Battery-grade lithium hydroxide monohydrate (LiOH·H₂O) is critical for lithium-ion batteries, yet its conventional causticization production faces sustainability challenges regarding waste management. This work systematically investigated the leaching kinetics and controlling mechanisms of lithium (Li) from lithium carbonate (Li₂CO₃) during causticization. The leaching behavior was found to follow the Avrami diffusion-controlled model, exhibiting a fast initial reaction rate with an apparent activation energy of 7.35 kJ·mol^-1. The leaching of Li was limited by the heterogeneous nucleation of calcium carbonate (CaCO₃) on Li₂CO₃ particles. An optimized strategy (CP-R) was proposed in this study, which involves calcining CaCO₃ precipitate to recover quicklime with a purity of 96.29%, and utilizing the Li₂CO₃ covered by the dense layer in the precipitate as additive for causticization, thereby achieving a closed-loop cycle of Li and Ca. Comprehensive economic analysis and life cycle assessment demonstrate that CP-R offers higher profitability and lower environmental impact compared to conventional causticization and electrodialysis bipolar membrane process. Overall, CP-R presents a compelling alternative for LiOH production by simultaneously addressing solid waste disposal and enabling closed-loop Li recycling.

Electrocatalysts for the hydrogen evolution reaction (HER) are critical for sustainable hydrogen production, yet simultaneously achieving high activity, atom-efficient noble-metal use and integrated fabrication remains challenging. Herein, we report a scalable strategy for fabricating integrated electrodes by combining 3D-printed hydrogel templating with ultrafast pulsed Joule heating. In detail, a 3D-printed hydrogel scaffold is transformed into an oxygen-functional hierarchical carbon support that anchors oxygen-coordinated Pt single atoms (SAs) with finely dispersed Pt nanoparticles (NPs). The resulting integrated SA/NP hybrid electrode exhibits greatly increased surface area and a micro-mesoporous architecture, which suppresses NP agglomeration, increases active-site density and improves charge transfer. First-principles calculations reveal that Pt NP primarily drives water dissociation and H^* generation, while adjacent Pt SA enhances the active-site utilization of Pt NP and facilitates OH^* transfer, together accelerating the HER pathway. As a result, the fabricated electrode delivers low overpotentials of 33, 103, and 173 mV at current densities of 10, 50, and 100 mA cm^-2, respectively, while also demonstrating remarkable durability. Beyond providing a practical route to atom-efficient HER electrodes, this integrated strategy uniquely combines a 3D-printed topological scaffold with ultrafast Joule heating to achieve synergistic Pt SA/NP sites, significantly enhancing both structural stability and catalytic kinetics, offering a great promise for next-generation energy-catalysis technologies.

Wearable electronics require compact, reliable, and sustainable power sources. Fabric-based triboelectric nanogenerators (TENGs) offer a promising solution by combining energy harvesting with the inherent softness and breathability of textiles. However, conventional functionalization methods, such as surface coatings or multilayer structures, inevitably diminish these essential properties. To address this issue, we developed a knitted fabric-based direct current triboelectric nanogenerator (KF DC-TENG) using whole-garment knitting technology and the air breakdown effect. This design allows direct application without complex post-processing and eliminates the need for external rectification. A single unit of KF DC-TENG (8 × 2.5 cm²), after structural optimization, is capable of lighting 744 series-connected Light Emitting Diodes (LEDs) when manually rubbed against polytetrafluoroethylene (PTFE) fabric. Integration with a low-cost power management circuit (PMC, ~1.5 CNY) further enhances the output power by 290 times. As a result, 17 s of friction can power an electronic watch for up to 8 min. Moreover, continuous sliding can sustain a 1.5 W LED without noticeable flickering. This work presents the first demonstration of integrating whole-garment knitting technology with DC-TENGs, offering a scalable and cost-effective strategy for self-powered wearable electronics.

Sulfurized polyacrylonitrile (SPAN) has been regarded as one of the most competitive cathode candidates for lithium-sulfur (Li-S) batteries, owing to its outstanding theoretical energy density, excellent structural durability, and minor self-discharge. Nevertheless, the intrinsically slow reaction kinetics of SPAN results in insufficient active sulfur utilization at high current rates, which severely restricts its rate performance and long-cycle stability. This study introduces FeNiS₂ Quantum Dots (QDs) as catalyst embedding in SPAN nanofibers (FeNiS₂ QDs@SPAN). Taking advantages of the ultra-small size, superior dispersibility and abundant catalytic sites of FeNiS₂ QDs, the redox kinetics and cycle performance of SPAN are significantly enhanced. Kinetic analyses and theoretical calculation demonstrate the uniformly dispersed FeNiS₂ QDs effectively reduce charge transfer resistance and facilitate conversion reaction. FeNiS₂ QDs@SPAN material exhibits high reversible capacity of 1,213 mAh g^-1 and an ultralow capacity decay of 0.034% per cycle over 1,000 cycles at 1 C. Remarkably, even at high rate of 5 C (8.37 A g^-1), it delivers a stable long-cycle capacity of 720 mAh g^-1 and demonstrates excellent cycling capability with a low fade rate of 0.029% per cycle over 450 cycles. FeNiS₂ QDs@SPAN material maintains good performance even under lean electrolyte conditions and a wide temperature range. This work underscores the significant potential of FeNiS₂ QDs as catalyst for achieving high performance sulfur cathode and advanced Li-S batteries.

The escalating plastic waste crisis has heightened the need for sustainable, scalable valorization strategies. Catalytic conversion of plastic waste into hydrogen offers dual benefits: waste mitigation and clean fuel generation. However, the variability of plastic feedstock and the complexity of reaction conditions pose significant challenges for designing efficient catalysts. Recent advances in artificial intelligence (AI) and machine learning (ML) are increasingly being employed to optimize process conditions for hydrogen production via electrolysis and traditional thermochemical pathways. ML models, such as neural networks and ensemble methods, have demonstrated high accuracy in predicting hydrogen yields and optimizing parameters for the gasification and pyrolysis of plastic waste. ML is also opening new avenues for accelerating catalyst discovery by enabling rapid prediction of catalyst performance, reaction pathways, and surface interactions. Computational tools and data-driven descriptors are being used to interpret complex catalytic systems and guide the design of more effective catalysts. However, their application to plastic-derived intermediates remains limited. Despite progress, significant gaps persist in applying ML to the unique challenges of plastic waste conversion, including catalyst discovery and the handling of heterogeneous feedstocks. Key limitations include the need for larger, high-quality datasets, improved model interpretability and the integration of domain-specific knowledge with advanced simulation techniques. In this review we critically summarized the current landscape of AI-driven catalyst design focusing on hydrogen production from plastic waste. It identified methodological and practical limitations and proposed a roadmap for integrating AI, domain-specific data, and catalysis simulations to unlock new catalysts for sustainable hydrogen production.