SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-026-4502-1
Near-infrared-II (NIR-II, 1000-1700 nm) luminescent materials are pivotal for deep-tissue bioimaging and optical communication, yet their performance is often limited by low quantum yields and thermal quenching. Here, we report a thermal-enhanced NIR-II luminescence in Sb3+/Er3+ co-doped Cs3GdCl6 microcrystals synthesized via a modified Bridgman method. Under ultraviolet excitation, the co-doped microcrystals exhibit intense NIR-II emission centered at 1532 nm corresponding to Er3+: 4I13/2 → 4I15/2 transition, with a maximum relative sensitivity of 1.2% K−1 at 303 K. Notably, the integrated NIR-II emission intensity increases by 2.3-fold from 298 K to 373 K, demonstrating anomalous thermal enhancement. This behavior is attributed to the thermally activated energy transfer from Sb3+ sensitizers to Er3+ activators, as confirmed by temperature-dependent photoluminescence spectra and decay kinetics. The energy transfer efficiency reaches 86% at room temperature and further improves with rising temperature. The microcrystals also show excellent photostability, retaining 95% of initial intensity after 120 min continuous UV irradiation. Furthermore, we demonstrate a proof-of-concept wireless optical communication link using the microcrystals as a NIR-II phosphor, achieving a signal-to-noise ratio of 30 dB at 400 Hz modulation frequency. These findings provide a new strategy for designing thermal-enhanced NIR-II luminescent materials and expand their potential in temperature sensing and optical communication.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3652-2
Existing robotic end-effector gripping technologies often encounter challenges such as poor adaptability to environmental changes, incomplete deformation sensing, and insufficient adhesion stability, which can compromise operational safety and reliability. Here, we present the bio-inspired self-sensing suction cup, in which the core self-sensing capability is achieved by combining high-performance, laser-induced graphene/Ag NWs flexible sensors with a Wheatstone bridge design. The flexible sensors provide high sensitivity, while the Wheatstone bridge circuit enables accurate and stable detection of deformation during the gripping process. Integrated into the octopus-inspired suction cup, this system allows for real-time monitoring of deformation and adsorption stability. The self-sensing suction cup demonstrates good performance across a 0–25 kPa negative pressure range, with outstanding linearity (R2 = 0.993) and high sensitivity (GF = 10.436 kPa−1). Experimental results confirm that the suction cup can achieve stable adsorption under varying loads and enable real-time monitoring of the suction cup status during the gripping process. This design provides a promising solution for intelligent gripping systems, logistics, and object recognition in challenging environments.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-4068-2
Eutectic high-entropy alloys (EHEAs) combine multi-principal-element compositions with regular lamellar microstructures, offering exceptional high-temperature stability and mechanical properties. However, conventional casting yields random solidification microstructures and inhomogeneous phase distributions, constraining strength-ductility synergy. This study employs directional solidification (DS) on Al19Fe20Co20Ni41 EHEA to achieve precise microstructural control, constructing a multi-level lamellar architecture with a herringbone-like alternating arrangement. This tailored microstructure refines interlamellar spacing, eliminates detrimental isolated B2 phases, and promotes slip continuity at interfaces, enhancing coordinated dislocation motion and uniform distribution across multiple slip systems. Consequently, the DS EHEA exhibits superior mechanical properties compared to most reported thermomechanically processed and directionally solidified HEAs. Micro-mechanistic analysis reveals that homogenized geometrically necessary dislocation (GND) density, interface-assisted crack deflection, and multi-stage strain-hardening from sequential dislocation activation collectively contribute to outstanding strength-ductility synergy. This work demonstrates that programming solidification paths enables design of unique multi-level lamellar architectures, serving as intrinsic microstructural composites that optimize dislocation management and crack propagation, offering a novel paradigm for developing ultra-robust EHEAs for extreme service environments.
Journal of Fuel Chemistry and Technology•2026•DOI: 10.1016/S1872-5813(26)60686-X
This study reports a streamlined route for synthesizing plate-like α-Al2O3 via co-combustion activation of coal gangue and corn stalk, enabling high-value utilization of solid wastes. The introduction of corn stalk significantly reduces the apparent activation energy of coal gangue combustion and increases the acid leaching yield of aluminum to 81.9%. Mechanism analysis reveals that titanium and iron ions in the co-combustion ash leachate act as natural morphology regulators, facilitating the formation of a plate-like structure in the alumina product, with titanium exhibiting leaching behavior consistent with that of aluminum. Furthermore, a high content of AlO6 structural units in the precursor effectively promotes the direct conversion into dense α-Al2O3 crystals during thermal treatment, thereby enhancing product density. Under optimized conditions (800 °C, 1 h), the as-prepared α-Al2O3 exhibits a plate-like morphology, with a median particle size (d50) of 5.70 μm and a density of 4.94 g/cm3. This work provides a new approach for the synergistic resource utilization of coal gangue and biomass waste.
Journal of Fuel Chemistry and Technology•2026•DOI: 10.3724/2097-213X.2026.JFCT.0006
The oxygen reduction reaction (ORR) is a critical cathode reaction in fuel cells and metal-air batteries, yet its sluggish kinetics and high overpotential severely limit device performance. Conventional platinum-based catalysts suffer from prohibitive cost (accounting for up to 40% of total fuel cell system cost), scarce reserves, and poor tolerance to methanol and carbon monoxide, impeding large-scale commercialization. This review systematically summarizes recent advances in biomass/coal-based carbon materials as ORR electrocatalysts, focusing on raw material characteristics, preparation methods, structural regulation, and performance evaluation. Biomass and coal precursors offer advantages of low cost, abundant availability, and natural heteroatom doping (N, P, S), enabling the design of high-performance, metal-free catalysts. Key challenges include ensuring raw material homogeneity, precise control of active sites, and scalable synthesis. Future research directions emphasize optimizing pore structure and surface chemistry to enhance four-electron selectivity and stability. The review provides theoretical guidance for developing cost-effective ORR catalysts to replace platinum, thereby accelerating the deployment of clean energy technologies.
SCIENCE CHINA Materials•2025•DOI: 10.1007/s40843-025-3507-9
Quantum dot (QD)-based memristors enable precise and energy-efficient neuromorphic computing through atomic-level control over electrical synapse performance. However, the stochastic nature of QD structures results in poor reliability of resistive switching, limiting practical applications. This work presents a data-driven QD synthesis optimization loop that integrates high-throughput density functional theory with machine learning to establish a cross-scale screening platform for precise QD synthesis. By minimizing structural disorder through pure phase, uniform size distribution, and highly preferred orientation, QD-based memristors demonstrate a 57% reduction in switching voltage, a two-order-of-magnitude increase in ON/OFF ratio, and endurance and retention degradation as low as 0.1% over 8.4 × 10^7 s of continuous operation and 10^5 rapid read cycles. The dynamic learning range and neuromorphic computing accuracy improve by 477% and 27.8% (reaching 92.23%), respectively. These findings establish a scalable, data-driven strategy for rational design of QD-based memristors, advancing next-generation reliable neuromorphic computing systems.