Methods for Electrochemical Mechanistic Analysis of Cyclic Voltammograms

Smart overview of the Invention

The patent application describes systems and methods for the automatic analysis of electrochemical mechanisms using cyclic voltammograms. These methods leverage deep learning to minimize manual analysis, allowing for characterization, categorization, and ranking of electrochemical mechanisms. This approach aims to provide qualitative, semi-quantitative, and quantitative insights into complex electrochemical systems, enhancing the traditional manual inspection process.

Background and Need

Cyclic voltammetry is a widely used technique for analyzing redox-active chemical systems, crucial in fields like sensing and energy storage. Traditionally, researchers manually analyze cyclic voltammograms to hypothesize mechanisms, a process that is labor-intensive and prone to human bias. The application addresses these limitations by introducing automated, deep learning-based analysis, which is essential for high-throughput screenings and more accurate assessments.

Methodology

The invention involves obtaining cyclic voltammograms, generating datasets, and evaluating these using machine learning models. The analysis determines the probability of various electrochemical mechanisms with high accuracy. The datasets include numerical values such as current and scan rate, allowing for detailed mechanistic insights. The method can handle complex reaction schemes, providing rapid and reliable results for various applications.

Applications and Benefits

The automated analysis is applicable in industries like catalysts, batteries, and fuel cells. It aids in identifying competing pathways, catalyst degradation, and turnover. The method supports processes like carbon dioxide reduction and hydrogen production. Its ability to analyze complex systems and detect subtle features offers significant advantages over manual methods, enabling semi-quantitative analysis and optimization of electrochemical transformations.

Technical Implementation

The deep learning-based process uses datasets rather than images, achieving higher accuracy in determining electrochemical mechanisms. It employs models such as residual neural networks (ResNet) for analysis. The output includes qualitative and quantitative characterizations, with probabilities indicating the prominence of various mechanisms. This approach facilitates the identification of competing reactions and transitions between mechanisms, enhancing the understanding of electrochemical processes.