Results 161 to 170 of about 369,121 (287)

Mixed Alkali Effect in Alkali Metasilicate Glasses [PDF]

Progress of Theoretical Physics Supplement, 1997
Junko Habasaki, Isao Okada, Yasuaki Hiwatari   +2 more
openaire   +1 more source

Ether‐Based Electrolytes and the Solvation Structure in Sodium Ion Batteries

Advanced Energy Materials, EarlyView.
Ether electrolytes enable high‐performance sodium‐ion batteries through their unique physicochemical properties, particularly distinctive solvation structures and co‐intercalation behavior. However, the localized solvation structure and its dynamic evolution during electrochemical processes remain a “black box”.
Jiaxin Ding, John T. S. Irvine
wiley   +1 more source

<i>GLK1</i> plays a positive role in regulating plant tolerance to alkali stress and salt-alkali mixed stress. [PDF]

Front Plant Sci
Bao Q, Jiang L, Wen Z, Du W, Wen J, Mysore KS, Sun J, Wang ZY.   +7 more
europepmc   +1 more source

The effect of alkalis on collagen [PDF]

Biochemical Journal, 1948
J H, Bowes, R H, Kenten
openaire   +2 more sources

Machine learning driven many‐objective moving horizon scheduling optimization

AIChE Journal, EarlyView.
Abstract Industrial electrification can decarbonize chemical manufacturing, but it exposes operations to volatile electricity prices and carbon intensities. This work develops a machine learning‐enhanced many‐objective moving horizon scheduling framework that predicts objective correlation groupings from 48‐hour price and emission‐intensity profiles ...
Hongxuan Wang, Andrew Allman
wiley   +1 more source

Admixtures for concrete and mortar with MgO-based binders: a review from RILEM TC 311-MBC. [PDF]

Mater Struct
Müller M, Rawat S, Nguyen H, Bouchaud-Deliot C, Kuhn R, Meng D, Quiatchon P, Qiu J, Ranjbar N, Zhou Z, Winnefeld F.   +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

Operando Identification of the Active Phase in Sr‐Promoted Lanthanum Cobaltite Perovskite‐Derived Catalysts for Ammonia Decomposition

Angewandte Chemie, EarlyView.
Ammonia decomposition catalyzed by base metals: Exsolution of cobalt from perovskites prepared by spray‐flame synthesis (SFS) yields highly active, SrO‐promoted Co/La2O3 catalysts with low cobalt content. Operando studies provide insight into the function of the promoter. Dynamic surface reconstruction under operating conditions increases the interface
Barış Alkan, Liseth Duarte‐Correa, Detre Teschner, Kassiogé Dembélé, Mino Borrelli, Anh Binh Ngo, Konstantinos Kappis, Mattia Scardamaglia, Zohreh Asadi, Wiebke Riedel, Thomas Risse, Frank Girgsdies, Gregor Koch, Olaf Timpe, Axel Knop‐Gericke, Thomas Lunkenbein, Annette Trunschke   +16 more
wiley   +2 more sources

Effect of Activator Modulus on the Performance, Reaction Products, and Microstructure of Biomass Power Plant Ash-Based Alkali-Activated Binders. [PDF]

ACS Omega
Zhao S, Chen Y, Ma T, Xia M.
europepmc   +1 more source

Interpretable Machine Learning for Bandgap Prediction and Descriptor‐Guided Design Rules of Phosphates

Advanced Intelligent Discovery, EarlyView.
An explainable CatBoost model was trained to predict the bandgaps of 474 phosphate crystals based on composition and density descriptors. SHAP analysis identified two key variables—d‐electron‐count dispersion and atomic‐density dispersion—as the primary drivers of the model's predictions.
Wenhu Wang, Abudukadi Tudi, Ran An, Zhihua Yang   +3 more
wiley   +1 more source