SCIENCE CHINA Materials•2025•DOI: 10.1007/s40843-025-3416-3
The development of high-efficiency perovskite solar cells (PSCs) demands comprehensive control of multi-scale factors influencing device performance. Artificial intelligence (AI), represented by machine learning (ML), has rapidly become a key tool for PSC design and optimization. However, current ML models often oversimplify PSC design at the device level, failing to capture multi-scale complexity. They are constrained by relatively small, specialized datasets, limiting generalizability across diverse architectures and fabrication methods. This work developed a full-process AI framework based on over 20,000 experimentally measured PSC samples and approximately 260 multi-scale features. The framework offers significant advantages in sample diversity and feature richness, combining material selection, fabrication processes, and environmental factors to provide accurate, comprehensive optimization solutions. Data diversity and heterogeneity challenges were addressed through feature engineering and model training, yielding a highly generalizable PSC performance prediction model with prediction error comparable to small-scale models. The framework enables precise optimization of specific features for any PSC and provides valuable insights for designing high-performance photovoltaic devices. Experimental validation fabricated 8 types of PSCs with new feature values; the framework searched corresponding optimization suggestions, resulting in an additional improvement of 0.92% to 2.43% in final power conversion efficiency (PCE) of fabricated devices. This demonstrates the framework's universality and sufficient learning and analytical capabilities for new data, adaptable to the rapid development of PSCs.
SCIENCE CHINA Materials•2025•DOI: 10.1007/s40843-025-3352-6
The relentless scaling of silicon transistors below 5 nm gate lengths has exposed fundamental limits: dangling-bond-induced interface scattering exacerbates short-channel effects, including direct source-drain tunneling and drain-induced barrier lowering, degrading power consumption, signal integrity, and reliability. Gate-all-around field-effect transistors (GAAFETs) mitigate these effects by fully enclosing the channel, but silicon's surface chemistry remains problematic. Two-dimensional (2D) semiconductors offer dangling-bond-free surfaces, atomic-level thickness uniformity, and high electron mobility, enabling sub-1-nm gate lengths without short-channel effects. Their van der Waals layered structure further permits monolithic 3D (M3D) integration for high-density, low-power circuits. Despite theoretical promise, 2D GAA devices face critical bottlenecks in source-drain contacts, gate dielectrics, and interface engineering. The deposition of high-quality, atomically uniform, low-trap-density high-k dielectrics on 2D surfaces is particularly challenging. Tang et al. addressed this by forming layered oxide Bi2SeO5 (k = 21) on Bi2O2Se via ultraviolet-assisted intercalative oxidation, creating an atomically smooth, lattice-matched van der Waals interface. The resulting fully encapsulated 2D Bi2O2Se/Bi2SeO5 GAA heterostructure enabled GAAFETs with equivalent oxide thickness below 0.5 nm, subthreshold swing of ~62 mV dec-1 over five orders of magnitude, electron mobility exceeding 280 cm2 V-1 s-1, and stable operation at 0.5 V with on-current exceeding 1 mA μm-1. These metrics demonstrate superior electrostatic control compared to silicon and other 2D GAAFETs, providing a viable route to M3D circuits beyond silicon.
SCIENCE CHINA Materials•2025•DOI: 10.1007/s40843-025-3499-6
Biomass-derived hard carbons (HCs) are promising anodes for sodium-ion batteries (SIBs) due to their low cost, renewable nature, and structural stability, yet their practical application is hindered by a low initial Coulombic efficiency (ICE) and inadequate rate capability. Herein, we report a tri-functional nitric acid treatment coupled with one-step carbonization to synthesize a hard carbon with a sp2-C-dominated structure. The process not only eliminates impurities but also selectively dissolves lignin in the biomass, thereby promoting the alignment of graphite microcrystals. At the same time, edge-N and C=O groups are grafted onto the carbon skeleton, which together produce an HC with an optimized interlayer spacing and abundant closed micropores. These structure modifications collectively increase Na+ adsorption kinetics in the sloping region and enable efficient sodium storage in the low-voltage plateau region, yielding a high ICE of 91.69% and a remarkable rate capability, with 83.9% capacity retention at 600 mA g−1. A full SIB cell using this HC anode with a Na3V2(PO4)3 cathode delivers an energy density of 213.14 Wh kg−1, demonstrating its practical potential. This work offers a simple and scalable engineering strategy to overcome the performance vs. manufacturing cost dilemma in developing HC anodes.