Results 161 to 170 of about 4,296 (259)

Hybrid Ionomer‐Free Porous Transport Electrodes With Catalyst Coated Membranes for Enhanced Water Electrolysis

Advanced Energy Materials, EarlyView.
We demonstrate how improved electrochemical performance during water electrolysis is achieved when a porous transport electrode (PTE) is coupled with a traditional catalyst coated membrane (CCM). Using operando neutron radiography, a more homogenous water distribution near the catalyst layer‐membrane interface is revealed in PTE‐based designs due to ...
Tess Seip, Lijun Zhu, Salvatore Ranieri, James Torres, Yuxuan Zhang, Roger Hobbs, Hassina Bilheux, Jean‐Christophe Bilheux, Aimy Bazylak   +8 more
wiley   +1 more source

Integrating preliminary test and Stein-type techniques to improve estimation in the time-dependent Cox model. [PDF]

PLoS One
Ramezani R, Rabiei MR, Arashi M.
europepmc   +1 more source

Automated generative process synthesis via transformer‐based dual‐loop simulation and optimization

AIChE Journal, EarlyView.
Abstract This study presents a novel framework for automated generative process synthesis, addressing the complexity of simultaneously optimizing discrete topologies and continuous operating variables. To overcome conventional superstructure limitations, we propose a dual‐loop architecture integrating generative transformers with rigorous process ...
Yeong Woo Son, Chan Kim, Ji Hun Pak, Hain Lee, Jong Min Lee   +4 more
wiley   +1 more source

Robust learning for ridge-penalized quasi-GLMs under non-identical distributions. [PDF]

Sci Rep
Zhang H, Tian W, Yao Q, Wang P, Zhang B.
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

Robust estimation methods for addressing multicollinearity and outliers in beta regression models. [PDF]

Sci Rep
Olaluwoye OT, Lukman AF, Alrasheedi MA, Nzomo WN, Farghali RA.   +4 more
europepmc   +1 more source

Factorization Machine‐Based Active Learning for Functional Materials Design with Optimal Initial Data

Advanced Intelligent Discovery, EarlyView.
This work investigates the optimal initial data size for surrogate‐based active learning in functional material optimization. Using factorization machine (FM)‐based quadratic unconstrained binary optimization (QUBO) surrogates and averaged piecewise linear regression, we show that adequate initial data accelerates convergence, enhances efficiency, and ...
Seongmin Kim, In‐Saeng Suh
wiley   +1 more source

Toward Predictable Nanomedicine: Current Forecasting Frameworks for Nanoparticle–Biology Interactions

Advanced Intelligent Discovery, EarlyView.
Predictive models successfully screen nanoparticles for toxicity and cellular uptake. Yet, complex biological dynamics and sparse, nonstandardized data limit their accuracy. The field urgently needs integrated artificial intelligence/machine learning, systems biology, and open‐access data protocols to bridge the gap between materials science and safe ...
Mariya L. Ivanova, Michael Nicholls, Nicola Russo, Gueorgui Mihaylov, Konstantin Nikolic   +4 more
wiley   +1 more source

Smooth and shape-constrained quantile distributed lag models. [PDF]

Biometrics
Jin Y, Molstad AJ, Wilson A, Antonelli J.   +3 more
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source