Voltammetry Prediction and Electrochemical Analysis of Carbon Material from "Salt-In-Water" to "Water-In-Salt".

Abstract

Cyclic voltammetry (CV) is a standard method for assessing electrochemical properties in the electrochemical cells, typically in conventional aqueous contexts like 1 m solutions ("salt-in-water"). However, recent advancements have extended electrochemistry into superconcentrated regimes, such as "water-in-salt" solutions with concentrations above 10 to 20 m, which require large amounts of salt for experiments. To address this, machine learning (ML) has been applied, coupled with in-house data collection using lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) electrolytes. This work demonstrates the electrochemistry of YEC-8B in LiTFSI, given their broad potential window of up to 3.0 V across concentrations from 1 to 20 m. The CV profiles were divided into two models: the upper curve for charging and the lower curve for discharging. Data were normalized and segmented by percentiles, and a decision tree model was developed to predict outputs based on input parameters like LiTFSI concentration, scan rates, and potential window. The model predicted nine target variables with a mean absolute percentage error of approximately 2% for both the upper and the lower CV profile curves. Trapezoidal rule was then used to calculate the system's capacitance. Additionally, tests showed a 75% accuracy in predicting the potential window and a suitable scan rate. Overall, the model effectively demonstrated the relationship between "water-in-salt" electrolytes and CV profiles in an electrochemical context using a simple machine learning (ML) algorithm, which continues to expand the integration of data science and electrochemistry.