Ionics最新文献

The commercialization of sodium-ion batteries (SIBs) is significantly impeded by critical challenges inherent to their anode materials, primarily including substantial volumetric fluctuations due to the larger ionic radius of Na⁺, intrinsically sluggish reaction kinetics, and low initial Coulombic efficiency (ICE), among other issues. This paper provides a comprehensive overview of anode materials, including carbon-based, metal compounds, alloy-based, and organic types, and analyzes their synthesis methods, microstructures, reaction mechanisms, and electrochemical properties. Based on a statistical analysis of the most cited papers for various anode materials over the past 7 years, this study gives the development trend and research focus for anode materials and their corresponding modification methods. Furthermore, for the highly studied anode materials, the correlation between their modification strategies and key electrochemical performance metrics, namely, Coulombic efficiency, rate capability, and cycling stability, was systematically examined. Finally, the advantages and limitations of different categories of materials and their modification methods are systematically summarized, providing forward-looking guidance and valuable references for future research on SIBs anode materials and their commercial applications.

The state of charge (SOC) of lithium-ion batteries is a critical parameter for ensuring safe and stable battery operation. Therefore, accurate estimation of lithium-ion battery SOC is required. The forgetting factor recursive least squares (FFRLS) algorithm and extended Kalman filter (EKF) algorithm are widely applied in SOC estimation. The selection of initial parameters matrix to be identified in the FFRLS algorithm and initial noise covariance matrices in the EKF algorithm directly affects the accuracy of SOC estimation. However, determining optimal initial matrices is particularly challenging. To more accurately estimate the SOC of lithium-ion batteries, the pattern search algorithm is implemented using the patternsearch function in MATLAB. This approach optimizes arbitrarily selected initial parameters matrix to be identified and initial noise covariance matrices. After optimization, the optimized initial matrices are used to perform online parameter identification and SOC estimation respectively. The simulation results demonstrate that after optimizing the initial matrices, the average SOC estimation accuracy under different temperature environments improved by 79.82% for Dynamic Stress Test (DST) and by 80.20% for Federal Urban Driving Schedule (FUDS) that simulates city driving environments. This optimization provides assurance for the accuracy and stability of SOC estimation using the FFRLS algorithm, its improved variants, and the EKF algorithm.

Optimizing the performance and lifespan of lithium-ion batteries (LIBs) is a key step toward advanced energy storage. Existing multiphysics models often miss important couplings, which limits simulation fidelity, and their intensive computations slow iterative design. This study presents a physics–data fusion framework for multi-objective optimization. A coupled ageing model—covering electrochemical, thermal, mechanical, and side-reaction effects—generates degradation data in COMSOL Multiphysics. These data are used to train machine learning (ML) surrogate models. Electrode thickness, solid phase volume fraction, and initial lithium-ion concentration are chosen by Latin Hypercube Sampling (LHS). SHapley Additive exPlanations (SHAP) interpretation quantifies each variable’s influence on the surrogate outputs. The surrogate is paired with a genetic algorithm to explore the design space, achieving a 28.47% increase in energy density (ED) and an 8.33% reduction in capacity loss (CL). This approach offers valuable insights for LIB structural design and provides potential guidance for improving battery manufacturing processes.

Solid polymer electrolytes (SPEs) composed of sodium carboxymethyl cellulose (NaCMC), sodium alginate (SA) and poly (vinyl alcohol) (PVA) integrated with zinc oxide (ZnO) nanoparticles were prepared via solution casting. FTIR and XRD analyses confirmed the interactions between the components and the presence of crystalline ZnO with a Wurtzite structure, while SEM and EDX revealed uniform dispersion of nanoparticles. Thermal analyses (DSC, TGA) indicated the enhanced thermal stability. The highest conductivity of 8.23 × 10^− 6 S cm⁻¹ is observed for the sample with 2 wt% ZnO for room temperature, which also showed increasing conductivity with temperature. Dielectric studies revealed space charge polarization effects and temperature-dependent relaxation behaviour. The ionic transference number was 0.74, indicating dominant ionic conduction. The best-performing sample, when employed in a primary battery, delivered a zero-load voltage of 1.373 V, area density of current 1946.9 µA cm⁻², specific energy of 21.57 Wh kg⁻¹, power flux density of 1.035 W kg⁻¹, and nominal capacity of 458.3 mAh g⁻¹, demonstrating its potential for energy storage applications.

The State of Health (SOH) of lithium-ion batteries is an important parameter of the battery management system and plays a decisive role in the reliability and safety of the batteries. This paper proposes an innovative northern goshawk optimization - hybrid neural network (NGO-HNN) algorithm for highly accurate SOH estimation. First, the convolutional neural network (CNN) layer extracts local features from the original battery data to capture important patterns during the battery charging process. Next, the bidirectional long short-term memory network (BiLSTM) layer learns the long-term dependencies of the battery data from both forward and backward directions to enhance the understanding of the temporal information. Then, the self-attention (SA) weights the output of the BiLSTM to highlight the features most relevant to the SOH estimation. Finally, the NGO algorithm globally optimizes the model’s hyperparameters by simulating the predatory behavior of the northern goshawk, avoiding getting trapped in local optimal solutions and further improving the model’s accuracy and generalization ability. The verification results on the National Aeronautics and Space Administration (NASA) dataset show that, compared with the hybrid neural network (HNN) algorithm, the proposed NGO - HNN algorithm reduces the maximum error (ME) by more than 37.27% in the single - battery verification and by more than 15.86% in the multi - battery cross - validation. This research provides an efficient and reliable solution for the SOH estimation of lithium-ion batteries. 

Although machine learning techniques have been widely employed for state-of-health (SOH) estimation of lithium-ion batteries (LIBs) in electric vehicles (EVs), the inherent complexity of LIBs’ reaction mechanisms and their highly nonlinear, time-dependent aging processes present significant challenges. Traditional SOH estimation methods, which depend on complete cycling data, are often impractical in real-world deployment scenarios due to data limitations. To address these issues, we propose a voltage-interval optimized SOH estimation approach based on incremental capacity analysis (ICA) and correlation feature selection. In this method, the charging voltage profile is divided into multiple diagnostic intervals, and the optimal voltage window is determined by analyzing the correlation between incremental capacity curve peak characteristics. This approach not only reduces data redundancy but also ensures high estimation accuracy by selecting degradation-sensitive features. Furthermore, a dynamic neural network-based SOH estimation model is developed and validated using both CALCE and NASA datasets. Experimental results demonstrate that the maximum error in SOH estimation is below 1.5%, and the computation time is reduced by approximately 66.67% compared to conventional methods. The proposed approach shows superior robustness, practical applicability, and strong generalization capability across various battery types and degradation conditions.

This article presents for the first time the scientific measurement and knowledge network analysis of the development and evolution of enzymatic fuel cell (EFC) technology in the past 41 years, based on information from 980 relevant articles in the Web of Science (WoS) Core Collection. WoS data have been edited for “Biblioshiny Software” using “R Programming” and then these edited data have been transferred to “Biblioshiny Software”. The results showed that the EFC research field exploded in 2017. The USA was the country with the strongest collaboration, and the author of the most cited article globally is Zebda A. The core findings from the analysis results showed that the research area was focused on the design of biobatteries, supercapacitors, micro- EFC designs, wearable and self-powered biosensors in last 4 years. Also, EFC with dual-mode sensing and EFCs developed for use as tumor biomarkers are new research areas. This article will help readers gain a quick perspective on the research structure and future directions by mapping the research knowledge on EFC through bibliometric analysis.

The development of efficient and stable electrocatalysts for the hydrogen evolution reaction (HER) is crucial for advancing sustainable hydrogen production. In this study, a polyaniline (PANI)-supported Pd–Au bimetallic film on pencil graphite (PGP), denoted as Pd–Au–PANI@PGP, was fabricated and evaluated for HER activity in 0.5 M H₂SO₄. The catalyst was characterized using Scanning Electron Microscopy (SEM) and Energy-Dispersive X-ray Spectroscopy (EDX), which confirmed the successful surface modification of PGP with Pd and Au, along with the polymeric network of PANI. The chemical composition and electronic structure were further examined by X-ray Photoelectron Spectroscopy (XPS), revealing a high proportion of metallic Pd and Au species. Electrochemical performance was assessed via linear sweep voltammetry (LSV), Tafel analysis, electrochemical active surface area (ECSA), and turnover frequency (TOF) measurements. The Pd–Au–PANI@PGP electrode exhibited an exceptionally low overpotential of 31.6 mV at 10 mA cm⁻², comparable to the benchmark Pt–C@GC catalyst (19 mV). The enhanced activity is attributed to the synergistic effect of Pd and Au, which facilitates electron transfer and accelerates catalytic kinetics. Tafel slope analysis confirmed that the HER process is primarily governed by the Volmer step, with Pd–Au–PANI@PGP exhibiting the lowest slope (91.44 mV dec⁻¹), indicative of improved reaction kinetics. The high exchange current density (4.65 mA cm⁻²) and large ECSA (1.64 cm²) further validate its superior catalytic activity. Moreover, the TOF of Pd–Au–PANI@PGP (0.0971 s⁻¹) significantly surpasses that of other modified electrodes, confirming its excellent intrinsic activity. Long-term stability, evaluated by chronoamperometry, showed negligible current degradation over 6 h, underscoring the durability of the catalyst. Overall, these results demonstrate that Pd–Au–PANI@PGP is a highly promising HER electrocatalyst, offering outstanding activity, rapid reaction kinetics, and excellent stability, making it a viable candidate for future hydrogen production applications.

This work demonstrates the use of recycled LiMn₂O₄-based cathode powder (SLBCP) from spent Li-ion batteries as a catalyst for the selective reductive cleavage of the azo dye Sunset Yellow (SY) under mild acidic conditions. The catalyst promotes cleavage of the –N = N– bond, yielding sulfanilic acid (SA) and 1-amino-2-naphthol-6-sulfonic acid (ANSA) as stable products, while COD analyses confirm that the overall organic load remains essentially unchanged. Kinetic analysis indicates an apparent first-order profile consistent with a Langmuir–Hinshelwood mechanism, and electrochemical studies reveal progressive passivation of Mn⁴⁺ sites that correlates with partial catalyst deactivation. These findings highlight a sustainable pathway that couples e-waste valorisation with dye upcycling, illustrating how spent battery materials can be repurposed to enable selective reduction processes in aqueous systems.

Graphical Abstract

The accurate estimation of state-of-charge (SoC) is critical within smart battery management systems (BMS). Despite numerous research articles discussing effective SoC estimation techniques, there remains a need to enhance the accuracy of the SoC estimation module, especially given its significance in various vehicular applications. In this context, the article examines machine learning operations (MLOps) for SoC estimation in Li-ion batteries, focusing on principles and practices aimed at effectively managing and implementing machine learning models in practical scenarios. Operational machine learning for SoC estimation involves leveraging real-time data from battery systems to continually enhance the precision and reliability of SoC predictions. Furthermore, the article extensively discusses the requirements, operations, and constraints associated with the models. It specifically addresses the challenge of model selection in MLOps, taking into account critical aspects of SoC estimation and performance metrics of machine learning models. The article aims to offer clarity on selecting, utilizing, and feasibly implementing MLOps models for advancing SoC estimation toward potential real-time applications. The study utilized real-life data from Panasonic 18650PF Li-ion battery cells to train and test the MLOps models under consideration. The machine learning application was implemented using Python.