Ionics最新文献

This study reports an eco-friendly microwave-assisted synthesis of cube-like mesoporous potassium tantalate (KTaO₃) nanoparticles using areca seed as a green fuel. Comprehensive structural and morphological characterizations confirmed the formation of highly crystalline, mesoporous KTaO₃ with a unique cubic morphology. Electrochemical evaluations demonstrated excellent lithium-ion battery anode performance, featuring a high reversible capacity of 610 mAh g⁻¹, outstanding cycling stability over 500 cycles, remarkable rate capability up to 3 C, and a coulombic efficiency of > 95%. Additionally, the material exhibited superior electrocatalytic activity for non-enzymatic glucose sensing, attributed to enhanced electron transfer and high surface area. The synthesis approach combines sustainability with multifunctionality, offering a promising pathway to develop advanced materials for energy storage and biomedical sensing applications.

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Sodium-ion batteries (SIBs) have emerged as a convincing alternative to lithium-ion batteries owing to the abundance and low cost of sodium resources. The prime requirement for improving the performance of SIBs is indeed the development of high-efficiency anode materials. This review provides a comprehensive overview of recent progress in anode materials for SIBs, including carbon-based materials, alloy-type materials, metal oxides, phosphides, sulfides, and organic compounds. Each material class is discussed in terms of structure, sodium storage mechanism, electrochemical performance, and key challenges. Particular attention is paid to carbonaceous anodes such as hard carbon, which currently leads the field due to their commercial viability, environmentally friendly nature, and economic benefits. Strategies such as heteroatom doping, surface modification, and nano-structuring are evaluated for their effectiveness in electrochemical performance. This review aims to guide researchers seeking to optimize anode materials for next-generation SIBs with high energy density, long cycle life, and robust safety performance.

The composite of transition metal oxides with ferrites shows promising potential as the electrode for energy storage applications. In this work, SiO₂ is dispersed in manganese cobalt ferrite (MnCoFe₂O₄), synthesized by a sol-gel auto-combustion method. The physicochemical characteristics of MnCoFe₂O₄/SiO₂ are evaluated using Fourier-transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), energy-dispersive X-ray (EDX), and BET analysis. The electrochemical behaviour is evaluated by cyclic voltammetry (CV), galvanostatic charge-discharge (GCD), and electrochemical impedance spectroscopy (EIS), representing the comparable efficiency and reversibility of the electrode materials. The electrochemical response of the optimized MCF-2% working electrode shows the highest specific capacitance of 296 F g^− 1 at a current density of 1 A g^− 1 and ~ 92% capacitance retention after 5,000 cycles at 100 mV s^− 1. The results suggest that the synthesized hierarchical porous MCF-2% was a promising candidate for supercapacitor electrode application.

Biochar made from Empty Fruit Bunch (EFB) and biochar/metal oxide composite has recently drawn interest as supercapacitor electrode material because of its high specific surface area, potential stability, and affordability. This research focuses on producing biochar from EFB using gasification method and activating it, followed by developing its composite materials of ZnO/biochar, along with developing high-performance supercapacitors. The physico-chemical properties of the produced biochar and ZnO/biochar composites with varying mass ratio were investigated with the help of Fourier-transform infrared spectroscopy (FTIR), X-ray Diffraction (XRD), field emission scanning electron microscopy (FESEM), and energy dispersive X-ray spectroscopy (EDS). The purpose of this investigation is to verify the successful integration of biochar and ZnO into a composite material, as well as to investigate the structural and morphological characteristics. Electrochemical analyses of cyclic voltammetry (CV), galvanostatic charge-discharge (GCD), and electrochemical impedance spectroscopy (EIS) were then carried out on the biochar and all synthesized ZnO/biochar composites with mass ratios of 1:1, 1:2, and 1:3. The electrochemical performance of biochar and ZnO/biochar is favourable according to the experimental results obtained. The 3-electrode system result has obtained a maximum specific capacitance of 145 Fg^− 1 for biochar and 295 Fg^− 1 for a ZnO/biochar ratio of 1:2 at a current density of 0.5 Ag^− 1. For 3-electrode system, the series resistance (Rs) of biochar and ZnO/biochar (1:2) is 1.12Ω and 1.95Ω respectively while the charge transfer resistance (Rct) of biochar and ZnO/biochar (1:2) is 18.54Ω and 0.85Ω respectively. The fabricated hybrid supercapacitor achieved the highest specific capacitance of 120 Fg^− 1 for biochar and 226 Fg^− 1 for ZnO/biochar (1:2). For fabricated hybrid supercapacitor, the series resistance (Rs) of biochar and ZnO/biochar (1:2) is 3.23Ω and 2.81Ω respectively while the charge transfer resistance (Rct) of biochar and ZnO/biochar (1:2) is 14.87Ω and 9.35Ω respectively. After 5000 charge-discharge cycles, the fabricated supercapacitor maintained 90% of capacitance for biochar and 93% for ZnO/biochar (1:2). Overall, the findings showed that biochar exhibits excellent potential for use in supercapacitors.

The development of efficient and low-cost electrocatalysts for hydrogen evolution reaction (HER), especially for alkaline water electrolyzers, is one of the key technical bottlenecks to promote the large-scale industrial production of hydrogen energy. Molybdenum disulfide (MoS₂), as a non-noble metal-based catalyst with abundant earth reserves, exhibits excellent HER catalytic performance in acidic media and is regarded as an ideal acidic system HER catalyst. However, it has the problem of high overpotential due to the slow reaction kinetics in alkaline electrolyte, which greatly limits its application in the field of alkaline water electrolysis. In response to this challenge, this study used a one-pot synthesis strategy to successfully prepare a Ni₃S₄-MoS₂ heterostructure catalyst. Abundant heterojunction structures are constructed inside the prepared Ni₃S₄-MoS₂ catalyst, which has been verified by X-ray photoelectron spectroscopy (XPS) and high-resolution transmission electron microscopy (HRTEM). HRTEM directly reveals the clear morphology of the interface between Ni₃S₄ and MoS₂, and XPS confirms the element interaction at the heterojunction interface from the chemical state level. Further combination of XPS analysis and density functional theory (DFT) calculations shows that the heterostructure has a significant interfacial electron redistribution phenomenon. This electronic structure regulation has laid a key structural foundation for optimizing the surface reaction energy barrier of the catalyst and improving the HER catalytic performance. In 1 M KOH alkaline electrolyte, the Ni₃S₄-MoS₂ heterostructure exhibits excellent HER catalytic performance, when the current density reaches 10 mA cm^− 2 the overpotential is only 66, and the corresponding Tafel slope is as low as 57 mV dec^− 1. After 25 h of continuous operation, the catalytic activity did not decrease significantly, showing good long-term stability.

The proton-conducting solid bio-membranes have been developed using Centella Asiatica Leaf (CAL) and ammonium nitrate (NH₄NO₃) via the solution-casting method. The crystalline/amorphous nature of the solid bio-membranes has been examined through X-ray diffraction analysis. The surface morphology of the prepared solid bio-membranes has been analyzed using scanning electron microscopy. The thermal properties and stability of the solid bio-membranes have been evaluated by differential scanning calorimetry and thermogravimetric analysis, respectively. The electrical, dielectric, and transport properties have been studied through AC impedance analysis. Transport parameters have been calculated using the Arof and Trukhan models. The solid bio-membrane CALAN3 (CAL + 0.4 M. wt% of NH₄NO₃) exhibits the highest proton conductivity of 3.37 × 10^− 3 S/cm at room temperature. The strength of CAL and CALAN3 has been determined with a dynamic testing machine. The electrochemical stability of the highest conducting solid bio-membrane (CALAN3) has been explored using linear sweep voltammetry. A primary proton-conducting battery has been constructed with the highest proton-conducting solid bio-membrane (CALAN3), showing an open circuit voltage (OCV) of 1.61 V. The battery’s discharge performance has been investigated with various loads. A single proton exchange membrane fuel cell (PEMFC) fabricated with the highest proton-conducting solid bio-membrane has been observed to have an open circuit voltage of 645 mV at the 10th cycle. The performance of the single PEMFC has been analyzed using the I-V polarization and I-P curves, resulting in maximum current density and power density of 6.44 mA/cm² and 2.23 mW/cm², respectively.

Developing appropriate catalysts to boost electrocatalytic reactions is of vital importance for accelerating the commercialization of advanced energy conversion technologies. Carbon nanoboxes (CNBs) are superior electrocatalysts due to their unique 3D hollow structure that offers high surface area, abundant active sites, and enhanced mass transfer. Their high conductivity promotes electron transfer and reaction kinetics. The carbon framework allows easy heteroatom doping (e.g., N, S) or metal nanoparticle loading for tunable electronic structure, activity, and selectivity. Inherent carbon stability and strength ensure durability in harsh environments. These properties enable outstanding performance in various electrochemical reactions. This review summarizes recent progress of employing CNBs as advanced electrocatalysts for energy conversion reactions, highlighting their distinctive advantages of extremely high specific surface area, excellent electron transfer capability, strong metal-substrate interaction, good structural stability, and controllable mass transport. Subsequently, the applications of CNBs-based catalysts for various electrochemical reactions are also manifested, highlighting the underlying structure-activity relationship. Finally, the challenges and future direction of engineering and study of CNBs are also discussed to offer guidance for the further development of more efficient CNBs-based catalysts.

Establishment of precise state-of-health (SOH) consists of a vital step to guarantee reliable, secure, and preventive maintenance of electric vehicle (EV) fleets and grid-associated energy storage systems based on lithium-ion batteries. Nevertheless, existing SOH estimation approaches primarily consider situations where complete sensor profiles or full charge/discharge conditions are available, which is not often the case due to sensor degradation, communication losses, or economic factors. This work introduces a novel Physics-Informed Neural Network (PINN) framework to enable robust SOH estimation under partial observability, leveraging minimal sensor data and physics-constrained learning to ensure both accuracy and interpretability. The model relies on an approximation of the PINN architecture that is able to embed the fundamental degradation mechanisms, such as solid electrolyte interphase (SEI) layer growth and capacity fading, as information that the training process can utilize through physical regularization. While PINN is semantically mixed with traditional purely data-driven models, it uses a composite loss function that leverages data consistency with underlying electrochemical laws. This enables accurate predictions even when no further capacity measurements or complete voltage profiles are available. The framework is tested in the case of publicly available lithium-ion battery data sets, which show superior generalization and the capacity to withstand situations where missing inputs or sensor noise are introduced under simulated sensor sparsity scenarios. The results of the experiments demonstrate the quality of the model, which enhances the precision of SOH estimation in conditions of partial observability and also increases the model’s interpretability and applicability in real-time in various operational settings. Statistical results show that the proposed method achieves a mean absolute error (MAE) of 0.008, root mean square error (RMSE) of 0.011, and a coefficient of determination (R²) of 0.96, outperforming baseline models such as CNN–LSTM and hybrid SE-NN by 25–40% in sparse data regimes. Furthermore, the framework exhibits high generalization capability across different chemistries and retains robustness with as little as 50% of the input features. These results underscore the practical potential of the proposed PINN approach for real-time, physically consistent battery health diagnostics in embedded battery management systems.

The accurate estimation of the state of charge (SOC) of lithium-ion batteries is a core technological challenge in battery management systems. However, data-driven SOC estimation methods have insufficient feature representation capabilities, and the attention mechanism in traditional Transformer models has limitations in capturing local dynamic features, making it difficult to differentiate the importance of features and to overly concentrate weight distribution in peak areas. To address these issues, this paper proposes an enhanced feature extraction method for lithium-ion battery SOC estimation. This method uses Singular Spectrum Analysis (SSA) to perform trend decomposition on input features and constructs a six-dimensional time-series feature matrix by combining the original input features to enhance the input representation ability. Additionally, an improved Transformer model, CCHformer (Causal Convolution-Channel Attention-Hilly Attention Transformer), is introduced, which incorporates a DCC Block (Dilated Causal Convolution with Channel Attention) in its architecture—this block extends the receptive field via dilated causal convolution and integrates a channel attention mechanism to enhance the model’s capability of capturing multi-scale local features and key information—while replacing the traditional self-attention mechanism with a Hilly Attention mechanism that optimizes weight distribution through a power-law transformation. Comparative experiments on a publicly available battery dataset show that the root mean square error (RMSE) of SOC estimation using this method remains stable below 0.85(%) at under three temperature conditions and two testing scenarios, with the coefficient of determination (R(^{2})) consistently above 99.88(%).

The layered oxide cathode materials of sodium-ion batteries have received extensive attention due to their high theoretical capacity and low production costs. However, the practical application of layered cathodes is limited by the poor structural reversibility. In this study, the O3-type NaNi_1/3Fe_1/3Mn_1/3O₂ cathodes were coated with TiNb₂O₇ by a simple solid-state sintering method. The TiNb₂O₇ coating layer suppresses side reactions and enhances the surface stability of the cathode material, and the trace doping of Ti⁴⁺ and Nb⁵⁺ alleviates lattice strain and enhances the migration of Na⁺. As a result, the modified cathode material demonstrates a reversible specific capacity of 140.6 mAh g^− 1 at 1 C, which is higher than that of pure phase material (112.3 mAh g^− 1). The capacity retention of the modified sample was improved by 13.4% after 100 cycles. This effective modification strategy enhances the practicality of layered oxide cathodes in sodium-ion batteries.