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基于超表面的中红外微光谱仪的多分析物检测。

Multianalyte Detection with Metasurface-Based Midinfrared Microspectrometer.

机构信息

School of Physics, University of Melbourne, Parkville, Victoria 3010, Australia.

ARC Centre of Excellence for Transformative Meta-Optical Systems (TMOS), University of Melbourne, Parkville, Victoria 3010, Australia.

出版信息

ACS Sens. 2024 Nov 22;9(11):5839-5847. doi: 10.1021/acssensors.4c01220. Epub 2024 Oct 30.

DOI:10.1021/acssensors.4c01220
PMID:39475063
Abstract

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.

摘要

中红外(2.5-25μm)光谱学是一种用于非破坏性识别化学物质的理想工具。传统的平台是傅里叶变换红外(FTIR)光谱仪,但对于许多应用来说,它体积庞大、昂贵且耗电。因此,对于用于现场的小型、轻便且具有成本效益的微光谱仪的需求不断增长。一个新兴的平台是滤光片阵列-探测器阵列微光谱仪。它将宽带探测器阵列与薄而刚性的光谱滤光片阵列配对,为实时原位传感提供了一个坚固、紧凑的平台。然而,大多数演示仅集中在识别单一化学物质与空白样品之间的差异上,尽管许多应用涉及多分析物检测。在这项工作中,我们展示了一种用超表面滤光片阵列微光谱仪同时跟踪多种分析物的罕见尝试。超表面由周期性的亚波长圆形孔的铝层组成,以创建一系列带通滤波器。使用现成的微测辐射热计通过反向透镜成像设置对滤光片阵列进行成像,以同时监测汽油中乙醇和甲醇的浓度。这代表了燃料质量监测的一个重要应用。化学计量模型(PLS 和 SVR)在含有乙醇和甲醇的汽油混合物上进行训练和测试,乙醇和甲醇的含量范围均为 0%至 20%(体积/体积)。发现具有立方核的支持向量机回归(SVR)模型具有最低的组合预测误差。乙醇和甲醇的预测均方根误差(RMSEP)分别为 1.23%和 1.84%(体积/体积);分别找到对应的单变量检测限为 4.22%和 6.86%(体积/体积)。这项工作将新兴的基于超表面的中红外光谱仪领域从单分析物检测扩展到多分析物检测,从而极大地扩展了它们的潜在应用范围。

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