传感器的气体选择性限制不足。
The design criteria of these individual multivariable sensors involve a sensing material with multiresponse mechanisms to different gases and a multivariable transducer with several partially or fully independent outputs to recognize these different gas responses. Performance capabilities of inorganic, organic, polymeric, biological, composite, and formulated sensing materials are discussed that have been explored for multivariable gas sensing. These sensing materials, when coupled with electrical, optical, and electromechanical transducers designed for operation in a multivariable mode, provide performance capabilities previously unavailable from conventional sensor systems. These new individual sensors quantify individual components in gas mixtures, reject interferences, and have a self-correction ability against environmental instabilities. These new performance characteristics will be attractive in established and emerging sensing scenarios.
这些单独的多变量传感器的设计标准包括具有对不同气体的多响应机制的传感材料,以及具有多个部分或完全独立输出的多变量传感器,以识别这些不同的气体响应。讨论了无机、有机、聚合物、生物、复合材料和配方传感材料在多变量气体传感中的性能能力。这些传感材料与设计用于多变量模式下的电、光学和机电传感器配合使用时,可提供传统传感器系统以前无法提供的性能。这些新型传感器可量化气体混合物中的单个成分,抑制干扰,并具有针对环境不稳定性的自我校正能力。这些新的性能特征在已建立和新兴的传感场景中将具有吸引力。
This Review has four broad goals in order to stimulate research in this rapidly expanding multidisciplinary area.
本综述有四大目标,以促进这一快速扩展的多学科领域的研究。
The first goal is to deliver critical analysis of recent developments of sensors based on the multivariable response principles as provided by the number of independent outputs generated by the sensor (also known as sensor dispersion).
第一个目标是根据传感器产生的独立输出数量(也称为传感器色散)提供的多变量响应原理,对传感器的最新发展进行批判性分析。
The second goal is to summarize the design criteria for a new generation of individual sensors based on diverse transducers (e.g., electrical, photonic, electromechanical, and other transducers) developed to operate in a multivariable mode when coupled with inorganic, organic, polymeric, and composite sensing materials.
第二个目标是总结基于各种传感器(例如,电气、光子、机电和其他传感器)的新一代单个传感器的设计标准,这些传感器在与无机、有机、聚合物和复合传感材料耦合时以多变量模式运行。
The third goal is to compare performance of individual multivariable sensors and conventional sensor arrays and to illustrate that modern multivariable sensors are a disruptive sensor technology.
第三个目标是比较单个多变量传感器和传统传感器阵列的性能,并说明现代多变量传感器是一种颠覆性的传感器技术。
The fourth goal is to present a 2025 roadmap for multivariable gas sensors with the analysis of the significant driving forces in sensor developments.
第四个目标是提出 2025 年多变量气体传感器路线图,并分析传感器发展的重要驱动力。
The Review is structured in 11 sections. Diverse applications and requirements for modern gas sensors are summarized in section 2. Existing sensing concepts are analyzed in section 3, followed by the discussion of principles of multivariable sensors in section 4. A critical analysis of diverse types of multivariable sensors and associated sensing materials is provided in sections 59. This analysis is accomplished by the type of multivariable transducer that governs design choices for particular sensing materials. Discussed multivariable sensors include nonresonant and resonant impedance sensors (section 5), electromechanical resonant sensors (section 6), field-effect transistors (section 7), photonic resonant sensors (section 8), and other multivariable sensor technologies (section 9). The design criteria for diverse types of multivariable sensors are summarized in section 10. Benefits of multivariable sensors are further summarized in comparison with conventional sensor arrays in section 11. The outlook and the roadmap for multivariable gas sensors are presented in section 12.
本次审查分为11个部分。第2节总结了现代气体传感器的各种应用和要求。第3节分析了现有的传感概念,第4节讨论了多变量传感器的原理。第 5-9 节对不同类型的多变量传感器和相关传感材料进行了批判性分析。该分析由多变量传感器类型完成,该传感器控制特定传感材料的设计选择。讨论的多变量传感器包括非谐振和谐振阻抗传感器(第5节)、机电谐振传感器(第6节)、场效应晶体管(第7节)、光子谐振传感器(第8节)和其他多变量传感器技术(第9节)。第10节总结了各种类型多变量传感器的设计标准。第11节进一步总结了多变量传感器与传统传感器阵列相比的优点。第12节介绍了多变量气体传感器的前景和路线图。

2 Diversity of Applications and Requirements for Modern Gas Sensors
2 现代气体传感器的应用和要求的多样性

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At present, “classic” analytical instruments based on gas chromatography (GC), mass spectrometry (MS), ion mobility spectrometry (IMS), and direct spectroscopy are preferred for high-selectivity detection, despite their relatively large power demands, cost, and size. (9, 10) These instruments could be inconvenient, even in portable configurations with the reduced carrier gas, vacuum, and power demands, (11) but are an unavoidable alternative over existing sensors.
目前,基于气相色谱(GC)、质谱(MS)、离子淌度谱(IMS)和直接光谱的“经典”分析仪器是高选择性检测的首选,尽管它们的功率要求、成本和尺寸相对较大。(9, 10)即使在便携式配置中,这些仪器也可能不方便,因为载气、真空和功率需求较低,(11),但与现有传感器相比,这些仪器是不可避免的替代方案。
Meanwhile, there are numerous scenarios when high-selectivity advantages of “classic” analytical instruments even in their microfabricated implementations would be canceled by specific application requirements (e.g., unobtrusive form factor, no external power for operation, no vacuum or carrier gases, no radioactive sources). The most prominent of these scenarios are Internet of Things and Industrial Internet applications. The Internet of Things is the network of everyday objects with embedded sensors and connectivity to increase the value of these objects by exchanging data with the users and other devices. (12, 13) The Industrial Internet is the integration of complex machinery with networked sensors. (14)
同时,在许多情况下,“经典”分析仪器的高选择性优势,即使在微加工实施中,也会被特定的应用要求(例如,不显眼的外形尺寸、无外部电源、无真空或载气、无放射源)所抵消。其中最突出的是物联网和工业互联网应用。物联网是具有嵌入式传感器和连接的日常物品网络,通过与用户和其他设备交换数据来增加这些物品的价值。(12, 13)工业互联网是复杂机械与联网传感器的集成。(14)
Exemplary existing and emerging applications of sensors are summarized in Figure 1 and include environmental monitoring and protection, industrial safety and manufacturing process control, monitoring of agricultural emissions, public safety, medical systems, wearable health and fitness, automation of residential homes and industrial buildings, transportation, and retail. (15-23) Examples of classes and types of measured gases and volatiles of interest for these applications include environmental background (e.g., O2, CO2, and H2O), transportation/industrial/agricultural atmospheric pollutants (e.g., CO2, CO, O3, H2S, NH3, NOx, SO2, CH4, industrial fumes, and waste odors), breath biomarkers (e.g., NO, H2S, NH4, acetone, ethane, pentane, isoprene, and hydrogen peroxide), and public/homeland safety hazardous volatiles (e.g., toxic industrial chemicals, chemical warfare agents, and explosives). (18, 24-28) Diverse types of volatiles need to be monitored over their broad range of concentrations ranging from part-per-trillion to percent, often mixed with chemical interferences such as ubiquitous variable background (indoor and outdoor urban air, industrial air, human odors and breath, exhaust of transportation engines, etc.) and at expected operation temperatures (ambient indoor and outdoor temperatures, body temperature, and exhaust of transportation engines).
图 1 总结了传感器的现有和新兴应用示例,包括环境监测和保护、工业安全和制造过程控制、农业排放监测、公共安全、医疗系统、可穿戴健康和健身、住宅和工业建筑自动化、运输和零售。(15-23) 这些应用所关注的测量气体和挥发物的类别和类型示例包括环境背景(例如,O 2 、CO 2 和H 2 O)、运输/工业/农业大气污染物(例如,CO 2 、CO、O 3 、H 2 S、NH 3 、NO x 、SO 2 、CH 4 、工业烟雾和废物气味)、呼吸生物标志物(例如,NO、H 2 S、NH 4 、丙酮、乙烷、戊烷、异戊二烯和过氧化氢)以及公共/国土安全危险挥发物(例如有毒工业化学品、化学战剂和爆炸物)。(18, 24-28)需要监测各种类型的挥发物,其浓度范围从万亿分之一到百分比不等,通常与化学干扰混合在一起,例如无处不在的可变背景(室内和室外城市空气、工业空气、人类气味和呼吸、运输发动机尾气等)和预期的工作温度(室内和室外环境温度、体温、 和运输发动机的排气)。

Figure 1 图1

Figure 1. Examples of diverse application scenarios of gas sensors in the Internet of Things and Industrial Internet applications. Image prepared by GE Global Research.
图 1.气体传感器在物联网和工业互联网应用中的多样化应用场景示例。图片由GE全球研究院提供。

At present, physical sensors dominate in Internet of Things applications—microphones, accelerometers, gyroscopes, and compasses are being shipped at ∼1 billion units each annually. (29) However, the market for physical, chemical, and biological sensors is expected to grow to a cumulative trillion units by 2022. (25, 30-32)
目前,物理传感器在物联网应用中占主导地位,麦克风、加速度计、陀螺仪和指南针的年出货量约为 10 亿台。(29) 然而,到 2022 年,物理、化学和生物传感器市场预计将累计增长到万亿台。(25, 30-32)
The top five requirements for modern sensors for Internet of Things and Industrial Internet applications include (1) reliability, to provide accurate readings in diverse environmental conditions; (2) low power, to extend battery life or to eliminate its need thus simplifying detection logistics; (3) low cost, to accommodate the need for their large deployed numbers; (4) appropriate real-time communication capability; and (5) data security. (33, 34) Applying a combination of these requirements to new sensors significantly enhances their value. In particular, having new sensors at low cost but with unreliable performance significantly limits their value. In contrast, a new generation of sensors is desired to be not only low cost but also as reliable as their more expensive traditional analytical devices and at a fraction of the power needed to operate. (35-39)
物联网和工业互联网应用对现代传感器的五大要求包括:(1)可靠性,在各种环境条件下提供准确的读数;(2)低功耗,延长电池寿命或消除其需求,从而简化检测流程;(3)成本低,以适应其部署人数大的需要;(4)适当的实时通信能力;(5)数据安全。(33, 34)将这些要求组合应用于新传感器可显著提高其价值。特别是,以低成本但性能不可靠的新传感器会大大限制其价值。相比之下,新一代传感器不仅成本低廉,而且与更昂贵的传统分析设备一样可靠,并且功率仅为运行所需功率的一小部分。(35-39)
Aligned with these requirements, (33, 34) developments of new sensors have been already focusing on reliability, low power, and low cost. Diverse sensing materials have been improved by understanding the key degradation mechanisms and reducing their effects. Recent reports include improvement of stability and poison resistance of metal oxide semiconducting (MOS) sensors (40) and improvement stability of ligand-capped metal nanoparticles by new ligand-attachment chemistry. (41) Significant power reduction has been demonstrated for sensors that operate at elevated temperatures of hundreds of degrees. Established available catalytic and MOS sensors operate with 0.1–1 W power requirements. (42-44) that allow their long-term applications in stationary and short-term applications in battery-powered systems. Recent reduction of needed power was achieved by reducing the duty cycle of operation (45-47) and applying self-heating principles of sensor operation. (48-51) Reduced power, size, and cost provided opportunities for MOS sensors to be integrated into smartphones for monitoring of air pollution. (52) Reducing the operating temperature of MOS sensors was also demonstrated under controlled lab conditions. (53-57) The reduced-temperature operation of such MOS sensors changes the gas-detection mechanisms (58) and requires further significant theoretical and practical validation. Cost reduction is targeted in many recent reports that include wafer-level fabrication, (28, 50, 59) printing, (60-62) roll-to-roll, (63, 64) self-assembly, (65, 66) and other techniques.
根据这些要求,(33,34)新传感器的开发已经集中在可靠性、低功耗和低成本上。通过了解关键的降解机制并减少其影响,改进了各种传感材料。最近的报道包括通过新的配体附着化学方法提高金属氧化物半导体 (MOS) 传感器的稳定性和抗毒性 (40) 以及改善配体封端金属纳米颗粒的稳定性。(41) 对于在数百度高温下工作的传感器,已经证明可以显着降低功耗。现有的催化和MOS传感器的工作功率要求为0.1–1 W。(42-44) 允许它们在固定和电池供电系统中的短期应用中长期应用。最近,通过降低工作占空比(45-47)和应用传感器工作的自热原理,实现了所需功率的降低。(48-51) 功耗、尺寸和成本的降低为MOS传感器集成到智能手机中以监测空气污染提供了机会。(52) 在受控的实验室条件下,还演示了降低MOS传感器的工作温度。(53-57) 这种MOS传感器的低温操作改变了气体检测机制(58),需要进一步进行重要的理论和实践验证。最近的许多报告都以降低成本为目标,包括晶圆级制造、(28、50、59)印刷、(60-62)卷对卷、(63、64)自组装、(65、66)和其他技术。
This Review is focused on the development of multivariable sensors to achieve an improved reliability of their performance by their enhanced selectivity. Several topics are outside the scope of this Review. In particular, design-for-manufacturability and cost reduction of sensors are the topics of a recent review (67) addressing the demands for Internet of Things and Industrial Internet applications in volumes of billions and even trillions of sensors. (30, 32) Aspects for low-power operation and energy-harvesting approaches for sensors have been also recently reviewed. (68-71) Diverse architectures of wireless sensor networks have been recently reviewed as well. (72)
本综述的重点是多变量传感器的开发,以通过增强选择性来提高其性能的可靠性。有几个主题超出了本综述的范围。特别是,传感器的可制造性和降低成本是最近一篇评论的主题(67),该评论解决了数十亿甚至数万亿个传感器对物联网和工业互联网应用的需求。(30, 32)最近还审查了传感器的低功耗操作和能量收集方法的各个方面。(68-71) 最近还对无线传感器网络的各种架构进行了审查。(72)

3 State of the Art of Existing Sensing Concepts
3 现有传感概念的最新技术

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In gas sensing, a proper combination of a sensing material and a physical transducer is needed to achieve a desired sensor response (Figure 2A). Some of the most widely studied types of sensing materials are illustrated in Figure 2B–G (73-75) as examples for detection of reducing or oxidizing gases, volatile organic compounds, and combustible and toxic gases. A summary of diverse vapor-response mechanisms of several classes of sensing materials such as inorganic, organic, polymeric, biological, and composites is presented in Table 1. To probe a sensing material response upon gas exposures, numerous types of transducers were developed. Figure 2H–S (100-103) illustrates examples of such transducers that include electrical nonresonant and resonant transducers, electromechanical resonators, and optical transducers.
在气体传感中,需要传感材料和物理传感器的适当组合才能实现所需的传感器响应(图2A)。图2B-G(73-75)展示了一些研究最广泛的传感材料类型,作为检测还原性或氧化性气体、挥发性有机化合物以及可燃和有毒气体的示例。表1总结了无机、有机、聚合物、生物和复合材料等几类传感材料的不同蒸汽响应机制。为了探测气体暴露时的传感材料响应,开发了多种类型的换能器。图2H-S(100-103)说明了此类换能器的示例,包括电非谐振和谐振换能器、机电谐振器和光学换能器。

Figure 2 图2

Figure 2. Anatomy of conventional gas sensors. (A) Required proper combination of a sensing material and a physical transducer to achieve a desired sensor response. (B–G) Examples of sensing materials: (B) semiconducting metal oxide, (C) metal–organic framework, (73) (D) single-walled carbon nanotubes, (E) graphene, (F) gold nanoparticles functionalized with organic ligand, (74) and (G) atomic-layered molybdenum disulfide. (75) (H–S) Examples of physical transducers: electrical nonresonant transducers (H) resistor with interdigital electrodes, (I) capacitor, and (J) field effect transistor; (100) electrical resonant transducers (K) resistor–inductor–capacitor (RLC) resonant transducer with an integrated circuit memory chip as a passive radio frequency identification tag and sensor operating at ∼13 MHz, (L) coil-based transducer operating at ∼100 MHz, (M) dual-split-ring-based transducer operating at ∼5 GHz; electromechanical resonant transducers (N) thickness shear mode device, (O) acoustic wave device, (101) (P) microcantilever; (102) optical transducers (Q) reflected light opto-pair, (103) (R) distributed fiber-optic transducer, (S) localized plasmon resonance transducer. (102) (C) Reprinted with permission from ref 73. Copyright 2014 Nature Publishing Group. (F) Reprinted with permission from ref 74. Copyright 2013 Wiley-VCH Verlag GmbH & Co KGaA. (G) Reprinted with permission from ref 75. Copyright 2015 American Chemical Society. (J) Reprinted with permission from ref 100. Copyright 2012 American Chemical Society. (L) Logo pictured courtesy of General Electric.
图2.传统气体传感器的解剖结构。(A) 需要传感材料和物理换能器的适当组合,以实现所需的传感器响应。(B-G)传感材料的例子:(B)半导体金属氧化物,(C)金属有机框架,(73)(D)单壁碳纳米管,(E)石墨烯,(F)用有机配体官能化的金纳米颗粒,(74)和(G)原子层状二硫化钼。(75) (H-S) 物理换能器的例子:电非谐振换能器 (H) 带指间电极的电阻器、(I) 电容器和 (J) 场效应晶体管;(100) 电谐振换能器 (K) 电阻-电感-电容 (RLC) 谐振换能器,带有集成电路存储芯片作为无源射频识别标签和传感器,工作频率为 ∼13 MHz,(L) 基于线圈的换能器,工作频率为 ∼100 MHz,(M) 基于双分裂环的传感器,工作频率为 ∼5 GHz;机电谐振换能器(N)厚度剪切模式装置,(O)声波装置,(101)(P)微悬臂;(102)光换能器(Q)反射光光对,(103)(R)分布式光纤换能器,(S)局部等离子体共振换能器。(102) (C) 经参考文献 73 许可转载。版权所有 2014 Nature Publishing Group.(F) 经参考文献 74 许可转载。版权所有 2013 Wiley-VCH Verlag GmbH & Co KGaA。(G) 经参考文献 75 许可转载。版权所有 2015 美国化学学会。(J) 经参考文献 100 许可转载。版权所有 2012 美国化学学会。(L)徽标图片由通用电气提供。

Table 1. Gas-Response Mechanisms of Representative Sensing Materials
表 1.代表性传感材料的气体响应机理
sensing material 传感材料gas-response mechanisms 气体反应机制ref
dielectric polymers 介电聚合物dispersion, polarizability, dipolarity, basicity, acidity, and hydrogen-bonding interactions
色散、极化率、偶极性、碱度、酸度和氢键相互作用
76
conjugated polymers 共轭聚合物changes in density and charge carrier mobility, swelling, and conformation transitions of chains
链密度和电荷载流子迁移率、膨胀和构象转变的变化
77, 78
metalloporphyrins, phthalocyanines, related macrocycles
金属卟啉、酞菁、相关大环
hydrogen bonding, polarization, polarity, metal center coordination interactions, π-stacking, and molecular arrangements
氢键、极化、极性、金属中心配位相互作用、π堆积和分子排列
79, 80
cavitands 卡维坦德斯intracavity host–guest complexation with hydrogen bonding, CH-π, and dipole–dipole as the main specific interactions
以氢键、CH-π和偶极-偶极子为主要特异性相互作用的腔内主客络合
81
zeolites 沸石molecular discrimination by size, shape, molecular kinetic diameter
通过大小、形状、分子动力学直径进行分子区分
82
metal–organic frameworks 金属-有机框架van der Waals interactions of the framework surface, coordination to the central metal ion, hydrogen bonding of the framework surface, size exclusion
框架表面的范德华相互作用,与中心金属离子的配位,框架表面的氢键,尺寸排阻
83, 84
metal oxides 金属氧化物physisorption, chemisorption, surface defects, and bulk defects depending on operation temperatures (ambient to ca. 1000 °C) and utilizing different metal oxides and dopants
物理吸附、化学吸附、表面缺陷和本体缺陷取决于工作温度(环境温度至约 1000 °C)并利用不同的金属氧化物和掺杂剂
85−87
monolayer-protected metal nanoparticles
单层保护金属纳米颗粒
electron tunneling between metal cores, charge hopping along the atoms of ligand shell
金属核之间的电子隧穿,电荷沿着配体壳层的原子跳跃
88, 89
carbon nanotubes 碳纳米管charge transfer from analytes and polarization of surface adsorbates, gas-induced Schottky barrier modulation
分析物的电荷转移和表面吸附的极化、气体诱导的肖特基势垒调制
90, 91
graphene 石墨烯charge transfer induced by adsorption/desorption of gaseous molecules acting as electron donors or acceptors, leading to changes in conductance
由充当电子供体或受体的气态分子的吸附/解吸引起的电荷转移,导致电导变化
92
molybdenum disulfide 二硫化钼charge-transfer mechanism involving transient doping of sensing layer
涉及传感层瞬态掺杂的电荷转移机理
93, 94
plasmonic nanoparticles with soft organic layers
具有软有机层的等离子体纳米颗粒
changes in interparticle spacing, refractive index of the organic layer, and reflectivity of the metal nanoparticle network film
金属纳米颗粒网络膜的颗粒间距、有机层折射率和反射率的变化
74
plasmonic nanoparticle/metal oxide nanocomposite films
等离子体纳米颗粒/金属氧化物纳米复合膜
charge exchange with the nanoparticles, change in the dielectric constant surrounding the nanoparticles, dependent on the type of a metal oxide and its morphology for operation at 300–800 °C
与纳米颗粒的电荷交换,纳米颗粒周围介电常数的变化,取决于金属氧化物的类型及其在300-800°C下操作的形态
95
colloidal crystals from core/shell nanospheres
来自核/壳纳米球的胶体晶体
vapor-induced changes of optical lattice constant of colloidal crystal with cores and shells of nanospheres responding to diverse vapors
胶体晶体与纳米球核壳响应不同气气的光晶格常数的气相变化
96
iridescent scales of tropical Morpho butterflies
热带 Morpho 蝴蝶的彩虹色鳞片
lamella/ridge nanostructures with gradient of surface chemistry induce spatial control of sorption and adsorption of analytes and probed with light interference and diffraction
具有表面化学梯度的薄片/脊纳米结构诱导了分析物吸附和吸附的空间控制,并通过光干涉和衍射探测
97, 98
bioinspired photonic interference-stack nanostructures
仿生光子干涉堆栈纳米结构
chemically functionalized nanostructures with weak optical loss induce spatial control of sorption and adsorption of analytes probed with light interference and diffraction
具有弱光损耗的化学功能化纳米结构诱导了光干涉和衍射探测的分析物的吸附和吸附的空间控制
99
Recent important advances in gas sensors include outstanding sensitivity in vacuum and clean carrier gas (-90, 92, 104-108) and rapid response times. (105, 109-114) These improvements in sensitivity and response times of sensing materials were demonstrated with the reduction of size of sensing features down to zero-dimensional nanoparticles, one-dimensional nanowires, two-dimensional sheets, and three-dimensional nanostructures. Such size reduction allowed not only the higher surface area for the analyte to interact with the material but also implementation of new physical phenomena on the nanometer scale. (48, 109, 115) The major differences in gas sensitivity between diverse materials are their mechanisms of interaction with different classes of gases such as reducing or oxidizing gases, volatile organic compounds, and combustible and toxic gases at different temperatures ranging from ambient to ∼1000 °C as presented in Table 1.
气体传感器的最新重要进展包括对真空和清洁载气(-90、92、104-108)的出色灵敏度和快速响应时间。(105, 109-114)传感材料的灵敏度和响应时间的这些改进通过将传感特征的尺寸减小到零维纳米颗粒、一维纳米线、二维片和三维纳米结构得到了证明。这种尺寸的减小不仅使分析物与材料相互作用的表面积更大,而且还可以在纳米尺度上实现新的物理现象。(48, 109, 115)不同材料之间气体敏感性的主要区别在于它们在从环境温度到∼1000 °C的不同温度范围内与不同类别的气体(如还原性或氧化性气体、挥发性有机化合物以及可燃和有毒气体)的相互作用机制,如表1所示。
Unfortunately, existing sensors have poor gas selectivity and insufficient stability. These features affect sensor reliability, which is one of the critical aspects for the broad acceptance of sensors. (33, 34) Often, new sensing materials respond not only to an intended analyte vapor but also to other vapors, for example, as shown in Figure 3A–C (116-118) exhibiting significant vapor cross-sensitivity. The origin of this limitation is in the conflicting requirements for sensor selectivity versus reversibility. (4) The full and fast reversibility of sensor response is achieved via weak interactions between the analyte and the sensing film, whereas the high selectivity of sensor response is achieved via strong interactions between the analyte vapor and the sensing film.
不幸的是,现有的传感器气体选择性差,稳定性不足。这些特性会影响传感器的可靠性,而传感器的可靠性是传感器被广泛接受的关键方面之一。(33, 34)通常,新的传感材料不仅对预期的分析物蒸气有反应,而且对其他蒸气也有反应,例如,如图3A-C(116-118)所示,表现出显著的蒸气交叉敏感性。这种限制的根源在于对传感器选择性与可逆性的相互冲突的要求。(4)传感器响应的完全快速可逆性是通过分析物与传感膜之间的弱相互作用实现的,而传感器响应的高选择性是通过分析物蒸气与传感膜之间的强相互作用来实现的。

Figure 3 图3

Figure 3. Typical gas cross-sensitivity patterns of new types of reversible sensing materials. (A) Chemiresistor with a p-type semiconductor NiO—preferential response to formaldehyde over other volatiles. (116) (B) Polymeric sensing film formulated with two types of fluorescent phosphonate cavitands—fluorescence response to vapors of different alcohols. (117) (C) Thickness shear mode resonators with two types of immobilized DNA—response to model analytes. (118) (A) Reprinted with permission from ref 116. Copyright 2015 Elsevier. (B) Reprinted with permission from ref 117. Copyright 2011 Wiley-VCH Verlag GmbH & Co KGaA. (C) Reprinted with permission from ref 118. Copyright 2015 Elsevier.
图3.新型可逆传感材料的典型气体交叉敏感度模式。(A) 具有 p 型半导体 NiO 的化学电阻器——对甲醛的响应优于其他挥发物。(116) (B) 由两种类型的荧光膦酸盐空体配制的聚合物传感膜——对不同醇的蒸气的荧光响应。(117) (C) 具有两种固定化DNA的厚度剪切模式谐振器——对模型分析物的响应。(118) (A) 经参考文献 116 许可转载。版权所有 2015 Elsevier。(B) 经参考文献 117 许可转载。版权所有 2011 Wiley-VCH Verlag GmbH & Co KGaA。(C) 经参考文献 118 许可转载。版权所有 2015 Elsevier。

Insufficient vapor selectivity is also known for existing sensors. For qualitative comparison with new sensing materials, typical gas cross-sensitivity patterns of the most widely implemented types of commercially available materials-based sensors such as electrochemical, MOS, and catalytic combustion sensors (40, 119, 120) are visualized in Figure 4A–C. (40, 119, 120) This information is typically available in the sensor product specification and is utilized to estimate the expected levels of false alarms in anticipated applications. Such comparison illustrates that neither new nor established single-output sensors have a desired minimal gas cross-sensitivity.
对于现有的传感器来说,蒸汽选择性不足也是众所周知的。为了与新型传感材料进行定性比较,图4A–C中显示了最广泛实施的基于材料的商用传感器类型(如电化学、MOS和催化燃烧传感器(40、119、120)的典型气体交叉灵敏度模式。 (40, 119, 120) 此信息通常在传感器产品规格中提供,用于估计预期应用中预期的误报水平。这种比较表明,无论是新的还是现有的单输出传感器,都没有所需的最小气体交叉灵敏度。

Figure 4 图4

Figure 4. Typical gas cross-sensitivity patterns of established types of reversible sensing materials. (A) Electrochemical sensor calibrated for ethylene oxide. (119) (B) Metal oxide semiconductor sensor calibrated for methane. (40) (C) Catalytic combustion sensor calibrated for methane. (120)
图4.已建立类型的可逆传感材料的典型气体交叉敏感度模式。(A) 经过环氧乙烷校准的电化学传感器。(119) (B) 针对甲烷进行校准的金属氧化物半导体传感器。(40) (C) 甲烷校准的催化燃烧传感器。(120)

This problem of poor selectivity can be reduced by implementing sensing materials that utilize strong irreversible or slow-recovery chemical reactions. Recent examples of such developments are illustrated in Figure 5A–C. (75, 121, 122) This approach allows operation of a single sensing element for several dosimetric measurements followed by element replacement or element resetting using an external UV, thermal, or other type of energy.
通过采用利用强不可逆或慢恢复化学反应的传感材料,可以减少选择性差的问题。(75,121,122)这种方法允许操作单个传感元件进行多次剂量测量,然后使用外部紫外线、热能或其他类型的能量更换元件或重置元件。

Figure 5 图5

Figure 5. Typical gas cross-sensitivity patterns of gas dosimeter materials based on irreversible or slow-recovery chemical reactions. (A) Response of a reduced graphene oxide-decorated cotton yarn to NO2 (analyte) and other volatiles. (121) (B) Response of an atomic-layered MoS2 to NO2 (analyte) and other volatiles. (75) (C) Response of colorimetric formulated composition to formaldehyde (analyte) and other volatiles. (122) Insets in (A–C) are output signals of dosimeters (1) before, (2) during, and (3) after exposures to analytes. (A) Reprinted with permission from ref 121. Copyright 2015 Nature Publishing Group. (B) Reprinted with permission from ref 75. Copyright 2015 American Chemical Society. (C) Reprinted with permission from ref 122. Copyright 2015 Institute of Electrical and Electronics Engineers.
图5.基于不可逆或慢恢复化学反应的气体剂量计材料的典型气体交叉敏感模式。(A)还原氧化石墨烯装饰棉纱对NO 2 (分析物)和其他挥发物的响应。(121) (B) 原子层状MoS 2 对NO 2 (分析物)和其他挥发物的响应。(75) (C) 比色配制组合物对甲醛(分析物)和其他挥发物的响应。(122) (A-C)中的插图是剂量计(1)暴露于分析物之前、(2)期间和(3)暴露于分析物之后的输出信号。(A) 经参考文献 121 许可转载。版权所有 2015 Nature Publishing Group.(B) 经参考文献 75 许可转载。版权所有 2015 美国化学学会。(C) 经参考文献 122 许可转载。版权所有 2015 电气和电子工程师协会。

An evolution of gas-sensing concepts that had led to multivariable sensors is depicted in Figure 6. “Classic” gas sensors based on a single output (i.e., zero-order sensors (123)) are schematically depicted in Figure 6A. Such sensors have been recently extensively reviewed. (124-130) To improve selectivity, individual sensors are assembled into arrays where the output of the array is processed with multivariate analysis tools (Figure 6B). Examples of the most widely used tools for multivariate analysis of sensing data are summarized in Table 2.
图 6 描绘了导致多变量传感器的气体传感概念的演变。基于单输出的“经典”气体传感器(即零阶传感器(123))示意性地如图6A所示。这种传感器最近得到了广泛的审查。(124-130)为了提高选择性,将单个传感器组装成阵列,其中阵列的输出使用多变量分析工具进行处理(图6B)。表2总结了最广泛使用的传感数据多变量分析工具示例。

Figure 6 图6

Figure 6. Evolution of gas-sensing concepts. (A) “Classic” single-output gas sensor with known insufficient selectivity. (B) Assembly of individual single-output sensors into an array and chemometric processing of the array output. (C) Individual gas sensor based on multivariable response principles.
图6.气体传感概念的演变。(A) “经典”单输出气体传感器,已知选择性不足。(B) 将单个单输出传感器组装到阵列中,并对阵列输出进行化学计量处理。(C) 基于多变量响应原理的单个气体传感器。

Table 2. Examples of Typical Chemometrics Tools Applied for Data Analysis of Sensor Arrays and Multivariable Sensors
表 2.用于传感器阵列和多变量传感器数据分析的典型化学计量学工具示例
algorithm 算法description 描述
principal component analysis (PCA)
主成分分析(PCA)
Unsupervised algorithm that reduces a multidimensional data set for its easier interpretation by calculating orthogonal principal components (PCs) oriented in the direction of the maximum variance within the data set. The first PC contains the highest degree of variance, and other PCs follow in the order of decreasing variance. Thus, PCA concentrates the most significant characteristics (variance) of the data into a lower dimensional space.
无监督算法,通过计算数据集内最大方差方向的正交主成分 (PC),减少多维数据集以便于解释。第一台 PC 包含最高程度的方差,其他 PC 按方差递减的顺序排列。因此,PCA 将数据的最显着特征(方差)集中到较低维的空间中。
discriminant analysis (DA)
判别分析 (DA)
Models the difference between the classes of data and maximizes the ratio of between-class variance to the within-class variance. Requires an input of distinction between independent variables and dependent variables.
对数据类别之间的差异进行建模,并最大化类间方差与类内方差的比率。需要输入区分自变量和因变量的输入。
artificial neural network (ANN)
人工神经网络 (ANN)
A system of a large number of simple highly interconnected processing elements (“neurons”) that exchange messages between each other to process information by their dynamic state response to external inputs. The connections have numeric weights that can be tuned based on experience, making neural nets adaptive to inputs and capable of learning.
一个由大量简单、高度互连的处理元件(“神经元”)组成的系统,它们相互交换消息,通过它们对外部输入的动态状态响应来处理信息。这些连接具有可以根据经验进行调整的数字权重,使神经网络能够适应输入并能够学习。
hierarchical cluster analysis (HCA)
分层聚类分析 (HCA)
Classifies samples using a dendrogram representation. Often, a Ward’s method is applied that shows the Euclidean distance between the samples. The Ward’s method is a minimum variance method, which takes into consideration the minimum amount of variance between the samples and gases (analyte and interferents) to define a cluster.
使用树状图表示对样本进行分类。通常,应用沃德方法显示样本之间的欧几里得距离。沃德法是一种最小方差法,它考虑样品和气体(分析物和干扰物)之间的最小方差来定义集群。
support vector machines (SVM)
支持向量机 (SVM)
Supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification, regression analysis, and outliers detection by finding the decision hyperplane that maximizes the margin between the classes. The vectors (cases) that define the decision hyperplane are the support vectors.
具有相关学习算法的监督学习模型,用于分析数据和识别模式,用于分类、回归分析和异常值检测,方法是找到最大化类之间边际的决策超平面。定义决策超平面的向量(事例)是支持向量。
independent component analysis (ICA)
独立成分分析 (ICA)
Separates a multivariate signal into additive subcomponents by assuming that the subcomponents are statistically mutually independent non-Gaussian signals. A powerful technique for revealing hidden factors that underlie sets of random variables, measurements, or signals.
通过假设多变量信号是统计上相互独立的非高斯信号,将多变量信号分割成加性子分量。一种强大的技术,用于揭示随机变量、测量值或信号集背后的隐藏因素。
partial least-squares (PLS) regression
偏最小二乘 (PLS) 回归
Determines correlations between the independent variables and the sensor response by finding the direction in the multidimensional space of the sensor response that explains the maximum variance for the independent variables. The key outputs of the developed multivariate models are residual errors of calibration and cross-validation.
通过在传感器响应的多维空间中查找解释自变量最大方差的方向,确定自变量与传感器响应之间的相关性。所开发的多变量模型的主要输出是校准和交叉验证的残余误差。
principal component regression (PCR)
主成分回归 (PCR)
Regression analysis technique based on PCA by regressing the dependent variables on a set of independent variables based on a standard linear regression model, but uses PCA for estimating the unknown regression coefficients in the model.
基于PCA的回归分析技术,通过基于标准线性回归模型的一组自变量上的因变量回归,但使用PCA来估计模型中的未知回归系数。
Since the 1980s, combining sensors into arrays (131-134) is a common compromise to mitigate poor selectivity of individual conventional sensors as shown in excellent studies with sensor arrays containing up to 65 536 elements. (131, 135-140) The field of sensor arrays (also known as electronic noses) has matured to an understanding of their applicability and limitations outside controlled laboratory conditions (e.g., an uncorrelated drift of each sensor in an array, inability to provide accurate quantitation of multiple vapors in their mixtures, and inability to operate in the presence of high levels of known and unknown interferences). The state of the art in sensor arrays and their prospects has been critically analyzed in “classic” and recent reviews. (141-166) Sensor arrays can be also complimented with hyphenated methodologies where sensing materials are interrogated with different transducers to probe diverse properties of the material. Several “classic” and recent examples include sensor arrays based on several transduction principles, (167-170) probing organic semiconducting materials with thickness-shear mode and field-effect transducers, (171) probing carbon nanotubes with acoustic and optical readouts, (172) probing nanopore-sorbed volatiles with opto-calorimetric readout, (173) and probing adsorbed volatiles with piezotransistive and photoacoustic readouts. (174)
自 1980 年代以来,将传感器组合成阵列 (131-134) 是一种常见的折衷方案,以减轻单个传统传感器的选择性差,正如对包含多达 65 536 个元件的传感器阵列的出色研究表明的那样。(131, 135-140)传感器阵列(也称为电子鼻)领域已经成熟,可以理解它们在受控实验室条件之外的适用性和局限性(例如,阵列中每个传感器的不相关漂移,无法准确定量其混合物中的多种蒸汽,以及无法在存在高水平的已知和未知干扰的情况下运行)。传感器阵列的最新技术及其前景已在“经典”和最近的评论中进行了批判性分析。(141-166) 传感器阵列也可以与连字符方法相辅相成,其中传感材料使用不同的换能器进行询问,以探测材料的不同特性。一些“经典”和最近的例子包括基于几种转导原理的传感器阵列,(167-170)用厚剪切模式和场效应换能器探测有机半导体材料,(171)用声学和光学读数探测碳纳米管,(172)用光量热读数探测纳米孔吸附挥发物,(173)用压电透体和光声读数探测吸附挥发物。(174)
On the basis of the developments of single-output sensors, their arrays, and hyphenated readouts, a new generation of gas sensors is emerging that utilizes multivariable response principles (Figure 6C). Multivariable sensors and microanalytical systems producing multivariable response were reviewed in “classic” (175, 176) and recent reviews. (177-179) Critical analysis of the recent developments in multivariable sensors based on diverse transduction principles and their critical comparison is the focus of the next sections of this Review.
在单输出传感器、其阵列和连字符读数的发展基础上,利用多变量响应原理的新一代气体传感器正在出现(图6C)。在“经典”(175,176)和最近的综述中回顾了产生多变量响应的多变量传感器和微分析系统。(177-179) 对基于不同转导原理的多变量传感器的最新发展及其批判性比较的批判性分析是本综述下一节的重点。

4 General Principles of Multivariable Sensors
4 多变量传感器的一般原理

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To overcome the insufficient selectivity limitation of existing sensors and sensor arrays and to improve their reliability, a new generation of gas sensors is emerging based on multivariable response principles. Multivariable sensors (also known as intelligent, (180) multiparameter, (181) high-order, (177) or multidimensional signatures (182) sensors, virtual multisensor systems, (183) or virtual sensor arrays (184, 185)) provide several partially or fully independent responses from a sensor. (63, 99, 179, 186) General design criteria for multivariable sensors involve roles of (1) a sensing material with diverse responses to different gases, (2) a multivariable transducer to provide independent outputs and to recognize these different gas responses, and (3) data analytics to provide multianalyte quantitation, rejection of interferences, and drift minimization. The common term “design” reflects a quantitative outcome of creating new materials or devices for particular applications as predicted by existing knowledge. Design of transducers and data analytics tools are two such examples. The complex nature of interactions between the composition, preparation, and end-use conditions of sensing materials often makes difficult their “design”. (187) Instead, “material tuning” is often performed to meet specific needs. (5, 187) Thus, at present, “design” of sensing materials should be viewed as an ultimate future goal rather than the currently available fully developed tool. While new computational tools are decoding the intricate interplay of mechanisms ranging from atomic to macroscopic scales with an increasing accuracy, these tools are not replacing yet the detailed experimental tuning of materials. (188-191) Selectivity and stability of sensing materials are important examples of remaining computational challenges that require experimental validations.
为了克服现有传感器和传感器阵列选择性不足的局限性,提高其可靠性,基于多变量响应原理的新一代气体传感器应运而生。多变量传感器(也称为智能、(180)多参数、(181)高阶、(177)或多维特征(182)传感器、虚拟多传感器系统(183)或虚拟传感器阵列(184、185))提供来自传感器的几个部分或完全独立的响应。(63, 99, 179, 186)多变量传感器的一般设计标准涉及以下角色:(1)对不同气体具有不同响应的传感材料,(2)提供独立输出并识别这些不同气体响应的多变量传感器,以及(3)数据分析,以提供多分析物定量、抑制干扰和漂移最小化。通用术语“设计”反映了现有知识所预测的为特定应用创造新材料或设备的定量结果。传感器和数据分析工具的设计就是这样两个例子。传感材料的组成、制备和最终使用条件之间相互作用的复杂性往往使其“设计”变得困难。(187)相反,通常执行“材料调整”以满足特定需求。(5, 187)因此,目前,传感材料的“设计”应被视为未来的最终目标,而不是目前可用的完全开发的工具。虽然新的计算工具正在以越来越高的精度解码从原子到宏观尺度的机制之间的复杂相互作用,但这些工具还没有取代材料的详细实验调整。 (188-191) 传感材料的选择性和稳定性是需要实验验证的剩余计算挑战的重要例子。
For new multivariable sensors, a subset of sensing materials, already applied to single-output sensors (Table 1), is also of interest because some of these materials have diverse vapor-response mechanisms that can be probed with a multivariable transducer. Further, tools for multivariate analysis of sensing data from individual multivariable sensors can be adapted from those used for sensor arrays (Table 2). Multivariable sensors provide performance capabilities previously unavailable from not only conventional single-output sensors but also from sensor arrays. These new individual sensors quantify individual components in gas mixtures, reject interferences, and have self-correction ability against environmental instabilities. (74, 99, 180, 192-194)
对于新的多变量传感器,已经应用于单输出传感器的传感材料子集(表1)也值得关注,因为其中一些材料具有不同的蒸汽响应机制,可以使用多变量传感器进行探测。此外,用于对来自单个多变量传感器的传感数据进行多变量分析的工具可以与用于传感器阵列的工具相适应(表2)。多变量传感器提供的性能是传统单输出传感器和传感器阵列以前无法提供的。这些新的单个传感器可量化气体混合物中的单个成分,抑制干扰,并具有针对环境不稳定的自我校正能力。(74, 99, 180, 192-194)
These capabilities are provided by the number of independent outputs (also known as dispersion, dimensionality, or order) generated by the sensor. A single-output sensor affords a single correlation between a gas concentration and a sensor output and provides a one-dimensional (1-D) response or 1-D dispersion. In such a sensor, gases with known cross-sensitivity produce different response magnitudes but without their discrimination (Figure 7A). Such sensors are valuable to measure known contaminants in the absence of interferences. Sensors with more than one independent output are critical for emerging applications. As the simplest case, Figure 7B depicts the response of a multivariable sensor with two outputs where different gases have their own unique response directions. These independent sensor outputs can be either raw sensor responses or can be weighted contributions of several partially independent outputs. Even this simplest 2-D dispersion allows discrimination between closely related analytes or correction for some environmental interferences. The value of the multivariable sensor increases with its ability to discriminate and quantify gases in the presence of known and unknown interferences and to correct for multiple environmental effects. This increased value is provided by the increased number of independent outputs leading to multidimensional dispersion (Figure 7C). Such value of individual multivariable sensors becomes higher than that of a sensor array not only because of similar (180, 195, 196) or better (99) short-term performance over sensor arrays but also because of an improved capability for long-term stability.
这些功能由传感器产生的独立输出数量(也称为色散、维数或阶数)提供。单输出传感器提供气体浓度和传感器输出之间的单一相关性,并提供一维 (1-D) 响应或一维色散。在这种传感器中,具有已知交叉灵敏度的气体产生不同的响应幅度,但没有区分(图7A)。这种传感器对于在没有干扰的情况下测量已知污染物很有价值。具有多个独立输出的传感器对于新兴应用至关重要。作为最简单的情况,图7B描述了具有两个输出的多变量传感器的响应,其中不同的气体具有自己独特的响应方向。这些独立的传感器输出可以是原始传感器响应,也可以是多个部分独立输出的加权贡献。即使是这种最简单的二维分散,也可以区分密切相关的分析物或校正某些环境干扰。多变量传感器的价值随着其在存在已知和未知干扰的情况下区分和量化气体以及校正多种环境影响的能力而增加。这种增加的值是由导致多维色散的独立输出数量的增加提供的(图7C)。单个多变量传感器的这种值变得高于传感器阵列的值,这不仅是因为与传感器阵列相似(180,195,196)或更好(99)的短期性能,而且还因为长期稳定性的能力有所提高。

Figure 7 图7

Figure 7. Importance of sensor response dispersion for reliable performance. (A) 1-D dispersion of a single-output sensor, sensor affords a single correlation between a gas concentration and sensor output. Gases with known cross-sensitivity have different response magnitudes without their discrimination. (B) 2-D dispersion of a multivariable sensor with two independent outputs; different gases have their own unique response directions, affording the possibility for correction for some environmental interferences. (C) 3-D dispersion of a multivariable sensor to monitor multiple gases in the presence of known and unknown interferences and closely related gases of different classes and to correct for multiple environmental effects.
图7.传感器响应色散对可靠性能的重要性。(A)单输出传感器的一维色散,传感器提供气体浓度和传感器输出之间的单一相关性。具有已知交叉敏感性的气体具有不同的响应幅度,而无需区分。(B) 具有两个独立输出的多变量传感器的二维色散;不同的气体有自己独特的响应方向,为校正某些环境干扰提供了可能性。(C) 多变量传感器的三维色散,用于监测存在已知和未知干扰的多种气体以及不同类别的密切相关气体,并校正多种环境影响。

In this Review, dispersion of the reported multivariable sensors was determined based on results of multivariate analysis of their individual outputs. The criterion for reporting dispersion of a multivariable sensor was its highest reported dispersion at which the sensor demonstrated the consistent diverse vapor-dependent responses. The most widely implemented multivariate analysis technique in multivariable sensors is PCA followed by DA, ANN, and other techniques summarized in Table 2. To illustrate the PCA approach, performance of a multivariable sensor has been simulated and processed using PCA as shown in Figure 8. This simulated sensor had a “spectrum” across 150 variables (a.k.a. dimensions such as frequencies or wavelengths) with an added random noise (Figure 8A). Three environmental effects (e.g., analyte vapor, humidity, and temperature) were assumed to change the peak height, peak position, and peak width of the spectrum (Figure 8A). Three levels of each of the effects were simulated (shown in Figure 8B), with three replicates per each level (not shown for simplicity). From the raw responses with a total of 30 samples of spectra (initial position, three effects at three levels, n = 3 for each), a PCA classification model was built. Several outputs from a PCA model are important for understanding and visualization of spectral changes in the sensor. One output is a scores plot that visualizes relations between measured spectra by presenting principal components (PCs) against each other or as a function of experimental time. A PC1 vs PC2 plot in Figure 8C illustrates that the contributions from the first two PCs (52.14% and 42.54%, respectively) did not cover 100% of the variance produced by the sensor. Thus, Figure 8D depicts a 3-D plot of PC1 vs PC2 vs PC3 illustrating that PC3 also correlated well with the three types of environmental effects. Another output is a signal-to-noise (S/N) plot of each PC in the model that determines which PCs have low S/N. Figure 8E depicts that the built PCA model had three PCs that had S/N ≫ 3. The third output is the loadings plot that reveals contributions of each variable to different PCs. Figure 8F depicts that loadings of the first three PCs had different spectral shapes.
在本综述中,报告的多变量传感器的离散度是根据其各自输出的多变量分析结果确定的。报告多变量传感器色散的标准是其报告的最高色散度,在该色散度下,传感器表现出一致的多样化蒸汽依赖性响应。在多变量传感器中应用最广泛的多变量分析技术是PCA,其次是DA、ANN和其他技术,如表2所示。为了说明PCA方法,使用PCA对多变量传感器的性能进行了仿真和处理,如图8所示。该模拟传感器具有跨越 150 个变量(也称为频率或波长等尺寸)的“频谱”,并增加了随机噪声(图 8A)。假设三种环境效应(例如,分析物蒸气、湿度和温度)会改变光谱的峰高、峰位置和峰宽(图8A)。模拟了每个效应的三个水平(如图8B所示),每个水平有三个重复(为简单起见,未显示)。从总共 30 个光谱样本的原始响应(初始位置,三个水平的三个效应,每个效应 n = 3)中,构建了 PCA 分类模型。PCA模型的几个输出对于理解和可视化传感器中的光谱变化非常重要。一个输出是分数图,它通过呈现主成分 (PC) 彼此之间或作为实验时间的函数来可视化测量光谱之间的关系。图8C中的PC 1 与PC 2 关系图表明,前两台PC的贡献(分别为52.14%和42.54%)并未覆盖传感器产生的100%方差。 因此,图8D描绘了 PC 1 vs PC 2 vs PC 3 的 3D 图,说明 PC 3 也与三种类型的环境影响密切相关。另一个输出是模型中每台 PC 的信噪比 (S/N) 图,用于确定哪些 PC 的信噪比较低。图 8E 描述了构建的 PCA 模型有三台 PC 的信噪比为 3≫。第三个输出是载荷图,它揭示了每个变量对不同PC的贡献。 图8F描述了前三台PC的载荷具有不同的光谱形状。

Figure 8 图8

Figure 8. Illustration of typical PCA results from a computer-simulated multivariable sensor. (A) “Spectrum” of a sensor with indicated changes in peak height, peak position, and peak width upon application of simulated environmental effects. (B) Three levels of spectral changes in peak height, peak position, and peak width. (C, D) Visualization of relations between measured spectra by presenting PCA scores plots of PC1 vs PC2 and PC1 vs PC2 vs PC3, respectively. (E) Plot of the signal-to-noise (S/N) of each PC in the model. (F) Loadings plot that depicts contributions of each variable to different PCs.
图8.计算机模拟多变量传感器的典型 PCA 结果图示。(A) 传感器的“光谱”,在应用模拟环境效应时,峰高、峰位置和峰宽会发生变化。(B) 峰高、峰位和峰宽三个层次的光谱变化。(C、D)通过分别呈现 PC 与 PC 1 2 和 PC 1 与 PC 与 PC 2 3 的 PCA 分数图,可视化测量光谱之间的关系。(E) 模型中每台 PC 的信噪比 (S/N) 图。(F) 描述每个变量对不同 PC 的贡献的加载图。

5 Multivariable Nonresonant and Resonant Impedance Sensors
5 个多变量非谐振和谐振阻抗传感器

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Multivariable impedance sensors typically measure vapor-modulated changes of resistance R and capacitance C of a sensing structure in nonresonant and resonant configurations. Measurements of R and C in a nonresonant configuration can be performed by using an electrode RC circuit structure (see Figure 9A) that incorporates a sensing film. (197-199) Adding an inductor L to such an electrical circuit provides a resistor–inductor–capacitor (RLC) resonator (see Figure 9B), where changes in R and C can be measured in a resonant circuit configuration. (200) Sensor-excitation conditions also can increase dispersion of sensor response. (201, 202) The advantage of resonators is sometimes a higher sensitivity over nonresonant structures. (203) Resonant response can be the resonance impedance spectrum (Figure 9C). Measurements can be done of the real part Zre and imaginary part Zim of resonance impedance, and representative parameters for multivariate analysis can be determined such as frequency Fp and magnitude Zp of the real part of the resonance impedance spectrum, the resonant F1 and antiresonant F2 frequencies of the imaginary part of the resonance impedance spectrum, and their impedance magnitudes Z1 and Z2, respectively. (200, 204)
多变量阻抗传感器通常测量非谐振和谐振配置中传感结构的电阻 R 和电容 C 的蒸汽调制变化。使用包含传感膜的电极RC电路结构(见图9A)可以对非谐振配置中的R和C进行测量。(197-199) 在这种电路中增加一个电感器L,可以得到一个电阻-电感器-电容器(RLC)谐振器(见图9B),其中R和C的变化可以在谐振电路配置中测量。(200) 传感器激励条件也会增加传感器响应的分散性。(201, 202)谐振器的优点有时是比非谐振结构具有更高的灵敏度。(203)谐振响应可以是谐振阻抗谱(图9C)。可以测量谐振阻抗的实部Z re 和虚部Z im ,并可以确定多变量分析的代表性参数,例如谐振阻抗谱实部的频率F p 和幅值Z p ,谐振阻抗谱虚部的谐振F 1 和反谐振F 2 频率, 以及它们的阻抗幅度分别为 Z 1 和 Z 2 。(200, 204)

Figure 9 图9

Figure 9. Design and operation of multivariable impedance vapor sensors. Simplified equivalent circuits of (A) nonresonant and (B) resonant sensor configurations to probe vapor-modulated changes of resistance Rs and capacitance Cs of a sensing material. (C) Resonance impedance spectrum (real part Zre and imaginary part Zim of resonance impedance) and representative parameters for multivariate analysis: frequency position Fp and magnitude Zp of the real part of the resonance impedance spectrum, the resonant F1 and antiresonant F2 frequencies of the imaginary part of the resonance impedance spectrum, and their impedance magnitudes Z1 and Z2, respectively.

To provide useful impedance response that will have diversity for different gases, the sensors should have the ability to change independently measured real and imaginary impedance values. These independent changes can originate from several types of contributions of the sensing structure. One type of contribution is the change in R and C from the bulk of the sensing material itself that is related to changes in the complex permittivity of the sensing material that can be correlated to a single or multiple analytes of interest and can provide correction of environmental effects. (194, 205) The real part of the complex permittivity of the sensing material is its “dielectric constant”. The imaginary part of the complex permittivity of the sensing material is its “dielectric loss factor” and is directly proportional to the conductivity of the sensing material. In impedance sensors, measurements can be done at a single (206) or at multiple frequencies. (207-210) While measurements at a single frequency provide a useful sensor, analysis at multiple frequencies could achieve an enhanced selectivity.
The change in R and C of portions of the sensing structure besides the bulk of the sensing material can also contribute to sensor response. These contributions can originate from several independent regions of the film/transducer system such as contact resistance and capacitance of the film/electrode interface, surface resistance and capacitance, and substrate/film interface resistance and capacitance (211) as visualized in Figure 10. As an example, parts A and B of Figure 11 show transmission electron microscope images of film/transducer interface, respectively, without and with a contact layer engineered to enhance sensor selectivity via an additional vapor-responsive RC circuit structure.

Figure 10

Figure 10. Details of the sensing region where a sensing material is applied onto the sensor electrodes. Effects of the sensing film are pronounced in resistance and capacitance related to the film width between electrodes, film thickness, contact resistance and capacitance, surface resistance and capacitance, and substrate/film interface resistance and capacitance.

Figure 11

Figure 11. Transmission electron microscope images of film/transducer interface without (A) and with (B) an engineered contact layer for the enhancement of sensor selectivity.

Impedance gas sensing was first demonstrated decades ago, (195, 212-215) including impedance measurements in gas mixtures, (180, 216) and has been the foundation for the modern RC and RLC sensor developments. These sensors can be probed using analyzers that can measure multiple output parameters from a single sensor. (217) Besides operating in the radio frequency (MHz) frequency range with RLC resonant sensors, (200) such sensors also have been demonstrated in the microwave (GHz) (218-220) frequency ranges. Numerous portable and single-chip analyzers have been developed to be applicable for these measurements. (221-233)
To date, multivariable impedance sensors are the most broadly explored multivariable sensors. They have been demonstrated with a wide range of sensing materials that exhibit independent or partially independent changes in their complex permittivity for discrimination between multiple gases, rejection of interferences, and correction for environmental effects. In this section, several diverse types of materials are analyzed to illustrate the power of multivariable sensing using electrical nonresonant and resonant impedance sensors. The most studied sensing materials include dielectric (198, 201, 202, 205, 215, 220, 234) and conjugated (192, 195, 204, 235-237) polymers, macrocycles, (215, 238) metal oxides, (182, 206, 239-241) carbon allotropes, (242, 243) and ligand-capped metal nanoparticles. (178, 192, 201, 207, 244) Less-explored materials are transition metal dichalcogenide monolayers MX2, with M being a transition metal atom (e.g., Mo, W) and X being a chalcogen atom (e.g., S, Se, Te). (245)

Dielectric Polymers

Dielectric polymers exhibit gas-sensing mechanisms such as dispersion, polarizability, dipolarity, basicity, acidity, and hydrogen-bonding interactions. (76) In single-output sensors, measurements are performed with capacitance readout (246, 247) unless these polymers are formulated with conducting nanoparticles to measure the change in sensor conductivity. (248) Such sensors have known insufficient selectivity; thus, they have been assembled into classic sensor arrays. (234, 249) Several reviews analyze performance of dielectric polymers in gas sensing. (76, 250)
In an important initial study using impedance measurements, dielectric polymers were deposited as films onto interdigital electrodes and exposed to several organic vapors. (215) While absorbed vapors had only negligible effect on the sensing film conductivity G, sensor resistance R exhibited significant changes due to the relation to sensor capacitance C as R = G/(G2 + (2πfC)2) where f was the measurement frequency. Following that initial study, polymer-coated nonresonant (198, 234) and RLC resonant sensors (201, 202, 205, 220) have been demonstrated for gas sensing. Examples of dielectric polymers that have been explored with multivariable RC and RLC sensors include ethylcellulose, poly(ethyl acrylate), poly(etherurethane), poly(vinyl acetate), cyanopropylmethylphenylmethyl silicone, dicyanoallyl silicone, and polysiloxane with pendant hexafluoro-2-propanol groups. (201, 202, 205, 215, 220)
Poly(etherurethane) has been used most extensively on RLC resonant transducers and has demonstrated important advancements in multivariable sensing using analysis of resonant spectra shown in Figure 9C. Diverse volatiles were discriminated with 2-D dispersion of the data shown by the PCA scores plot in Figure 12 (201) with PCA responses roughly related to vapor dielectric constant. Self-correction against fluctuations of ambient temperature was demonstrated (Figure 13) (194, 251) by taking advantage of different temperature- and gas-induced effects on the equivalent circuit components of the sensor (dielectric sensor substrate, metal sensor coil, dielectric sensing film, and semiconductor memory chip). Such an approach will be attractive in applications where temperature stabilization of a gas sensor or addition of auxiliary temperature or uncoated reference sensors is prohibitive. When RLC sensors were tested with relatively high concentrations of vapors, 3-D sensor dispersion was achieved even using only three model vapors (water, toluene, and tetrahydrofuran) as illustrated in Figure 14. (201) The PC3 contribution of that sensor was 0.01% but was correlated with concentrations of vapors. Another RLC sensor was based on a conventional flexible passive radio frequency identification (RFID) tag with an integrated circuit memory chip and its antenna inlay laminated with poly(etherurethane) film. Using four vapors (toluene, acetone, ethanol, and water), this sensor had 3-D dispersion and demonstrated a strong PC3 contribution of 13% as shown in Figure 15A (252) due to the carefully selected sensor-excitation conditions. Using a roll-to-roll manufacturing process, ∼5000 flexible RFID sensors were made (Figure 15B). (63) Binary and ternary mixtures of model vapors were quantified using RLC sensors with poly(etherurethane) sensing films. (201)

Figure 12

Figure 12. 2-D dispersion of a multivariable RLC resonant sensor with poly(etherurethane) sensing film presented as PCA scores plot in discrimination of eight volatiles at their single concentrations. (201) Dielectric constants of respective solvents are shown in parentheses.

Figure 13

Figure 13. Response of the developed multivariable resonant RLC sensor to various concentrations of water vapor at variable temperatures. (A) PCA scores plot. (B) Actual vs predicted values of water vapor concentrations at four different temperatures. Reprinted with permission from ref 194. Copyright 2013 Elsevier.

Figure 14

Figure 14. 3-D dispersion of a multivariable RLC resonant sensor with poly(etherurethane) sensing film presented as PCA scores plots in discrimination of three volatiles at their various concentrations. (201) Dielectric constants of respective solvents are shown in parentheses.

Figure 15

Figure 15. 3-D dispersion of a multivariable RLC resonant sensor with poly(etherurethane) sensing film presented as PCA scores plot and an example of manufactured sensors. (A) 3-D sensor dispersion in discrimination of four volatiles at their various concentrations. Dielectric constants of respective solvents are shown in parentheses. (B) Example of roll-to-roll manufactured sensors on a flexible substrate. Reprinted with permission from ref 63. Copyright 2012 The Royal Society of Chemistry.

Resonant RLC sensors also can be evaluated by measurements of response delay, resonant frequency, and other parameters. (253-255) In recent multivariable sensors operating in the microwave range, measurements of response delay were combined with resonant frequency and response amplitude. (220) Response signatures of sensors coated with cyanopropylmethylphenylmethyl silicone and polysiloxane with pendant hexafluoro-2-propanol groups are illustrated in Figure 16 as scores plots of PCA models. These results illustrate 2-D dispersion of these sensors where the chosen polymer modulates the selectivity of the sensor and its contributions to PC1 and PC2.

Figure 16

Figure 16. PCA scores plots illustrating 2-D dispersion of multivariable RLC resonant sensors based on measurements of response delay, resonant frequency, and response amplitude. Sensing films: (A) cyanopropyl methyl phenylmethyl silicone and (B) polysiloxane with pendant hexafluoro-2-propanol groups. Data adapted and reproduced with permission from ref 220. Copyright 2015 Elsevier.

Conjugated Polymers

Conjugated polymers (also known as conducting polymers (256, 257) or intrinsically conducting polymers (258)) exhibit several mechanisms of gas response including changes in density and charge carrier mobility, polymer swelling, and conformational transitions of polymer chains. (77, 78) Sensors with single-output measurements of resistance (259, 260) or capacitance (261) of conjugated polymers have known insufficient selectivity; thus, they have been assembled into classic sensor arrays. (259, 260) Several reviews analyze applications of conjugated polymers in single-output sensors and their arrays. (256-258, 262)
In one important early study, impedance measurements at relatively low frequencies (20 Hz–10 kHz) were utilized to explore changes of relative permittivity of a “classic” conjugated polymer such as polypyrrole upon exposure to different volatiles (methanol, acetone, ethyl acetate, and ethanol). (235) The patterns of R and C responses to these volatiles were different and comparable to those produced using conventional multisensor arrays. Impedance measurements were also explored at high frequencies in resonant sensor configurations using polypyrrole (195) and polyaniline. (196) In these sensors, the dissipation factor was an indicator of the change in dipole–dipole interactions upon sorption of vapors into polymers. Figure 17A shows the dissipation factor vs frequency response of polypyrrole to methanol, ethyl acetate, and acetone vapors. (195) A specific frequency of the sensor dissipation factor was related to vapor type, while the response magnitude was proportional to vapor concentration. It was suggested that these specific frequency responses may be correlated to the permittivity of the polymer film. This technique was further applied to analyze mixtures of vapors (Figure 17B) and was coupled with ANN to determine concentrations of individual vapors in binary, tertiary, and quaternary mixtures. (180)

Figure 17

Figure 17. Dissipation factor measurements using multivariable RLC resonant sensors with conjugated polymers. (A) Response to individual vapors of methanol, acetone, and ethyl acetate. (B) Response to individual vapors of acetone and methanol and their binary mixtures. (A) Reprinted with permission from ref 195. Copyright 1995 Institute of Physics. (B) Reprinted with permission from ref 180. Copyright 1997 Elsevier.

Following these early experiments, conjugated polymers have been explored with recently developed passive RLC resonator-based RFID sensors. (192, 204, 236) For detection of diverse volatiles using these RFID sensors, several conjugated polymers have been utilized such as poly(3,4-ethylenedioxythiophene), polyaniline, poly(fluorene)diphenylpropane, and others, (192, 204, 236, 237) demonstrating 2-D sensor dispersion.

Macrocycles

Macrocycles (e.g., metalloporphyrins and metallophthalocyanines) exhibit gas-sensing ability by π-stacking of gas molecules into organized layers of the flat macrocycles or by gas coordination to the metal center without the cavity inclusion. Mechanisms of gas response of these materials include hydrogen bonding, polarization, polarity interactions, metal center coordination interactions, and molecular arrangements. (79, 80) Performance of porphyrins and cyanines in single-output gas sensing has been recently reviewed. (263)
Impedance sensing was investigated for detection of organic vapors using a nonconductive film of tetrakis-t-butyl phthalocyaninatonickel deposited onto interdigital electrodes. (215) Similar to other tested nonconductive films, absorbed vapors had only negligible effect on the conductivity of the metallophthalocyanine sensing film, but both sensor resistance and capacitance had significant changes when exposed to model organic solvent vapors (dichloromethane, chloroform, trichloroethene, toluene, and ethanol), providing the response pattern illustrated in Figure 18A. (215)

Figure 18

Figure 18. Applications of metallophthalocyanines for multivariable vapor sensing. (A) Diversity of resistance and capacitance responses obtained from nonresonant impedance measurements of tetrakis-t-butyl phthalocyaninatonickel film. (215) (B) Diversity of dissipation spectra obtained from resonant measurements of cobalt phthalocyanine film. (238) (A) Reprinted with permission from ref 215. Copyright 1996 Elsevier. (B) Reprinted with permission from ref 238. Copyright 2006 American Institute of Physics.

Impedance sensing has been further used to identify vapors using resonant sensor structures coated with metallophthalocyanine films. (238) Three model vapors (methanol, ethanol, and isopropanol) were differentiated using a cobalt phthalocyanine sensing material based on the frequency shifts due to the charge, dielectric, and dipolar relaxation behavior of the sensing material. The spectra of the sensor exposed to the three alcohols at their saturation concentrations are shown in Figure 18B. (238)

Metal Oxides

Metal oxides have different gas-response mechanisms (e.g., physisorption, chemisorption, surface defects, and bulk defects) when such sensors operate at temperatures ranging from ambient to ∼1000 °C and utilize different types of metal oxides and dopants. (85-87) Examples of metal oxides include single-metal oxides (e.g., SnO2, ZnO, CuO, CoO, TiO2, ZrO2, CeO2, WO3, MoO3, In2O3, and Ga2O3), perovskite oxide structures with two differently sized cations (e.g., SrTiO3, CaTiO3, BaTiO3, LaFeO3, LaCoO3, and SmFeO3), and mixed metal oxide compositions (e.g., CuO-BaTiO3 and ZnO-WO3). (87, 124, 264-266) The majority of metal oxides in gas sensing are n-type oxide semiconductors, with only a handful of materials that are p-type oxide semiconductors (e.g., NiO, CuO, Cr2O3, Co3O4, and Mn2O4). (267) The junctions between p- and n-type semiconductors are also attractive for gas sensing based on their work functions, bandgaps, and electron affinities. (267)
Historically, gas response in MOS sensors has been measured as the change in sensor resistance. (268) There are also examples of capacitance measurements in metal oxides ranging from single-metal oxides (e.g., In2O3 and SnO2) to mixed metal oxide compositions (e.g., CuO-BaTiO3 and ZnO-WO3). (269-271) Sensors based on traditional single-output measurements of resistance or capacitance of metal oxides have known insufficient selectivity; as a result, they have been assembled into classic sensor arrays as recently reviewed. (148)
Different techniques to improve the selectivities of individual MOS sensors have been explored. One such technique utilized a carefully designed temperature modulation of a metal oxide sensing film to take advantage of different mechanisms of gas response of metal oxides and their gas selectivity at different temperatures. (239, 272-277) For example, this approach provided an impressive ability to resolve numerous odors with a single sensor using a conventional resistance readout but by rapidly (<10 s) cycling a sensor through multiple temperatures ranging from ∼50 to ∼480 °C. (239) The response of a SnO2 sensor at multiple temperatures was analyzed by using a linear DA, demonstrating the ability to resolve different odors in a 3-D space of the built model (Figure 19). (276)

Figure 19

Figure 19. Gas selectivity of a conventional SnO2 sensing film by temperature modulation demonstrated using a linear discrimination analysis (LDA) plot for hydrogen cyanide (HCN) discrimination from a variety of backgrounds containing HCN and eight types of interfering species (1–8). Reprinted with permission from ref 276. Copyright 2007 Elsevier.

Impedance measurements of metal oxide sensing films were introduced two decades ago, resulting in improvement of gas selectivity. (240) Recently, to explore the opportunities to improve selectivity of Pt-doped CeO2 nanofibers, single-frequency impedance measurements were performed and compared with conventional resistance measurements. The comparison was done by detecting CO reducing gas and testing O2, CO2, NO, and SO2 as interferences. (206) Resistive response to CO had a significant interference from O2 (Figure 20A). Impedance measurements at 100 kHz eliminated this interference effect (Figure 20B).

Figure 20

Figure 20. Gas selectivity of Pt-doped CeO2 nanofibers to CO using (A) conventional resistance and (B) single-frequency impedance measurements at 100 kHz. Reprinted with permission from ref 206. Copyright 2013 Elsevier.

When resistance and capacitance measurements were combined, 2-D responses were obtained using a sensor based on In2O3 single-metal oxide upon exposure to five gases such as NO2, NH3, CO, NO, and acetone at their single concentrations. (182) The time-dependent conductance vs capacitance plots provided the ability to discriminate between individual gases (Figure 21). The ability to discriminate between mixtures of gases was also explored using mixtures of NO2 and NH3 at different ratios. It was found that responses from mixtures did not correspond to a simple addition of responses according to their gas-phase composition and were much closer to that for the NO2 alone, possibly due to different adsorption coefficients of NO2 and NH3 molecules. Further, an addition of a surface work function measurements provided 3-D responses achieved for individual gases. It was also suggested that a 4-D response could be possible by adding adsorption-induced stress detection, which has been widely used for microcantilevers.

Figure 21

Figure 21. Discrimination between individual gases using an In2O3 sensing material coupled with capacitance and conductance measurements at 1 kHz upon exposure to NO2, NH3, CO, NO, and acetone at their single concentrations. Reprinted with permission from ref 182. Copyright 2011 Elsevier.

Using a Pd-doped SnO2, a 2-D response from an individual sensor was obtained by measurements of changes in properties of a resonant RLC sensor upon exposure to five gases such as hydrogen peroxide, ethanolamine, water, chlorine dioxide, and ammonia at their multiple concentrations. (241) As shown in Figure 22, the PCA scores plot produced from responses of a single sensor demonstrated the sensor ability to discriminate between five gases based on the number of their valence electrons.

Figure 22

Figure 22. Discrimination between individual gases using a Pd-doped SnO2 sensing material coupled with resonant impedance measurements followed by PCA upon exposure to hydrogen peroxide, ethanolamine, water, chlorine dioxide, and ammonia at their various concentrations. The sensor discriminated between five gases based on the number of their valence electrons as shown in parentheses. (241)

Carbon Allotropes

Carbon allotropes (e.g., amorphous carbon, fullerenes, carbon nanotubes, graphene, and graphite) can act either as a main component in the sensing film to facilitate analyte recognition and provide signal transduction or as an additive in the sensing film matrix to provide signal transduction. Sensors based on traditional single-output measurements of resistance or capacitance of carbon allotropes have known insufficient selectivity; as a result, they have been assembled into classic sensor arrays. (278) Recent reviews summarize single-output sensors and arrays based on carbon allotropes. (279-286)
Carbon allotropes have been explored for multivariable sensing because they can exhibit at least partially independent changes in their different electrical properties at room temperature. Simultaneous measurements of capacitance and conductance of single-walled carbon nanotubes (SWCNTs) grown by chemical vapor deposition and exposed to diverse vapors demonstrated the existence of two distinct physiochemical properties of adsorbed vapors such as charge transfer and polarizability. (242) The ratio of conductance to capacitance response was found to be a concentration-independent intrinsic property of a specific gas, useful as a parameter for gas identification (Figure 23).

Figure 23

Figure 23. Ratio of conductance to capacitance response of SWCNTs grown by chemical vapor deposition and exposed to diverse individual vapors is a concentration-independent intrinsic property of a specific gas, useful as a parameter for gas identification. Data adapted and reproduced with permission from ref 242. Copyright 2005 American Chemical Society.

Following these initial observations, application of SWCNTs onto RLC resonators provided a 2-D dispersion of individual sensors. In one study, SWCNTs functionalized with carboxylic acid were used for sensing of model vapors such as water, methane, and toluene, selected as examples of different polarities and types of SWCNT–vapor interactions. The sensor resonant spectra were processed as described in Figure 9C. All volatiles were discriminated by using only two PCs in a PCA scores plot (Figure 24).

Figure 24

Figure 24. PCA scores plot of an RLC sensor with SWCNTs functionalized with carboxylic acid upon exposure to varying concentrations of water, methane, and toluene vapors.

In another study that involved SWCNTs functionalized with polyaminobenzenesulfonic acid, other vapors were selected such as water, acetonitrile, dichloromethane, and chloroform. These vapors were selected as examples of different polarities of SWCNT–vapor interactions. Results of PCA illustrated that all four volatiles were discriminated by generating 2-D response dispersion (Figure 25). (243)

Figure 25

Figure 25. PCA scores plot of an RLC sensor with SWCNTs functionalized with polyaminobenzenesulfonic acid upon exposure to varying concentrations of water, acetonitrile, dichloromethane, and chloroform vapors.

Ligand-Capped Metal Nanoparticles

Ligand-capped metal nanoparticles (also known as colloidal metal–insulator–metal ensembles (287) or monolayer-protected metal nanoparticles (288)) have the gas-sensing mechanisms that involve electron tunneling between metal cores through the dielectric ligand shell and charge hopping along the shell. Sensor response diversity can be tuned by designing different ligands (e.g., organothiol derivatives, dendrimers, organoamines, and mercaptocarboxylic acids) surrounding metallic cores. (289-295) Ligands can be broadly divided into two classes, with soft and rigid linkers. Soft linkers change their length as a function of the amount of sorbed vapor modulating film resistance. Rigid linkers restrain swelling of sensing films and boost effects of analyte-dependent changes of the film dielectric constant. (296-298) Several recent reviews detail conductivity measurements using ligand-capped metal nanoparticles. (283, 299-301)
Although chemiresistor measurements are very attractive with ligand-capped metal nanoparticles because of simplicity, (287) other transduction modalities have also been implemented such as capacitance, impedance, and RLC resonators, (192, 201, 207, 244) as well as gravimetric detection (see section 6) and optical detection (see section 8).
In a recent study, resistance and capacitance responses of metal nanoparticles capped with an organic (2-mercapto-benzylalcoholthiol) ligand were compared using impedance spectroscopy measurements. (207) As expected, (178) humidity effects were more pronounced in capacitance vs resistance responses, which is especially important to take into account in real-world applications. The applicability of biological capping ligands to gold nanoparticles for selective vapor sensing was demonstrated using gold nanoparticles capped with a peptide AYSSGAPPMPPF deposited onto an RLC resonant sensor. (244) As model toxic vapors and chemical warfare agent simulants, acetonitrile, dichloromethane, and methyl salicylate were used. The PCA scores plot demonstrated 2-D dispersion with almost opposite response directions to dichloromethane and methyl salicylate vapors even though they have very similar dielectric constants (i.e., εr = 9.1 and 9.0, respectively). A possible explanation of this effect may be that methyl salicylate is more bulky than dichloromethane, resulting in different rigidity of the linker.
Discrimination of vapors of the first nine linear alcohols of their homologous series and water was further accomplished with Au nanoparticles capped with a 1-octanethiol ligand deposited onto a RLC resonant sensor (Figure 26). (201) Detection of these closely related alcohol vapors is not straightforward even using gas chromatography and sensor arrays. (302) The sensor was exposed to four increasing concentrations of 10 vapors each starting from water, to methanol, ethanol, 1-propanol, 1-butanol, 1-pentanol, 1-hexanol, 1-heptanol, 1-octanol, and 1-nonanol as vapors 1–10. The single sensor provided several diverse responses Fp, F1, F2, and Zp that are illustrated in Figure 26A–D. The red dotted lines in Figure 26A–D depict the patterns of each of the responses of the multivariable sensor to 10 vapors. The knowledge of gas-induced effects on permittivity (192, 201, 207, 244) and conductivity (287) of ligand-capped metal nanoparticles was important motivation to visualize the relation between raw sensor responses. Figure 26E depicts the plot of Zp vs Fp that was chosen because sensor response Zp was known to be correlated with the resistance of ligand-capped Au nanoparticles sensing film while Fp was correlated with its capacitance. This plot of raw responses of Zp vs Fp illustrates an excellent ability of the sensor to discriminate between nine linear alcohols of their homologous series and water. Interestingly the raw Fp response had minimal effect from 1-nonanol (vapor 10, Figure 26A), while the raw Zp response had minimal effect from methanol (vapor 2, Figure 26D). Thus, the plot of Zp vs Fp had respective horizontal and vertical responses for vapors 10 and 2 (Figure 26E). A PCA classification model was further developed using Fp, F1, F2, and Zp responses. A scores plot of the first two PCs (Figure 26F) demonstrated an excellent general resemblance with the plot of raw responses Zp vs Fp (Figure 26E), confirming the vapor-discrimination power of this sensor. However, the PCA model also had a small contribution from PC3 (Figure 26G) that should be important for analysis of mixtures.

Figure 26

Figure 26. Discrimination between 10 individual vapors (9 alcohols from their homologous series and water as interferent) using an individual resonant sensor. (A–D) Individual sensor responses Fp, F1, F2, and Zp as representative examples. (E) Plot of raw responses of Zp vs Fp. PCA scores plots of (F) PC1 vs PC2 and (G) PC1 vs PC2 vs PC3 of the developed PCA model upon sensor exposure to various concentrations of vapors: (1) water, (2) methanol, (3) ethanol, (4) 1-propanol, (5) 1-butanol, (6) 1-pentanol, (7) 1-hexanol, (8) 1-heptanol, (9) 1-octanol, and (10) 1-nonanol.

To tune selectivity of a resonant multivariable sensor to a particular linear alcohol, several ligands have been computationally selected for preferential detection of a specific alcohol vapor and to benefit from the multivariable response of resonant impedance sensors. To achieve this goal, the linear solvation energy relationships (LSER) modeling (303, 304) was implemented. The LSER is an effective guide for determination of vapor responses of polymers, (187, 305-307) fullerenes, (308) ligand-capped metal nanoparticles, (309) and DNA. (118) The LSER method calculates material/vapor partition coefficients K as a linear combination of terms that represent several molecular types of interactions: log K = c + rR2 + 2H + a∑α2H + b∑β2H + l log L16, where R2, π2H, α2H, β2H, and log L16 are solvation parameters of the vapor in the sensing material, coefficients r, s, a, b, and l are the corresponding sensing material parameters, and c is the constant. (303, 304) The partition coefficients K were calculated by using R2, π2H, α2H, β2H, and log L16 solvation parameters of alcohol vapors (304, 310) and r, s, a, b, and l parameters of different ligands. (309) Further, the volume fractions of vapors of interest in φA the sensing material were calculated using values of liquid analyte molar mass M and density ρ, partial pressure of analyte vapor PA, and gas constant R as φA = K(M/ρ)(PA/(RT)). (311) Next, various combinations of r, s, a, b, and l were generated to evaluate the LSER values for possible new ligand candidates for tuned vapor response. These computed parameters of the sensing material provided the design features for new ligands for the detection of homologous series of alcohols. Figure 27 illustrates examples of predicted ligands for enhanced volume fractions of methanol, 1-butanol, 1-hexanol, and 1-nonanol and suppressed volume fractions for other alcohol vapors and water vapor as an interference. Thus, the tunable sensor selectivity was demonstrated to particular linear alcohols by using computationally selected ligands.

Figure 27

Figure 27. Results of LSER tuning of the volume fractions of vapors of interest φA in ligand-capped metal nanoparticles as sensing materials for multivariable sensors. LSER-predicted functionality of ligand-capped metal nanoparticles preferentially sensitive to model vapors of interest: (A) methanol, (B) 1-butanol, (C) 1-hexanol, and (D) 1-nonanol as highlighted by arrows. The predicted ligand structures shown in A–D may have different stability.

A 3-D dispersion was achieved in another RLC resonant sensor that utilized Au nanoparticles capped with an organic soft linker ligand. (312) The time plots of the first four PCs of the developed PCA model (Figure 28) showed the distinct recognition pattern for PC1, PC2, and PC3 and more noisy results for PC4 between four model vapors (water, methyl salicylate, toluene, and acetone).

Figure 28

Figure 28. High-dispersion response of an individual sensor illustrated as PC1–PC4 time traces. Vapors: (1) H2O, (2) methyl salicylate (MeS), (3) toluene, and (4) acetone at various concentrations. (312)

Significantly suppressed humidity effects have been also demonstrated using octanethiol-capped metal nanoparticles on resonant structures. Using toluene and 1-nonanol as model vapors with high and low vapor pressures, the ability of sensors to operate in the presence of variable ambient humidity and to reject effects of ambient humidity was evaluated in detail. (192, 201) It was found that these sensors rejected up to ∼3 000- and 2 000 000-fold overloading from water vapor when doing measurements of toluene and 1-nonanol, respectively. (192, 201) Such rejection of an excess of an interferent is a significant advancement not only in the development of individual multivariable sensors but also to the development of sensors in general. Previously, numerous types of sensors were shown to suffer from water interference pronounced by a significant baseline offset and analyte signal suppression. (178)
In summary, the majority of reported multivariable impedance nonresonant and resonant sensors demonstrate 2-D response dispersion. (192, 200, 201, 204, 207, 215, 220, 236-238, 241, 243, 244) Examples of reported 3-D (63, 182, 201, 312) and 4-D (180) response dispersion are stimulating for further developments and improvements.

6 Electromechanical Multivariable Resonant Sensors

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Electromechanical resonant sensors measure the vapor-modulated changes of mass, viscoelasticity, and electrical loss of a sensing material. The most widely demonstrated designs of these sensors include resonant structures such as thickness shear mode resonators (also known as quartz crystal microbalances, QCMs), acoustic wave devices, tuning forks, and microcantilevers. (313-318) Electromechanical resonant sensors can be described using an RLC circuit with several components such as R (energy loss), L (mass/motional inertia of bare resonator), C (substrate elasticity), and CP (resonator parasitic capacitance) that form acoustic and electrical branches (Figure 29). (319) A sensing material applied to the resonator adds new components such as inductance LS (related to additional mass) and resistance RS (related to acoustic energy dissipation). (319)

Figure 29

Figure 29. Simplified equivalent RLC circuit of an electromechanical multivariable resonant sensor. Circuit components: R (energy loss), L (mass/motional inertia of bare resonator), C (substrate elasticity), and CP (resonator parasitic capacitance) form acoustic and electrical branches of the resonator. Sensing material adds inductance LS (related to additional mass) and resistance RS (related to acoustic energy dissipation). Reprinted with permission from ref 319. Copyright 2004 Elsevier.

Numerous types of sensing materials have been explored with electromechanical resonant transducers (e.g., dielectric and conjugated polymers, (313, 316, 317, 320, 321) ionic liquids, (185) cage compounds, (319, 321) polycyclic aromatic hydrocarbons, (315) ligand-capped metal nanoparticles, (322) nanostructured materials, (318) and composite films (314, 323, 324)). Recent reviews cover advances in electromechanical resonant single-output sensors. (325, 326)
Different types of electromechanical resonant structures have been demonstrated as multivariable sensors when coated with different types of sensing materials. (185, 313, 314, 327, 328) Independent output parameters of individual devices were measured to create multivariable sensors, for example, frequency, absolute quality factor, and amplitude of oscillation, (329) wave propagation velocity and attenuation, (327) resonant frequency and resonant admittance, (330) circuit resistance, and resonant frequency. (319, 328, 331, 332) Also, broadband excitation was applied to tuning forks and microcantilevers followed by the Fourier transform of their output to quantify frequency, quality factor, and amplitude of the mechanical resonance, capacitance, and transmitted electrical phase shift. (313) Further, multiple-frequency resonant devices have been demonstrated over the MHz–GHz range to correct for fluctuations of environmental effects and to improve discrimination of vapors. (333-335)
Recently, an important advancement in discrimination between different types of polar and nonpolar vapors has been demonstrated using an individual QCM resonator coated with a composite film comprising cellulose acetate and a solid-phase organic salt. (314) The salt was 1-n-butyl-2,3-dimethylimidazolium hexafluorophosphate from a Group of Uniform Materials Based on Organic Salts (GUMBOS). Multivariable response of the QCM was achieved by measurements of the changes in resonance frequency Δf and the changes in motional resistance ΔR. (319, 328, 331, 332) Unlike earlier results with only two (319, 332) or three vapors, (331) these recent studies (314) demonstrated the ability to discriminate between a large number of model polar and nonpolar vapors such as methanol, acetonitrile, ethanol, acetone, 1-propanol, toluene, chloroform, and carbon tetrachloride (Figure 30). It was also found that the ratio ΔfR was a concentration-independent quantity proportional to the molecular weight of the absorbed vapors.

Figure 30

Figure 30. Measurements of circuit resistance ΔR and resonant frequency Δf of a QCM sensor for discrimination between different types of polar and nonpolar vapors at various concentrations. Sensing material: composite film comprising cellulose acetate and a solid-phase organic salt. Reprinted with permission from ref 314. Copyright 2012 The Royal Society of Chemistry.

An elegant approach to improve selectivity of vapor sensors was demonstrated by taking advantage of multiharmonic measurements of QCM sensors with dissipation monitoring (QCM-D) based on a ring-down technique. (185) Operation of QCM-D transducers at multiple harmonics (329) has already extensively benefited in the biological studies of biological films. (336) For multivariable sensing of multiple vapors, a QCM-D transducer was coated with a film of an ionic liquid (1-octyl-3-methylimidazolium bromide or 1-octyl-3-methylimidazolium thiocyanate) and its response was measured at multiple harmonics. The sensor was exposed to 18 different organic vapors such as alcohols, hydrocarbons, chlorohydrocarbons, and nitriles. The resulting frequency shifts were measured at seven harmonics and evaluated using PCA and DA. An example of a canonical plot of a DA model is presented in Figure 31. (185) The 2-D and 3-D response dispersions were demonstrated with good vapors classification.

Figure 31

Figure 31. Canonical plot of a DA model based on measurements of resonant frequency Δf at multiple harmonics of a QCM-D sensor for discrimination between different vapors. Sensing material: ionic liquid. Reprinted with permission from ref 185. Copyright 2015 American Chemical Society.

Electrical and electromechanical RLC resonant sensors have been compared for their ability for multivariable operation using gold nanoparticles capped with a peptide AYSSGAPPMPPF as a sensing material. (337) Both resonators were exposed to eight model analyte vapors (acetonitrile, dichloromethane, methyl salicylate, ethanol, toluene, 1-pentanol, chloroform, and salicylaldehyde). From the developed PCA models, it was found that that the electrical RLC resonant sensor was more selective than the electromechanical QCM sensor. The contribution of PC2 in the electrical sensor was four times larger over the QCM sensor: 87.88% and 11.90% for PC1 and PC2 in the electrical sensor and 95.70% and 2.87% for PC1 and PC2 in the QCM sensor, respectively.
In summary, reported multivariable electromechanical resonant sensors typically demonstrate 2-D response dispersion. (314, 319, 331, 337) Sensors based on multiharmonic measurements and ionic liquid sensing materials have been shown to exhibit 3-D response dispersion. (185)

7 Multivariable Field-Effect Transistor Sensors

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Field-effect transistors (FETs) for gas sensing were introduced in the 1970s (338) when interactions of gases with an active region of the transistor (e.g., its gate) were recognized to produce significant changes of the electronic work function of the sensing material affecting the transistor threshold voltage. Compared to chemiresistors, transistors can have an amplified response, due to the current modulation by the gate electrode, (339) allowing a 100-fold boost in sensitivity. (340)
Initially, the gate materials were inorganic films of catalytic metals (e.g., Pt, Ir, and Pd) and metal oxides (e.g., SnO2 and Ga2O3) at elevated operation temperatures to avoid difficulties with adsorbed water molecules and to speed up the response. (341, 342) These studies resulted in commercialized single-output FET gas sensors. (343) Over the years, the range of sensing materials on FETs has expanded to include Ba1–xSrxTiO3, Ir/WO3, NaNO2, and other materials. (344-346) Semiconducting 1D nanomaterials (e.g., Si, TiO2, ZnO, SnO2, and V2O5) have been also demonstrated (340, 347, 348) as well as 2D materials (e.g., graphene oxide and molybdenum disulfide). (349, 350) Organic, polymeric, and oligomeric thin-film semiconductors are also attracting attention as sensing materials for FETs (e.g., oligo- and polythiophenes, polyaniline, naphthalenes, phthalocyanines, naphthalocyanines, pentacene, polypyrrole, poly(phenylene ethynylene), polycyclic aromatic hydrocarbons, and hexyltrichlorosilanes). (351-354) Excellent “classic” and recent reviews describe the state of the art in field-effect transistors for gas sensing. (256, 339, 341, 352, 355-362)
The role of field-effect transistors in multivariable gas sensing is steadily increasing. The notable initial examples include studies of SnO2 gate (363) and metal catalytic gate (364) transistors at different bias conditions. Measurements of four output parameters from an organic thin-film transistor were demonstrated that included the bulk conductivity of the organic sensing film, the field-induced conductivity, the threshold voltage, and the field-effect mobility. (181)
A platinum gate SiC-FET was studied for quantification of hazardous indoor air pollutants such as benzene, naphthalene, and formaldehyde independent of the level of background humidity using temperature cycling with three plateaus at 180, 200, and 220 °C. (365) Linear discriminant analysis of resulting response patterns has been performed (Figure 32A), (365) demonstrating 2-D dispersion in detection of these selected volatiles in synthetic air. The temperature-cycled operation of the platinum gate SiC-FET was also combined with its gate bias modulation to provide sensor selectivity for discrimination of CO, NO2, and NH3 (Figure 32B). (366) This SiC-FET sensor also demonstrated 2-D dispersion but with relatively small contribution of the second discriminant function. The temperature-cycled operation of the single-crystalline SnO2 nanowires FET was also combined with its gate control to provide sensor selectivity for discrimination of acetone, ethanol, and methyl ethyl ketone. (367)

Figure 32

Figure 32. Platinum gate SiC-FET as a multivariable sensor for discrimination of different volatiles upon analysis of the response patterns using LDA. (A) Results of temperature cycling of the sensor for discrimination of benzene, naphthalene, and formaldehyde at different humidity levels. (365) (B) Results of temperature cycling combined with gate bias modulation of the sensor for discrimination of CO, NO2, and NH3. (366) (A) Reprinted with permission from ref 365. Copyright 2015 Elsevier. (B) Reprinted with permission from ref 366. Copyright 2014 Elsevier.

Analysis of noise spectral density of individual chemiresistors was shown to have a potential to identify various vapors. (368) Such an approach was recently adapted for analysis of low-frequency noise spectra of a graphene gate FET upon its exposure to vapors of different solvents such as methanol, ethanol, tetrahydrofuran, chloroform, acetonitrile, toluene, and methylene chloride. (100) Some volatiles (e.g., methanol, tetrahydrofuran, chloroform, and acetonitrile) exhibited noise spectra with distinctive Lorentzian-shape components due to the vapor-induced charge traps. These results indicated that the low-frequency noise allowed discriminating vapors with a single graphene gate FET. The same approach of discrimination of vapors was also applied to a MoS2 gate FET. (369) However, it was found that, in contrast to graphene, noise spectra changed only upon exposure to acetonitrile vapor.
In another study, a copper phthalocyanine film on a FET with a multivariable readout was used for discrimination of hydrogen peroxide, organic peroxide di-tert-butyl peroxide (TBP), water, and dimethyl methylphosphonate (DMMP) vapors. (370) Multivariable measurements were performed of the source-drain current, mobility, and threshold voltage. Such an approach showed distinct patterns for two peroxides, water, and DMMP vapors (Figure 33A). The positive shifts in the source-drain current and threshold voltage were the diagnostic parameters for peroxides. The results illustrated a dual-response mechanism in which the peroxide molecularly chemisorbed and subsequently catalytically decomposed, forming reactive products and increasing fixed positive charge. A developed PCA model based on the response pattern of these four vapors exhibited 3-D sensor dispersion (Figure 33B).

Figure 33

Figure 33. Copper phthalocyanine gate FET as a multivariable sensor for discrimination of hydrogen peroxide, organic peroxide, water, and dimethyl methylphosphonate (DMMP) vapors. (A) Results of multivariable measurements of the source-drain current, mobility, and threshold voltage demonstrating distinct response patterns for four vapors. (B) PCA scores plot demonstrates 3-D sensor dispersion. Adapted and reproduced with permission from ref 370. Copyright 2012 American Chemical Society.

For selective detection of vapors in single-component and multicomponent mixtures, Si nanowire FETs modified with hexyltrichlorosilanes were used. (193) Several parameters of modified SiNW FETs were measured such as voltage threshold, hole mobility, subthreshold swing, and source-drain current at a minimal gate voltage. An example of diversity of these responses is illustrated in Figure 34A–D. These multiple responses by a single modified SiNW FET created a distinct pattern for each vapor. By analyzing these responses using PCA, a 3-D sensor dispersion was achieved that allowed discrimination of most of the 11 vapors at their single tested concentrations (Figure 34E). Using four outputs of the sensor, an ANN model was also developed based on Euclidean distances to evaluate sensor selectivity. Analysis of binary and ternary mixtures was also performed using a combination of hexane, octane, and hexanol—challenging for the sensor because of the similarity of structures of these vapors. As shown in Figure 34F, ANN was able to identify individual vapors and their binary and ternary mixtures. Such a detection approach has been further explored to detect volatiles that are linked with gastric cancer conditions in exhaled breath and to discriminate them from environmental odors with >85% accuracy. (371)

Figure 34

Figure 34. Si nanowire FET modified with hexyltrichlorosilane for discrimination of 11 diverse vapors. Results of measurements of (A) voltage threshold, (B) subthreshold swing, (C) hole mobility, and (D) source-drain current at a minimal gate voltage demonstrate distinct response patterns for 11 vapors. (193) (E) PCA scores plot demonstrates 3-D sensor dispersion. (F) ANN analysis of binary and ternary mixtures of hexane, octane, and hexanol demonstrates the ability to identify individual vapors and their binary and ternary mixtures. Adapted and reproduced with permission from ref 193. Copyright 2014 American Chemical Society.

In summary, reported multivariable field-effect transistors with inorganic and organic sensing films typically demonstrate 2-D (365, 366) and 3-D (193, 370) response dispersion.

8 Multivariable Photonic Resonant Sensors

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Photonic multivariable sensors can be categorized as material- and structure-based. Material-based sensors utilize materials with multifunctional properties that allow several partially or fully independent responses. Such materials comprise units that are much smaller than the wavelength of interrogation light. Structure-based sensors utilize physical structures that are responsible for the multivariable performance of these sensors. Such structures comprise units that are comparable with the wavelength of interrogation light.

Material-Based Multivariable Photonic Sensors

Material-based multivariable photonic sensors go back to ratiometric probes that provided light-intensity- (372) or temperature-independent (373) measurements. Follow-up developments included sensing materials formulated with several reporter moieties. (374) Multiwavelength luminescent and colorimetric reporter moieties were demonstrated to respond to different chemicals provided by several reporter units in a moiety. Such reporters were developed to exhibit absorbance, fluorescence, electroluminescence, and other changes in relation to detected chemicals. (375, 376) Compounds for dual-, triple-, and quadruple-channel sensing of volatile and nonvolatile analytes have been demonstrated. (377-383)
Developments in nanotechnology provided controlled synthesis of plasmonic nanoparticles for resonant vapor sensing. Classic sensor arrays based on plasmonic nanoparticles functionalized with soft (384) and rigid (385) layers have been reported. Plasmonic nanoparticles have been functionalized with “soft” organic layers for multivariable gas sensing at room temperature. (74, 386) Nanocomposites of plasmonic nanoparticles with “rigid” inorganic layers of metal oxides provided the ability for gas sensing at elevated temperatures. (95, 387)
The mechanisms of optical vapor response using plasmonic nanoparticles functionalized with a soft organic layer include (1) variation in the interparticle spacing related to the type and concentration of the vapor, (2) variation in the refractive index of the organic layer related to the partition coefficients of vapors sorbed into this ligand shell, and (3) variation of the reflectivity of the metal nanoparticle network film affected by the variations of the film thickness (74) (Figure 35A). These effects allowed discrimination of individual vapors and their mixtures using gold nanoparticles functionalized with a 1-mercapto-(triethylene glycol) methyl ether ligand, selected for its amphiphilic properties to respond to both polar and nonpolar vapors. Six diverse vapors (water, methyl salicylate, tetrahydrofuran, dimethylformamide, ethyl acetate, and benzene) produced plasmonic responses affecting the peak and the short- and long-wavelength shoulders of the plasmonic band. The PCA scores plot illustrated that this sensing film discriminated most of the tested vapors (Figure 35B) ranked in the order of the refractive index of the respective solvent except for water vapor, likely because water was the only polar protic solvent among tested vapors. Sensor response to these volatiles produced only 2-D dispersion. This sensing film was further utilized to quantify ethyl acetate and benzene in their mixtures using PLS.

Figure 35

Figure 35. 3-D network of organothiol-functionalized plasmonic nanoparticles for multivariable gas sensing. (A) Mechanisms facilitating vapor response selectivity involve vapor-induced modulation of interparticle spacing D, dielectric constant εr and refractive index n of the ligand shell, and film reflectivity R. (B) Discrimination of six individual vapors using a scores plot of a developed PCA model. Each vapor concentration is represented by two replicate exposures. Dashed line is film response to different vapors in the order of the refractive index n of the corresponding solvent. The sensor was exposed to varying concentrations of vapors: (1) water, (2) methyl salicylate, (3) tetrahydrofuran, (4) dimethylformamide, (5) ethyl acetate, and (6) benzene. Reprinted with permission from ref 74. Copyright 2013 Wiley-VCH Verlag GmbH & Co KGaA.

The mechanisms of vapor response using plasmonic nanoparticles in nanocomposite films with metal oxides and operating at high temperatures (300–800 °C) involve the charge exchange with the nanoparticles or a change in the dielectric constant surrounding the nanoparticles, dependent on the type of metal oxide and its morphology. (95) The material of reported nanoparticles was typically gold or gold alloys; examples of metal oxides in such plasmonic films include CuO, ZnO, TiO2, NiO-SiO2, SiO2, BaO, CeO2, and yttria-stabilized zirconia (YSZ, ZrO2, and Y2O3). (95, 385, 387-393)
Most of the reported nanocomposite plasmonic films with metal oxides have not been explored yet for their multivariable responses to different gases. Only several metal oxides have been used in multivariable plasmonic sensing. They did show important results such as 3-D dispersion. Au-CeO2 nanocomposite films were used for multivariable sensing of individual H2, CO, and NO2 gases at 500 °C. (95) Spectral multivariate analysis was used to gauge the inherent selectivity of the film between the separate analytes. The PCA model had three PCs shown in Figure 36 (95) and demonstrated identifiable responses for each gas. Similarly, the use of a Au-TiO2 nanocomposite film at 500 °C also provided three PCs in its PCA model. (385) To eliminate the need for a white light source and to take advantage of a thermal emission of the sensing material at high temperature, lithographically patterned Au nanorods were used upon tuning of their absorption peak into the near-infrared. An Au nanorod–YSZ nanocomposite film was used for multivariable sensing of H2, CO, and NO2 gases at 500 °C. (387) The plasmonic absorption spectrum of the sensing film overlapped with the thermal energy emitted by the tube furnace and the calculated spectral irradiance from Planck’s distribution, providing opportunities for the detection of white light and thermal spectra from the sensing film over the 600–1000 nm wavelength range. The multivariable gas response achieved by using a passive thermal emission of the film was compared to results using a white light source for film illumination. Illumination with the white light source provided a slightly better discrimination between three gases versus thermal imaging where the two reducing gases were not discriminated well.

Figure 36

Figure 36. Au-CeO2 nanocomposite film for multivariable gas sensing at high temperature. Model gases: H2, CO, and NO2 at 500 °C. PCA scores plots showing the data projected onto the PC axes: (A) PC1 vs PC2 and (B) PC2 vs PC3. Nonoverlapping clusters indicate a unique response to each of the three analytes. Data marker size increases with increasing concentration. Reprinted with permission from ref 95. Copyright 2012 American Chemical Society.

Other plasmonic materials attractive but not demonstrated yet for multivariable sensing include nanostructured metal hydrates, (394) template-fabricated plasmonic nanoholes on analyte-sensitive substrates, (395) and hybrid nanocomposites. (396)

Structure-Based Multivariable Photonic Sensors

Structure-based multivariable photonic sensors have been recently reported to demonstrate improvements over conventional single-output sensors and sensor arrays. Previous photonic structure-based vapor sensors operated on single-output vapor quantitation principles based on detection of wavelength shift of the resonance peak measured with a spectrometer (397, 398) or signal-intensity change measured at a single wavelength. (399) These sensors were based on porous silicon, self-assembled colloidal particles, mesoporous photonic crystals, inverse opals, and high-Q resonators as analyzed in recent reviews. (400-411) Traditionally, for monitoring of multiple analytes, such sensors were combined in an array with each sensor having a partial response selectivity to a certain class of analytes. (397, 412)
Recently, multivariable vapor sensing has been accomplished using a composite structurally colored colloidal crystal film self-assembled from polystyrene core nanospheres with a sol–gel shell (Figure 37). (96) The detection mechanism was based on the vapor-induced changes of the optical lattice constant of this composite colloidal crystal array where the polystyrene cores of the nanospheres were preferentially responding to nonpolar vapors while the sol–gel shells were affected mostly by polar vapors. The associated vapor-induced variations in the shape of the Bragg diffraction band were resolved using PCA of differential reflectance spectra. For the evaluation of the response of the sensor, four model vapors of different polarities were selected (water, acetonitrile, dichloromethane, and toluene). The best selectivity was obtained between nonpolar vapors, with less resolution of polar vapors. The built PCA model provided a 3-D response dispersion of the sensor.

Figure 37

Figure 37. Structurally colored colloidal crystal film formed from composite core/shell nanospheres for multivariable gas sensing. (A–D) Differential reflectance spectra for four vapors: water, acetonitrile (ACN), dichloromethane (DCM), and toluene, respectively, at various concentrations. (E) PCA scores plot of response of the colloidal sensor film for four tested vapors demonstrating 3-D dispersion. (F) Reflected light image of the sensing film on a Teflon support. Reprinted with permission from ref 96. Copyright 2008 Institute of Electrical and Electronics Engineers.

Natural biological nanostructures have been the recent focus of growing attention for bioinspired sensing and other technological applications. (97, 413-415) This interest is driven by the design features of these nanostructures that provided geometries that are difficult to reproduce using existing nanofabrication tools but attractive for sensing. In particular, iridescence of tropical Morpho butterflies (Figure 38A) (99) is attractive because it is produced by microscopic scales that have a nanostructure with an open-air architecture (98) that allows vapors to interact with all its regions. Ridges of the scales act as a diffraction grating and lamella of the ridges act as multilayer interferometric nanoreflectors. (416) These biological nanostructures were visualized using electron microscopy of bare (Figure 38B) (98) and Al2O3-coated (Figure 38C) samples.

Figure 38

Figure 38. High vapor-selectivity of natural Morpho scales. (A) Iridescent coloration of Morpho sulkowskyi scales. Images of (B) bare and (C) Al2O3-coated ridge nanostructures with lamellae. (D) Image of conformal epicuticle on the lamellae. An out-of-plane microrib is also visible. (E) Schematic of the tree-like tapered structure of natural butterfly scales with its chemical gradient of surface polarity. (A, E) Reprinted with permission from ref 99. Copyright 2015 Nature Publishing Group. (B, D) Reprinted with permission from ref 98. Copyright 2013 The National Academy of Sciences USA.

Initially, iridescent scales of the Morpho butterflies provided an unexpected diverse optical response to different vapors. (97) This finding inspired further studies in this direction. (414, 417-427) The origin of this vapor selectivity was found to be a gradient of surface polarity of the <10 nm thick epicuticle layer of the ridge structure (Figure 38D). (98) This polarity gradient runs from the polar tops to the less-polar bottoms of ridges. Diverse types of lipids, proteins, and other biomolecules are spatially distributed in the epicuticle layer, forming a polarity gradient and facilitating adsorption and absorption effects of vapors. The mechanism of selective vapor response of the Morpho scales was described to involve preferential sorption of vapors of different polarities onto the corresponding regions of the nanostructured ridge (Figure 38E). (99) Such vapor interactions are expressed in the corresponding regions of the reflected light spectrum, responsible for the unusual vapor response selectivity of the Morpho scales.
Differential spectral reflectance responses ΔR of the Morpho scales to vapors of closely related polarity (water, methanol, and ethanol) showed spectral differences, mostly in the violet–blue regions (Figure 39A–C). (97) The PCA results illustrated the discrimination of all the vapors (Figure 39D) demonstrating strong 3-D dispersion. (97) To explain the experimentally observed results, spectral responses were computed upon a uniform and gradient coverage of the Morpho structure with different adsorbed vapors. (98) With a uniform coverage, computed spectra did not explain the experimental results. Simulations of gradient coverage produced reflectance spectra that formed individual response directions in the PCA scores plot, similar to experimental results. (97)

Figure 39

Figure 39. High vapor selectivity of natural Morpho scales. (A–C) Differential reflectance spectra upon exposure to water, methanol, and ethanol vapors, respectively, at various concentrations. (D) PCA scores plot illustrating discrimination of water, methanol, and ethanol vapors with a 3-D dispersion. Reprinted with permission from ref 97. Copyright 2007 Nature Publishing Group.

Besides using scales of Morpho butterflies, vapor-sensing experiments were also performed using other types of iridescent butterflies. (425, 428) Scales of Polyommatus icarus were utilized for measurements of responses to diverse model vapors (Figure 40A). (425) When a 5 nm thick conformal Al2O3 coating was applied onto the photonic structure of the Polyommatus icarus butterfly, this thin film intended to isolate the epicuticle from the vapors. Indeed, the vapor-response pattern was significantly modified (Figure 40B), demonstrating the possibility for tuning of vapor interactions using the Al2O3 coating. The contributions of PC3 in bare and Al2O3-modified wing scales were 8.67% and 4.84%, respectively.

Figure 40

Figure 40. Tuning of vapor selectivity of Polyommatus icarus butterfly scales. PCA scores plots of spectral responses to diverse vapors at their various concentrations of (A) bare wing scales and (B) wing scales conformally covered by 5 nm Al2O3. Adapted and reproduced with permission from ref 425. Copyright 2014 Elsevier.

Bioinspired interference-stack sensors were fabricated utilizing electron beam lithography (Figure 41A) with physical and chemical features for vapor-selectivity control. (99) The physical design utilized not only optical interference and diffraction in the fabricated periodic nanostructures but also a small optical loss in the nanostructure that provided signatures of reflectance spectra from different regions of the ridge. The chemical design utilized spatially controlled nanostructure functionalization to promote distinct interactions of diverse vapors within the sensor. Similar to iridescence of Morpho scales (Figure 38A), structure-dependent iridescence was observed from fabricated nanostructures (Figure 41B).

Figure 41

Figure 41. Fabricated photonic sensors inspired by Morpho butterflies. (A) Scanning electron microscopy (SEM) image of fabricated six-lamellae nanostructure. (B) Iridescent coloration of fabricated nanostructures. Shown are six regions of nanostructures that were fabricated with and without lamella; each region was 2 × 2 mm. Upon illumination with a white light, three replicate regions of nanostructures with lamella reflected blue light while three other replicate regions of nanostructures without lamella (with only ridges) reflected red light. Reprinted with permission from ref 99. Copyright 2015 Nature Publishing Group.

Selectivity of fabricated nanostructured sensors was tested in numerous scenarios of increasing complexity. Tests with vapors of diverse nature were performed using benzene, methyl ethyl ketone, acetonitrile, methanol, and water, producing differential reflectance spectra ΔR for each vapor (Figure 42A–E). (99) The PCA results illustrated the discrimination of all the vapors with 4-D response dispersion (Figure 42F, G). (99) These sensors were further tested with the challenging combination of methanol and ethanol and with variable water vapor background. Fabricated nanostructured sensors had excellent discrimination of methanol and ethanol vapors even when vapors were mixed with water vapor. Figure 43 illustrates the sensor ability to quantify methanol and ethanol in the presence of variable water vapor background. (99)

Figure 42

Figure 42. High vapor selectivity of a fabricated photonic sensor inspired by Morpho butterflies. (A–E) Differential reflectance spectra upon exposure to benzene (Ben), acetonitrile (ACN), methyl ethyl ketone (MEK), methanol (MeOH), and water (H2O) vapors at their various concentrations. (F) PCA scores plot of PC1 vs PC2 vs PC3 illustrating discrimination of vapors. (G) PCA scores plot of PC2 vs PC4 illustrating discrimination of vapors with a 4-D response dispersion. Reprinted with permission from ref 99. Copyright 2015 Nature Publishing Group.

Figure 43

Figure 43. Fabricated photonic sensor inspired by Morpho butterflies detects individual methanol (MeOH) and ethanol (EtOH) vapors and their mixtures in the presence of different levels of water vapor background. (A–D) ΔR sensor responses at 393, 546, 563, and 950 nm, respectively. Correlation plots between the actual and predicted concentrations of (E) MeOH and (F) EtOH vapors in the presence of different levels of background water vapor. Reprinted with permission from ref 99. Copyright 2015 Nature Publishing Group.

In summary, reported multivariable photonic resonant sensors demonstrate 2-D, (74, 387) 3-D, (95, 96, 98, 385, 421, 425, 427-429) and 4-D (99) response dispersion.

9 Other Multivariable Sensor Technologies

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Several other types of multivariable sensors have been also developed with outputs of either the same or different types of energy such as optical, electrical, mechanical, or thermal. Two types of optical outputs were combined in a sensor that provided simultaneous measurements of guided optical wave scattering and reflection. (430) Optical and electrical outputs were combined in several types of multivariable sensors. One such sensor was developed to monitor the resonance wavelength of a porous silicon microcavity and its electrical conductivity. (431) Another dual-transduction-mode sensor utilized electrical and optical output signals from a light-emitting organic field-effect transistor with a vapor-sensitive layered structure. (432) It was suggested that using several output variables such as channel conductance, trans-conductance, threshold voltage, light emission intensity, spectrum of the emitted light, location of the emission zone in the channel region, and temporal characteristics of different transduction properties upon exposure to the analyte should enhance sensor dispersion. Thermal and mechanical outputs were combined in another sensor that measured heat dissipation and resonant damping in a microcantilevers. (433) Electrical and mechanical outputs were combined in a sensor that measured conductance and bending of the microhot plate due to volume change of functional layers. (434)
Two types of electrical outputs were combined in a sensor that measured conductance and surface work function of a nanostructured graphite film. (435) These measurements were applied to discriminate several individual model gases as illustrated in Figure 44. (435) It was suggested that this approach can identify gases irrespective of their concentration. Two types of electrical outputs were combined in another sensor that provided simultaneous measurements of resistance and Seebeck coefficient (i.e., thermoelectric sensitivity) of a SnO2 gas sensing material, allowing identification of different reducing gases and determination of their concentrations (436) as illustrated in Figure 45.

Figure 44

Figure 44. Measurements of conductance and surface work function of a nanostructured graphite film for concentration-independent discrimination of individual gases. Sensitivity to respective vapors (as mV/% change of conductance) is shown in parentheses. Reprinted with permission from ref 435. Copyright 2008 American Institute of Physics.

Figure 45

Figure 45. Measurements of resistance and Seebeck coefficient of a SnO2 gas sensing material for identification of different reducing gases and determination of their concentrations. Reprinted with permission from ref 436. Copyright 1998 Elsevier.

While these discussed sensors exhibited typically 2-D response dispersion, (430-436) their design principles may be further expanded toward higher response dispersion in future sensor designs. (432)

10 Design Criteria for Multivariable Sensors

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Over the century of development of gas sensors, (338, 437-440) three key learnings have emerged about their selectivity. First, the requirement for sensor selectivity was found to conflict with the requirement for sensor reversibility. (4) As a result, modern commercially successful sensors include extensive data on gas interferences. (40, 119, 120)Second, the breakthrough in gas selectivity was achieved by using sensor arrays, (131-134) but problems with their drift limited their acceptance in practical applications. (144, 146, 161, 177, 441, 442)Third, carefully designed multivariable transducers with independent outputs (63, 99, 179-186) were found to play the key role in boosting selectivity of individual gas sensors. The general design criteria of such multivariable sensors can be refined to include transducer design, transducer excitation, and material-specific details based on the analysis of reported multivariable sensors, as presented in sections 59.

Design Criteria for Multivariable Nonresonant Impedance Sensors

Design criteria for multivariable nonresonant impedance sensors involve the changes in complex permittivity of sensing films when exposed to diverse gases that lead to the predictable variation in capacitance and resistance of the impedance sensor circuit. Semiconducting and conducting sensing films can exhibit several response mechanisms to diverse vapors to independently change their capacitance and conductivity. Dielectric polymers do not exhibit significant changes of their conductivity upon interactions with vapors. However, because of the frequency-related interrelation between the conductivity of the sensing film and the resistance of the sensor circuit, both sensor capacitance and resistance of the sensor circuit do change when dielectric polymers are used as sensing films. Applying such sensing materials onto interdigital electrodes in impedance sensors typically results in 2-D sensor dispersion. To increase dispersion, additional engineering of the transducer/sensing material interface can be done and coupled with the carefully chosen spectral range of sensor excitation. Such sensor design can provide more than a single RC constant of the sensor circuit, where each of the RC constants in the sensor circuit contributes from different portions of the sensor (e.g., bulk of the sensing material and contact capacitance and resistance). When these RC constants are designed to produce different predictable responses to numerous gases of interest and interferences, dispersion above 2-D can be achieved.

Design Criteria for Multivariable Resonant Impedance Sensors

Adding a stand-alone inductor element to the impedance sensor typically does not enhance sensor dispersion beyond its nonresonant operation. However, if the inductor is an integral part of the sensing region, sensor dispersion can be enhanced by changing analyte-induced dimensional properties of the inductor. In addition, if a proximity wireless readout is employed between the sensor inductor coil and the pickup coil of the sensor reader, gas-induced modulation of coupling between these inductors (e.g., by flexing of the sensor as a cantilever) can further enhance sensor dispersion. Multifrequency excitation over the broad frequency ranges in the radio frequency and microwave regions can also provide the capability to take advantage of the spectral dispersion of sensing materials upon their exposure to different gases. The use of power modulation of the sensor excitation also can improve sensor dispersion.

Design Criteria for Multivariable Electromechanical Resonant Sensors

Design criteria for multivariable electromechanical resonant sensors involve the changes in mass, elasticity, and conductivity of sensing films produced by diverse gases that lead to the predictable variation in multiple parameters of the electromechanical sensor circuit and its typical 2-D sensor dispersion. Multifrequency excitation of harmonics over the broad frequency range improves sensor dispersion to 3-D.

Design Criteria for Multivariable Field-Effect Transistor Sensors

Design criteria for multivariable field-effect transistors are steadily evolving and maturing. The variation of gate bias leads to variation of several measured parameters that can provide independent or partially independent outputs leading to 2-D and 3-D response dispersion. Examples of measured parameters include the drain current, drain voltage, and threshold voltage (with inorganic sensing materials operated at high temperature) as well as the bulk conductivity, field-induced conductivity, threshold voltage, and field-effect mobility (with organic sensing materials operated at ambient room temperature).

Design Criteria for Multivariable Photonic Sensors Based on Functionalized Plasmonic Nanoparticles

Design criteria for multivariable photonic sensors based on functionalized plasmonic nanoparticles can be related to plasmonic nanoparticles functionalized with soft organic layers and their operation at ambient room temperature as well as to plasmonic nanoparticles functionalized with metal oxides and their operation at high temperature. The use of soft organic layers involves modulation of the interparticle spacing, refractive index of the organic layer, and reflectivity of the metal nanoparticle network film as a function of the type and concentration of vapors. The use of metal oxides involves modulation of the charge exchange with the nanoparticles or a change in the dielectric constant surrounding the nanoparticles as a function of the type and concentration of vapors. Such effects lead to the change in the shape of the plasmonic band and 2-D and 3-D response dispersion.

Design Criteria for Multivariable Photonic Composite Colloidal Crystal Film Sensors

Design criteria for multivariable photonic composite colloidal crystal film sensors involve composite nanospheres with their cores and shells fabricated from materials that sorb vapors and change their optical lattice constant either by a change in their refractive index (hard core or shell) or by swelling (soft core or shell). When these composite nanospheres are self-assembled into structurally colored colloidal crystal film, diverse vapors induce changes of the optical lattice constant of this composite colloidal crystal array that lead to the associated vapor-induced variations in the shape of the Bragg diffraction band and 3-D response dispersion.

Design Criteria for Multivariable Photonic Interference-Stack Sensors

Design criteria for multivariable photonic interference-stack sensors involve physical and chemical design variables. The physical design uses not only optical interference and diffraction due to the fabricated periodic nanostructures but also a small component of optical loss in the nanostructure. This loss gives rise to distinct signatures of reflectance spectra that are induced by the optical attenuation when light propagates between the top and bottom regions of the interference stack. The chemical design uses spatially controlled nanostructure functionalization to promote distinct interactions of diverse vapors within the sensor. Such design experimentally demonstrated 4-D dispersion and 10-D dispersion in simulations. The use of angle modulation of the sensor excitation also can improve sensor dispersion.

11 Benefits of Multivariable Sensors

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Achievements in multivariable individual sensors that are analyzed in this Review illustrate that existing to-date multivariable sensors often have 2-D dispersion, similar to the most reported sensor arrays. Such a level of response dispersion may be sufficient in monitoring situations in a relatively simple background or without the need to quantify multiple gases in their mixtures. Some of the multivariable sensors achieve 3-D and 4-D dispersion, comparable with some advanced sensor arrays. (443) Such high dispersion levels should allow quantitation of gases in their binary or ternary mixtures and correction for environmental interferences such as variable chemical background and temperature. However, the biggest achievements in multivariable individual sensors are three-fold.
First, when a recent multivariable sensor was compared with conventional sensor arrays such as QCM and MOS sensors arrays (selected because of their diverse vapor-detection modalities that include both vapor-sorption and vapor-adsorption effects), it was found that the multivariable sensor demonstrated several performance advantages over the sensor arrays. (99) A fabricated multivariable sensor had the ability to reliably quantify vapors in their mixtures and a better linearity of response. Figure 46A depicts a designed map of concentrations of two model vapors and their binary mixtures mixed with water vapor for studies of responses of all sensors. The multivariable sensor resolved individual vapors as well as their binary and ternary mixtures with variable water vapor levels (Figure 46B). The QCM array resolved well only individual vapors but not vapor mixtures (Figure 46C). The MOS sensors array resolved individual vapors and their mixtures with significant nonlinearities (Figure 46D). Thus, the individual multivariable sensor demonstrated the key performance advantages over tested conventional sensor arrays.

Figure 46

Figure 46. Quantitation of individual vapors and their mixtures in the presence of water vapor background using a recently developed multivariable sensor and two conventional sensor arrays. (A) Designed map of concentrations of two individual vapors and their binary mixtures mixed with water vapor; PCA scores plots of responses of (B) multivariable sensor, (C) QCM sensors array, and (D) MOS sensors array. Vapors: 1, methanol (CH3OH); 2, ethanol (CH2CH3OH) at varying concentrations. Reprinted with permission from ref 99. Copyright 2015 Nature Publishing Group.

Second, the most advanced multivariable individual sensors have a response dispersion compatible or even higher than the most advanced sensor arrays. Recently developed bioinspired photonic multivariable sensors (99) compete with disposable one-time-use sensor arrays that involve strong chemical interactions between sensing materials and gases. (444, 445)Figure 47A illustrates a 9-D dispersion achieved with one such array of dosimeter spots where materials in a 36-element array (Figure 47B) strongly ligate tested vapors, leading to device operation as a disposable vapor dosimeter or a chemical fuse. (135) In contrast to such a dosimeter that has limited applicability because of its complexity and strong competition from established simple visual colorimetric test tubes and strips, an individual multivariable sensor has an even better response dispersion based on the new design principles of individual multivariable sensors. Figure 47C illustrates a computed response dispersion of one of such multivariable sensors that had a bioinspired structure with 10 lamella and a preferential adsorption of 10 vapors onto corresponding lamella regions. (446) Inspired by these computed results, a respective multilamella photonic nanostructure has been fabricated as shown in Figure 47D (447) for its further detailed studies. Additional types of photonic nanostructures were also fabricated using diverse techniques and materials as illustrated in Figure 48. (447-455) These structures can allow further tuning of dispersion of sensor response by physical and chemical design principles.

Figure 47

Figure 47. Comparison of response dispersion of a dosimeter array and a multivariable sensor. (A) Measured 9-D dispersion of a disposable one-time-use dosimeter array. (B) White light image of a 36-element vapor dosimeter array. (C) Computed 11-D dispersion of a multivariable sensor based on bioinspired interference-stack design principles. (446) (D) Fabricated multilamella photonic nanostructure for detailed studies of high dispersion of sensor response. (447) (A, B) Reprinted with permission from ref 135. Copyright 2009 Nature Publishing Group.

Figure 48

Figure 48. Examples of different materials and techniques implemented for fabrication of photonic nanostructures inspired by Morpho and other butterflies. (A) Diamond-like carbon structure fabricated using focused ion-beam chemical vapor deposition. (448) (B) Poly(methyl methacrylate) line-array structure with a lift-off resist fabricated using single-step electron-beam lithography. (449) (C) Poly(methyl methacrylate) superlattice structure fabricated using electron-beam lithography with alternate development/dissolution of poly(methyl methacrylate) and lift-off resist. (450) (D) Poly(methyl methacrylate) structure with the alternating lamellae pattern fabricated using electron-beam lithography. (451) (E) Fifteen-lamella poly(methyl methacrylate) structure fabricated using electron-beam lithography. (447) (F) Inorganic structure with thin ridge fabricated using chemical vapor deposition, UV lithography, and chemical etching. (452) (G) Inorganic structure with thin lamella fabricated using selective under-etching of atomic layer deposited material. (453) (H) Porous hierarchical inorganic structure fabricated using chemical vapor deposition, UV lithography, and chemical etching. (452) (I) Inorganic structure with nine large-area lamella fabricated using conventional photolithography and chemical etching. (J) Inorganic structure fabricated on a 10 cm silicon wafer using conventional photolithography and chemical etching. (K) Photoresist structure fabricated utilizing laser interference lithography. (454) (L) UV-curable epoxy scalloped microplates fabricated utilizing double-molding process. (455) (M) Poly(3-hexylthiophene) structure after soft lithography replication from an inorganic master. (452) (N) Inorganic structure with three materials of lamella fabricated using conventional photolithography and chemical etching. (A) Reprinted with permission from ref 448. Copyright 2005 The Japan Society of Applied Physics. (B) Reprinted with permission from ref 449. Copyright 2007 Wiley-VCH Verlag GmbH & Co KGaA. (C) Reprinted with permission from ref 450. Copyright 2015 Nature Publishing Group. (D) Reprinted with permission from ref 451. Copyright 2013 The Optical Society of America. (F, H, M) Reprinted with permission from ref 452. Copyright 2012 American Vacuum Society. (G) Reprinted with permission from ref 453. Copyright 2016 Institute of Physics. (K) Reprinted with permission from ref 454. Copyright 2015 The Optical Society of America. (L) Reprinted with permission from ref 455. Copyright 2014 The National Academy of Sciences U.S.A.

Third, in long-term applications, the drift of response of sensor arrays is a serious unsolved problem (144, 146, 161, 177, 441, 442) because each sensor in an array has a different sensing material with its own drift and aging, uncorrelated with other sensors. This uncorrelated drift of each sensor leads to significant challenges in keeping sensor arrays within their original calibrations. (144, 146, 161, 177, 441, 442) This problem is minimized down to a single multivariable sensor where instabilities in each output in this sensor are correlated due to the use of only one sensing material and one transducer. These instabilities are corrected more effectively as compared to the uncorrelated drift of each sensor in an array. Figure 49A compares calculation results of the prediction error of an analyte concentration for an exemplary multivariable sensor with five outputs and for arrays of 5, 10, and 20 single-output sensors. (102) In the multivariable sensor, its five outputs were calculated with a correlated drift in the range from 0 to 10% (e.g., assuming sensing material aging). The same range of drift was used to calculate the prediction error of an analyte concentration for sensor arrays but taking into the account their uncorrelated drift. When the drift was zero (i.e., fresh sensors), the prediction error for any sensor array or a multivariable sensor was minimal and the same. However, in the presence of uncontrolled long-term drift, sensor arrays lose their prediction accuracy much faster than a single multivariable sensor. This advantage of multivariable sensors can be a deal-breaker between acceptance and rejection of sensor technologies for demanding applications that are highlighted in Figure 1.

Figure 49

Figure 49. Simulation results of long-term performance of individual multivariable sensors vs sensor arrays. (A) Prediction error and (B) probability of false alarms for an exemplary individual multivariable sensor with five outputs and for arrays of 5, 10, and 20 single-output sensors.

Figure 49B depicts the calculated results of the probability of false alarms for the exemplary multivariable sensor with five outputs and for arrays of 5, 10, and 20 single-output sensors. (102) Even for the same number of outputs, a multivariable sensor had lower false alarms. The advantage of the multivariable sensors increases further versus sensor arrays with even larger number of elements. These calculations demonstrate the impact of multivariable sensors for the improved sensor stability with experimental data collection to follow to validate these theoretical predictions. Such multivariable gas sensors with significantly improved performance over existing individual single-output sensors and sensor arrays would be attractive in scenarios illustrated in Figure 1 when high-selectivity advantages of “classic” analytical instruments would be canceled based on the demanding application requirements.

12 Summary and Development Trends of Single-Output and Multivariable Sensors

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Looking Back

Single-output gas sensors have been under development for over 100 years, beginning with sensors based on catalytic metals, metal oxides, polymers, and formulated materials. (338, 437-440) The strongest forces for commercialization of gas sensors have been regulatory, safety, pollution control, and industrial productivity needs. (32, 456) The most widely utilized transduction principles in commercial sensors have been resistance, capacitance, potentiometry, amperometry, and work function, as implemented in single-output MOS, catalytic combustion, electrochemical, and FET sensors.
Sensor arrays (131-134) were introduced ∼70 years after the first individual single-output sensors to mitigate their poor selectivity in gas-detection situations that demand low false alarms. Limitations of sensor arrays outside controlled laboratory conditions have been identified. (141, 142, 457) An excellent summary was compiled about 10 years ago containing over 25 types of sensor arrays from over 20 manufacturers of sensor-array-based and mass-spectrometer-based electronic noses. (143)
Multivariable gas sensors were reported ∼15 years after sensor arrays. (180, 195, 196) Most of those multivariable sensors demonstrated 2-D dispersion that allowed discrimination of individual gases. (195, 196) Some multivariable sensors were reported to discriminate up to four components in mixtures, (180) due to the higher level of sensor dispersion.

Looking at the Present

Single-output gas sensors based on existing concepts of sensing materials and transduction principles have been significantly improved as a result of productive efforts of scientists and engineers focused on the key unmet needs important to users for indoor and outdoor applications of sensors. For example, stability of MOS sensors has been increased, (40) their resistance to poisons has been enhanced, (40) and their power consumption and size have been both significantly reduced. (46) These and other advancements in the “classic” metal oxide resistive sensing concept have boosted these relatively nonselective sensors toward their applications as smartphone-integrated detectors of total air quality in countries with high levels of urban air pollution. (52) Other types of classic sensors, such as electrochemical sensors, also have been advanced. Their power, size, and cost have been significantly reduced with the concurrent enhancements of sensor reliability—all these advancements have led to their broader applications. (458) Examples of commonly used practical solutions to improve reliability of sensors include integration of gas filters onto sensing elements (459, 460) and self-calibration of portable gas sensors during their periodic recharging. (461)
Only a few new sensor detection concepts or sensing materials that were initially demonstrated in the laboratories 10–20 years ago have found their acceptance in practical applications. One of such rare examples is a sensor for explosives detection based on a conjugated polymer at an elevated temperature. (462) Other sensors (e.g., based on fullerenes, carbon nanotubes, semiconducting nanowires, graphene, room-temperature conjugated polymers, and formulated colorimetric sensing materials) have yet to find their wide practical acceptance.
Data acquisition from sensors has been demonstrated using ubiquitous electronics components or systems, initially developed for high-volume applications. Examples of such approaches include cell phone cameras, (463) business card readers, (464) and computer screens (465) for illumination and measurements of gas-induced colorimetric changes of sensing materials, mobile devices such as personal digital assistants (103) and smartphones (466) with auxiliary connected or device-integrated (52) gas sensors, and RFID tags with added gas-sensing capabilities. (23, 194, 200, 278, 467-471) The near-field communication (NFC) protocol that is available in many modern smartphones has been implemented in commercially available NFC RFID sensors for quantitation of chemical and physical parameters (e.g., moisture, humidity, temperature, and glucose). (192, 472-475)
While the number of research publications on sensor arrays and electronic noses has a steady upward trend, at present, electronic noses based on sensor arrays are rarely employed in routine applications outside of scientific laboratories. (161) From the summary of commercial electronic noses compiled about 10 years ago, (143) only mass-spectrometer-based electronic noses are still available; most of the companies that manufactured sensor-array-based electronic noses discontinued their offerings.
Multivariable gas sensors have expanded to the demonstration of diverse principles that can be broadly described as nonresonant and resonant. The nonresonant principles include impedance spectroscopic sensors and multioutput field-effect transistors. The resonant sensors include those that utilize electromagnetic responses (i.e., radio frequency, microwave, terahertz, and optical) and electromechanical responses (i.e., tuning forks, thickness shear mode devices, and acoustic-wave devices). While many of these sensors have only 2-D dispersion, similar to past developments, there is a growing number of multivariable sensors with higher dispersion. Recent experimental examples of 3–4-D dispersion include radio frequency and optical resonant sensors as well as field-effect transistors. A summary of recent multivariable sensors with their levels of experimentally achieved dispersion is presented in Table 3. A comparison of different types of multivariable sensors with respect to their dispersion is presented in Figure 50. While the nonresonant and resonant impedance sensors have the most examples in the literature, the vast majority of these sensors have 2-D dispersion followed by examples of 3-D and 4-D dispersion. Sensors that have the most examples of 3-D dispersion are photonic sensors.

Figure 50

Figure 50. Comparison of different types of reported multivariable sensors with respect to their dispersion. The areas of each segment are proportional to the number of publications.

Table 3. Summary of Dispersion of Different Types of Multivariable Sensors
type of multivariable sensorliterature references on 2-D dispersionliterature references on 3-D dispersionliterature references on 4-D dispersion
electrical multivariable nonresonant and resonant impedance sensors 192, 200,201, 204, 207, 215, 220, 236−238, 241, 243, 244 63, 182, 201, 312 180
electromechanical multivariable resonant sensors 314, 319, 331, 337 185 
multivariable field-effect transistor sensors 365, 366 193, 370 
multivariable photonic resonant sensors 74, 387 95, 96, 98, 385, 421, 425, 427−429 99
other multivariable sensor technologies 430−436  
At present, based on operational requirements, selectivity either may be designed utilizing existing knowledge of materials and transducers or may seek additional future knowledge. In one example, selective monitoring of indoor humidity in the presence of expected nonpolar volatiles in industrial facilities can be successfully designed by using hydrophilic polymers or aluminum oxides coupled with capacitance transducers. (-476-479) However, in another example, selective detection of particular alcohols for determination of molds or fruits condition (480-483) or particular hydrocarbons to determine biogenic or thermogenic origin of their emissions (484) is still a significant scientific challenge for a true design of selective sensors.
Selectivity against the most abundant interferent such as water vapor remains a challenge for most single-output sensors. Humidity effects and their mitigation approaches were detailed in section 5 and touched upon in section 8 because of the most advanced development stages of multivariable impedance and photonic sensors.
As individual sensors, single-output sensors remain the only commercial offering. No multivariable sensors have been broadly commercialized yet.

Looking into the Future

Single-output and multivariable gas sensors will continue to be an attractive opportunity for numerous application scenarios in Internet of Things, Industrial Internet, and other areas when certain specific requirements (e.g., unobtrusive form factor, no external power for operation, no vacuum or carrier gases, and no radioactive sources) cancel the advantages of “classic” analytical instruments. Gas sensors based on sensing materials coupled with an appropriate transducer will continue to compete with microfabricated designs of “classic” analytical instruments such as GC, MS, IMS, and direct spectroscopic gas detectors. This competition will be more enhanced due to the steady advancements in those technologies in their miniaturization, sample handling, and power reduction. (485-487) At the same time, important learnings obtained in those areas (e.g., temperature stabilization, signal conditioning, reduction of fouling, and others) should be more proactively adapted in gas sensors. Sensor arrays will be at the point where they either will demonstrate their cost–benefit capabilities in practical applications or will remain as a laboratory curiosity with yet to be solved practical analytical problems. One of the possible solutions for sensor arrays will be to follow the developments of multivariable sensors—to minimize the number of sensors in an array for its best stability.
Dosimeter-type sensors and their arrays with strong chemical interactions (135) may find more acceptance depending on the cost–benefit proposition in anticipated applications. Classic dosimeters (488, 489) will benefit from the ability to detect numerous gases with one dosimeter cartridge (147) and the ability to store temporally varying chemical information. (490) Dosimeter-type sensors will benefit from new approaches for their resetting by applying different types of energy (e.g., thermal, ultraviolet, and visible). (491-493)
Multivariable gas sensors should continue to advance with their four main thrusts—sensing materials, transducer designs, data analytics, and manufacturability. A system solution for these four main thrusts will provide practical, user-accepted sensor designs for indoor and outdoor applications.
New sensing materials should be explored in three main areas. First, new materials are needed with diverse multiresponse mechanisms for detection of analytes in complex environments. Established materials research tools such as computational, combinatorial, and high-throughput materials design (309, 494-501) should be more proactively utilized for identifying new materials. Future ability to design sensing materials should include quantitative understanding of the mechanisms on atomic and macroscopic scales that describe not only desired interactions between the sensing material and the gas (e.g., sensitivity and selectivity) but also undesired effects (e.g., aging and poisoning). Second, stability and reliability of new sensing materials should be taken into account as early as possible (502-505) in order to properly gauge the next steps in sensor testing because sensor reliability is one of the main requirements for ubiquitous sensors. Third, new concepts should be developed to improve rejection of gaseous interferences—as standalone filters based on new materials or as multiple sensing layers on a transducer. (506)
New transducer designs should be developed to expand sensor dispersion dimensionality beyond 2D dispersion of numerous reported multivariable sensors. These new developments will be in the areas of resonant and nonresonant devices. Exploring different excitation conditions of new transducer designs should assist in identifying new opportunities to enhance sensor dispersion. Excitation will play an increasingly important role for modulation of selectivity of sensing materials by utilizing electrical, optical, temperature, and other stimuli. For example, electrical stimuli can include excitation frequency or excitation voltage; optical stimuli can include light power density, excitation wavelength, or excitation angle; and temperature stimuli can include temperature modulation. Excitation conditions will be also designed to switch between detection mechanisms. For example, operation temperature of semiconducting metal oxide sensors can be utilized to switch material response mechanisms between physisorption at low temperatures, chemisorption at medium temperatures, and bulk effects at high temperatures. Optical excitation of reagent-doped polymeric materials can be utilized to switch between extinction and swelling response mechanisms.
New data analytics will continue to be the key enabler in achieving high-value performance. Different chemometrics tools have been widely accepted in the sensor-development community to demonstrate initial capabilities of sensor systems. However, most of these tools have only little acceptance among users of sensors, not because of the lack of their knowledge of chemometrics but rather due to challenges of reliability of the built chemometric models in routine practical implementations. Results in Figures 21, 26E, 30, 44, and 45 illustrate 2-D sensor dispersion by using original physical signals from multivariable sensors without performing PCA or other types of multivariate analysis. Such ability has been achieved by using the knowledge of diverse response mechanisms of the utilized sensing materials and matching these responses with the appropriate transducer type and its excitation conditions. The underlying principles in the gas-material interactions exploited in these cases involved the previously known diversity of chosen responses to different vapors and the new ability to visualize these responses using matched transduction. Future developments in data analytics should focus on better understanding of trade-offs between using physical signals with and without the multivariate analysis layer to achieve high sensor dispersion and better understanding of noise and drift sources from each of the outputs of a multivariable sensor. Different techniques of conditioning of individual outputs from a multivariable sensor also should be explored with the goal of providing more reliable sensor performance. The best practices in data analytics of dynamic and steady-state features should be shared more proactively across different types of multivariable and other sensors.
Sensor manufacturability, including not only a sensing element but also a sensor reader, will continue to be a critical aspect in acceptance of sensors in practical applications. Approaches for cost-effective manufacturing of sensing elements such as wafer-level fabrication, printing, roll-to-roll, self-assembly, and other techniques (28, 50, 59-66) should be evaluated based on their tolerances, manufacturing volumes, and other critical parameters. Cost analysis of multivariable sensors based on different sensing principles illustrates that sensing elements and sensor readers for different types of sensors can be of similar cost at large manufacturing volumes. (252) Two examples of evolution of sensor readers from the excellent broad-use desktop systems to the low-power microsystems are presented in Figures 51 and 52. (507-509) Electrical sensor readers have diversified from desktop and field-deployable (Figure 51B–D) to discrete-components wearable (Figure 51E, F), and to integrated circuit (Figure 51G, H) readers. The integrated circuit readers can be integrated into mobile and other networked devices (226, 227, 312) or can be connected to these devices. (510) Optical sensor readers have diversified from desktop (Figure 52A) to hand-held (Figure 52B), to microfabricated (Figure 52C), and to integrated attachments for mobile networked devices (Figure 52D). Smartphone camera modules (511-513) and application-specific photonic integrated circuits (32, 252) also can provide desired unobtrusive and cost-effective solutions.

Figure 51

Figure 51. Examples of electrical readout devices for interrogation of multivariable sensors. (A) General view of different devices, (B) desktop network analyzer, (C) fieldable network analyzer, (D) portable impedance analyzer, (E) handheld/wearable impedance analyzer, (F) bluetooth mini network analyzer, (G) integrated circuit impedance analyzer on an evaluation board, and (H) low-power integrated circuit with resistance and capacitance inputs. as a part of an RFID sensor. The scale bars in (B−H) are 2 cm. Logos pictured courtesy of Keysight Technologies, Inc. and WiMo Antennen and Elektronik GmbH.

Figure 52

Figure 52. Examples of optical readout devices for interrogation of multivariable sensors. (A) Portable spectrometer, (B) handheld spectrometer with integrated illumination using multiple light-emitting diodes, (507) (C) ultracompact spectrometer, (508) and (D) smartphone attached spectrometer. (509) Logo pictured courtesy of Ocean Optics, Inc.

These advances in sensing materials, transducer designs, data analytics, and manufacturability will allow new multivariable sensors to outperform not only existing single-output sensors but also sensor arrays in their stability and immunity to humidity and other interferences in numerous practical indoor and outdoor deployments. Multivariable sensors can be considered as a disruptive sensor technology (Figure 53) where high dispersion of an individual sensor response contributes to its high reliability and ability to operate without false alarms.

Figure 53

Figure 53. Disruptive multivariable sensor technologies to complement and ultimately to replace conventional single-output sensors and sensor arrays.

A 2025 roadmap for the development of gas sensors is summarized in Figure 54 with highlighted needed advances in sensor reliability, reduction of sensor cost, and needed operation power. The significant driving forces in these developments will continue to be numerous application scenarios in Internet of Things and Industrial Internet with needed trillions of sensors. (25, 30-32)

Figure 54

Figure 54. 2025 roadmap for development of gas sensors.

Competition between different microanalytical systems for particular types of applications will be enhanced due to significant advances in microfabricated designs of “classic” analytical instruments such as GC, MS, IMS, and direct spectroscopic gas detectors. The key metric for the acceptance of certain technologies in diverse indoor and outdoor applications will remain to be the cost–benefit ratio to the users. The value of multivariable sensors will continue to increase with their ability to discriminate and quantify multiple gases in the presence of known and unknown interferences and closely related gases of different classes and to correct for multiple environmental effects without the added system hardware complexity.

Biography

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Radislav A. Potyrailo

Dr. Radislav Potyrailo is a Principal Scientist at GE Global Research in Niskayuna, New York, leading the growth of industrial, wireless, wearable, and harsh environment sensing technologies for GE applications. He holds an Optoelectronics degree from Kiev Polytechnic Institute and Ph.D. in Analytical Chemistry from Indiana University. Radislav has been serving as the GE Principal Investigator on U.S. Government programs funded by AFRL, DARPA, DHS, NETL, NIH, NIOSH, and TSWG. Radislav delivered 80+ invited lectures and nine keynote/plenary lectures at National and International Meetings, has 100+ granted U.S. Patents and 150+ publications, and coauthored/coedited eight books. He serves as an editor of the Springer book series Integrated Analytical Systems. His recent awards include the 2010 Prism Award by SPIE and the 2012 Blodgett Award by GE Research. In 2011 Radislav was elected SPIE Fellow for achievements in fundamental breakthroughs in optical sensing and analytical systems. In 2013 Radislav was elevated to the grade of Senior Member of the IEEE.

Acknowledgment

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The author is thankful for the fruitful discussions with many Program Managers at U.S. and non-U.S. Government Agencies and GE businesses who were querying about capabilities and limitations of existing and future sensors and sensor arrays to deliver field-deployable monitoring solutions for demanding existing and emerging applications with a desirable cost/performance ratio. This Review has been inspired by those discussions. Results by General Electric collaborative teams that were cited in this Review were accomplished by creative scientists and engineers who coauthored original referenced contributions: J. Ashe, B. Bartling, V. Bromberg, A. Burns, M. Butts, J. Carter, J. Cella, K. Chichak, V. Cotero, J. Cournoyer, T. Deng, R. Diana, J. Dieringer, Z. Ding, K. Dovidenko, G. Gach, S. Genovese, S. Go, G. Goddard, J. Grande, H. Ehring, S. Hasan, B. Kandapallil, S. Klensmeden, H. Lam, M. Larsen, L. Le Tarte, A. Leach, Y. Lee, K. Lindh, D. Monk, W. Morris, H. Mouquin, N. Nagraj, E. Olson, M. Palacios, V. Pizzi, N. Rao, O. Riccobono, D. Sexton, T. Sivavec, K. Sundaresan, C. Surman, Z. Tang, I. Tokarev, H. Tomlinson, A. Vertiatchikh, M. Vincent, T. Wortley, S. Zalubovsky, S. Zhong, and G. Zorn (General Electric); H. Ghiradella, R. Bonam, and J. Hartley (State University of New York, Albany); T. Starkey and P. Vukusic (University of Exeter); R. Naik, T. Bunning, D. Gallagher, J. Hagen, N. Kelley-Loughnane, W. Lyon, D. Phillips, J. Slocik, and M. Vasudev (Air Force Research Laboratory); D. J. Lee and E. McGinniss (Avery Dennison); H. Boudries and H. Lai (Morpho Detection); G. Hieftje, T. Danielson, M. Johnson, and A. Szumlas (Indiana University); V. Mirsky (Brandenburg University of Technology); S. Rumyantsev and M. Shur (Rensselaer Polytechnic Institute); A. Balandin and G. Liu (University of California—Riverside). Special thanks go to W. Morris and M. Schulmerich for critical contributions on automation of sensor data acquisitions; M. Larsen and Z. Tang for fabricating and analysis of structures in Figure 11; H. Lam for computing data in Figure 27; L. Le Tarte, T. Deng, and S. Zhong for fabricating and analysis of structure in Figure 38C; G. Piszter, K. Kertész, Z. Vértesy, Z. Bálint, and L. Biró for providing Figure 40; T. Starkey for computing spectra processed in Figure 47C; R. Bonam for fabricating structures in Figures 47D and 48E; T. Deng, S. Zhong, and W. Shang for fabricating structures in Figures 48I and 48N; A. Minnik for fabricating structure in Figure 48J; M. Nayeri for computing data in Figure 49; and J. Ashe, W. Morris, J. Iannotti, S. Bulumulla, K. Dufel, and D. Sexton for design of handheld/wearable analyzer in Figure 51E.

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