Due to low optical activity and structural simplicity,
synchronous
chiral discrimination and identification of aliphatic amino acids
(AAs) are still challenging yet demanding. Herein, we developed a
novel surface-enhanced Raman spectroscopy (SERS)-based chiral discrimination-sensing
platform for aliphatic AAs, in which l- and d-enantiomers
are able to discriminately bind with quinine to generate distinct
differences in the SERS vibrational modes. Meanwhile, the plasmonic
sub-nanometer gaps supported by the rigid quinine enable the maximization
of SERS signal enhancement to reveal feeble signals, allowing for
simultaneously acquiring the structural specificity and enantioselectivity
of aliphatic amino acid enantiomers in a single SERS spectrum. Different
kinds of chiral aliphatic AAs were successfully identified by using
this sensing platform, demonstrating its potential and practicality
in recognizing chiral aliphatic molecules.
In situ rapid detection of contaminants in environmental water is crucial for protecting the ecological environment and human health; however, it is always hindered by the complexity of sample matrices, trace content, and unknown species. Herein, we demonstrate a deep learning-based multicapturer surface-enhanced Raman scattering (SERS) platform on plasmonic nanocube metasurfaces for multiplex determination of organophosphorus pesticides (OPPs) residues. Poly(vinylpyrrolidone), 4mercaptobenzoic acid, and L-cysteine are assembled on Ag nanocubes (AgNCs) and act as capturers to chemically define OPPs. Meanwhile, the OPPs-captured AgNCs efficiently close the interparticle distance and generate plasmonic metasurfaces, guaranteeing ultrasensitive and reproducible SERS analysis. Furthermore, by strategically combining all capturer−OPP SERS spectra, comprehensive "combined-SERS spectra" are reconstructed to enhance spectral variations of each OPP. Based on the combined-SERS spectra, a deep learning model is trained to predict OPPs, which significantly improve the qualitative and quantitative analysis accuracy. We successfully identified multiple OPPs in farmland, river, and fishpond water using this strategy. The whole detection procedure requires only 30 min, including sampling, SERS measurements, and deep learning analyses. This combination of a multicapturer SERS platform with the deep learning algorithm creates a rapid and reliable analytical strategy for multiplex detection of target molecules, providing a potential paradigm shift for environment-related research.
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