Results 101 to 110 of about 589,086 (216)

Rationally Design Thermoelectric Materials Based on Ingenious Machine Learning Methods

Advanced Electronic Materials, EarlyView.
A machine learning framework is developed to accurately predict thermoelectric performance of materials. By combining high‐quality data, advanced feature engineering, and machine learning, the model identifies promising candidates like CsCdBr3 and TlBSe3.
Yuqing Sun, Xiaorui Chen, Jianzhi Gao, Wenliang Zhu, Minghu Pan   +4 more
wiley   +1 more source

State‐of‐the‐Art Machine Learning Technology for Sustainable Lithium Battery Cathode Design: A Perspective

Advanced Energy Materials, EarlyView.
Machine learning applications in Li‐ion batteries. Abstract Technology for lithium‐ion batteries (LIBs) is developing rapidly, which is essential to modern devices and renewable energy sources. The latest development focuses on the optimization of cathode materials, which is critical in determining battery performance and durability.
Adil Saleem, Leon L. Shaw, Zhiqian Chen, Wei Lai   +3 more
wiley   +1 more source

Solvent Engineering for Scalable and Sustainable Fabrication of Lead‐tin Perovskite Solar Cells

Advanced Energy Materials, EarlyView.
Scaling up Pb‐Sn PSCs from the laboratory to an industrial scale requires addressing challenges related to scalable preparation technologies and solvent sustainability. This work introduces two innovative, low‐toxic solvent mixtures for a two‐step blade‐coating deposition process of the active perovskite layer, resulting in efficient and stable Pb‐Sn ...
Lijun Chen, Filippo Tavormina, Lorenzo Di Mario, Matteo Pitaro, Giuseppe Portale, Norberto Masciocchi, Antonietta Guagliardi, Maria Antonietta Loi   +7 more
wiley   +1 more source

Synthesis of Pt Carbon Aerogel Electrocatalysts with Multiscale Porosity Derived from Cellulose and Chitosan Biopolymer Aerogels via Supercritical Deposition for Hydrogen Evolution Reaction

Advanced Energy and Sustainability Research, EarlyView.
The synthesis of platinumcarbon aerogel electrocatalysts with multiscale porosity derived from biopolymer aerogels via supercritical deposition is presented. The electrocatalysts demonstrate similar overpotentials to commercial alternatives with significantly higher platinum loadings, while exhibiting superior stability due to enhanced porosity ...
Ala Alsuhile, Philip Sidney Pein, Şansim Bengisu Barım, Selmi Erim Bozbağ, Irina Smirnova, Can Erkey, Baldur Schroeter   +6 more
wiley   +1 more source

Surveying the Efficacy of an Open Access Biomedical Informatics Boot Camp. [PDF]

Appl Clin Inform
Resendez SD, Franklin G, Tomlin C, Stephens R, Maness H, Chamala S, Koppel R, Elkin PL.   +7 more
europepmc   +1 more source

Detection and Quantification of Over‐Humidification in Polymer Electrolyte Fuel Cells: Insights into Simulation, Imaging, and Sensors

Advanced Energy and Sustainability Research, EarlyView.
This review highlights advanced methods for detecting and managing over‐humidification in polymer electrolyte fuel cells (PEFCs), emphasizing innovative sensors, simulation techniques, and imaging methods. By addressing the impact of water management on fuel‐cell performance and durability, this study outlines practical and sustainable solutions for ...
Maximilian Käfer, Viktor Hacker, Merit Bodner   +2 more
wiley   +1 more source

Deformation mechanism of soft rock based on mineral crystal models. [PDF]

Sci Rep
Wang B, Zhao Q, Liang G, Lan B, Lu L.
europepmc   +1 more source

Biodegradable Poly(butylene adipate‐co‐terephthalate)/Poly(lactic) Acid Mulch Film with Soy Waste Filler for Improved Biodegradation and Plant Growth

Advanced Energy and Sustainability Research, EarlyView.
The study finds that mulch films made with poly(butylene adipate‐co‐terephthalate)/poly(lactic acid) and 10% soy waste degrade faster than those without soy. Plants grown with the soy‐containing films are 49% taller, suggesting that the soy acts as a biostimulant.
Kerry Candlen, Caralyn Conrad, Pongkhun Prommart, Aidan Doherty, Aderlyn Castillo, Yanfen Li, Jo Ann Ratto, Robina Hogan, Wan‐Ting Chen   +8 more
wiley   +1 more source

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

Advanced 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

Multi-mode geometrically gated encryption with 4D morphing hydrogel. [PDF]

Nat Commun
Wen X, Zhang K, Wu B, Chen G, Zheng N, Wu J, Yang X, Xie T, Zhao Q.   +8 more
europepmc   +1 more source