Please use this identifier to cite or link to this item: https://doi.org/10.1021/acsnano.2c10163
Title: Midinfrared Spectroscopic Analysis of Aqueous Mixtures Using Artificial- Intelligence-Enhanced Metamaterial Waveguide Sensing Platform
Authors: Lee, Chengkuo 
Zhou, Jingkai 
Zhang, Zixuan 
Dong, Bowei 
Ren, Zhihao 
Liu, Weixin 
Keywords: Science & Technology
Physical Sciences
Technology
Chemistry, Multidisciplinary
Chemistry, Physical
Nanoscience & Nanotechnology
Materials Science, Multidisciplinary
Chemistry
Science & Technology - Other Topics
Materials Science
mid-infrared spectroscopy
waveguide sensors
artificial intelligence
metamaterial
mixture analysis
SILICON PHOTONICS
CASCADE LASER
CHIP
Issue Date: 28-Dec-2022
Publisher: AMER CHEMICAL SOC
Citation: Lee, Chengkuo, Zhou, Jingkai, Zhang, Zixuan, Dong, Bowei, Ren, Zhihao, Liu, Weixin (2022-12-28). Midinfrared Spectroscopic Analysis of Aqueous Mixtures Using Artificial- Intelligence-Enhanced Metamaterial Waveguide Sensing Platform. ACS NANO 17 (1). ScholarBank@NUS Repository. https://doi.org/10.1021/acsnano.2c10163
Abstract: As miniaturized solutions, mid-infrared (MIR) waveguide sensors are promising for label-free compositional detection of mixtures leveraging plentiful absorption fingerprints. However, the quantitative analysis of liquid mixtures is still challenging using MIR waveguide sensors, as the absorption spectrum overlaps for multiple organic components accompanied by strong water absorption background. Here, we present an artificial-intelligence-enhanced metamaterial waveguide sensing platform (AIMWSP) for aqueous mixture analysis in the MIR. With the sensitivity-improved metamaterial waveguide and assistance of machine learning, the MIR absorption spectra of a ternary mixture in water can be successfully distinguished and decomposed to single-component spectra for predicting concentration. A classification accuracy of 98.88% for 64 mixing ratios and 92.86% for four concentrations below the limit of detection (972 ppm, based on 3σ) with steps of 300 ppm are realized. Besides, the mixture concentration prediction with root-mean-squared error varying from 0.107 vol % to 1.436 vol % is also achieved. Our work indicates the potential of further extending this sensing platform to MIR spectrometer-on-chip aiming for the data analytics of multiple organic components in aqueous environments.
Source Title: ACS NANO
URI: https://scholarbank.nus.edu.sg/handle/10635/239178
ISSN: 1936-0851
1936-086X
DOI: 10.1021/acsnano.2c10163
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