Using voltammetry augmented with physics-based modeling and Bayesian hypothesis testing to identify analytes in electrolyte solutions
Using voltammetry augmented with physics-based modeling and Bayesian hypothesis testing to identify analytes in electrolyte solutions
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DOI：
10.1016/j.jelechem.2021.115751
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发表时间：
2021-12-24
影响因子：
4.5
通讯作者：
Brushett, Fikile R.
中科院分区：
文献类型：
--
作者：
Fenton, Alexis M., Jr.;Brushett, Fikile R.
伏安法是一种基本的电化学技术，可以定性和定量地探测溶液中的电活性物质，因此已被用于许多研究领域。最近，自动化已被引入，以通过贝叶斯参数估计和化合物识别等方法来扩展伏安分析的能力。然而，存在机会使更通用的方法在更广泛的溶液组成和实验条件。在这里，我们提出了一个协议，使用实验伏安法，物理驱动的模型，二进制假设检验，贝叶斯推理，使强大的多组分解决方案中的分析物的标记跨多种技术。我们首先描述了这个协议的发展，我们随后验证的方法在一个案例研究，涉及五个N-官能化吩噻嗪衍生物。在该分析中，该方案从循环伏安图和循环方波伏安图中正确标记了各自含有10 H-吩噻嗪和10-甲基吩噻嗪的溶液，证明了识别多组分溶液中氧化还原活性成分的能力。最后，我们确定了进一步改进的领域，如实现更高的检测精度和未来的应用，以潜在地提高原位或手术诊断工作流程。
Voltammetry is a foundational electrochemical technique that can qualitatively and quantitatively probe electroactive species in solutions and as such has been used in numerous fields of study. Recently, automation has been introduced to extend the capabilities of voltammetric analysis through approaches such as Bayesian parameter estimation and compound identification. However, opportunities exist to enable more versatile methods across a wider range of solution compositions and experimental conditions. Here, we present a protocol that uses experimental voltammetry, physics-driven models, binary hypothesis testing, and Bayesian inference to enable robust labeling of analytes in multicomponent solutions across multiple techniques. We first describe the development of this protocol, and we subsequently validate the methodology in a case study involving five N-functionalized phenothiazine derivatives. In this analysis, the protocol correctly labels solutions each containing 10H-phenothiazine and 10-methylphenothiazine from both cyclic voltammograms and cyclic square wave voltammograms, demonstrating the ability to identify redox-active constituents of a multicomponent solution. Finally, we identify areas of further improvement-such as achieving greater detection accuracy-and future applications to potentially enhance in situ or operando diagnostic workflows.