Ionics最新文献

With the rapid increase in retired lithium iron phosphate (LiFePO₄, LFP) batteries and the rising demand for carbon neutrality, efficient and sustainable recycling of spent LFP cathodes has become a pressing research priority. This review systematically compares three representative recycling approaches—pyrometallurgical, hydrometallurgical, and direct regeneration—highlighting their respective mechanisms, advantages, and limitations. Pyrometallurgy, while industrially mature, suffers from high energy consumption and elemental loss. Hydrometallurgy enables high recovery yields but generates significant chemical waste. In contrast, direct regeneration preserves the cathode structure and offers notable advantages in energy efficiency, environmental impact, and cost-effectiveness. Life cycle assessment results based on the EverBatt model show that direct regeneration significantly reduces greenhouse gas emissions and process costs compared to other methods. Finally, the review outlines current challenges and research directions toward scalable, green, and economically viable LFP recycling technologies.

Zinc-ion batteries offer environmentally friendly and reliable energy storage alternatives. They may also lower the cost of producing next-generation battery technologies. Transition metal oxide-based materials have large theoretical capacities, are abundant in nature, are inexpensive, and have effective redox reactions, making them promising cathode materials for zinc-ion batteries. V₂O₅ and V₂O₅-based materials are being studied for cathode materials in zinc ion batteries due to their high theoretical capacitance and efficient electrochemical properties. Researchers are exploring doping, composite synthesis, and electrolyte optimization for improving the electrochemical potential of zinc-ion batteries. The functioning of zinc-ion batteries, the possibilities of V₂O₅-based materials as cathodes, certain V₂O₅-based hybrid materials, substantial and continuing studies, and future challenges have been adequately covered in this article.

Recycling spent lithium iron phosphate (LiFePO₄) batteries, ubiquitous in electric vehicles and energy storage, is crucial for sustainability. However, the prevalent carbon coating, essential for battery performance, presents significant challenges during recycling processes. The melt growth of LiFePO₄ crystals from a carbon-decorated LiFePO₄ amorphous powder precursor via the Bridgman method was studied. The electrochemical performance of the regenerated LiFePO₄ materials is significantly degraded which should be attributed to the presence of Fe-related defects. The results suggest decarbonization as a necessary step for achieving phase-pure crystalline LiFePO₄ from wasted LiFePO₄ batteries.

MnPO₄∙H₂O is an ideal precursor for the preparation of LiMnPO₄. However, the instability of Mn³⁺ in an aqueous solution necessitates the implementation of the existing preparation methods, which are carried out in ethanol and produce toxic gases such as NO and NO₂. In this work, a radical-oxidation coupled phosphate stabilization strategy is proposed for synthesizing MnPO₄∙H₂O in an aqueous solution. The strategy involves the initial oxidation of Mn²⁺ to Mn³⁺ by sulfate radicals, which are produced through the thermal activation of Na₂S₂O₈. Subsequently, Mn³⁺ is stabilized by H₃PO₄, leading to the formation of MnPO₄∙H₂O. By implementing this strategy, the mesoporous MnPO₄∙H₂O precursor can be readily obtained through a reaction at 90 °C for 5 h. Subsequently, the prepared LiMnPO₄/C inherits the mesoporous structure of the MnPO₄∙H₂O precursor, exhibiting excellent electrochemical performance. Specifically, the mesoporous LiMnPO₄/C delivers an initial capacity of 115.8 mAh g⁻¹ with a capacity retention of 83.1% after 100 cycles at 10 C. The enhanced performance is mainly attributed to the mesoporous structure, which facilitates electrolyte penetration, reduces interfacial charge transfer impedance, and accelerates Li⁺ diffusion. The environmentally benign and scalable strategy presented here opens a new approach to the synthesis of high-performance LiMnPO₄/C.

Proper modeling, control and optimization of Proton Exchange Membrane Fuel Cells (PEMFCs) depends on the correct extraction of parameters. This is a nonlinear identification problem that is complex and involves the estimation of seven interdependent parameters using empirical voltage-current data. The paper suggests a new metaheuristic approach, the Starfish Optimization Algorithm (SFOA) based on the regenerative and coordinated feeding behaviors of starfish. The algorithm is strictly tested by reducing the sum-square error (SSE) of the model predictions and experimental data of twelve different PEMFC stacks, whose power was 12 W to 6 kW. The effectiveness of SFOA is proved by the comparative analysis with nine state-of-the-art algorithms (GJO, COA, RSA, PO, SO, YDSE, AOA, RIME, DE). The results consistently indicate that SFOA has the most accuracy with the smallest mean error of 0.0255 being the lowest, the smallest SSE as well as being very robust with a small standard deviation of 1.69E-05. Moreover, SFOA is computationally highly efficient and can reach the optimal solution in 0.38 s, much faster than any of the benchmarked algorithms. The reliability and accuracy of SFOA are validated statistically through convergence curves, box plot analysis and Friedman ranking tests. This study has made SFOA a powerful new instrument in improving the accuracy of fuel cell models, which is essential to create real-time control schemes, system design optimization, and critical monitoring processes.

Adequate energy storage and transformation are crucial for supporting a stable society. Supercapattery devices offer a viable approach to bridging the gap between conventional batteries and supercapacitors (SCs). Metal-organic frameworks (MOFs) are durable networks with interconnected pores formed by metal ions and organic linkers. MXenes are 2-D transition metal carbides or nitrides with high conductivity and reactivity. This study explores the synergy of MOFs, MXenes, and graphene quantum dots (GQDs) doping as advanced electrode materials. In a three-electrode design, the ZIF-90/Nb₂C@GQD electrode recorded 1391 C g⁻¹ in specific capacity when tested at 1 A g⁻¹. A supercapattery device (ZIF-90/Nb₂C@GQD//AC) was fabricated using ZIF-90/Nb₂C@GQD as the anode (positive electrode) and activated carbon (AC) as the cathode (negative electrode). The device sustained an energy density of 70 Wh kg⁻¹ at a power output of 900 W kg⁻¹ and preserved 83.7% of its capacity after 12,000 charge–discharge cycles. The extracted b-values (0.54–0.70) from power-law analysis confirm the hybrid nature of the charge-storage process. The ZIF-90/Nb₂C@GQD composite showed strong hydrogen evolution reaction (HER) performance, with a low Tafel slope of 45.72 mV dec⁻¹. The slight Tafel slope demonstrates the rapid kinetics of the electrocatalytic process. The ZIF-90/Nb₂C@GQD composites demonstrate strong potential for advanced energy storage systems and catalytic energy conversion.

Conventional proton-exchange membranes (PEMs) like Nafion face conductivity limitations (~ 0.1 S·cm⁻¹). This study explores thermal compression of vermiculite membranes (VMs) as a potential alternative. Controlled thermal compression at 300–500 °C reduced interlayer spacing and improved nanosheet alignment, which make nanochannel dimensions approaching the critical scale for ballistic transport. Consequently, proton conductivity is significantly enhanced. The membrane treated at 400 ℃ achieved a proton conductivity of 1.77 S·cm⁻¹ at 90 °C—far exceeding Nafion under identical conditions. However, temperatures at 500 °C appeared to induce crystallization, increasing proton transport barriers. This suggests that there is a trade-off between the interlayer spacing and the crystallinity. These findings indicate that thermally engineered 2D membranes could offer new pathways for high-conductivity PEM design.

Appropriate operating conditions are essential to ensure the stable performance of proton exchange membrane fuel cells (PEMFCs). In this study, the coupled influence of temperature, pressure, and humidity on the performance and stability of a 3-cells stack were investigated through testing under different conditions at a constant current density of 1600 mA/cm². Although increasing temperature accelerates electrochemical reactions, it exacerbates membrane dehydration, increasing internal resistance and reducing performance. For instance, under low-pressure and low-humidity conditions, the voltage drops from 0.540 V to 0.472 V (a 12.6% decrease) as temperature rises from 75 to 84℃. Increasing pressure not only improves cell performance but also enhances stack consistency while mitigating the impacts of temperature and humidity. Under high-temperature and low-humidity conditions, the voltage increases 37.9%, i.e., from 0.472 V(@70/60 kPa) to 0.651 V (@170/150 kPa). Increasing humidity notably improves performance under low-pressure conditions, but its effect diminishes under high pressure due to the reduced proportion of water vapor partial pressure in the total gas pressure. So, for long-term stable operation of the stack under high-temperature conditions, priority should be given to increasing operating pressure, followed by dynamic adjustment of humidity levels.

This review provides an in-depth exploration of recent advancements in lithium-ion battery (LIB) technology, specifically focusing on graphene-based anode materials and lithium iron phosphate (LiFePO₄) cathodes. The transition from conventional graphite anodes to graphene is emphasized, highlighting its theoretical specific capacity of 744 mAh g⁻¹, which is nearly double that of graphite. The review critically examines the challenges associated with graphene’s use, such as its tendency to aggregate and issues related to its volumetric expansion and low electron mobility. Strategies to address these challenges, including chemical modifications and hybrid material formations, are discussed in detail. The role of LiFePO₄ cathodes in enhancing energy density and ensuring safety, especially for electric vehicle applications, is also explored. Furthermore, modifications to electrolytes, including the incorporation of solid-state and gel electrolytes, are presented as solutions to improve the thermal stability and safety of LIBs. The review also investigates the future prospects of next-generation batteries, such as lithium-sulfur (Li-S) and lithium-air batteries, which offer potential for significantly higher energy densities. Overall, the review underscores the critical advancements and challenges that need to be addressed to enhance the efficiency, safety, and sustainability of LIBs, with a special focus on the electrolyte modifications that are key to achieving these goals.

Accurately estimating the state of health (SOH) of lithium-ion batteries is crucial for ensuring safety, improving economic efficiency, and optimizing battery system operation. However, existing data-driven SOH prediction methods rely on expert experience to extract artificial features. Meanwhile, unsupervised learning-based SOH prediction methods are difficult to capture the long-term dependence of capacity degradation and are prone to overfitting. To address these issues, this paper proposes a novel SOH estimation method based on automatic feature extraction and BiLSTM-SA. In this method, firstly, a Squeeze-and-Excitation block is added to the Temporal Convolutional Network to dynamically adjust the channel weights, and combined with the multi-scale technique to enhance the extraction of battery features, so as to construct an Improved Temporal Convolutional Network (ITCN). Subsequently, the ITCN is fused with an Autoencoder (AE) to form the ITCN-AE structure, and the representative features can be automatically extracted by directly taking the data after wavelet denoising and truncation-alignment operations as the input to the ITCN-AE. Next, the extracted features are fed into a Bidirectional Long Short-Term Memory Network (BiLSTM) to further mine the features, and mapped to the battery health state using a fully connected layer after assigning feature weights through the self-attention (SA) mechanism. Finally, on both the NASA and Oxford lithium-ion battery datasets, the proposed model achieves an average estimation error within 1%. Compared with transformer-based method and the latest LSTM variants, the error is reduced by at least 25.2%.