Results 231 to 240 of about 1,662,275 (307)

3D investigation and modeling of the geometric effects on porosity in packed beds

open access: yesAIChE Journal, EarlyView.
Abstract In porous beds, physical boundaries restrict particle arrangement, leading to inhomogeneous porosity. This paper reports on the porosity profiles that are the result of geometric effects on monodisperse packed beds in cylindrical and cubic arrangements. Special focus is given to the influence of edges and corners in cubic geometries.
Bastian Oldach   +3 more
wiley   +1 more source

Spheres, tears, and spears: Regulating the perimeter and circularity of millimeter‐sized alginate hydrogel beads

open access: yesAIChE Journal, EarlyView.
Abstract Generating hydrogel beads pertains to many engineering applications. We examined two alginate‐based fluids at three concentrations of alginate, cAG$$ {c}_{\mathrm{AG}} $$. We used the “Map of Misery” to determine which material property (viscosity, elasticity, and inertia) drives droplet formation.
Conor G. Harris   +5 more
wiley   +1 more source

Monitoring and control of a continuous, integrated filtration‐drying system with in‐line mass spectrometry via PharmaPy

open access: yesAIChE Journal, EarlyView.
Abstract This article demonstrates the integration of in‐line mass spectrometry as a process analytical technology (PAT) tool with model‐based soft sensors in a continuous filtration‐drying carousel system for solid–liquid separation (SLS) of crystal slurries.
Inyoung Hur   +3 more
wiley   +1 more source

BabelFSH-a toolkit for an effective HL7 FHIR-based terminology provision. [PDF]

open access: yesJ Biomed Semantics
Wiedekopf J   +3 more
europepmc   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

open access: yesAdvanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang   +4 more
wiley   +1 more source

A Generalized Framework for Data‐Efficient and Extrapolative Materials Discovery for Gas Separation

open access: yesAdvanced Intelligent Discovery, EarlyView.
This study introduces an iterative supervised machine learning framework for metal‐organic framework (MOF) discovery. The approach identifies over 97% of the best performing candidates while using less than 10% of available data. It generalizes across diverse MOF databases and gas separation scenarios.
Varad Daoo, Jayant K. Singh
wiley   +1 more source

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