Results 291 to 300 of about 131,686 (354)
Provenance and distribution of potentially toxic elements (PTEs) in stream sediments from the eastern Hg-district of Mt. Amiata (central Italy). [PDF]
Meloni F+6 more
europepmc +1 more source
Reconfigurable physical unclonable function (PUF) integrating optical and electrical responses in organic field‐effect transistor is developed by using unique optical fingerprint textures and random molecular alignment of the semiconductive smectic liquid crystal. This approach enhances security by enabling hierarchical authentication, providing robust
Hee Seong Yun+8 more
wiley +1 more source
A binary merger product as the direct progenitor of a Type II-P supernova
Niu* Z+17 more
europepmc +1 more source
Mechanical Homogenization Promoting Dual‐Directional Upcycling of Layered Oxide Cathodes
This work introduces an efficient dual‐directional upcycling scheme enabled through a mechanical homogenization pretreatment. It enables various layered oxide cathodes to be reprocessed into fresh NCM cathodes with tailored Ni contents through boosted atomic diffusion in just 4 h of solid‐state sintering.
Nianji Zhang+3 more
wiley +1 more source
In situ crystallographic mapping constrains sulfate precipitation and timing in Jezero crater, Mars. [PDF]
Jones MWM+27 more
europepmc +1 more source
From Be X-Ray Binaries to Double Neutron Stars: Exploring the Spin and Orbital Evolution
Yungang Zhou, Dehua Wang, Chengmin Zhang
openalex +1 more source
This work engineered a bi‐heterojunction noise‐enhanced negative transconductance (BHN‐NTC) transistor using a half‐PTCDI‐C13 layer, achieving expanded and tunable noise characteristics. This advancement enables efficient multi‐bit TRNGs for AI‐driven image generation and enhances logic circuit applications.
Youngmin Han+6 more
wiley +1 more source
Electromagnetic follow-up of gravitational waves: review and lessons learned. [PDF]
Nicholl M, Andreoni I.
europepmc +1 more source
AI‐Driven Defect Engineering for Advanced Thermoelectric Materials
This review presents how AI accelerates the design of defect‐tuned thermoelectric materials. By integrating machine learning with high‐throughput data and physics‐informed representations, it enables efficient prediction of thermoelectric performance from complex defect landscapes.
Chu‐Liang Fu+9 more
wiley +1 more source