School of Medical Science and Technology, Indian Institute of Technology, Kharagpur 721302, West Bengal, India.
ACS Appl Mater Interfaces. 2023 Sep 13;15(36):43060-43074. doi: 10.1021/acsami.3c03899. Epub 2023 Aug 29.
The sensing behavior of a MoS-functionalized paper sensor towards dopamine was explored through a combinatorial approach of theoretical analysis, subsequent experimental validation, and machine-learning-driven predictive modeling of the measured electrochemical outputs. The suitability of the chosen 2D material for efficient detection of dopamine was confirmed using density functional theory. The physisorption behavior along with electrostatic interaction due to the incorporation of dopamine on MoS was unraveled under the purview of theoretically estimated noncovalent interaction and charge density difference plot. The theoretical Löwdin population analysis elucidates the alteration in oxidation potential of dopamine, as observed in electrochemical experiments. The electrochemical responses of the developed sensor with the spiked serum samples showed an average accuracy of more than 96% with a limit of detection of 10 nM. Furthermore, implementation of a machine-intelligent interactive web app interface improved the resolution of the sensing platform significantly with an enhanced accuracy of nearly 99%.
通过理论分析、后续实验验证以及对测量电化学输出进行机器学习驱动的预测建模的组合方法，研究了 MoS 功能化纸张传感器对多巴胺的传感行为。使用密度泛函理论证实了所选二维材料在高效检测多巴胺方面的适用性。在理论估计的非共价相互作用和电荷密度差图的范围内，揭示了多巴胺在 MoS 上的物理吸附行为以及静电相互作用。理论上的 Löwdin 配分分析阐明了电化学实验中观察到的多巴胺氧化电位的变化。开发的传感器与加标血清样本的电化学响应显示平均准确率超过 96%，检测限为 10 nM。此外，实现机器智能交互式网络应用程序接口显著提高了传感平台的分辨率，准确率提高了近 99%。
