Unlocking new possibilities in ionic thermoelectric materials: a machine learning perspective. [PDF]
Wu Y +7 more
europepmc +1 more source
Thermoradiative Diodes Using Black Phosphorus van der Waals Materials
We demonstrate thermoradiative diodes based on black phosphorus (bP${\rm bP}$)/molybdenum disulfide (MoS2$MoS_2$) van der Waals heterojunctions which generate power by exchanging photons with a colder environment. The bandgap of black phosphorus is well positioned to exchange photons with outer space through the mid‐wave atmospheric window.
Yuchen Sun +13 more
wiley +1 more source
Recent trends and future perspectives of thermoelectric materials and their applications. [PDF]
Baskaran P, Rajasekar M.
europepmc +1 more source
A low‐cost, self‐driving laboratory is developed to democratize autonomous materials discovery. Using this "frugal twin" hardware architecture with Bayesian optimization, the platform rapidly converges to target lower critical solution temperature (LCST) values while self‐correcting from off‐target experiments, demonstrating an accessible route to data‐
Guoyue Xu, Renzheng Zhang, Tengfei Luo
wiley +1 more source
Facile development of carbon nanotube (CNT)-based flexible thermoelectric materials for energy-harvesting applications. [PDF]
Naeem N +9 more
europepmc +1 more source
Harnessing Digital Microstructure for Simulation‐Guided Optimization of Permanent Magnets
An experimental‐to‐computational workflow is presented that transforms experimental 3D focused ion beam‐scanning electron microscopy data into a simulation‐ready digital microstructure for multiphase functional materials. Using heavy‐rare‐earth‐free Nd–Fe–B magnets as a model system, the approach quantifies grain connectivity across complex secondary ...
Nikita Kulesh +4 more
wiley +1 more source
Insights into One-Dimensional Thermoelectric Materials: A Concise Review of Nanowires and Nanotubes. [PDF]
Latronico G +3 more
europepmc +1 more source
A Critical Assessment of Bonding Descriptors for Predicting Materials Properties
The impact of new bonding descriptors in machine learning models for predicting material properties is assessed. Improvements are validated using significance tests, and new, intuitive descriptors for screening lattice thermal conductivity and projected force constants are introduced.
Aakash Ashok Naik +6 more
wiley +1 more source
Wenzhou TE: A First-Principle-Calculated Thermoelectric Materials Database. [PDF]
Fang Y, Shao H.
europepmc +1 more source
Physics‐Guided Modeling of Temperature‐Dependent Electrical Conductivity of Functional Oxides
This complete workflow presents an automated, data‐driven screening of functional oxides based on their predicted electrical conductivity. A curated dataset is fed into TriCondNet, a charge‐transport, physics‐guided neural network that classifies an input composition's conduction mechanism and then simulates its conductivity–temperature profile.
Elias Assaad +6 more
wiley +1 more source

