International Journal of Electrochemical Science最新文献

The urea oxidation reaction (UOR) has potential application in water electrolysis-assisted hydrogen generation, fuel cells, and the treatment of urea-containing wastewater. In this work, the composite (Cr-NiO/CWF) was synthesized by anchoring Cr and NiO onto carbonized wood fiber (CWF) by hydrothermal method combined with pyrolysis, utilizing biomass wood fiber (WF) as the precursor for the carbon substrate. The optimal Cr-NiO/CWF has significant UOR activity, and the current density (j) can reach 310.40 mA cm⁻² at 1.67 V, while the required potential for UOR is 1.36 V at the j value of 10 mA cm⁻². After 12 h of long-term chronoamperometry (CA) testing, the j retention rate of Cr-NiO/CWF is 90 %. The excellent properties of Cr-NiO/CWF composites are mainly ascribed to the effective modulation of the electronic structure by Cr doping, which optimizes the adsorption behavior of the reactants and products. Cr-NiO nanoparticles enhance the carrier mobility and accelerate the electron transfer rate between Cr-NiO and carbon substrate. Moreover, Cr-NiO nanoparticles are tightly anchored onto the nitrogen-doped carbon substrate to enhance the stability of the composite.

In this work, the effects of Zn²⁺ on the electrochemical activity of positive electrolyte were researched by cyclic voltammetry(CV), AC impedance. The results of CV showed that the positive electrolyte containing 2 wt% Zn²⁺ as additive had the best electrochemical activity. Compared with the electrolyte without any additive, the electrolyte with 2 wt% Zn²⁺ obviously enhanced electrochemical activity and reversibility of Farady reaction. In addition, the kinetic study of the mass transfer process indicated that the diffusion coefficient obviously increased. Besides, the results of electrochemical impedance spectroscopy implied that the internal charge transfer resistance of the electrolyte electrochemical system containing Zn²⁺ as an additive was significantly reduced. This indicated that the charge transfer was significantly accelerated, which was beneficial to the improvement of the electrochemical activity. Moreover, the study of the electrolyte stability showed that the electrolyte containing Zn²⁺ had superior stability yet after 30 cycles’ scanning.

This study investigates the rancidity development in four edible oils (corn, mustard, soybean, and sunflower) over a 12-month storage period using a novel approach combining electrochemical techniques and machine learning. Cyclic voltammetry, electrochemical impedance spectroscopy, and differential pulse voltammetry were employed to characterize oil oxidation. Electrochemical parameters showed strong correlations with traditional chemical indicators, such as the DPV peak current at +0.2 V with p-anisidine value (r = 0.94, p < 0.001). A Random Forest model, trained on electrochemical data, accurately predicted Total Oxidation (TOTOX) values, achieving an R² of 0.96 and RMSE of 2.18 for the test set. The model effectively captured oxidation trends across oil types, with the highest accuracy for mustard oil (MAE: 1.21) and lower performance for sunflower oil (MAE: 2.15). Feature importance analysis revealed charge transfer resistance and DPV peak currents as the most influential predictors. This approach offers rapid, non-destructive assessment of oil quality, potentially improving quality control in the food industry. However, challenges such as electrode fouling and complex sample preparation need to be addressed for practical implementation.

Timely diagnosis of micro short circuit (MSC) faults is crucial for ensuring the safe operation of lithium-ion battery energy storage systems. Existing diagnostic methods face limitations such as high dependency on battery models, difficulty in determining accurate diagnostic thresholds, or low computational efficiency. This work presents a model-free approach for the detection and quantitative assessment of MSCs in lithium-ion battery packs, with incremental capacity (IC) and unsupervised clustering. First, the IC is extracted from charging voltage data to effectively characterize MSC faults in lithium-ion batteries. Next, principal component analysis is used to map the high-dimensional feature space onto a two-dimensional plane to facilitate fault detection and result visualization. Then, an unsupervised clustering algorithm is employed for anomaly detection to identify MSC cells within the battery pack. For the detected MSC cells, a method based on the maximum charging voltage difference between adjacent cycles is designed to estimate the MSC resistance, quantitatively assessing the severity and evolution stage of the MSC. Experimental results show that the accuracy of MSC detection is 99.17 % and the minimum relative error of short-circuit resistance estimation is 1.20 %, which demonstrates the effectiveness and feasibility of the proposed method.

Organic–inorganic composites of (E)-3,5-di(9 H-carbazol-9-yl)-N-(4-(diphenylamino)benzylidene)aniline/dopamine-modified WO₃ (P(TPACz)/WO₃-PDA) were prepared by electrochemical polymerisation. The as-prepared P(TPACz)/WO₃-PDA composites showed good electrochromic and electrochemical performance. The prominent electrochemical performance of P(TPACz)/WO₃-PDA represents a high areal capacitance (32.15 mF cm⁻² at 0.1 mA cm⁻²) and wide range of potential windows (-2.0−1.6 V). Additionally, symmetric supercapacitor devices based on P(TPACz)/WO₃-PDA composite films were successfully constructed, which exhibited a high specific capacitance (13.88 mF cm⁻² at 0.02 mA cm⁻²) and an energy density of 7.71 × 10⁻³ mWh cm⁻² in n-doped station. The remarkable electrochromic and electrochemical performances are due to the synergy between the organic polymer and WO₃-PDA. A complete large-area composite film structure with high conductivity promises fast electronic transport. This study provides a method for preparing multifunctional composite electrode materials, offering technical support for intelligent displays and energy storage technologies.