Ionics最新文献

Alkali-metal-rich Prussian blue analogues (PBAs) with low symmetry structure have attracted growing interest for the development of high-performance cathode materials of Na/K-ion batteries. In this study, the K-rich K₂Cu[Fe(CN)₆] (KCuHCF-T) in triclinic phase has been synthesized by a facile co-precipitation reaction. It reveals that the chelating agent K₂EDTA plays a key role in controlling the lattice symmetry of K₂Cu[Fe(CN)₆]. When tested as cathode material for Na-ion batteries, the K-rich triclinic KCuHCF-T with a negligible content of [Fe(CN)₆]⁴⁻ vacancy and interstitial water delivers much higher reversible capacity and rate capability compared to the cubic phase counterpart. Moreover, the KCuHCF-T cathode enables an excellent capacity retention of 95.1% over 3000 cycles at 0.5 A g⁻¹. The good long-term stability can be ascribed to the pillar effect of K⁺ ions that can stabilize the framework structure. The results provide valuable information on the electrochemical Na-storage behavior of the alkali-metal-rich PBAs.

Lithium-ion battery research is vital for advancing modern electronics, electric vehicles, renewable energy storage, and sustainable energy systems; however, the nonlinear and dynamic characteristics of lithium-ion batteries have significantly increased the difficulty of accurately estimating the state of charge. This study proposes a dual-scale deep learning model for estimating SOC. To begin with, the input voltage and current are decomposed by complementary ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and then denoised by detrended fluctuation analysis and adaptive wavelet threshold denoising. Following this, the SOC is decomposed into high- and low-frequency components by performing K-means clustering on the sample entropy of the CEEMDAN decomposition results. Subsequently, a bidirectional long short-term memory network is applied to estimate the high-frequency components, while an AdaBoost hybrid kernel extreme learning machine is employed to estimate the low-frequency components. Experiments based on data from the McMaster University show that the proposed model achieves higher estimation accuracy compared to other models. Specifically, the root mean square error is reduced from 1.92% to 0.88%, and the mean absolute error is reduced from 1.5% to 0.73% at 25 °C.

The practical implementation of Li-S batteries is significantly impeded by pronounced shuttle effects and suboptimal active material utilization rates. However, the development of modified interlayers presents a viable solution to these challenges. In this study, a hydrothermal approach was employed to synthesize two-dimensional hydrophilic GNPs@MoS₂@CoS₂. The resulting GNPs@MoS₂@CoS₂ features a unique hierarchical architecture that not only improves ion mobility but also enhances cell conductivity and facilitates the trapping of polysulfides. Furthermore, the reduction and oxidation peaks observed in cells utilizing the hydrophilic GNPs@MoS₂@CoS₂ were more pronounced compared to those with solely hydrophilic GNPs or MoS₂@CoS₂, indicating superior redox kinetics. The elevated absorption energy associated with GNPs@MoS₂@CoS₂ ensures an improved lithiation process relative to other configurations. Density functional theory (DFT) calculations reveal that the enhanced mobility of Li ions and the effective adsorption of lithium polysulfide chains within GNPs@MoS₂@CoS₂ position it as a promising candidate for the development of high-performance Li-S batteries. The conductive CoS₂ and stable MoS₂ are combined to create an interconnected MoS₂@CoS₂ composite, featuring an electroactive interface that is developed on a Mo substrate. This composite serves as a high-performance electrode material, exhibiting both electrochemical and mechanical stability. The band gap and density of states of MoS₂@CoS₂, as determined by density functional theory simulations, suggest an enhancement in electrical conductivity.

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.