文献类型：期刊文献

英文题名：Holistic Prediction of AuNP Aggregation in Diverse Aqueous Suspensions Based on Machine Vision and Dark-Field Scattering Imaging

作者：Wang, Xiao-Yuan[1];Hong, Qin[1];Zhou, Ze-Rui[1];Jin, Zi-Yue[1];Li, Da-Wei[1];Qian, Ruo-Can[1]

机构：[1]East China Univ Sci & Technol, Feringa Nobel Prize Scientist Joint Res Ctr, Frontiers Sci Ctr Materiobiol & Dynam Chem, Sch Chem & Mol Engn,Key Lab Adv Mat,Joint Int Lab, Shanghai 200237, Peoples R China

年份：2024

卷号：96

期号：4

起止页码：1506

外文期刊名：ANALYTICAL CHEMISTRY

收录：;EI(收录号：20240315398111);WOS:【SCI-EXPANDED(收录号:WOS:001154765400001)】；

基金：This research was supported by National Natural Science Foundation of China (21977031, 21974046, 22176058), Shanghai Science and Technology Committee (23ZR1416100, 22ZR1416800, 19520744000), the Program of Introducing Talents of Discipline to Universities (B16017), and the Fundamental Research Funds for the Central Universities (222201717003).

语种：英文

外文关键词：Agglomeration - Deep learning - Learning systems - Predictive analytics - Surface plasmon resonance - Suspensions (fluids)

摘要：The localized surface-plasmon resonance of the AuNP in aqueous media is extremely sensitive to environmental changes. By measuring the signal of plasmon scattering light, the dark-field microscopic (DFM) imaging technique has been used to monitor the aggregation of AuNPs, which has attracted great attention because of its simplicity, low cost, high sensitivity, and universal applicability. However, it is still challenging to interpret DFM images of AuNP aggregation due to the heterogeneous characteristics of the isolated and discontinuous color distribution. Herein, we introduce machine vision algorithms for the training of DFM images of AuNPs in different saline aqueous media. A visual deep learning framework based on AlexNet is constructed for studying the aggregation patterns of AuNPs in aqueous suspensions, which allows for rapid and accurate identification of the aggregation extent of AuNPs, with a prediction accuracy higher than 0.96. With the aid of machine learning analysis, we further demonstrate the prediction ability of various aggregation phenomena induced by both cation species and the concentration of the external saline solution. Our results suggest the great potential of machine vision frameworks in the accurate recognition of subtle pattern changes in DFM images, which can help researchers build predictive analytics based on DFM imaging data.