SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-026-4479-8
The von Neumann architecture is increasingly constrained by energy consumption and data-transfer efficiency as artificial intelligence and data-intensive applications expand. Neuromorphic computing, inspired by the human brain's information-processing mechanisms, offers an alternative paradigm. Two-dimensional (2D) ferroelectric materials are promising candidates due to their intrinsic non-volatility, atomic-scale thickness, ultra-low power consumption, excellent fatigue endurance, and dangling-bond-free surfaces. This review examines recent advances in 2D ferroelectric materials and associated device architectures for neuromorphic applications. It first introduces ferroelectric mechanisms and representative 2D ferroelectrics, then surveys key device architectures including ferroelectric tunnel junctions, diodes, transistors, and photovoltaic devices. Their applications in in-memory computing and in-sensor neuromorphic systems are discussed, with emphasis on artificial neural networks, spiking neural networks, reservoir computing, and neuromorphic perception for efficient information processing and intelligent sensing. The unique properties of 2D ferroelectrics enable integrated sensing, memory, and computing functionalities, demonstrating potential for future neuromorphic and brain-inspired intelligent systems.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-026-4283-0
Smart windows are critical for building energy conservation, yet existing technologies cannot simultaneously satisfy the diverse requirements of light transmission, thermal insulation, and privacy protection across varying scenarios, such as daytime transparency and nighttime heat retention with opacity. Herein, we report a thermo- and electro-responsive ionogel fabricated via one-step photopolymerization, integrating the electrochromic viologen derivative (Pa-PhV)(TFSI)2 with a thermoresponsive matrix. The (Pa-PhV)(TFSI)2 delivers excellent electrochromic performance, featuring dual-band light modulation, a high coloration efficiency of 433.6 cm2 C-1, and a fast coloration time of 2.52 s. The ionogel exhibits three stable switchable states under thermal and electrical regulation, fulfilling core practical demands for full-spectrum photothermal management. Model house tests verify its excellent seasonal adaptability, with a maximum indoor temperature reduction of up to 15 °C in a simulated summer environment. Furthermore, the ionogel enables dual-encrypted data storage via UCST and voltage triggering. This work broadens the application scope of viologen derivatives and offers a competitive strategy for multifunctional smart windows and encrypted data storage.
Chinese Journal of Environmental Engineering•2026•DOI: 10.12030/j.cjee.202506010
To facilitate accurate understanding and implementation of the provisions in the Technical Specification for Comprehensive Utilization of Titanium Gypsum (GB/T 45015-2024), and to promote technological capability in comprehensive utilization while effectively controlling environmental risks during utilization, this paper analyzes the current status and existing problems of titanium gypsum generation, management, and utilization technologies in China. The standard is systematically interpreted. It is concluded that the implementation of this standard will promote resource utilization of titanium gypsum, foster energy conservation and carbon reduction in the titanium dioxide industry, and further safeguard ecological and environmental security. China produces over 3,120×10^4 t of titanium gypsum annually (2023), yet its comprehensive utilization rate is only about 10%, far lower than that of phosphogypsum (~40%) and desulfurization gypsum (~80%). The standard, as the first national standard dedicated to titanium gypsum resource utilization, establishes technical pathways for building materials and ecological restoration, sets limits for soluble impurities, and specifies pollution control indicators throughout the utilization process. It addresses the long-standing gaps in technical standards, product quality variability, and environmental supervision, providing critical support for the green and low-carbon transformation of the sulfuric acid process titanium dioxide industry.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3982-6
Skin wounds are refractory due to antibiotic-resistant bacterial infection. Although photodynamic therapy (PDT) offers noninvasiveness, high efficiency, and no drug resistance, its therapeutic effect is constrained by the complex structure of wound tissue and diffuse drug distribution. The proinflammatory cytokine tumor necrosis factor-like weak inducer of apoptosis (TWEAK) regulates tissue repair by engaging its receptor Fn14, which is highly expressed in wounds. In this study, we developed a novel photosensitizer, the selenoviologen-TWEAK conjugate (SeV-Tp), to enhance selective enrichment and synergistically promote wound healing. In vitro analyses demonstrated that SeV-Tp, under visible light, generated high levels of reactive oxygen species, resulting in potent antibacterial activity against both Gram-positive and Gram-negative bacteria. Notably, SeV-Tp selectively bound to Fn14 and amplified fibroblast activation via photodynamic cooperation. In a mouse model of antibiotic-resistant Pseudomonas aeruginosa-infected wound, SeV-Tp accelerated healing by reducing bacterial burden, modulating the immune microenvironment, promoting collagen deposition, and stimulating hair follicle regeneration. Moreover, SeV-Tp preferentially accumulated within wound tissues with minimal adverse effects. SeV-Tp represents a strategy that selectively enriches and harnesses synergistic benefits from both components, positioning SeV-Tp as a promising photosensitizer for the treatment of refractory wounds.
Journal of Fuel Chemistry and Technology•2026•DOI: 10.1016/S1872-5813(26)60655-X
Defect-induced nonradiative recombination critically restricts the power conversion efficiency (PCE) and stability of perovskite solar cells (PSCs). Lewis base additives show great promise in defect passivation, but current screening methods rely heavily on empirical trial and error and lack clear design principles, making it difficult to efficiently discover high-performance candidate materials. Here, we present a machine learning (ML) framework to intelligently screen Lewis base molecules for defect passivation. We trained six ensemble models on a dataset of 146 experimental data points, with Light Gradient Boosting Machine (LightGBM) yielding the best classification performance (87% accuracy). Shapley Additive Explanations (SHAP) interpretability analysis subsequently identifies the highest occupied molecular orbital (HOMO) energy (−7.5 to −6.3 eV), additive concentration (2.5 to 6.5 mg/mL), and simplified molecular backbones (O atom ≤ 2, C atom < 5) as critical design criteria. The ML prediction was experimentally validated: (S)-pyrrolidine-3-carboxylic acid ((S)-PCA) and 2-methyl-1,3-cyclopentanedione (MCPD) (Class Ⅱ) improved PCE by 2.22% and 2.01%, respectively, while 3-hydroxymethyl-3-methylbutanenitrile (3-HMBN) (Class Ⅰ) showed minimal gain. Density functional theory (DFT) calculations further confirmed the stronger binding affinities and elevated defect formation energies of Class Ⅱ additives. Notably, the champion (S)-PCA device achieved a PCE of 24.05%. This work established an ML-accelerated paradigm for the rational design of defect passivators, bridging data science and photovoltaics.