Arbitrary-cross-section rotary inductive coils for microparticle detection: analytical modeling, optimization and design

Xue, Bo; Yu, Changli; Hua, Geyang; Zhang, Xingming; Zhang, Hongpeng

doi:10.1007/s00158-023-03606-9

Cited by 1 publication

(1 citation statement)

References 26 publications

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“…The true ample scope of AI is to tackle the inverse problems which can instruct the design and optimization of SERS substrates according to the demands on optical properties [ 63,67,169 ] (Figure 2a). Supervised algorithms perform the inverse task generally by interpolating some on‐demand structures within a large training set.…”

Section: Ai For Sers Substrate Designmentioning

confidence: 99%

Artificial Intelligence for Surface‐Enhanced Raman Spectroscopy

Bi,

Lin,

Chen

et al. 2023

Small Methods

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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.

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Section: Ai For Sers Substrate Designmentioning

confidence: 99%