Multimodal pre-training is driving the technological revolution in the field of drug discovery
Peer-Reviewed Publication
Updates every hour. Last Updated: 19-Apr-2026 09:16 ET (19-Apr-2026 13:16 GMT/UTC)
Multimodal pre-training models open a new avenue for drug discovery.
Thermoelectric (TE) materials, being capable of converting waste heat into electricity, are pivotal for sustainable energy solutions. Among emerging TE materials, organic TE materials, particularly conjugated polymers, are gaining prominence due to their unique combination of mechanical flexibility, environmental compatibility, and solution-processable fabrication. A notable candidate in this field is poly(2,5-bis(3-alkylthiophen-2-yl)thieno[3,2-b]thiophene) (PBTTT), a liquid-crystalline conjugated polymer, with high charge carrier mobility and adaptability to melt-processing techniques. Recent advancements have propelled PBTTT’s figure of merit from below 0.1 to a remarkable 1.28 at 368 K, showcasing its potential for practical applications. This review systematically examines strategies to enhance PBTTT’s TE performance through doping (solution, vapor, and anion exchange doping), composite engineering, and aggregation state controlling. Recent key breakthroughs include ion exchange doping for stable charge modulation, multi-heterojunction architectures reducing thermal conductivity, and proton-coupled electron transfer doping for precise Fermi-level tuning. Despite great progress, challenges still persist in enhancing TE conversion efficiency, balancing or decoupling electrical conductivity, Seebeck coefficient and thermal conductivity, and leveraging melt-processing scalability of PBTTT. By bridging fundamental insights with applied research, this work provides a roadmap for advancing PBTTT-based TE materials toward efficient energy harvesting and wearable electronics.
In a paper published in Science Bulletin, a Chinese team of scientists estimated carbon sequestration rates of urban forests in China from 1995 to 2060 under three climate scenarios.
The challenge of photosynthesizing hydrogen peroxide (H2O2) from water and oxygen lies in the high O–H bond dissociation energy in water molecules, which leads to sluggish kinetics in hydrogen donor reactions. This study presents a novel catalytic strategy by constructing single-atom palladium sites (Pd–CNO2) within a keto-form anthraquinone-based covalent organic framework (KfAQ-Pd), effectively promoting water oxidation reactions and achieving efficient H2O2 photosynthesis in neutral aqueous environments, with a high yield of 3828 μmol h−1 g−1. Experimental evidence and theoretical simulations reveal that the Pd–CNO2 sites enhance the hydrophilicity of KfAQ, disrupt the hydrogen-bond network in surface-adsorbed water clusters, and facilitate the cleavage of O–H bonds. The generated H3O+ can be reduced by photoexcited electrons, promoting the anthraquinone hydrogenation reaction and enabling the efficient H2O2 synthesis cycle. This study provides a new approach for solar-driven green hydrogen peroxide synthesis under neutral conditions.
Scientists from the City University of Hong Kong have found that nanoplastics can enter zebrafish via two pathways: waterborne exposure and dietary exposure. These tiny particles can cross biological barriers to enter the circulatory system, and then translocate to and accumulate in various organs, including the blood, brain, gills, liver, intestines, gonads, and muscles. The gills and intestines are the most important absorption organs, while the intestines serve as the primary excretion organ.
Strain engineering precisely tunes van der Waals materials' electronic and optical properties for flexible electronics. This review covers uniaxial, biaxial, and localized strain methods, analyzing their effects on band structure, carrier mobility, and phase transitions. Applications include high-performance sensors, transistors, memristors, photodetectors, and LEDs. Future challenges involve scalable material growth, efficient strain transfer, and integration toward intelligent wearable systems.
Researchers at UCLA have developed an artificial intelligence tool that can use electronic health records to identify patients with undiagnosed Alzheimer’s disease, addressing a critical gap in Alzheimer’s care: significant underdiagnosis, particularly among underrepresented communities.