Revolutionizing ionic thermoelectrics: machine learning unlocks high-performance materials
Peer-Reviewed Publication
Updates every hour. Last Updated: 27-Jun-2025 16:10 ET (27-Jun-2025 20:10 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 Science Bulletin, Researchers discovered a unique signature of the planar Hall effect (PHE) in magnetic Weyl semimetals, where PHE conductivity is determined by a global quantity: the Chern number of Weyl point. Due to the robustness of the Chern number, the PHE conductivity here is independent of many material details, leading to a planar Hall plateau, which is rare and has great potential applications. By revealing completely new physical signatures, their work significantly advances the field of Hall transport.
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.
University of Groningen materials scientists studied a ‘twisted’ 2D material and discovered that it defied theoretical predictions.