Ionics最新文献

Flexible counter electrodes (FCEs) emerge as a promising alternative to the conventional, rigid, and expensive platinized FTO-based CEs in wearable electronics. In flexible dye-sensitized solar cells (DSSCs), the CE must have a large surface area with high conductivity for efficient electron transfer, strong catalytic activity for redox reactions, and stability in the electrolyte. Platinum (Pt) is still the standard CE material based on its excellent catalysis and conductivity. However, its rarity, high cost, and low mechanical flexibility prevent large-scale commercialization, especially in flexible DSSCs. This study critically reviews the recent developments in carbon-based materials, including carbon nanotubes (CNTs), graphene, and mesoporous carbon (MC), as well as conducting polymers, such as polyaniline (PANI), polypyrrole (PPy), and poly (3,4-ethylene dioxythiophene) (PEDOT), and transition metals. This review also examines composite FCE materials and their structural modification. Comparative understanding underlines how nanostructured carbons, polymer composites, and transition-metal-based hybrids are coming forward as sustainable alternatives that offer efficiency reaching or exceeding Pt benchmarks without losing flexibility. Lastly, this review presents existing challenges, such as interfacial stability, mass production, and long-term operating reliability, and offers future directions for the design of next-generation FCE materials to facilitate low-cost, high-efficiency DSSCs in wearable and portable energy devices.

Porous carbon fibers possess the advantages of high conductivity, rich channels for mass transport and electron transfer, and abundant anchoring sites for loading highly active components. These favorable merits enable porous carbon fibers to be promising candidates for serving as advanced electrocatalysts. To gain a deeper understanding of the merits and potentials of porous carbon fibers for boosting electrocatalytic reactions, this review summarizes recent advances in modifying them to boost electrocatalytic reactions. The review is started by discussing the typical synthesis methods of porous carbon fibers, which include the electrospinning-carbonization, in-situ composite growth, and chemical etching methods. Subsequently, some effective strategies for the modification of porous carbon fibers are also discussed, such as heteroatom doping, defect engineering, single-metal and their compounds doping, and coupling with other compounds. Moreover, the applications of porous carbon fibers for boosting electrocatalytic reactions (e.g., ORR, OER, HER, CO₂ reduction, nitrate reduction) are also comprehensively discussed, highlighting the significance of porous carbon fibers for boosting electrocatalytic reactions. Finally, the review also lists some challenges of this interesting field and proposes the direction for guiding the synthesis of more efficient electrocatalysts.

The CeO₂/AgI/PPy ternary composite was synthesized via in situ polymerization using preformed CeO₂/AgI as a structural template. The material’s crystalline structure, surface properties, and photoactivity were systematically characterized. Photocatalytic tests indicated that the CeO₂/AgI/PPy composite exhibited exceptional visible-light-driven activity for Rhodamine B (RhB) degradation. It achieved a remarkable removal efficiency of 98.3% within 40 min, which significantly outperformed pure PPy and the CeO₂/AgI nanocomposite. Kinetic analysis revealed that its apparent rate constant reached 0.0996 min⁻¹, approximately 2.6 times higher than that of the CeO₂/AgI composite. Moreover, after five consecutive cycles of recycling experiments, the composite retained 85% of its initial degradation efficiency, indicating excellent stability. Finally, mechanistic analysis revealed the synergistic effects of enhanced charge separation and interfacial electron transfer within the ternary system. This study deepens the fundamental understanding of interfacial charge dynamics in multicomponent photocatalysts while offering practical guidelines for engineering high-performance photocatalytic systems. The developed composite demonstrates strong potential for scalable implementation.

Aqueous zinc-ion batteries (AZIBs) have attracted significant interest due to their high specific capacity, low cost, and environmental compatibility. However, their widespread application is hindered by limited cycle stability and poor rate capability. Enhancing the electrochemical performance of cathode materials remains a critical and sustainable strategy to overcome these challenges. This study investigates the influence of hydrothermal reaction time on the structural, morphological, and electrochemical properties of α-MnO₂ cathodes for AZIBs. The α-MnO₂ synthesized under optimized conditions, specifically, a 6-h hydrothermal reaction at 140 °C (MnO₂-6 h), exhibited a pure single-phase structure, expanded tunnel dimensions, high specific surface area, and enlarged pore volume, resulting in markedly improved electrochemical performance relative to samples prepared with shorter or longer reaction times. These findings provide a foundational understanding crucial for the subsequent development of strategies aimed at enhancing cycle life and rate capability of α-MnO₂-based cathodes in AZIB systems.

Aqueous zinc-ion batteries (AZIBs) have emerged as a research focus in large-scale energy storage due to their advantages of high safety, low cost, and abundant zinc resources. However, manganese dioxide (MnO₂) cathode materials suffer from poor cycle stability and insufficient rate capability, limiting their practical applications. Herein, β-MnO₂ cathode materials with different Eu doping contents were prepared via a microwave hydrothermal method. Pure-phase β-MnO₂ exhibited a slender nanorod-like structure but suffered from agglomeration, delivering a specific capacity of only 142 mAh g⁻¹ at 0.1 A g⁻¹. In contrast, Eu-doped MnO₂ materials formed a tunnel structure with a larger lattice constant, along with more uniformly distributed nanorods and reduced agglomeration. Electrochemical tests revealed that the Eu-doped MnO₂ cathode achieved a specific capacity of 425 mAh g⁻¹ at 0.1 A g⁻¹ (three times that of pure β-MnO₂). After 1000 cycles at 1 A g⁻¹, it retained 59.4% of its initial capacity, significantly outperforming the pure phase (44.7%). Kinetic analysis indicated that Eu doping enhanced the surface pseudocapacitive effect, shifted the reaction mechanism toward diffusion-capacitance mixed control, and improved reversibility and active site utilization efficiency remarkably.

Accurate estimation of the state of health (SOH) of lithium-ion batteries is crucial for ensuring their safety and usage. This paper proposes a lithium-ion battery state of health (SOH) estimation method using multi-feature fusion and the Swin Transformer model. Key health factors (HFs) related to capacity degradation are extracted from charge and discharge curves, and data preprocessing is performed using the Isolation Forest algorithm and different interpolation methods. The CEEMDAN method is employed to extract residual components that reflect battery degradation. The effectiveness of these health factors and residual components is verified through Pearson and Spearman correlation analysis, and key features are selected to construct a multi-feature fusion dataset. The paper also innovatively combines 1D CNN with 1D Swin Transformer to build a 1D CNN-Swin Transformer hybrid model, which fully integrates the local perception ability of convolutional layers with the Swin Transformer’s advantage in modeling long-range dependencies. The Swin Transformer reduces computational complexity through its shifted window design, enhancing computational efficiency while maintaining model performance. The proposed method is tested on NASA and CALCE datasets, showing significant improvements. On the NASA dataset, the RMSE metric effectively decreases by 11.83 to 32.14%, compared to LSTM, and on the CALCE dataset, RMSE metric effectively decreases by 40.64 to 58.76%.

The development of Na₃V₂(PO₄)₃ (NVP) as a cathode material for sodium-ion batteries is significantly hindered by its intrinsically low electronic conductivity, structural instability during cycling, and poor kinetics. To address these issues, a novel strategy of co-doping NVP with Al and Y ions via a sol–gel method is proposed in this study. While substituting V³⁺(0.64 Å) with Al³⁺ (0.51 Å) enhances electronic conductivity, thereby improving rate capability, this smaller ionic radius may compromise ionic conductivity. Simultaneously, introducing a small amount of larger Y³⁺ (0.90 Å) for V³⁺(0.64 Å) stabilizes the crystal structure by expanding the unit cell volume, which facilitates Na⁺ diffusion. The synergistic effect of Al and Y co-doping systematically enhances the structural stability of NVP, effectively improves electron transfer and ion diffusion kinetics, and boosts structural robustness. Consequently, the optimized Na₃V_1.793Al_0.2Y_0.007(PO₄)₃/C sample exhibits superior electrochemical and kinetic performance. It delivers a high reversible capacity of 116.1 mAh/g at 0.1 C and retains 83.9 mAh/g even at 30 C. Furthermore, the optimized Na₃V_1.76Al_0.2Y_0.04(PO₄)₃/C sample shows an initial capacity of 103.5 mAh/g at 1 C and maintains 98 mAh/g after 500 cycles, corresponding to an impressive capacity retention of 94.68%. This work provides a promising approach for developing high-performance cathode materials for sodium-ion batteries, advancing their application potential in energy storage systems.

Accurately predicting the remaining useful life (RUL) of lithium-ion batteries (LiBs) is paramount for optimizing maintenance schedules and ensuring the reliability of energy storage systems. However, achieving high-precision RUL prediction remains critically dependent on the selection of features extracted from the data and the efficacy of model training strategies. To address these challenges, this paper proposes a novel RUL prediction method based on feature optimization and an ensemble deep learning model, CGLA (CNN-GRU-LSTM-AM). Initially, a systematic health features (HFs) extraction and correlation analysis is conducted. The minimum redundancy-maximum relevance (MRMR) algorithm is then employed to select representative HFs, ensuring both strong correlation with battery capacity degradation and minimal inter-feature redundancy. Subsequently, to enhance the capability of latent information extraction, both manually engineered HFs and features automatically learned by a convolutional neural network (CNN) are fused, significantly improving the relevance and quality of the input features for the subsequent RUL prediction model. Furthermore, to achieve high-accuracy RUL prediction, a novel CGLA ensemble model is proposed, combining CNN, gated recurrent unit (GRU), long short-term memory (LSTM) networks, and an attention mechanism (AM) to capture complex temporal dependencies and focus on critical degradation patterns. Finally, the proposed method is rigorously validated using the CALCE, MIT, and NASA datasets across three representative stages of LiBs’ lifespan (early, middle, and late). Experimental results demonstrate exceptional prediction accuracy, with MAE, RMSE, and MAPE consistently maintained below 0.0064, 0.0082, and 0.0036, respectively. These findings underscore that the proposed method substantially improves both the accuracy and generalization capability of LiBs RUL prediction.

Lithium-ion batteries, with their exceptional electrochemical performance, have emerged as the dominant technology in energy storage, sparking intense global research interest. Extensive studies have demonstrated that the design and optimization of electrolytes play a pivotal role in enhancing battery performance. The deliberate design of solvation structures has become a fundamental strategy in battery research, complementing the solid electrolyte interphase (SEI) and cathode electrolyte interphase (CEI) theory. This solvation engineering approach, based on classical solvation theories, impacts multiple critical aspects of battery operation. Therefore, a deeper understanding of electrolyte engineering holds significant scientific and practical importance. This review provides novel insights into the design principles and performance optimization strategies for lithium-ion battery electrolytes from the perspective of solvation engineering. The discussion systematically elucidates the physicochemical properties, functional mechanisms, and structural requirements of key electrolyte components. It identifies the driving forces governing solvation structure formation, categorizes lithium-ion solvation structures, and clarifies the impact of solvation processes on electrochemical performance. Furthermore, the review presents a detailed analysis of electrolyte solvation processes and proposes targeted optimization strategies to enhance battery performance, aiming to establish a theoretical foundation and technical guidance for developing high-performance lithium-ion batteries.

This review outlines a standardized, AI-accelerated architecture for real-time battery characterization, integrating neutron and synchrotron techniques with coordinated multi-modal analysis. Case studies spanning lithium-ion and solid-state batteries demonstrate how this framework enhances phase mapping, degradation modeling, and the detection of early failures. The approach incorporates modular commercial cell formats, FAIR-compliant metadata structures, and beamline automation to achieve integrated datasets within < 1 month, enabling a 3–5 × improvement in cycle-accurate diagnostic resolution. By combining advanced instrumentation, intelligent data pipelines, and global scheduling protocols, this roadmap advances reproducibility, real-time predictability, and translational impact in next-generation battery research.