Results 121 to 130 of about 166,709,915 (273)

Atomic Defects in Layered Transition Metal Dichalcogenides for Sustainable Energy Storage and the Intelligent Trends in Data Analytics

Advanced Science, EarlyView.
This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo, Ying Yang, Zizhen Fu, Fengqi Liu, Susu Fang, Kele Xu, Shanshan Wang   +6 more
wiley   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

Adressing Energy Demand and Climate Change through the Second Law of Thermodynamics and LCA towards a Rational Use of Energy in Brazilian Households. [PDF]

Entropy (Basel), 2022
Pereira MTRM, Carvalho M, Mady CEK.
europepmc   +1 more source

Data‐Driven Modeling of Composition–Processing–Microstructure Relations for Recycled Aluminum Cast Alloys

Advanced Science, EarlyView.
Interpretable machine learning reveals how composition and processing govern the formation and microstructural burden of Fe‐rich intermetallic compounds in recycled Al–Si–Fe–Mn alloys. By separating morphology selection from morphology‐conditioned burden partitioning, this framework shows that identical Fe contents can yield different intermetallic ...
Jaemin Wang, Waleed Mohammed, Dierk Raabe   +2 more
wiley   +1 more source

Physics‐Constrained Constitutive Learning of Rate‐Limiting Timescales for Efficient Hydrogen‐Based Direct Reduction for Green Steel Making

Advanced Science, EarlyView.
A conversion‐resolved constitutive framework is developed for the hydrogen‐based direct reduction of iron oxide pellets. Effective reaction and transport timescales are inferred directly from measured trajectories and mapped against operating conditions, pellet architecture, and composition. The analysis reveals how late‐stage transport control emerges
Anurag Bajpai, Barak Ratzker, Pasquale Cavaliere, Dierk Raabe   +3 more
wiley   +1 more source

Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics

Advanced Science, EarlyView.
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai, Ri He, Junxian Liu, Liangzhi Kou   +3 more
wiley   +1 more source

Wafer‐Scale Atomically‐Thin Gold: Transferable Platform for Flexible Optoelectronics, Thermal Management and Bio‐Interfaces

Advanced Science, EarlyView.
Combining template stripping with a graphene‐inspired transfer yields continuous, transferable gold films at 6‐inch wafer scale with thicknesses approaching the atomic limit. The atomically smooth, near‐bulk‐quality films offer above 86% transmittance and sub‐20 Ω/square sheet resistance, serving as a universal platform for flexible optoelectronics ...
Dmitry Yakubovsky, Mikhail Mironov, Georgy Ermolaev, Aleksandr Khrebtov, Aleksandr Slavich, Prashant Narute, Pritom Chowdhury, Roman Kirtaev, Igor Khramtsov, Anton Minnekhanov, Gleb Tselikov, Dmitriy Grudinin, Maria Sidorova, Nikolay Pak, Pavel Maslov, Andrey Vyshnevyy, Hongkai Zhou, Vasyl G. Kravets, Alexander N. Grigorenko, Ivan Kruglov, Nikita Orekhov, Dmitry Kireev, Aleksey Arsenin, Kostya S. Novoselov, Valentyn Volkov   +24 more
wiley   +1 more source

Second law of thermodynamics and arithmetic-mean-geometric-mean inequality

, 1999
The application of the second law of thermodynamics to a typical irreversible process of a thermally isolated system shows that the Arithmetic-mean-geometric-mean (AM-GM) inequality, a powerful mathematical inequality, follows logically from the second ...
Wang, L
core  

Machine Learning–Guided Surface Strain Engineering in Connected Platinum–Nickel Nanoparticle Catalysts for Advanced Oxygen Reduction Performance

Advanced Science, EarlyView.
Machine learning‐guided strain engineering enables highly active, durable, support‐free Pt–Ni nanonetwork catalysts for the oxygen reduction reaction. Analysis of a Pt‐based catalyst dataset identifies surface compressive strain as an effective descriptor associated with enhanced activity and provided practical design guidelines.
Aparna Chitra Sudheer, Gopinathan M. Anilkumar, Hidenori Kuroki, Yuuki Sugawara, Takeo Yamaguchi   +4 more
wiley   +1 more source

The Second Law: From Carnot to Thomson-Clausius, to the Theory of Exergy, and to the Entropy-Growth Potential Principle

, 2017
At its origins, thermodynamics was the study of heat and engines. Carnot transformed it into a scientific discipline by explaining engine power in terms of transfer of “caloric”. That idea became the second law of thermodynamics when Thomson and Clausius
Lin-Shu Wang
core   +1 more source