The study introduces a high-performance NIR photodetector that optimizes interfacial energy alignment and suppresses leakage current. The optimized device achieves an ultra-low dark current density of 6.1×10−8 A cm−2, the lowest value reported for AgBiS2 CQD photodetectors so far.
Researchers developed a 'beehive inspired' nano carrier that solves the recyclability issue of single-chain nano-catalysts. The system uses a dynamic Diels Alder covalent bond to anchor copper-loaded SCNP onto a glass surface, releasing catalysts into the reaction medium and then re-grafting them for recovery.
Researchers develop a bio-inspired nanonetwork strategy to improve the mechanical performance of carbon nanotube composite films. The approach results in ultra-high dynamic toughness and enhances strength, toughness, and damage tolerance.
Scientists create a two-step anion-exchange strategy to synthesize metastable quasi-1D van der Waals material W6Te6 on a wafer scale. The method utilizes a WS2 template and overcomes the thermodynamic limitation of conventional tellurization, allowing for uniform growth of the material.
Researchers developed a conductive e-skin patch that enables real-time wireless monitoring of physiological signals, accelerates wound healing, and promotes smart wound dressing. The patch demonstrates exceptional 99% wound healing rate within 14 days in rat full-thickness wound models.
Researchers at Shandong University developed a stepwise growth strategy to fabricate high-quality monolayer graphene on silicon carbide. By decoupling buffer layer formation from subsequent graphene growth, they stabilize the SiC surface, eliminate giant step bunching, and achieve uniform graphene growth. The approach enables wafer-sca...
Researchers developed a self-powered blue-light photodetector that operates at zero bias, enabling low-power underwater imaging and optical sensing. The device features an asymmetric electrode design and achieves high responsivity and quantum efficiency, making it suitable for real-world tasks.
A team from Tsinghua University developed the 'calculus of intelligence' framework to break down complex problems in agentic AI. This framework provides a mathematical approach to decomposing tasks into smaller, manageable subtasks that can be solved independently and then combined for a coherent whole.
Researchers developed a transparent strain-insensitive stretchable ionic temperature sensor with improved sensitivity and stability. The sensor's strain-insensitive performance is attributed to a crack counteraction mechanism, which offsets the promoting effect of an enlarging electrode surface area as strain increases.
Researchers have developed high-quality field-effect transistors using violet phosphorus, a layered semiconductor material. The devices showed robust unipolar n-type conduction and maintained stable performance over time, making them suitable for future electronic and optoelectronic applications.
A highly linear and durable flexible strain sensor is developed using MoS2 van der Waals films, enabling reliable signal output under cyclic loading. The sensor's performance is stable over 40000 stretching cycles, making it suitable for applications requiring accurate deformation sensing in soft machines.
Researchers have developed a breakthrough tandem electrode structure that integrates anodic and cathodic electrochromic materials on a single substrate, enabling seamless switching between transparent, brown, and blue states. This innovation allows smart windows to control both visible light and heat-producing near-infrared radiation f...
Researchers developed a new catalyst that combines porous aromatic frameworks with Co-N4 active sites, achieving high efficiency in converting nitric oxide to ammonia. The PAF-TPP-Co catalyst optimizes adsorption energetics, lowers reaction barriers, and accelerates kinetics.
Researchers developed a high-performance catalyst using more abundant materials, exhibiting superior HER activity and remarkable stability across a wide pH range. The optimized catalyst achieved record-low overpotentials of 3 mV in alkaline and 17.3 mV in acidic media with exceptional durability.
A research team has developed a practical and scalable strategy for high-sensitivity tactile sensors by elucidating the effects of ionic conductivity and excitation frequency on iontronic pressure sensing. The findings reveal that sensitivity can be enhanced by as much as 8772% solely through electrical parameter tuning.
Researchers develop ferroelectric reconfigurable homojunctions for energy-efficient in-Sensor computing, achieving sub-fJ programming energy and exceptional image processing performance. The device integrates sensing and processing units, enabling simultaneous optimization of functional reconfigurability and energy efficiency.
High-capacity lithium-ion batteries face challenges during operation, particularly activation, where capacity drops initially and recovers gradually. Researchers found that particle size plays a decisive role in determining whether activation occurs, with larger particles exhibiting pronounced behavior.
A team at University Akron used AUC to study molecular clusters and their interactions with biomolecules, revealing key findings on hydration shells and intermolecular distances. The technique offers a powerful tool for characterizing complex solution systems, overcoming limitations of traditional methods.
A new type of conductive hydrogel has been developed using gelatin to stabilize liquid metal particles, enabling high sensitivity in motion sensing and real-time handwriting recognition. The material also harvests solar energy via photothermal conversion.
Researchers have developed an ultrafine ruthenium nanocluster catalyst that selectively converts lignin-derived compounds into 1,4-cyclohexanediol using only pure water. The process achieves a 96.7% yield, exceeding current industrial benchmarks and commercial noble metal catalysts.
A team of material scientists developed a new catalyst that simultaneously achieves high activity and remarkable stability for CO2-to-CO conversion. The ZnS@Fe-NSC catalyst features atomically dispersed iron active sites synergistically modified by sulfur atoms and adjacent zinc sulfide nanoparticles.
A novel 'point-to-point adsorption' technique facilitates scalable fabrication of ordered gold nanoparticle arrays for next-generation data storage, resulting in superior device performance. The approach leverages self-assembly behavior of block copolymers to create templates for precisely positioning AuNPs.
Researchers develop a physics-informed neural network to simulate fluid-structure interaction for an Euler-Bernoulli beam under steady flow. The framework achieves accurate results even with sparse data, showing great potential as a data-efficient method for numerical simulation.
Researchers have discovered that oscillatory pressure accelerates densification and inhibits grain growth in Al2O3/TiCp composites, resulting in improved mechanical properties and near-full density. The study integrates sintering kinetics with metal hysteresis theory to elucidate the promoting mechanism of dynamic pressure.
The study presents a novel method for synthesizing fluorescent polymer nanoparticles (FPNs) using microwave irradiation, enabling precise regulation of particle size and fluorescence wavelength. The approach achieves a 24-fold improvement in reaction efficiency compared to traditional thermal polymerization methods.
Researchers develop a new photocatalytic material by engineering sulfur defects in ZnIn2S4 nanosheets using pulsed laser ablation. The defect-rich nanosheets show a 15-fold enhancement in converting CO2 to carbon monoxide.
Researchers developed boron-doped carbon nanotubes to boost NO3- reduction reaction performance, achieving high ammonia yields and positive-shifted onset potentials. The doping strategy offers a new way to tune complex electrocatalytic reactions.
The HULU framework accelerates clean energy materials discovery by integrating machine learning potentials with adsorption simulations. Researchers found that universal machine-learning potentials are reliable across different chemical systems, providing a roadmap for material screening.
Researchers have developed a fully flexible sensing-memory integrated system for wearable physiological sensing, which integrates a pressure sensor with a floating-gate organic thin-film transistor memory. The device offers a short response time and long data retention capability compared to previously reported OTFT-based pressure sens...
Researchers developed a thermal engineering method to modify ZnMgO nanoparticles, reducing oxygen vacancies and increasing nanoparticle size. This approach improved QLED electroluminescence uniformity, brightness, and stability, tripling the device lifetime to 20,000 hours.
Researchers from Tsinghua University Press outline a roadmap for developing practical single-molecule spintronics devices, offering a pathway toward ultra-compact, low-power spintronic technologies. The review highlights various spin-related quantum effects that have been demonstrated in single-molecule devices.
A new review explores innovative strategies for achieving ultralow-resistance Ohmic contacts in 2D materials, including van der Waals integration and interfacial doping. The study aims to overcome Fermi-level pinning and Schottky barrier limitations, enabling better devices and industrial realities.
Scientists develop a biomimetic coating mimicking human skin, combining superior water resistance with high-efficiency fire retardancy. The coating exhibits impressive mechanical durability and exceptional flame-retardant performance, including self-extinguishing capabilities.
Researchers have demonstrated large strain tunability of excitons in ZrSe3 under cryogenic conditions, showcasing exceptional strain sensitivity and potential for band structure engineering. This breakthrough highlights ZrSe3 as a promising material for next-generation Straintronics and flexible optoelectronic devices.
A team from Xi'an Jiaotong University creates a novel flexible film with multiply indented, hybrid 3D-nanobowls, achieving ultra-high haze and excellent light transmittance. The roll-to-roll manufacturing process enables large-scale production at low cost.
Researchers developed a composite material with gradient electronic structure to efficiently absorb electromagnetic waves. The material exhibits exceptional performance, including high reflection loss and broad absorption bandwidth.
Researchers designed a novel organic-inorganic composite electrolyte to improve lithium metal batteries' safety, reliability, and electrochemical performance. The asymmetric quasi-solid-state composite electrolyte reduces internal short-circuit risks and enhances overall battery life.
Scientists develop a new palladium-polyoxometalates cluster catalyst, Pd-PMo10V2, which enhances the selective hydrogenation of phenol to cyclohexanone. The catalyst achieves high phenol conversion rates and maintains structural integrity through multiple cycles.
Metal-organic frameworks (MOFs) and polyoxometalate-based MOFs (POMOFs) have been shown to selectively activate inert C-H bonds, overcoming long-standing challenges in organic synthetic chemistry. POMOFs enable ultrafast electron injection and generate reactive oxygen species with high selectivity.
Researchers used reinforcement learning to control wave energy converters, generating up to 13.9% more energy with improved efficiency and stability. The approach uses short-term wave forecasts to adapt in real-time, avoiding the need for precise models.
Research finds that one-pedal driving in electric vehicles yields narrower distributions of speed and acceleration when integrated with Adaptive Cruise Control, improving traffic flow stability. However, manual control produces pronounced speed oscillations and sharper decelerations, highlighting the need for automation.
Researchers developed a multi-feature fusion filtering framework to improve defect detection in heavy-haul lines, achieving accurate localization in over 90% of test cases. The method outperforms single-feature methods by reducing missed detections and large localization deviations.
iFuture, a new academic journal, debuts at WAIC 2026, focusing on foundational AI theories and cutting-edge interdisciplinary research. The journal's supporting platform Oscholar uses AI to support scholarly communication and collaboration, aiming to become a globally recognized high-impact platform for scholarly innovation.
This study reveals that off-the-shelf large language models do not exhibit universal risk profiles and can be affected by prompt language, leading to systematic distortions. The findings highlight the need for rigorous empirical calibration before deploying these models in computational social science and choice modeling.
Knowledge graphs provide explicit reasoning and traceable decision support, while large language models excel in semantic understanding and contextual generalization. The study reveals the growing synergy between these two AI paradigms, promising a more robust and trustworthy autonomous driving system.
A study reveals a contrast in decision-making pathways between fuel vehicle and EV owners, highlighting pragmatic benefits for FV owners and habit-driven loyalty among EV owners. The research suggests segment-specific strategies to convert fuel-to-EV owners and retain existing EV users.
Researchers introduced CogDrive, a cognition-driven framework for autonomous vehicles to think ahead and make structured reasoning about multiple possible future behaviors. The framework achieves state-of-the-art performance in trajectory prediction and demonstrates smoother maneuvers in dense urban environments.
A new study highlights a potential enforcement gap in the UK Contracts for Difference scheme, which supports low-carbon electricity generation. The study shows that termination payments under the CfD scheme may be large in contractual terms but difficult to recover in practice due to financial distress or insolvency.
A study by Tsinghua University explores China's power supply security amid a shift to renewable energy. The research suggests that current capacity payment mechanisms may not support emerging flexible resources like battery storage, and instead recommends piloting market-based capacity mechanisms.
The COMPASS framework offers a unified and collaborative modeling approach, balancing macroeconomic consistency and technological granularity. It has been widely applied to evaluate the socioeconomic impacts of climate policies and simulate sectoral energy transition pathways.