利用基于元表面的中红外显微光谱仪进行多分析物检测

IF 8.2 1区 化学 Q1 CHEMISTRY, ANALYTICAL ACS Sensors Pub Date : 2024-10-30 DOI:10.1021/acssensors.4c01220
Henry Tan, Jiajun Meng, Kenneth B. Crozier
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引用次数: 0

摘要

中红外(2.5-25 μm)光谱是以无损方式鉴定化学品的理想工具。传统的平台是傅立叶变换红外(FTIR)光谱仪,但这种仪器过于笨重、昂贵,而且耗电量大,不适合许多应用。因此,对用于现场的小型、轻便、高性价比的微型光谱仪的需求日益增长。滤波器阵列探测器阵列微光谱仪就是一种新兴的平台。它将宽带探测器阵列与薄而坚固的光谱滤波器阵列配对,为实时现场传感提供了一个坚固耐用的紧凑型平台。然而,尽管许多应用都涉及多分析物检测,但大多数演示都只侧重于针对空样本识别单一化学物。在这项工作中,我们展示了利用元表面滤波器阵列显微光谱仪同时跟踪多种分析物的罕见尝试。元表面由铝层中亚波长圆形孔的周期性晶格组成,形成一个带通滤波器阵列。滤波器阵列通过反透镜成像装置与现成的微测辐射热计成像,以同时监测汽油中乙醇和甲醇的浓度。这是燃料质量监测的一项重要应用。在乙醇和甲醇含量均为 0% 至 20% v/v 的汽油混合物上对化学计量模型(PLS 和 SVR)进行了训练和测试。结果发现,采用立方核的支持向量机回归(SVR)模型的综合预测误差最小。乙醇和甲醇的预测均方根误差(RMSEP)分别为 1.23% 和 1.84% v/v;相应的假变量检测限分别为 4.22% 和 6.86% v/v。这项工作将基于元表面的中红外光谱仪的新兴领域从单一分析物检测扩展到多分析物检测,从而大大扩展了其潜在应用范围。
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Multianalyte Detection with Metasurface-Based Midinfrared Microspectrometer
Midinfrared (2.5–25 μm) spectroscopy is an ideal tool for identifying chemicals in a nondestructive manner. The traditional platform is a Fourier transform infrared (FTIR) spectrometer, but this is too bulky, expensive, and power-hungry for many applications. There is therefore a growing demand for small, lightweight, and cost-effective microspectrometers for use in the field. One emerging platform is the filter-array detector-array microspectrometer. It pairs a broadband detector array with a thin and rigid array of spectral filters to offer a robust, compact platform for real-time in situ sensing. However, most demonstrations have only focused on identifying a single chemical against a null sample, even though many applications would involve multianalyte detection. In this work, we show a rare attempt at simultaneously tracking multiple analytes with a metasurface filter-array microspectrometer. The metasurface consists of periodic lattices of subwavelength circular apertures in an aluminum layer to create an array of bandpass filters. The filter array is imaged with an off-the-shelf microbolometer via a reverse-lens imaging setup to simultaneously monitor the concentration of ethanol and methanol in gasoline. This represents an important application of fuel quality monitoring. Chemometric models (PLS and SVR) are trained and tested on gasoline blends with ethanol and methanol contents, both ranging from 0% to 20% v/v. A support vector machine regression (SVR) model with a cubic kernel was found to have the lowest combined prediction errors. The root-mean-square-error of prediction (RMSEP) for ethanol and methanol are 1.23% and 1.84% v/v; the corresponding pseudounivariate limit of detection is found to be 4.22% and 6.86% v/v, respectively. This work takes the emerging field of metasurface-based mid-infrared spectrometers from single- to multianalyte detection, thereby considerably expanding their range of potential applications.
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来源期刊
ACS Sensors
ACS Sensors Chemical Engineering-Bioengineering
CiteScore
14.50
自引率
3.40%
发文量
372
期刊介绍: ACS Sensors is a peer-reviewed research journal that focuses on the dissemination of new and original knowledge in the field of sensor science, particularly those that selectively sense chemical or biological species or processes. The journal covers a broad range of topics, including but not limited to biosensors, chemical sensors, gas sensors, intracellular sensors, single molecule sensors, cell chips, and microfluidic devices. It aims to publish articles that address conceptual advances in sensing technology applicable to various types of analytes or application papers that report on the use of existing sensing concepts in new ways or for new analytes.
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