Revolutionizing ionic thermoelectrics: machine learning unlocks high-performance materials
Peer-Reviewed Publication
Updates every hour. Last Updated: 25-Jun-2025 06:10 ET (25-Jun-2025 10:10 GMT/UTC)
A novel machine learning framework accelerates the discovery of ionic thermoelectric materials, achieving precise Seebeck coefficient predictions. This groundbreaking method identified a waterborne polyurethane-potassium iodide ionogel with a Seebeck coefficient of 41.39 mV/K. Insights into key molecular features promise rapid advancements in waste-heat recovery and thermal sensing technologies.
In a paper published in National Science Review, an advanced catalytic team of scientists present a novel heterogeneous catalytic process that efficiently converts polyurethane waste into important chemicals like aromatic amines and lactones by combining methanolysis and hydrogenation with a CO2/H2 reaction medium. The intermediate chemicals were then transformed into functional polymers—polyimide and polylactone.
A research team led by Professors Wu and Cai at the State Key Laboratory of Transducer Technology, AIRCAS has developed a flexible serpentine electrode probe that enables stable, long-term neural monitoring. This study was published in National Science Review.
Presentation videos are widely used for sharing information but can be difficult to search, analyze, and store efficiently. To address this, researchers developed PV2DOC, software that transforms video content into structured, searchable PDF documents. The software integrates visual and audio data, including text, images, figures, and formulas, to create a concise summary of the video, making information more accessible while reducing storage requirements.
Bio-inspired wind sensing using strain sensors on flexible wings could revolutionize robotic flight control strategy. Researchers at Institute of Science Tokyo have developed a method to detect wind direction with 99% accuracy using seven strain gauges on the flapping wing and a convolutional neural network model. This breakthrough, inspired by natural strain receptors in birds and insects, opens up new possibilities for improving the control and adaptability of flapping-wing aerial robots in varying wind conditions.