Developing lightweight superalloys with excellent high-temperature creep resistance has long been a significant challenge for aerospace and energy applications. In this study, we present a single-crystal highentropy superalloy (HESA) characterized by a pronounced dual-high-entropy effect where both the gamma and gamma ' phases feature multi-component configurations. The resulting HESA exhibits a remarkable synergy of low mass density (8.16 g cm(-3)) and an elevated gamma ' solvus temperature (1238 degrees C), with an outstanding creep performance at 850-900 degrees C under compressive stress of 50 0-40 0 MPa. Notably, the minimum creep rate at 850 degrees C/40 0 MPa (similar to 4.0 x 10(-9) s(-1)) is nearly two orders of magnitude lower than that of conventional W-free Co-based alloys and comparable to that of Re-containing Ni-based single crystals. Such superior creep resistance could be attributed to a dual high-entropy effect on both thermodynamic stability and deformation kinetics. Thermodynamically, the entropy-driven stabilization enhances the microstructural stability of the alloy and suppresses the formation of detrimental phases during the prolonged creep process. Kinetically, the atomic-scale, chemically complex environment creates a rugged fault energy landscape that effectively influences dislocation dynamics, promoting multiple deformation mechanisms, including dense interfacial dislocation networks, antiphase-boundary-coupled superpartials, and microtwins. These substructures and their interactions further inhibit the motion of dislocations for a sustained creep resistance. Furthermore, we identified a novel microtwinning mechanism mediated by the coordinated glide of a pair of dissimilar 90 degrees and 30 degrees 1/6 < 112 > Shockley partials on consecutive {111} planes, coupled with a unique one-step atomic shuffling process. Our findings offer mechanistic insights into creep behaviors of HESAs and demonstrate a promising strategy for the development of lightweight superalloys with superb high-temperature capabilities. (c) 2026 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
In recent years, room-temperature gallium-based liquid metals (Ga-based LMs) have rapidly advanced in fields such as flexible electronics, soft robotics, micro/nanofabrication, and biomedicine, owing to their combination of high electrical/thermal conductivity and fluidic plasticity. Despite the surge of related studies, systematic clarification of how and why LM particles deform at the micro/nanoscale remains limited. Insights derived from the macroscale cannot be directly extrapolated to micro/nano dimensions, where identical stimuli (e.g., heating, acid treatment, or template confinement) often induce new phase formation and irreversible morphological locking. Typical outcomes include the generation of α-GaOOH and β-Ga2O3, core-shell reconstruction, and shape fixation. To address this gap, this review systematically summarizes the evolutionary modes of Ga-based LM micro/nanoparticles. Using “whether new phases emerge” and “whether the deformation is reversible” as criteria, these modes are classified into two categories: physical deformation and interfacial chemical reconstruction. Furthermore, the distinctions between the application scenarios associated with each mechanism are clarified. This review aims to provide practical guidelines for the design and fabrication of LM micro/nanomaterials, while establishing a unified classification and reference for the development of the mechanism and the application.
Parkinson’s disease (PD) affects tens of millions of people globally, yet current clinical management remains fragmented, relying on subjective assessments and episodic laboratory measurements that fail to capture disease dynamics in real-world settings. Here, we present a smart textile-integrated multimodal interface (STMI) that seamlessly combines tremor quantification with real-time therapeutic drug monitoring through a wearable wristband platform. The system integrates an array of enhanced triboelectric nanogenerators with optimized bead-on-string nanofiber architecture for sensitive tremor detection, coupled with fiber-based electrochemical sensors for simultaneous monitoring of levodopa (LD), pH, and sodium levels in sweat. By leveraging deep learning algorithms (bidirectional LSTM), the STMI achieves accurate discrimination between healthy controls, prodromal Parkinson’s patients, and PD patients—including early detection of prodromal stages currently undetectable by standard imaging. Longitudinal tracking in PD patients demonstrates real-time correlation between tremor suppression and LD pharmacokinetics, enabling quantitative assessment of medication efficacy and identification of wearing-off phenomena. The textile-based form factor ensures ergonomic wearability and mechanical robustness for continuous monitoring, while the integrated multimodal sensing paradigm establishes a new standard for personalized PD management. This work demonstrates how intelligent wearable systems can bridge the gap between motor symptom assessment and pharmacological profiling, transforming PD care from episodic clinical visits to continuous, data-driven therapeutic optimization.
Recent advancements in Large Language Models (LLMs)-based text embedding models primarily focus on data scaling or synthesis, yet limited exploration of training techniques and data quality, thereby constraining performance. In this work, we propose KaLM-Embedding-V2, a series of versatile and compact embedding models, systematically incentivizing advanced embedding capability in LLMs by superior training techniques and high-quality data. For model architecture, we implement the models on a 0.5B compact size with simple mean-pooling to produce fixed-length embeddings and remove the causal attention mask to enable fully bidirectional representation learning. For training techniques, we propose a progressive multi-stage training pipeline: pre-training on weakly supervised large-scale datasets, fine-tuning with supervised high-quality datasets, and contrastive distillation with fine-grained soft signals, integrated with focal-style reweighting and online hard-negative mixing to emphasize difficult samples and enrich hard negatives, respectively. For training data, we curate over 20 categories for pre-training and 100 categories for fine-tuning and contrastive distillation, to improve both performance and generalization, leveraging task-specific instructions, hard-negative mining, and example-based multi-class labeling to ensure high quality. Combining these techniques, our KaLM-Embedding-V2 series achieves state-of-the-art performance on the Massive Text Embedding Benchmark, outperforming models of comparable size and rivaling models 3-26x larger, setting a new standard for versatile and compact embedding models under 1B parameters. The code, data, and models will be publicly available to facilitate academic research.
This study examines the impact of China’s 2019–2023 climate policies on the stock returns of sectors within the carbon-neutral industry chain. By employing contagion tests and network analysis, we explore how policy shocks propagate from the carbon-neutral sector to its upstream, midstream and downstream sectors. Our analysis also quantifies the importance of climate policy in driving policy shocks and identifies the main driver within the carbon-neutral industry chain. The empirical findings indicate that the 2019 persuasive strategy policy demonstrates the most robust evidence of policy contagion, followed by the 2020 cap-and-trade scheme and the 2022 and 2023 carbon offset mechanisms. In contrast, the regulatory measures exhibit the weakest evidence of policy contagion. Notably, climate policies have a greater impact on upstream sectors, which consist of carbon-intensive industries, compared with the midstream and downstream sectors, which comprise renewable energy sectors.
