Electrochem

Electrolyte degradation and trace water contamination critically affect the lifetime and safety of lithium-ion batteries. In organic-based electrolytes such as acetonitrile (MeCN), even small amounts of water can trigger ${\mathrm{PF}}_6^{-}$ hydrolysis, producing HF, POF₃, and related species that contribute to electrolyte ageing and alter interfacial reactions. This study explores the electrochemical signatures of ageing and moisture contamination in Bu₄NPF₆- and LiPF₆-based MeCN electrolytes through a systematic cyclic voltammetry protocol. Platinum electrodes with different surface morphologies—flat, Nafion-coated, and nanostructured—were compared to assess their sensitivity to water-induced degradation. Cathodic Faradaic currents appearing around −0.7 to −1.0 V vs. Ag/AgCl were attributed to the protonic species generated by ${\mathrm{PF}}_6^{-}$-induced hydrolysis. The presence of LiPF₆, commonly used in battery electrolytes, further increases the concentration of anions responsible for the protonic species, therefore contributing to the acceleration of the electrolyte degradation. Experiments using a Nafion proton-conductive membrane assess the protonic origin of these peaks. Meanwhile, nanostructured platinum exhibits approximately four times higher current responses and enhanced sensitivity to water additions, reflecting the influence of surface roughness and active area. Overall, the findings indicate that electrode morphology significantly influences the detectability of ageing- and water-driven reactions, supporting the potential of nanostructured Pt as a diagnostic material for in situ monitoring.

In this study, Ni/Mn-doped cobalt–reduced graphene oxide (Co-RGO) composites were successfully synthesized as advanced electrode materials for supercapacitors. The structural and morphological properties of the composites were characterized using FTIR, XRD, SEM, TEM, and UV–Vis spectroscopy. Their electrochemical performance was evaluated through electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and galvanostatic charge–discharge (GCD). Among the prepared samples, Co-RGO doped with Ni/Mn at a 40:10 ratio exhibited the most outstanding capacitive behavior, achieving a specific capacitance of 7414 F g⁻¹ at a current density of 10 A g⁻¹, along with a high energy density of 565 Wh kg⁻¹ and a power density of 4998 W kg⁻¹. The high capacitance arises from faradaic pseudocapacitive reactions rather than electric double-layer capacitance, eliminating the need for a large surface area. These results confirm that Ni doping significantly enhances pseudocapacitance and conductivity in the Co-RGO matrix, making Ni/Mn (40:10)–Co-RGO a potential material for advanced energy storage systems.

Accurate modeling of proton exchange membrane fuel cells (PEMFCs) is essential for predicting system performance under diverse operating conditions. This study introduces a refined semi-empirical modeling that combines experimental validation with an enhanced parameter estimation method based on the Cloud Drift Optimization (CDO) algorithm. The approach focuses on identifying seven key parameters of the nonlinear PEMFC model by minimizing the difference between experimentally measured and simulated cell voltages. To assess its effectiveness, the proposed CDO-based estimator was compared with several established metaheuristic algorithms, including the particle swarm optimizer and the tetragonula carbonaria optimization algorithm. The evaluation was performed using three commercial PEMFC stacks rated at 250 W, 500 W, and the NedStack PS6, as well as experimental data obtained from the Renewable Energy Laboratory at A’Sharqiyah University. Results demonstrate that the CDO algorithm consistently produced the lowest sum of squared errors (SSE) of 1.0337 and exhibited stable convergence across multiple independent runs with a standard deviation of 1.2114 × 10⁻⁷. Its reliable performance under both normal and degraded conditions confirms the algorithm’s robustness and adaptability, establishing CDO as an efficient and dependable technique for PEMFC modeling and parameter identification.