Results 161 to 170 of about 3,025,022 (290)

Robust Spot Melting by 3D Spot Arrangements in Electron Beam Powder Bed Fusion

Advanced Engineering Materials, EarlyView.
This work proposes an approach to replace separately melted contours for spot melting in electron beam powder fusion. Adapting the spot arrangements close to the contour combined with stacking yields a comparable surface quality without the inherent challenges of separate contours, as demonstrated, by electron optical images and roughness measurements.
Tobias Kupfer, Lukas Spano, Sebastian Pohl, Carolin Körner, Matthias Markl   +4 more
wiley   +1 more source

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

A Topology Optimization Framework for the Inverse Design of Nonlinear Mechanical Metamaterials

Advanced Engineering Materials, EarlyView.
This work uses topology optimization to design unit cells for mechanical metamaterials with a prescribed nonlinear stress–strain response. The framework adds contact and postbuckling modeling to synthesize microstructures for three highly nonlinear responses, including pseudoductile behavior, monostable with snap‐through buckling, and bistable ...
Charlie Aveline, Matthew Santer, Robert Hewson   +2 more
wiley   +1 more source

High‐Temperature Nanoindentation of Metals: Assessing Thermal Drift, Frame Compliance, and Chemical Composition Effects on the Reported Mechanical Properties

Advanced Engineering Materials, EarlyView.
Do not let thermal drift and instrument artifacts deceive high‐temperature nanoindentation results. We compare classical Oliver–Pharr and automatic image recognition analyses across steels and a Ni alloy to quantify these effects. Accounting for artifacts reveals systematic softening with temperature, while Cr and Ni additions boost resistance ...
Velislava Yonkova, Steffen Brinckmann, Ruth Schwaiger   +2 more
wiley   +1 more source

Lipid Nanoparticles for the Delivery of CRISPR/Cas9 Machinery to Enable Site‐Specific Integration of CFTR and Mutation‐Agnostic Disease Rescue

Advanced Functional Materials, EarlyView.
Lipid nanoparticles (LNPs) are optimized to co‐deliver Cas9‐encoding messenger RNA (mRNA), a single guide RNA (sgRNA) targeting the endogenous cystic fibrosis transmembrane conductance regulator (CFTR) gene, and homologous linear double‐stranded donor DNA (ldsDNA) templates encoding CFTR.
Ruth A. Foley, Paul G. Ayoub, Vrishti Sinha, Colin Juett, Alicia Sanoyca, Emily C. Duggan, Lindsay E. Lathrop, Priyanka Bhatt, Kevin Coote, Beate Illek, Brigitte N. Gomperts, Donald B. Kohn, Steven J. Jonas   +12 more
wiley   +1 more source

The Research Imperative: Medical Library Association policy and the curricula of schools of library and information science. [PDF]

J Med Libr Assoc, 2009
McKnight M, Hagy CR.
europepmc   +1 more source

GSLIS Magazine Summer 2011 [PDF]

, 2011
Graduate School of Library and Information Science. University of Illinois at Urbana-Champaign.
core  

Band Alignment in In‐Oxo Metal Porphyrin SURMOF Heterojunctions

Advanced Functional Materials, EarlyView.
Porphyrin core metalation in indium‑oxo SURMOFs enables systematic tuning of band edge positions without altering the crystal structure. First‑principles calculations reveal type‑I and type‑II heterostructures as well as multi‑junction energy cascades, establishing a modular strategy for exciton funneling and charge separation in optoelectronic ...
Puja Singhvi, Nina Vankova, Thomas Heine
wiley   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source