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Artificial Intelligence for Surface-Enhanced Raman Spectroscopy
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DOI：10.1002/smtd.202301243.png)
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摘要
En 中文
Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track. Recent years, artificial intelligence has greatly accelerated the advancement of surface-enhancement Raman spectroscopy in the whole pipeline. This review highlights the artificial intelligence-assisted progress in surface-enhanced Raman spectroscopy (SERS) substrates, reporter molecules, synthesis planning, instrumentations, spectral preprocessing, and SERS-related applications in a wide range of fields. Challenges and perspectives are also provided for future development.image
Keyword:
artificial intelligence
biomedicine
environmental protection
food safety
sensing
surface-enhanced Raman spectroscopy
期刊
IF:
9.1
论文数:
4.4K
被引数:
2.2W
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