分析物
等离子体子
材料科学
拉曼光谱
拉曼散射
基质(水族馆)
表面增强拉曼光谱
胶体金
激光烧蚀
纳米颗粒
纳米技术
分析化学(期刊)
化学
激光器
色谱法
光电子学
光学
物理
地质学
海洋学
作者
Reshma Beeram,Dipanjan Banerjee,Linga Murthy Narlagiri,S. Venugopal Rao
出处
期刊:Analytical Methods
[The Royal Society of Chemistry]
日期:2022-01-01
卷期号:14 (18): 1788-1796
被引量:24
摘要
Given the intrinsic nature of low reproducibility and signal blinking in the surface enhanced Raman scattering (SERS) technique, especially while detecting trace/ultra-trace amounts, it remains a major challenge to quantify the analyte under study. Here we present a simple and economically viable, flexible hydrophobic plasmonic filter paper-based SERS substrate for the quantification of two trace analytes [crystal violet (CV) and picric acid (PA)] using machine learning techniques and SERS data. The wettability of the substrate was modified with an easy and low-cost technique of coating it with silicone oil. Gold nanoparticles were synthesized using a femtosecond laser ablation in water technique. The prepared nanoparticles were characterized using UV, TEM, and SEM techniques and subsequently loaded onto filter papers before using them for SERS studies. We have considered the SERS intensities of the analytes at different concentrations with over 900 spectra to train the model. Principal component analysis (PCA) was used to reduce the dimensionality and, hence, the complexity of the model. Furthermore, support vector regression was used to quantify the analyte molecules and we achieved an R2 error of 0.9629 for CV and 0.9472 for PA. In conjunction with a portable Raman spectrometer and a computation time of less than <10 s, we believe that this is an affordable and rapid method for quantification of analytes using the SERS technique.
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