Zhenming Xu

Chemical recycling of brominated plastic waste necessitates the concurrent mitigation of hazardous brominated compounds and the preservation of valuable hydrocarbons, which remains unachievable with conventional thermochemical methods. Here, we report that such a dilemma has been resolved by a solar-driven method using a bifunctional material comprising nanoscale zerovalent iron (nZVI) dispersed on a HZSM-5 zeolite. Under sole light irradiation, the nZVI intensively absorbs photons and raises its temperature to trigger the photothermochemically partial cleavage of C-C/C-H bonds in polyolefins and polystyrene to produce condensable hydrocarbon fragments over HZSM-5, and immobilizes dissociated C-Br as a solid, stable FeBr₂ phase with >90% debromination efficiency. We demonstrate the scalability of this process with a 0.5 m × 0.5 m solar-driven apparatus that achieves a rapid plastic processing rate of 1.93 g_plastic cm^-2·h^-1. This solar-driven one-step strategy offers a sustainable path for decontaminating and valorizing real-world additive-laden plastic waste.

This research optimized the recovery process through machine learning and SHAP analysis, utilizing fly ash to replace toxic chlorinating agents, thereby achieving efficient and environmentally sound recovery of multiple scattered metals.

Anthropogenic mineral circularity offers a synergistic solution to dual challenges of resource supply and waste management in photovoltaic development. However, the global supply potential of secondary materials remains underexplored, limiting future informed decision-making. Here, we present the global analysis of secondary material supply potential in five photovoltaic technologies under different energy scenarios, using the tailored modeling framework. Results show that cumulative material demand and waste generation are projected to reach 705–1879 megatonnes and 238–529 megatonnes, respectively, by 2050. With the circularity strategy, the annual supply ratio of secondary materials is expected to increase from 3.3% in 2020 to 43.4%–101.6% by 2050, with silver and tellurium potentially in surplus. Additionally, circularity brings 6.6%–55.0% decrease in metal criticality, 321–700 billion US Dollars economic potential, and 697.0–1546.1 megatonnes CO₂-equivalent emissions reductions. This study quantifies anthropogenic mineral circularity’s role in photovoltaic development and provides insights for energy transition.

The lithium-ion battery (LIB) industry faces a critical challenge in achieving sustainable resource circularity amid surging global demand. While LIB recycling technologies have advanced, a disconnect persists between academia and industry: novel methods remain confined to labs, while industry relies on traditional hydrometallurgy with high pollution risks. This review redefines LIB recycling technologies as “lithium-last technology” (LLT) and “lithium-first technology” (LFT) paradigms, establishing a technical mapping to bridge this gap. LLT, driven by transition metal (TM) recovery, employs hydrogen-/nitrogen-based extractants or bio-extractants but suffers from inefficiency and environmental costs. LFT prioritizes selective lithium extraction via product- or process-oriented strategies, enhancing sustainability and shortening recycling routes. However, industrial adoption of LFT is hindered by operational complexity, impurity management, and scalability barriers. Life-cycle assessments reveal that emerging oxidative extraction and direct regeneration show promise for low-carbon development, yet traditional methods dominate due to rigid infrastructure and market immaturity. Future challenges include handling low-value LIBs (e.g., LFP), mitigating overcapacity, and integrating AI for pre-evaluation. This work provides actionable insights to align academic innovation with industrial needs, fostering eco-friendly and economically viable LIB recycling.

As the retirement of new energy vehicles peaks, efficient recycling of spent lithium-ion batteries (LIBs) is significant for environmental sustainability. Roasting integrated with water leaching is a popular process for metal recovery from spent LIBs. However, traditional optimization experiments face the challenges of lengthy and costly procedures due to the variability of waste materials and the intricate interplay between process variables. This study breaks through the barriers by introducing machine learning (ML) to establish a smart prediction model for efficient metal recovery from LIBs. Based on the 8921 data collected, the model incorporates 18 input features, encompassing waste particle size, components, and roasting–water leaching parameters, and predicts Li, Co, Mn, and Ni recovery efficiencies. Four ML algorithms are compared to determine the best prediction models with R² values of 0.81–0.98 in the training and test datasets. The intricate interaction mechanisms of each feature with metal recovery were revealed, providing a deeper understanding of the recovery process. Furthermore, we developed a user-friendly GUI that instantly suggests optimal parameters for maximizing the metal recovery efficiency, simply by inputting waste particle sizes and components. Finally, the reliability and practicability of the GUI are verified by experiments. This work dispenses with the traditional and extensive optimization experiments and decreases the recovery costs, which achieves efficient and intelligent spent LIB recycling.