Results 211 to 220 of about 672,031 (310)

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

Benefits of Microwave-Assisted Heat Treatment for Sintered Diopside Glass-Ceramics. [PDF]

Materials (Basel)
Karamanov A, Colombini E, Ferrante D, Georgiev I, Raykovska M, Karamanova E, Atanasova S, Veronesi P, Leonelli C.   +8 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

Pollucite Ceramics and Glass-Ceramics as Advanced Wasteforms for the Immobilization of Cs-Loaded IONSIV Wastes. [PDF]

Environ Sci Technol
Bahmanrokh G, Whitelock E, Dayal P, Aughterson RD, Peristyy A, Sutton P, Farzana R, Abraham JL, Degeling J, Page M, Sorrell CC, Koshy P, Gregg DJ.   +12 more
europepmc   +1 more source

Silicon‐Based Anodes for Sulfide Solid‐State Batteries: Failure Mechanisms and Multiscale Design Strategies

Advanced Energy Materials, EarlyView.
Silicon anodes in sulfide SSBs face coupled electrochemo‐mechanical failure by interface instability. This review examined recent advances and proposed mitigation strategies via material‐, electrode/interface‐, and cell‐level‐ engineering. We further evaluate scalable synthesis of sulfide SEs.
Murugesan Karuppaiah, JaeWook Seo, SeEun Park, Seung Hyeok Chu, Jung Kyoo Lee   +4 more
wiley   +1 more source

Random Lasers Based on Tellurite and Germanate Glasses and Glass-Ceramics Doped with Rare-Earth Ions. [PDF]

Micromachines (Basel)
da Silva DM, Câmara JG, Wetter NU, Dipold J, Kassab LRP, de Araújo CB.   +5 more
europepmc   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

Controllable Nano-Crystallization in Fluoroborosilicate Glass Ceramics for Broadband Visible Photoluminescence. [PDF]

Nanomaterials (Basel)
Xiang Y, Long Y, Cen P, Liu S, Fang Z, Jiao R.   +5 more
europepmc   +1 more source

AI‐BioMech: Deep Learning Prediction of Mechanical Behavior in Aperiodic Biological Cellular Materials

Advanced Intelligent Discovery, EarlyView.
AI‐BioMech is a deep learning framework that predicts the mechanical behavior of biological cellular materials directly from 2D images. By replacing traditional finite element analysis with semantic segmentation, it identifies stress and strain distributions with 99% accuracy, offering a high‐speed, scalable alternative for analyzing complex, aperiodic
Haleema Sadia, Marcelo A. Dias, Parvez Alam   +2 more
wiley   +1 more source

The influence of different factors on the bond strength of lithium disilicate-reinforced glass-ceramics to Resin: a machine learning analysis. [PDF]

BMC Oral Health
Liu J, Tu S, Wang M, Chen D, Chen C, Xie H.   +5 more
europepmc   +1 more source