Results 191 to 200 of about 531,323 (307)

A Lightweight Procedural Layer for Hybrid Experimental–Computational Workflows in Materials Science

Advanced Engineering Materials, EarlyView.
We unveil a prototype hybrid‐workflow framework that fuses automatedcomputation with hands‐on experiments. Built atop pyiron, a lightweight, parameterized layer translates procedure descriptions into executable manual steps, syncing instrument settings, human interventions, and data capture in real‐time today.
Steffen Brinckmann, Sarath Menon, Raphael Röske, Johannes Steinhülb, Michael Selzer, Jan Janssen, Tilmann Hickel, Ruth Schwaiger, Jörg Neugebauer   +8 more
wiley   +1 more source

PASTA‐ELN: Simplifying Research Data Management for Experimental Materials Science

Advanced Engineering Materials, EarlyView.
Research data management faces ongoing hurdles as many ELNs remain complex and restrictive. PASTA‐ELN offers an open‐source, cross‐platform solution that prioritizes simplicity, offline access, and user control. Its in tuitive folder structure, modular Python add‐ons, and open formats enable seamless documentation, FAIR data practices, and easy ...
S. Brinckmann, G. Winkens, R. Schwaiger
wiley   +1 more source

Beyond Infrastructure: Shared Resources as Drivers of Impact and Collaboration in the NCI Cancer Center Community. [PDF]

J Biomol Tech
Springer BC, Ambulos NP, Gosky DM.
europepmc   +1 more source

Modular Critical Element Recycling Platform Using a Nanoporous Additively Manufactured Gyroid

Advanced Engineering Materials, EarlyView.
A modular recycling platform integrates 3D‐printed nanoporous gyroid structures to enable efficient critical element recovery. This system utilizes a hierarchical architecture, combining macroscopic channels with polymerization‐induced nanoscale porosity. By systematically tuning structural wall thickness and resin formulation, the platform achieves an
Xiangyu Gao, Keldy Mason, Mert Gülçür, Rachel Paddock‐Lamb, Prince Alvin Fofanah, Zachariah A. Page, Michael Cullinan   +6 more
wiley   +1 more source

Revisiting the LSER Approach in the Era of Machine Learning: Insights from IAM Chromatography. [PDF]

Mol Pharm
Nisterenko W, Greber KE, Kierkowicz M, Ciura K.   +3 more
europepmc   +1 more source

Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

Advanced Engineering Materials, EarlyView.
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran, Vincent Nebel, Dominik Perius, Kshitij Kumar, Yannis Heim, Laura Bies, Tobias Kraus   +6 more
wiley   +1 more source

Structural analysis and causal loop modelling: an integrated systems approach to analysing perinatal service delivery in carceral settings in Ontario, Canada. [PDF]

Health Res Policy Syst
Bancroft X, McGee A, Morgan L, Ferron Parayre A, Peterson WE.   +4 more
europepmc   +1 more source

Inverse Identification of Energy‐Dependent Laser Absorptivity in NiTi Laser Powder‐Bed Fusion via Calibrated Melt Pool Simulation

Advanced Engineering Materials, EarlyView.
A combined experimental–computational framework identifies energy‐dependent laser absorptivity for NiTi in laser powder‐bed fusion, applicable to conduction and transition modes. Single‐track experiments and thermofluid smoothed particle hydrodynamics simulations are coupled through inverse analysis of melt pool geometry.
Mohamadreza Afrasiabi, Hadi Moutablaleh, Ignatius A. Setiawan, Mehrshad Mehrpouya   +3 more
wiley   +1 more source

A systems approach to understanding injury care in LMICs using causal loop diagrams. [PDF]

BMJ Glob Health
Davies JI, Chu K, D'Ambruoso L, Bojke L, English R, Geduld H, Lahri S, Mahomed H, Matzopoulos R, Tako AA.   +9 more
europepmc   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source