Bakal Viktoriia, Gusliakova Olga, Kartashova Anastasia, Saveleva Mariia, Demina Polina, Kozhevnikov Ilya, Ryabov Evgenii, Bratashov Daniil, Prikhozhdenko Ekaterina
Science Medical Centre, Saratov State University, 83 Astrakhanskaya Str., 410012 Saratov, Russia.
Sensors (Basel). 2025 Jul 3;25(13):4143. doi: 10.3390/s25134143.
In recent years, non-invasive methods for the analysis of biological fluids have attracted growing interest. In this study, we propose a straightforward approach to fabricating silver nanoparticle (AgNP)-coated non-woven polyacrylonitrile substrates for surface-enhanced Raman scattering (SERS). AgNPs were synthesized directly on the substrate using borohydride reduction, ensuring uniform distribution. The optimized SERS substrates exhibited a high enhancement factor (EF) of up to 10 for the detection of 4-mercaptobenzoic acid (4-MBA). To enable glucose sensing, the substrates were further functionalized with glucose oxidase (GOx), allowing detection in the 1-10 mM range. Machine learning classification and regression models based on gradient boosting were employed to analyze SERS spectra, enhancing the accuracy of quantitative predictions (R = 0.971, accuracy = 0.938, limit of detection = 0.66 mM). These results highlight the potential of AgNP-modified substrates for reliable and reusable biochemical sensing applications.
近年来,用于生物流体分析的非侵入性方法越来越受到关注。在本研究中,我们提出了一种直接的方法来制备用于表面增强拉曼散射(SERS)的银纳米颗粒(AgNP)包覆的聚丙烯腈非织造基底。使用硼氢化物还原法将AgNP直接合成在基底上,确保其均匀分布。优化后的SERS基底对4-巯基苯甲酸(4-MBA)的检测显示出高达10的高增强因子(EF)。为了实现葡萄糖传感,用葡萄糖氧化酶(GOx)对基底进行进一步功能化,使其能够在1-10 mM范围内进行检测。采用基于梯度提升的机器学习分类和回归模型来分析SERS光谱,提高了定量预测的准确性(R = 0.971,准确率 = 0.938,检测限 = 0.66 mM)。这些结果突出了AgNP修饰基底在可靠且可重复使用的生化传感应用中的潜力。