Machine learning-aided Cu₃(HHTP)₂/COOH-MWCNTs electrochemical sensing platform for simultaneous detection of dopamine and uric acid in human serum.

IF 6.7 1区化学 Q1 CHEMISTRY, ANALYTICAL

Talanta Pub Date : 2026-12-01 Epub Date: 2026-06-15 DOI:10.1016/j.talanta.2026.130163

Zihao Zhao, Jianlong Li, Jinmi Zhang, Qiang Li, Sheng Li, Yingbo Shi, Xiaoli Xiong

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引用次数: 0

Abstract

The simultaneous determination of dopamine and uric acid in complicated biological matrices remains challenging due to overlapping electrochemical signals and interference from coexisting species. Herein, a machine learning-assisted electrochemical sensor based on a Cu₃(HHTP)₂/COOH-MWCNTs hybrid is developed for the simultaneous determination of these analytes. Benefiting from the unique π-π stacking and π-d conjugation characteristics of Cu₃(HHTP)₂, as well as the excellent coordination effect between COOH-MWCNTs and Cu²⁺ and the good dispersibility of carbon nanotubes themselves, the composite material exhibits more prominent surface activity, significantly boosting the exposure of active sites and accelerating the electron transfer kinetics at the electrode-electrolyte interface. On this basis, we constructed an electrochemical sensing platform for the simultaneous detection of DA and UA in human serum samples. The sensor show excellent performance with dual linear ranges of 0.5-15 and 15-80 μM for DA, and 1-200 and 200-350 μM for UA, achieving low LODs of 0.039 μM and 0.083 μM, respectively (S/N = 3). Furthermore, machine learning (ML) algorithms were employed to optimize electrochemical signal processing, enabling more accurate and robust analysis of complex data. The proposed approach achieved successful application in the simultaneous determination of DA and UA in human serum matrix samples.

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同时检测人血清中多巴胺和尿酸的机器学习辅助Cu3(HHTP)2/COOH-MWCNTs电化学传感平台。

由于电化学信号重叠和共存物种的干扰，复杂生物基质中多巴胺和尿酸的同时测定仍然具有挑战性。本文开发了一种基于Cu3(HHTP)2/COOH-MWCNTs混合物的机器学习辅助电化学传感器，用于同时测定这些分析物。得益于Cu3(HHTP)2独特的π-π堆叠和π-d共轭特性，以及COOH-MWCNTs与Cu2+之间良好的配位效应和碳纳米管本身良好的分散性，复合材料表现出更突出的表面活性，显著促进了活性位点的暴露，加速了电极-电解质界面的电子转移动力学。在此基础上，我们构建了同时检测人血清样品中DA和UA的电化学传感平台。该传感器的双线性范围为0.5 ~ 15 μM和15 ~ 80 μM， UA的双线性范围为1 ~ 200 μM和200 ~ 350 μM，检测负载分别为0.039 μM和0.083 μM （S/N = 3）。此外，采用机器学习（ML）算法优化电化学信号处理，使复杂数据的分析更加准确和稳健。该方法已成功应用于人血清基质样品中DA和UA的同时测定。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Talanta 化学-分析化学

CiteScore

12.30

自引率

4.90%

发文量

861

审稿时长

29 days

期刊介绍： Talanta provides a forum for the publication of original research papers, short communications, and critical reviews in all branches of pure and applied analytical chemistry. Papers are evaluated based on established guidelines, including the fundamental nature of the study, scientific novelty, substantial improvement or advantage over existing technology or methods, and demonstrated analytical applicability. Original research papers on fundamental studies, and on novel sensor and instrumentation developments, are encouraged. Novel or improved applications in areas such as clinical and biological chemistry, environmental analysis, geochemistry, materials science and engineering, and analytical platforms for omics development are welcome. Analytical performance of methods should be determined, including interference and matrix effects, and methods should be validated by comparison with a standard method, or analysis of a certified reference material. Simple spiking recoveries may not be sufficient. The developed method should especially comprise information on selectivity, sensitivity, detection limits, accuracy, and reliability. However, applying official validation or robustness studies to a routine method or technique does not necessarily constitute novelty. Proper statistical treatment of the data should be provided. Relevant literature should be cited, including related publications by the authors, and authors should discuss how their proposed methodology compares with previously reported methods.