Results 161 to 170 of about 8,361 (290)

Solution Synthesis of Actinide Chalcogenide and Oxychalcogenide Nanoparticles

Angewandte Chemie, EarlyView.
We report the synthesis of UOS, UOSe, and US2 nanoparticles under colloidal conditions. The magnetic properties were measured of all three materials and compared to prior studies of UO2 nanoparticles. Nanoparticle phases were determined using powder diffraction, and size by transmission electron microscopy.
Cheyenne Orozco, Orlando C. Stewart Jr., Nicole Vanagas, Greg Morrison, Hans‐Conrad zur Loye, Hope E. Smith, Karah Knope, Sarah L. Stoll   +7 more
wiley   +2 more sources

Photoredox Coupling of CO₂ Reduction with Benzyl Alcohol Oxidation over Ternary Metal Chalcogenides (Zn_mIn₂S_3+m, m = 1-5) with Regulable Products Selectivity. [PDF]

Molecules, 2023
Du Z, Gong K, Yu Z, Yang Y, Wang P, Zheng X, Wang Z, Zhang S, Chen S, Meng S.   +9 more
europepmc   +1 more source

Structure and Spectroscopic Characterisation of Phenanthroline‐Based Iodobismuthate(III) Complexes Utilised for Raw Acoustic Signal Classification

Advanced Intelligent Discovery, EarlyView.
Memristors based on trimethylsulfonium (phenanthroline)tetraiodobismuthate have been utilised as a nonlinear node in a delayed feedback reservoir. This system allowed an efficient classification of acoustic signals, namely differentiation of vocalisation of the brushtail possum (Trichosurus vulpecula).
Ewelina Cechosz, Gisya Abdi, Andrzej Sławek, Marlena Gryl, Konrad Szaciłowski   +4 more
wiley   +1 more source

Transition Metal Chalcogenides as a Versatile and Tunable Platform for Catalytic CO₂ and N₂ Electroreduction. [PDF]

ACS Mater Au, 2021
Giuffredi G, Asset T, Liu Y, Atanassov P, Di Fonzo F.   +4 more
europepmc   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source

Deep Learning–Based Extraction of Promising Material Groups and Common Features from High‐Dimensional Data: A Case of Optical Spectra of Inorganic Crystals

Advanced Intelligent Discovery, EarlyView.
We report a novel interpretation method for deep learning models based on feature extraction and clustering. Applying this method to an atomistic line graph neural network (ALIGNN) model trained on optical absorption spectra of 2,681 inorganic compounds obtained from first‐principles calculations, we successfully identify key factors underlying ...
Akira Takahashi, Yu Kumagai, Arata Takamatsu, Fumiyasu Oba   +3 more
wiley   +1 more source

Atomic Wires of Transition Metal Chalcogenides: A Family of 1D Materials for Flexible Electronics and Spintronics. [PDF]

JACS Au, 2021
Shang C, Fu L, Zhou S, Zhao J.
europepmc   +1 more source

From Data to Discovery: Machine Learning–Enabled Intelligent Characterization of Two‐Dimensional Materials

Advanced Intelligent Discovery, EarlyView.
Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley   +1 more source

Structure-Property-Application Correlations of Early Transition Metal Chalcogenides: A Dichalcogenide-Centered Perspective. [PDF]

Small
Jaidka S, Gupta A, Shelly D, Kaur Y, Garg A, Lee SY, Park SJ.   +6 more
europepmc   +1 more source

Physics‐Grounded Probabilistic Bits for Hardware‐Efficient Intelligent Inference and Optimization

Advanced Intelligent Systems, EarlyView.
Si–SiNx interface traps are harnessed as a complementary metal–oxide–semiconductor‐compatible source of controllable randomness for probabilistic bits. Pulse‐width‐programmed stochastic capture converts nanoscale defect dynamics into Boltzmann‐consistent binary outputs, while a physics‐based Simulation Program with Integrated Circuit Emphasis model ...
Dokyoung Lee, Jun‐Young Park, Joon‐Kyu Han, Sungho Kim   +3 more
wiley   +1 more source