Results 71 to 80 of about 58,585 (300)

A review on thermochemical seasonal solar energy storage materials and modeling methods

open access: yesInternational Journal of Air-Conditioning and Refrigeration
In the current era, national and international energy strategies are increasingly focused on promoting the adoption of clean and sustainable energy sources.
Abdullah   +3 more
doaj   +1 more source

Unique thermodynamic relationships for ΔfHo and ΔfGo for crystalline inorganic salts. I, Predicting the possible existence and synthesis of Na2SO2 and Na2SeO2 [PDF]

open access: yes, 2012
The concept that equates oxidation and pressure has been successfully utilized in explaining the structural changes observed in the M2S subnets of M2SOx (x = 3, 4) compounds (M = Na, K) when compared with the structures (room- and high-pressure phases ...
Beck   +51 more
core   +1 more source

Unraveling Hydride‐Driven Multiphasic Reduction Toward Tunable Germanium Structures for Lithium‐Ion Batteries

open access: yesAdvanced Science, EarlyView.
The sodium hydride (NaH)‐mediated reduction of germanium dioxide yields distinct Ge structures depending on the amount of reductant, revealing the dual roles of NaH in the reduction pathways. The synthesized Ge under off‐stoichiometric conditions exhibits a porous morphology and reduced crystallinity, which effectively mitigates volume expansion during
Gijung Lee   +12 more
wiley   +1 more source

Discovery of materials for solar thermochemical hydrogen combining machine learning, computational chemistry, experiments and system simulations

open access: yesnpj Computational Materials
This study integrates first-principles calculations, computational chemistry, system simulations, experiments, and machine learning to identify redox perovskite oxides for solar thermochemical hydrogen production.
Jonathan Perry   +9 more
doaj   +1 more source

Understanding the Interplay Between Thermal Activation, Diffusion, and Phase Segregation of Molecular Dopants Blended with Polymeric Semiconductors

open access: yesAdvanced Electronic Materials, EarlyView.
The use of air stable but thermally labile molecules provides an efficient strategy for the N‐type doping of organic semiconductors with relatively low electron affinities. Design criteria for efficient dopants should also take into account diffusion and phase segregation that cannot be decoupled from thermally activated doping.
Francesca Pallini   +15 more
wiley   +1 more source

Metal oxide candidates for thermochemical water splitting obtained with a generative diffusion model

open access: yesJPhys Energy
Generative diffusion models (DMs) for inorganic crystalline materials are being actively investigated for their potential to expand the chemical and structural design spaces for known functional materials.
Matthew D Witman   +3 more
doaj   +1 more source

Simulating the Martian Chemical Enivronment [PDF]

open access: yes, 2018
We report on new analogue materials to simulate Martian rocks and soils, especially under realistic redox ...
Olsson-Francis, K.   +3 more
core  

Leveraging Wind Energy for Electricity and Hydrogen Production: A Sustainable Solution to Power Shortages and Eco‐Friendly Alternative Fuel

open access: yesAdvanced Energy and Sustainability Research, EarlyView.
The article attracts researchers, policymakers, and industry leaders in renewable energy, hydrogen, and sustainable development, as it addresses the critical need for clean, efficient energy solutions. By evaluating the technical, environmental, and economic feasibility of integrating wind energy with hydrogen production. The findings offer significant
Mohamed Elnaggar   +4 more
wiley   +1 more source

Thermochemical ablation of rocket nozzle insert materials Final report [PDF]

open access: yes
Resistance of rocket nozzle throat insert materials to corrosion and ...
Clark, K. J.   +3 more
core   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

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