多孔性
咖啡酸
纳米纤维
复合数
材料科学
制作
电化学
化学工程
纳米技术
复合材料
化学
工程类
有机化学
物理化学
电极
病理
替代医学
抗氧化剂
医学
作者
K.V. Kavya,Raju Suresh Kumar,Ramasamy Thangavelu Rajendra Kumar,Sivalingam Ramesh,Woochul Yang,Vijay Kakani,Yuvaraj Haldorai
标识
DOI:10.1016/j.microc.2024.110490
摘要
Artificial intelligence, including machine learning, can offer creative solutions for problems that sensors must solve to anticipate the concentrations of analyte automatically. In this article, a machine learning approach was used to predict the sensing performance of the 1D gold nanofibers decorated 2D amine-terminated chromium metal–organic framework (MIL-101(Cr)–NH2) composite for the determination of caffeic acid (CA). The MIL-101(Cr)–NH2 surface was decorated with Au nanofibers with an average diameter of 12 nm, according to the morphological examination. The composite demonstrated a good linear range of CA concentrations from 0.5 to 100 μM with a detection limit of 0.011 µM and a sensitivity of 2.53 µA/µM/cm2. The electrode's production of current for the interfering substances was incredibly low. The spiked CA in the coffee powder and red wine samples recovered exceptionally well using the composite electrode. The machine learning design forecasted the sensing efficiency of CA to support the experimental results. Linear regression, the most trivial machine learning algorithm, produced predictions that closely matched the experimental data. The composite's porosity and potential electrochemical traits were also investigated using computer vision and artificial intelligence-based algorithms and compared with the experimental results.
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