Results 191 to 200 of about 3,497 (260)

Pathway-priming of intermediate phases in FACs-based wide-bandgap perovskites for Si tandem solar cells. [PDF]

Nat Commun
Huang N, Fang J, Wang X, Hu S, Li S, Xie G, Gan L, Peng H, Lin X, Zhang X, Liu F, Yang Z, Zhao P, Huang P, Li W, Qiu L.   +15 more
europepmc   +1 more source

Challenges and enablers in fluidization technology

AIChE Journal, EarlyView.
Abstract Gas–solid fluidized beds provide excellent heat and mass transfer for high‐throughput operations from coating to catalytic conversion and underpin emerging low‐carbon technologies. Yet industrial reliability, scale‐up, and control lag scientific understanding, particularly as finer, stickier, and more variable feedstocks increasingly challenge
J. Ruud van Ommen, Jia Wei Chew
wiley   +1 more source

Complexity analyses of geomagnetic and solar activity indices time series. [PDF]

Sci Rep
Javaherian M, Mollaei S, Zolghadr N, Moradpour H, Yousefzadeh M.   +4 more
europepmc   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Forecasting solar energetic particles using multi-source data from solar flares, CMEs, and radio bursts with machine learning approaches. [PDF]

Sci Rep
Ali MA, Abdelkawy AGA, Shaltout AMK, Beheary MM.   +3 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

The delayed arrival of faster solar energetic particles as a probe into the shock acceleration process. [PDF]

Natl Sci Rev
Li Y, Guo J, Pacheco D, Wang Y, Temmer M, Ding Z, Wimmer-Schweingruber RF.   +6 more
europepmc   +1 more source

Materials Representation Learning Based on a Material–Motif Network and Heterogeneous Graphs

Advanced Intelligent Discovery, EarlyView.
Structure motifs in materials are used to construct a bipartite material–motif network that links each material to its constituent motifs and establishes connectivity among materials sharing common motifs. Network analysis reveals material clusters associated with different functional applications and supports motif‐guided screening of materials.
Anoj Aryal, Weiyi Gong, Huta Banjade, Qimin Yan   +3 more
wiley   +1 more source

Multi-objective techno-economic and environmental optimization of hydrogen-based hybrid renewable energy system using osprey optimization algorithm. [PDF]

Sci Rep
Ermiş S, Taşdemir O, Al-Hajj R.
europepmc   +1 more source