Results 181 to 190 of about 3,969 (258)

Selective Extraction Process of Niobium and Tantalum from Tin Slag Derived from the Thermal Processing of Cassiterite. [PDF]

ACS Omega
Cheje Machaca DM, Juyo Salazar RB, Flores-Calla SS, Botelho Junior AB, Romano Espinosa DC, Tenório JAS.   +5 more
europepmc   +1 more source

Emerging Materials and Future Strategies for Solid Oxide Electrochemical Cells

Advanced Energy Materials, EarlyView.
Solid oxide electrochemical cells operate under strongly coupled electrochemical and thermodynamic conditions, where performance is constrained by interactions among crystal structure, defect chemistry, and interfacial evolution. This review, based on a structure‐defect‐property‐durability framework, reveals the roles of lattice symmetry and defect ...
Qiuchun Lu, Yufei Zhang, Han Zhao, Pooi See Lee, Daniel H.C. Chua   +4 more
wiley   +1 more source

Deciphering Intricacies in Directional CO2 Conversion From Electrolysis to CO2 Batteries

Advanced Energy Materials, EarlyView.
This review will delve into the inherent connections and distinctions of CO2‐directed conversion in ECO2RR and CO2 batteries, in terms of product types, catalyst selection, catalytic mechanisms, and electrochemical performances, while proposing a benchmarking framework for the evaluation of CO2 batteries and innovative CO2 battery configurations for ...
Changfan Xu, Huaping Zhao, Lizhuang Chen, Weidong Shi, Guosheng Shao, Yong Lei   +5 more
wiley   +1 more source

A chromatic-switching Fe₂Nb₂O₇ pyrochlore cage nanozyme for robust peroxidase-mimetic activity and selective hydrogen peroxide detection in complex media. [PDF]

RSC Adv
Ahmad MI, Nawaz MH, Hayat A, Khalid I, John P, Nasir M.   +5 more
europepmc   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Niobium-Doped Nanofiber Reinforcement of Low-Viscosity Bulk-Fill Resin Composites: Physicomechanical Properties and Mineral Deposition Potential. [PDF]

Polymers (Basel)
Rodrigues MS, Nascimento TRL, Mondelli RFL, Alhotan A, Alhijji S, Brondino NC, Velo MMAC.   +6 more
europepmc   +1 more source

Surface Studies of Niobium Oxides

APPROVED
openaire   +1 more source

A Robust Deep Temporal Causal Discovery Platform for Single‐Cell Gene Regulatory Network Reconstruction

Advanced Intelligent Discovery, EarlyView.
scTIGER2.0 is a deep‐learning framework that infers gene regulatory networks from single‐cell RNA sequencing data. By integrating correlation, pseudotime ordering, deep learning and bootstrap‐based significance testing, it reduces false positives and reveals directional gene interactions.
Nishi Gupta, Madison Dautle, Shaoqiang Zhang, Yong Chen   +3 more
wiley   +1 more source

Damage-free van der Waals metal/NbO_x-NbSe₂ integration for reliable and flexible memristor in neuromorphic computing. [PDF]

Nano Converg
Phan TL, Nguyen MC, Dang DX, Vu VT, Vu TTH, Yun HW, Li H, Lee J, Lemme MC, Aggarwal P, Yu WJ.   +10 more
europepmc   +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