Recycling lithium from spent batteries is challenging because of problems with poor purity and contamination. Here, we propose a green and sustainable lithium recovery strategy for spent batteries containing LiFePO ₄ , LiCoO ₂ , and LiNi _0.5 Co _0.2 Mn _0.3 O ₂ electrodes. Our proposed configuration of “lithium-rich electrode || LLZTO@LiTFSI+P3HT || LiOH” system achieves double-side and roll-to-roll recycling of lithium-containing electrode without destroying its integrity. The LiTFSI+P3HT-modified LLZTO membrane also solves the H ⁺ /Li ⁺ exchange problem and realizes a waterproof protection of bare LLZTO in the aqueous working environment. On the basis of these advantages, our system shows high Li selectivity (97%) and excellent Faradaic efficiency (≥97%), achieving high-purity (99%) LiOH along with the production of H ₂ . The Li extraction processes for spent LiFePO ₄ , LiNi _0.5 Co _0.2 Mn _0.3 O ₂ , and LiCoO ₂ batteries is shown to be economically feasible. Therefore, this study provides a previously unexplored technology with low energy consumption as well as high economic and environmental benefits to realize sustainable lithium recycling from spent batteries.

Sodium metal is one of the ideal anodes for high-performance rechargeable batteries because of its high specific capacity (~ 1166 mAh·g⁻¹), low reduction potential (−2.71 V compared to standard hydrogen electrodes), and low cost. However, the unstable solid electrolyte interphase, uncontrolled dendrite growth, and inevitable volume expansion hinder the practical application of sodium metal anodes. At present, many strategies have been developed to achieve stable sodium metal anodes. Here, we systematically summarize the latest strategies adopted in interface engineering, current collector design, and the emerging methods to improve the reaction kinetics of sodium deposition processes. First, the strategies of constructing protective layers are reviewed, including inorganic, organic, and mixed protective layers through electrolyte additives or pretreatments. Then, the classification of metal-based, carbon-based, and composite porous frames is discussed, including their function in reducing local deposition current density and the effect of introducing sodiophilic sites. Third, the recent progress of alloys, nanoparticles, and single atoms in improving Na deposition kinetics is systematically reviewed. Finally, the future research direction and the prospect of high-performance sodium metal batteries are proposed.

Room temperature sodium-sulfur (Na-S) batteries, known for their high energy density and low cost, are one of the most promising next-generation energy storage systems. However, the polysulfide shuttling and uncontrollable Na dendrite growth as well as safety issues caused by the use of organic liquid electrolytes in Na-S cells, have severely hindered their commercialization. Solid-state electrolytes instead of liquid electrolytes are considered to be the most direct and effective solution to solve the above problems. However, its practical application is still greatly challenged due to the poor interfacial compatibility between the all-solid-state electrolytes and the anode/cathode, ionic conductivity, and the shuttle effect caused by the presence of liquid phase in the quasi-solid-state electrolytes. This paper presents a comprehensive review of solid-state Na-S batteries from the perspective of regulating interfacial compatibility and improving ionic conductivity as well as suppressing polysulfide shuttle. According to different components, solid-state electrolytes were divided into five categories: solid inorganic electrolytes, solid polymer electrolytes, polymer/inorganic solid hybrid electrolytes, gel polymer electrolytes, and liquid–solid inorganic hybrid electrolytes. Finally, the prospect of developing high performance solid-state electrolytes to improve the cycling stability of room temperature Na-S cells is envisaged.

Accurate battery capacity estimation can contribute to safe and reliable operations of lithium-ion battery systems. The incremental capacity (IC) based techniques provide promising estimates of battery capacity. However, curve smoothing algorithms are usually required in the IC-based methods, which introduce additional errors and are computationally burdensome. To address this issue, this work proposes a novel approach using multi-voltage-interval IC peaks combined with a back-propagation neural network (BPNN) for battery capacity estimation. Multiple voltage intervals covering relatively narrow and wide values are applied for computing IC curves to enhance the estimation robustness. In particular, there is no need to employ smoothing algorithms. A BPNN is then applied to approximate the correlation between multi-voltage-interval IC peak and capacity. Besides, a five-point moving window technique is proposed to capture multi-voltage-interval IC peaks online effectively. Experimental results show capacity estimates with the majority of relative errors of ±1% and the maximum error of 2%.