Steady-state current–potential curves are often used to evaluate electrocatalysts. Steady-state current is generally expressed as a sum of reciprocals. Previously, we proposed that the catalytic reaction should be considered and that the inverse of the steady-state current can be expressed as the reciprocal sum of the diffusion-controlled and the electrocatalytic-controlled limiting current (i_ec) (Y. Yokoyama, et al., Electrochemistry, 90, 103002 (2022)). In this paper, we report a further modification to the i_ec that reveals it involves the adsorption/desorption steps of reactants/products to/from the catalyst. The relationship between limiting current and the reversibility of the electrode reaction is also discussed.

With unfolding the large scale energy storage, expanding the electrodes with high Li⁺ storage capacity and cost-effectiveness features is becoming a significantly hot research topic. According to the double-template method, carbon anode materials having high storage capacity were successfully prepared by using the coal tar pitches, Na₂CO₃ and SiO₂. It is intriguingly observed that the two templates manifest the different roles constructing the structures of carbon materials. Meanwhile, the fabricated CTPC-1-2 material possesses the three-dimensional porous, and its pore size distributions are in a range (3.94–16.14 nm). After cycling 100 times, the CTPC-1-2 shows the Li⁺ storage capacity of 611.3 mAh g⁻¹ at a current density of 0.1 A g⁻¹. The Li⁺ storage capacity accomplishes 250.6 mAh g⁻¹ at a large current density of 1 A g⁻¹, after cycling 500 times. These results indicatethat only using the coal tar pitches is able to fabricate the carbon materials with the high Li⁺ storage capacity, which provides a new idea for enlarging the application of coal tar pitches in the domains of the fabrications of anode materials.

Accurate temperature prediction plays a vital role in the thermal management and safety assurance of lithium-ion battery systems. This study proposes a hybrid temperature prediction model that integrates Fully Connected Networks (FCN) and Gradient Boosting Machines (GBM) to capture temperature evolution under varying discharge rates. A nonlinear electrothermal simulation framework based on the Nonlinear Thermal Generalized Kalman (NTGKF) model is first constructed to analyze the electrothermal coupling behavior of batteries under discharge rates ranging from 1 to 5 times the nominal capacity. Leveraging the nonlinear feature extraction capability of FCN and the ensemble learning robustness of GBM, an FCN–GBM hybrid model is developed and evaluated using different input configurations, including voltage alone, internal resistance alone, and the combination of both. Simulation and prediction results demonstrate that the evolution of voltage and internal resistance closely aligns with temperature variation, indicating their suitability as key features for temperature modeling. The proposed FCN–GBM model is applied to predict discharge temperature profiles of LiFePO₄ (LFP) and LiNi_0.8Co_0.15Al_0.05O₂ (NCA) cells. The combination of voltage and resistance as input features significantly enhances prediction performance. Under the 20 % training data condition, the LFP model achieves a mean absolute error (MAE) of 0.4576 K and a root mean square error (RMSE) of 0.5411 K, compared to 1.3404 K and 1.5727 K for the baseline GBM model. For the NCA battery, the FCN–GBM model achieves an MAE of 0.3025 K and an RMSE of 0.9973 K, also outperforming GBM with respective errors of 0.8447 K and 1.5878 K. Moreover, the use of single input features leads to larger prediction errors. These results confirm the effectiveness of the FCN-enhanced GBM model and the advantage of feature fusion using voltage and resistance, providing practical insights for improving thermal management and risk mitigation in lithium-ion battery systems.