Ionics最新文献

Designing hybrid battery systems based on Li/Na coexisting silicate framework with synergized lithium’s high energy density with sodium’s economic advantages is still challenging. Herein, a series of Li_2-xNa_xFeSiO₄ (where x = 0, 0.25, 0.5, and 1.0) cathode materials were constructed through vibratory ball milling-assisted solid-state synthesis. The optimized sample at the composition x = 0.25 showed a single-phase monoclinic P2₁-Li₂FeSiO₄ phase, with exceptional electrochemical performances. By contrast, higher sodium contents (x ≥ 0.5) resulted in dual-phase mixtures of Na₂FeSiO₄ and Li₂FeSiO₄, along with some undesirable impurities of Li₅FeO₄ and Na₆Si₂O₇. The electrochemical characterization revealed that the introduction of sodium ions in the deintercalation reaction increased the interfacial charge transfer resistance (R_ct) due to the Na⁺ barrier, but also significantly improved the Li⁺ diffusion coefficient (D_Li⁺), suitable for enhancing ionic utilization efficiency for an optimized specific capacity. Overall, strategically incorporating sodium at lithium sites can effectively increase the storage capacity while reducing dependence on lithium resources for economical energy storage devices.

With high theoretical discharge specific capacity, excellent reversibility of electrochemical reaction, and suitable oxygen evolution overpotential, α-Ni(OH)₂ is an auspicious cathode material for alkaline batteries, which supports the development of high-capacity nickel-based batteries. Nevertheless, α-Ni(OH)₂ exhibits substandard structural stability and is susceptible to crystalline transformation, leading to capacity degradation, affecting the battery charge/discharge performance and cycle life. In this study, the TiO₂-nickel hydroxide (TiO₂-α-Ni(OH)₂) is prepared by high-energy ball milling. Among them, TiO₂ can be uniformly dispersed on the surface of α-Ni(OH)₂, which can effectively prevent the collapse of the α-phase structure. Benefiting from the unique elemental doping technology, the TiO₂-α-Ni(OH)₂ composites as a nickel-metal hydride (Ni-MH) battery cathode can release a reversible specific capacity of 390 mAh g⁻¹ at a 2 C rate for 200 cycles. A reversible specific capacity of 210 mAh g⁻¹ is still released after 200 cycles at a 5 C rate. Consequently, TiO₂-α-Ni(OH)₂ is a promising candidate for high-power Ni-MH batteries.

Energy is an essential factor for human survival and development. With the development of new energy sources, the demand for energy storage devices has been increasing. In recent years, supercapacitors have gained more attention in the field of energy storage. Electrodes, as the core components in the energy storage process of supercapacitors, play a crucial role, and the design and preparation of electrode materials are key areas of research. Common electrode materials mainly involve carbon materials, conductive polymers, and transition metal oxides. Among them, cobalt-based nanomaterials are considered a highly promising pseudocapacitive electrode material due to their diverse valence states and excellent electrochemical performance. This article primarily reviews the research status of cobalt-based oxides, cobalt-based sulfides, cobalt-based hydroxides, cobalt-based inorganic salts, and cobalt-based phosphides as electrode materials for supercapacitors from the perspectives of synthesis methods, material morphology, and electrochemical performance. It summarizes the advantages and disadvantages of these materials, modification methods, and discusses the prospects and challenges of cobalt-based compounds and their composite materials in supercapacitor applications. Compared with previous reviews, this work integrates the most recent research findings from 2023 to 2025 and places particular emphasis on morphology control strategies (such as hollow, core–shell, and layered structures), composite design (such as integration with carbon materials or conductive polymers), and the correlation between structural features and electrochemical behavior. The goal is to provide up-to-date theoretical guidance and design strategies for the development of high-performance cobalt-based electrode materials.

Rechargeable aqueous zinc-ion batteries (RAZIBs) have emerged as promising candidates for large-scale energy storage systems, owing to their low cost, high safety, and environmental benignity. However, their practical application is hindered by intrinsic limitations of cathode materials, including sluggish Zn²⁺ diffusion kinetics, poor electrical conductivity, and structural degradation during cycling. Herein, we report a facile hydrothermal strategy to synthesize Ag-doped MnO₂ cathode with uniform urchin-like nanostructures surrounded by nanowires as high-performance cathodes for RAZIBs. The optimal 5 wt% Ag doping yields denser nanowire arrays, namely 5Ag/MnO₂, increases accessible active sites, and introduces lattice defects while preserving the tetragonal MnO₂ crystal structure (I4/m). The electrochemical characterization demonstrated that 5Ag/MnO₂ exhibited significantly enhanced kinetics, including lower charge-transfer resistance, reduced polarization, and higher Zn²⁺ diffusion coefficient. Consequently, the 5Ag/MnO₂ cathode delivered a high initial discharge capacity of 153.4 mAh g⁻¹ at 1 A g⁻¹ and superior long-term cycling stability (the discharge specific capacity remained at 135.5 mAh g⁻¹ and 88.3% capacity retention after 500 cycles). Mechanism studies via ex situ XRD, XPS, and CV reveal a reversible Zn²⁺/H⁺ storage mechanism involving phase transformation between MnO₂ and ZnMn₂O₄, with mixed capacitive and intercalation behavior. The enhanced performance is attributed to Ag doping facilitating rapid ion diffusion, improving electronic conductivity, and stabilizing the hierarchical structure. This work provides a viable strategy for designing high-performance MnO₂-based cathodes for advanced zinc-ion batteries, advancing the practical application of RAZIBs.

The prediction of the remaining useful life (RUL) of lithium-ion batteries (LIBs) is essential for effective battery management systems (BMS), safety assurance, and timely maintenance, particularly in ensuring the reliable operation of electric vehicles (EVs). This study aims to develop an accurate prediction model using Optuna for hyper-tuning with the Kolmogorov Arnold Network (KAN) to assess battery health and estimate its lifespan. KAN, an advanced neural network, introduces a novel approach to machine learning by replacing traditional linear weights with univariate functions parameterised by splines. This enables the model to flexibly capture complex activation patterns, significantly enhancing its predictive capabilities. In this study, we propose Opt-KAN-XAI as an effective method to accurately estimate the RUL in energy storage devices. To further enhance interpretability, we integrate explainable artificial intelligence (XAI) techniques using Shapley additive explanations (SHAP) values. This analysis examines the influence of key features, such as temperature, cycle index, voltage, and current, on RUL predictions, highlighting the substantial impact of temperature on discharge capacity. The findings of this research underscore the potential of machine learning models in LIBs management within the XAI framework, demonstrating their strategic role in optimising energy storage systems. To validate the Opt-KAN-XAI method, we conducted experiments on the NASA and CALCE datasets and compared the results with other existing approaches. The experimental results confirm the model’s high accuracy and robustness, achieving a minimum test loss, root mean square error (RMSE) and a minimum mean absolute error (MAE), demonstrating its effectiveness in the precise estimation of RUL.

Accurate estimation of the State of Health (SOH) of lithium-ion batteries enables battery management systems to effectively monitor battery status, thereby preventing the occurrence of battery safety accidents. To address the challenges of difficult acquisition of complete charge–discharge data and low estimation accuracy under actual operating conditions, this study proposes an SOH estimation method based on time–frequency analysis and charging voltage segments. Health-related features are extracted within the voltage interval with the highest frequency in the charging data, and Discrete Wavelet Transform (DWT) is utilised to perform time–frequency decomposition on the input features. Each decomposed component is transmitted to the Temporal Convolutional Network (TCN) and Bidirectional Long Short-Term Memory (BiLSTM) branches respectively. Concurrently, the Transformer is used to capture global information, and finally, the SOH estimation value is output through the fully connected layer. Relatively accurate estimation of battery SOH can be achieved using only charging voltage data with a length of 0.1 V. Validation and analysis on the CACLE dataset demonstrate that the mean absolute error is within 1%. Generalisation verification is completed on the Oxford dataset, indicating that the proposed model exhibits excellent generalisation performance.

The CuO/SnO₂ nanocomposites (NCs) were synthesized using a simple hydrothermal process to produce high-performance photocatalytic and electrochemical applications. X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR), field emission scanning electron microscopy with energy-dispersive X-ray spectroscopy (FESEM-EDX), high-resolution transmission electron microscopy with selected area electron diffraction (HRTEM-SAED), UV–visible diffuse reflectance spectroscopy (UV-DRS), and X-ray photoelectron spectroscopy (XPS) investigations were used to characterize the properties of the prepared NCs. The XRD patterns ensured tetragonal and combined (monoclinic/tetragonal) phases for SnO₂ and CuO/SnO₂ composites, respectively. The estimated crystallite sizes were in the nanometer range and decreased from 29 to 21 nm with increasing CuO content. FTIR spectra were used to identify the metal oxide peaks that corresponded to SnO₂ and CuO. The FESEM image for 1CS7 NC shows spherically agglomerated particles, whereas for 2CS7 and 3CS7, NCs exhibit a few rod structures along with spherical-shaped particles. The HRTEM images revealed the spherical morphology for optimized 1CS7 NCs. The oxidation states of synthesized CuO/SnO₂ composites were confirmed using XPS investigations. Utilizing Kubelka–Munk method, the band gap values were determined for 1CS7, 2CS7, and 3CS7 NCs that rise from 2.66, 3.33, and 3.50 eV, respectively. The photocatalytic behavior of CuO/SnO₂ NCs was studied by degrading methyl violet (MV) dye under solar irradiation for 70 min. The 1CS7 NC had higher degradation efficiency (91%) than other synthesized NCs. Furthermore, at a current density of 0.5 Ag⁻¹, the 1CS7 electrode exhibited a specific capacitance of 770 Fg⁻¹, and this electrode shows enhanced cyclic stability, maintaining 93.4% up to 2000 cycles. This investigation reveals that the 1CS7 composite is the outstanding material for photocatalytic and supercapacitor applications.

Accurate prediction of the State of Health (SOH) is essential for the safety and reliability of lithium-ion batteries. Traditional single-model architectures encounter challenges related to noise resistance and prediction accuracy when processing complex battery aging patterns. This paper proposes a DSwin-Transformer architecture that integrates relaxation voltage analysis with deep learning techniques. The method combines a denoising autoencoder (DAE) for feature dimensionality reduction with a hierarchical window attention mechanism to capture local degradation details and global aging dependencies. In this framework, the relaxation voltage is selected as the primary aging feature. Additionally, representative auxiliary aging features are identified from variables such as constant current charging time and voltage area using Pearson correlation analysis, mutual information, and SHAP values. Experimental validation across the CALCE and Tongji datasets demonstrates performance with mean absolute errors below 0.95%, showing improvements over Convolutional Neural Networks (CNN)-Gated Recurrent Units, CNN-Long Short-Term Memory, and CNN-Transformer baseline methods across various battery cells and training volumes. Ablation studies reveal the contribution of the DAE module, with the root mean square error (RMSE) improving from 0.179 to 0.0084 on the CALCE dataset for complex degradation patterns. The model maintains accuracy during both capacity decay and recovery periods while requiring a footprint of 15.6 MB. The results indicate that combining relaxation voltage features with adapted computer vision architectures provides a practical approach for predicting battery SOH.

The extensive application of the electrolyte for solid oxide fuel cells (SOFCs) at intermediate temperatures (IT) is limited mainly owing to low conductivity. The effects of the Yb₂O₃ content on the phase stability, microstructure and ionic conductivity of the Yb₂O₃ and Sc₂O₃ co-doped ZrO₂ (YbScSZ) ceramic electrolyte synthesized by the sol–gel-hydrothermal and calcination methods were investigated through X-ray diffraction (XRD), scanning electron microscopy (SEM), and electrochemical impedance spectroscopy (EIS) in this paper. The results show that the 3Yb₂O₃–8Sc₂O₃–89ZrO₂ (mol%, 3Yb8ScSZ) exhibits a density of 5.80 g cm⁻³, an activation energy of 0.578 eV, and an ionic conductivity of 1.02 × 10⁻¹ S cm⁻¹ at 800 ℃. It is attributed to the good phase stability and high density of the cubic Yb₂O₃ and Sc₂O₃ co-doped ZrO₂, and the low activation energy. Certain Yb³⁺ replacements for Sc³⁺ can increase the grain size and uniformity without reducing the oxygen vacancy density, which is beneficial for enhancing ionic conduction.