Health diagnosis and recuperation of aged Li-ion batteries with data analytics and equivalent circuit modeling.

Battery health assessment and recuperation play crucial roles in the utilization of second-life Li-ion batteries. However, due to ambiguous aging mechanisms, it is challenging to estimate battery health and devise an effective strategy for cell rejuvenation. This paper presents aging and reconditioning experiments of 62 commercial lithium iron phosphate cells, which allow us to use machine learning models to predict cycle life and identify important indicators of recoverable capacity. An average test error of 16.84% ± 1.87% (mean absolute percentage error) for cycle life prediction is achieved by gradient boosting regressor. Some of the recoverable lost capacity is found to be attributed to the non-uniformity in electrodes. An experimentally validated equivalent circuit model is built to demonstrate how such non-uniformity can be accumulated, and how it can give rise to recoverable capacity loss. Furthermore, Shapley additive explanations (SHAP) analysis also reveals that battery operation history significantly affects the capacity recovery.

Closed-Loop Multi-Objective Optimization for Cu-Sb-S Photo-Electrocatalytic Materials' Discovery.

Copper antimony sulfides are regarded as promising catalysts for photo-electrochemical water splitting because of their earth abundance and broad light absorption. The unique photoactivity of copper antimony sulfides is dependent on their various crystalline structures and atomic compositions. Here, a closed-loop workflow is built, which explores Cu-Sb-S compositional space to optimize its photo-electrocatalytic hydrogen evolution from water, by integrating a high-throughput robotic platform, characterization techniques, and machine learning (ML) optimization workflow. The multi-objective optimization model discovers optimum experimental conditions after only nine cycles of integrated experiments-machine learning loop. Photocurrent testing at 0 V versus reversible hydrogen electrode (RHE) confirms the expected correlation between the materials' properties and photocurrent. An optimum photocurrent of -186 µA cm-2 is observed on Cu-Sb-S in the ratio of 9:45:46 in the form of single-layer coating on F-doped SnO2 (FTO) glass with a corresponding bandgap of 1.85 eV and 63.2% Cu1+ /Cu species content. The targeted intelligent search reveals a nonobvious CuSbS composition that exhibits 2.3 times greater activity than baseline results from random sampling.

Machine learning-assisted optimization of multi-metal hydroxide electrocatalysts for overall water splitting.

Green hydrogen produced via electrochemical water splitting is a suitable candidate to replace emission-intensive fuels. However, the successful widespread adoption of green hydrogen is contingent on the development of low-cost, earth-abundant catalysts. Herein, machine learning models built on experimental data were used to optimize the precursor ratios of hydroxide-based electrocatalysts, with the objective of improving the product's electrocatalytic performance for overall water splitting. The Neural Network-based models were found to be the most effective in predicting and minimizing the overpotentials of the catalysts, reaching a minimum in two iterations. The relatively mild reaction conditions of the synthesis procedure, coupled with its scalability demonstrated herein, renders the optimized catalyst relevant for industrial implementation in the future. The optimized catalyst, characterized to be a molybdate-intercalated CoFe LDH, demonstrated overpotentials of 266 and 272 mV at 10 mA cm-2 for oxygen and hydrogen evolution reactions respectively in alkaline electrolyte, alongside unwavering stability for overall water splitting over 50 h. Overall, our results reflect the efficacy and advantages of machine learning strategies to alleviate the time and labour-intensive nature of experimental optimizations, which can greatly accelerate electrocatalysts research.