Tech & Engineering
Updates every hour. Last Updated: 16-Dec-2025 21:11 ET (17-Dec-2025 02:11 GMT/UTC)
Unveiling non-thermal catalytic origin of direct current-promoted catalysis for energy-efficient transformation of greenhouse gases to valuable chemicals
National Institutes of Natural SciencesPeer-Reviewed Publication
- Journal
- The Journal of Physical Chemistry Letters
- Funder
- JSPS KAKENHI, Grant-in-Aid for Early-Career Scientists, JSPS KAKENHI, Grant-in-Aid for Transformative Research Areas (A), JSPS KAKENHI, Grant-in-Aid for Scientific Research (A), JSPS KAKENHI, Grant-in-Aid for Specially Promoted Research, JSPS KAKENHI, Fund for the Promotion of Joint International Research (International Leading Research), JST CREST, Japan, JST ACT-X, Japan, BL3U of UVSOR Synchrotron Facility, Institute for Molecular Science (IMS program), Demonstration Project of Innovative Catalyst Technology for Decarbonization through Regional Resource Recycling, the Ministry of the Environment, Japan
Beyond small data limitations: Transfer learning-enabled framework for predicting mechanical properties of aluminum matrix composites
Songshan Lake Materials LaboratoryPeer-Reviewed Publication
A research team led by Chang Keke from the Ningbo Institute of Materials Technology and Engineering (NIMTE), Chinese Academy of Sciences (CAS), has developed an innovative machine learning framework (PAMCs-MP) for predicting the mechanical properties of particle-reinforced aluminum matrix composites (PAMCs). Despite limited existing datasets, the approach uses extensive pre-training on larger aluminium alloy datasets to guide multi-objective optimization tasks effectively. The model achieves high predictive accuracy, R² values of over 92% for ultimate tensile strength and over 90% for elongation, demonstrating its robustness and reliability. The platform not only accelerates the design cycle but also offers profound insights into material behaviour, facilitating the development of high-strength, ductile aluminum composites tailored to specific application needs.
- Journal
- Materials Futures
AI in primary care: experts warn of safety risks as tech outpaces regulation
University of SydneyPeer-Reviewed Publication
From digital scribes to ChatGPT, artificial intelligence (AI) is rapidly entering GP clinics. New University of Sydney research warns that technology is racing ahead of safety checks, putting patients and health systems at risk.
- Funder
- National Health and Medical Research Council, National Institute for Health and Care Research Applied Research Collaboration Oxford and Thames Valley, NIHR Imperial Biomedical Research Centre
Exposing the most dangerous dams in the US
American Geophysical UnionReports and Proceedings
Video: Drivers struggle to multitask when using dashboard touch screens, study finds
University of WashingtonReports and Proceedings
To get lifelike movement from synthetic materials, researchers can embrace chaos
University of MichiganPeer-Reviewed Publication
- Journal
- Physical Review Letters
- Funder
- U.S. National Science Foundation, Army Research Office, Office of Naval Research