Sodium-layered oxides are a promising category of cathodes for sodium-ion batteries with high energy densities. The solid-state method is the typical approach to synthesizing these oxides because of its simple procedure and low cost. Although the reaction conditions have usually been understated, the effect of reagents has often been overlooked. Thus, fundamental insight into the chemical reagents is required to perform well. Here we report in situ structural and electrochemical methods of studying the effect of using different reagents. The materials have a composite structure containing layered NaMnO₂ and Li₂MnO₃ components, where oxygen anionic redox can be triggered at high voltage by forming Na-O-Li configurations. The samples synthesized via MnCO₃-based precursors form the Li₂MnO₃ phase at evaluated temperature and perform better than those through MnO₂-based precursors. This work demonstrates that the reagents also impact the structure and performance of sodium-layered oxides, which provides new insight into developing high-energy cathode material.

Lithium carbonate, a solid discharge product, is closely associated with the discharge performance of oxygen-involved lithium-carbon dioxide batteries that exacerbates concentration polarization and electrode passivation. Although numerous strategies to enhance battery performance have progressed, the mechanistic understanding of lithium carbonate on oxygen-involved lithium-carbon dioxide batteries is still confusing. Herein, the effects of lithium carbonate over past decades are traced, including the lithium carbonate product morphology, reaction pathway, formation intermediate, and growth mechanism. The lithium carbonate nucleation and growth are crucial factors that influence battery performance. This perspective proposes a brand-new growth mechanism coupling of solution and surface mechanisms based on experimental results and theories, which extends the growth space of the product and enhances the discharge capacity. Developing advanced technologies are expected to reveal complex lithium carbonate formation pathways and spearhead advanced oxygen-involved lithium-carbon dioxide batteries.

Potassium batteries are very appealing for stationary applications and domestic use, offering a promising alternative to lithium-ion systems. To improve their safety and environmental impact, gel polymer electrolytes (GPEs) based on bioderived materials can be employed. In this work, a series of biobased membranes are developed by crosslinking pre-oxidized Kraft lignin as bio-based component and poly(ethylene glycol) diglycidyl ether (PEGDGE) as functional linker with 200, 500, and 1000 g mol⁻¹ molecular weight. The influence of PEGDGE chain length on the physicochemical properties and electrochemical performance of GPEs for potassium batteries is investigated. These membranes exhibit thermal stability above 240°C and tunable glass transition temperatures depending on the PEGDGE molecular weight. Their mechanical properties are determined by rheology measurements in dry and swollen states, evidencing a slight decrease of elastic modulus (G′) by increasing PEGDGE chain length. An approximately one-order-of-magnitude lower G′ value is observed in swollen membranes versus their dry counterpart. Upon successful activation of the lignin-based membranes by swelling in the liquid electrolyte embedding potassium salts, these GPEs are tested in potassium metal cell prototypes. These systems exhibit ionic conductivity of ~10⁻³ S cm⁻¹ at ambient temperature. Interestingly, battery devices equipped with the GPE based on PEGDGE 1000 g mol⁻¹ withstand current densities as high as 1.5 mA cm⁻² during operation. Moreover, the same devices reach specific capacities of 130 mAh g^‒1 at 0.05 A g⁻¹ in the first 100 cycles and long-term operation for over 2500 cycles, representing outstanding achievements as bio-sourced systems for potassium batteries.

Silicon-based anodes are among the most appealing possibilities for high-capacity anode materials, considering that they possess a high theoretical capacity. However, the significant volumetric changes during cycling lead to rapid capacity degradation, hindering their commercial application in high-energy density lithium-ion batteries (LIBs). This research introduces a novel organic-inorganic cross-linked binder system: sodium alginate-lithium borate-boric acid (Alg-LBO-BA). This three-dimensional network structure effectively buffers the volumetric changes of Si particles, maintaining overall electrode stability. LBO serves as prelithiation agent, compensating for irreversible lithium consumption during SEI formation, and the Si−O−B structure offers a plethora of Lewis acid sites, enhancing lithium-ion transport and interfacial stability. At a current activation of 0.2 A g⁻¹, the optimized silicon anode shows an initial coulombic efficiency (ICE) of 91%. After 200 cycles at 1 A g⁻¹, it retains a reversible capacity of 1631.8 mAh g⁻¹ and achieves 1768.0 mAh g⁻¹ at a high current density of 5 A g⁻¹. This study presents a novel approach to designing organic-inorganic binders for silicon anodes, significantly advancing the development of high-performance silicon anodes.

Two-dimensional (2D) lead halide perovskites (LHPs) have captured a range of interest for the advancement of state-of-the-art optoelectronic devices, highly efficient solar cells, next-generation energy harvesting technologies owing to their hydrophobic nature, layered configuration, and remarkable chemical/environmental stabilities. These 2D LHPs have been categorized into the Dion-Jacobson (DJ) and Ruddlesden-Popper (RP) systems based on their layered configuration respectively. To efficiently classify the RP and DJ phases synthetically and reduce reliance on trial/error method, machine learning (ML) techniques needs to develop. Herein, this work effectively identifies RP and DJ phases of 2D LHPs by implementing various ML models. ML models were trained on 264 experimental data set using 10-fold stratified cross-validation, hyperparameter optimization with Optuna, and Shapley Additive Explanations (SHAP) were employed. The stacking classifier efficiently classified RP and DJ phases, demonstrating a minimal variation between the sensitivity and specificity and achieved a high Balance Accuracy (BA) of (0.83) on independent test data set. Our best model tested on 17 hybrid 2D LHPs and three experimental synthesized 2D LHPs aligns well experimental outcomes, a significant advance in cutting edge ML models. Thus, this proposed study has unlocked a new route toward the rational classification of RP and DJ phases of 2D LHPs.

Lithium batteries constitute a pivotal component in electric vehicles (EVs) owing to their rechargeability and high-power output capabilities. Despite their advantageous features, these batteries encounter longevity challenges, posing disposal complications and an insufficient sustainable supply chain ecosystem to address the growing demand for lithium batteries. One potential solution to address this issue is the implementation of a circular economy model. This study aims to identify and assess the key barriers to optimizing a sustainable supply chain in the lithium-ion battery circular economy using an integrated Gray Multi-Criteria Decision Making approach within the automotive sector. The novelty of this research lies in its application of Gray Possibility Comparison and Gray Possibility of degree to address these uncertainties. By integrating Gray DEMATEL (Decision Making Trial and Evaluation Laboratory) and Gray ANP (Analytic Network Process) methods, this study offers a more flexible and adaptive framework for identifying and analyzing the interrelationships among barriers. The research process involves validating the identified barriers through the Gray Delphi method, followed by the application of Gray DEMATEL to establish the cause-effect relationships among the barriers. Finally, Gray ANP is used to assign weights and prioritize the barriers into primary and secondary categories. The results indicate that the barrier “Lack of supportive policies and standards” holds the highest importance and influence, with a weight of 0.101225.