SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3648-2
Transient energy storage devices represent an emerging class of biodegradable power systems that provide temporary energy for implantable medical electronics before safely degrading in vivo. From early transient primary batteries to contemporary rechargeable batteries integrated with wireless charging systems, these devices have evolved to enable stable prolonged power supply. Through rational transient design and structural engineering, they achieve desirable electrochemical performance, tunable degradation rates, and mechanical compatibility with soft, irregular, and dynamic biological tissues. This work provides a critical review of state-of-the-art transient energy storage devices, including transient primary batteries, transient secondary batteries, and transient supercapacitors, with emphasis on their electrodes, electrolytes, encapsulation materials, fabrication processes, and applications. We critically analyze material selection strategies, transient design principles, and architecture design for various transient batteries and capacitors. Finally, we discuss existing challenges and outline future directions to guide the clinical translation of biodegradable power solutions for biomedical implants.
Chinese Journal of Environmental Engineering•2026•DOI: 10.12030/j.cjee.202506020
The rotary kiln roasting of lepidolite for lithium extraction faces challenges of unstable lithium conversion rates and high energy consumption. To address this, a multi-objective optimization method coupling improved neural network simulation with a multi-objective genetic algorithm was proposed, targeting the synergistic optimization of lithium conversion rate (TRLi) and natural gas consumption intensity (EIng). Using long-term industrial time-series data of batching parameters and kiln operating variables, back-propagation (BP) neural network and its particle swarm optimization (PSO) improved variant were developed to model TRLi and EIng. The PSO-BP model demonstrated superior accuracy in capturing the complex nonlinear relationships, reducing mean absolute percentage errors (MAPE) to 0.278 and 0.284 for TRLi and EIng, respectively. Subsequently, the non-dominated sorting genetic algorithm II (NSGA-II) was employed to construct a multi-objective optimization model, yielding a Pareto-optimal set of process parameters that maximize TRLi and minimize EIng. The results revealed that under NSGA-II optimized conditions, TRLi could be stabilized between 82.45% and 87.96%, an average increase of 3.61 percentage points over baseline operations, while EIng could be reduced to 53.7 m3 per ton of clinker. For an annual processing capacity of 3.2×105 tons of lepidolite concentrate and sulfate mixture, this corresponds to an additional 127.1 tons of lithium metal recovery, a reduction of 1,964,912 m3 in natural gas consumption, and a decrease of 3,763.84 tons in CO2 emissions annually. This study provides theoretical and technical support for the green, high-quality, and low-carbon supply of critical raw materials for the lithium battery new energy industry.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-4057-4
The escalating use of ionizing radiation in medical and industrial applications necessitates lead-free, flexible, and sustainable shielding materials. Current development relies on empirical trial-and-error, which is inefficient. This study introduces a machine learning-assisted Monte Carlo simulation strategy for rapid optimization of metal filler compositions for X-ray attenuation across 40–120 kV. Guided by this AI-driven approach, polyvinyl alcohol (PVA)-based gels containing uniformly dispersed Bi/W/Gd2O3 nanoparticles were developed, forming within 1 minute at -20°C using a PVA-DMSO/H2O co-solvent system. The optimized gel with 50 wt% metal loading exhibits exceptional mechanical properties: tensile strength of 1.76 MPa, toughness of 6.3 MJ m−3, and elongation of 600%. It achieves >98% X-ray shielding efficiency at 5 mm thickness, outperforming lead composites at 120 kV. The physically cross-linked network provides recyclability and anti-freezing capability, retaining flexibility at -50°C. This work establishes a data-driven paradigm for designing high-performance radiation-shielding materials, demonstrating AI's potential to accelerate materials discovery and enable scalable fabrication of eco-friendly protective systems.