Interfacial Self-Assembly of Surfactant-Free Au Nanoparticles as a Clean Surface-Enhanced Raman Scattering Substrate for Quantitative Detection of As 5+ in Combination with Convolutional Neural Networks.
Guoqiang FangWuliji HasiXuanyu ShaGuangxu CaoSiqingaowa HanJinlei WuXiang LinZhouzhou BaoPublished in: The journal of physical chemistry letters (2023)
Surface-enhanced Raman scattering (SERS) is a highly sensitive tool in the field of environmental testing. However, the detection and accurate quantification of weakly adsorbed molecules (such as heavy metal ions) remain a challenge. Herein, we combine clean SERS substrates capable of capturing heavy metal ions with convolutional neural network (CNN) algorithm models for quantitative detection of heavy metal ions in solution. The SERS substrate consists of surfactant-free Au nanoparticles (NPs) and l-cysteine molecules. As plasmonic nanobuilt blocks, surfactant-free Au NPs without physical or chemical barriers are more accessible to target molecules. The amino and carboxyl groups in the l-cysteine molecule can chelate As 5+ ions. The CNN algorithm model is applied to quantify and predict the concentration of As 5+ ions in samples. The results demonstrated that this strategy allows for fast and accurate prediction of As 5+ ion concentrations, and the determination coefficient between the predicted and actual values is as high as 0.991.
Keyphrases
- convolutional neural network
- sensitive detection
- quantum dots
- heavy metals
- deep learning
- loop mediated isothermal amplification
- label free
- gold nanoparticles
- high resolution
- aqueous solution
- machine learning
- health risk assessment
- risk assessment
- health risk
- water soluble
- real time pcr
- reduced graphene oxide
- fluorescent probe
- energy transfer
- computed tomography
- mental health
- physical activity
- raman spectroscopy
- ionic liquid
- climate change
- mass spectrometry
- liquid chromatography
- neural network
- human health
- tandem mass spectrometry
- molecular dynamics simulations