Results 131 to 140 of about 437,980 (285)

Multimodal Data‐Driven Microstructure Characterization

Advanced Engineering Materials, EarlyView.
A self‐consistent autonomous workflow for EBSP‐based microstructure segmentation by integrating PCA, GMM clustering, and cNMF with information‐theoretic parameter selection, requiring no user input. An optimal ROI size related to characteristic grain size is identified.
Qi Zhang, Santiago Benito, Özge Özgün, Sebastian Weber, Markus Stricker   +4 more
wiley   +1 more source

Microstructure Design of Steel‐Spinel Composites via Spark Plasma Sintering Process

Advanced Engineering Materials, EarlyView.
This study illustrates how spark plasma sintering at reduced oxygen partial pressure enables controlled formation of steel‐spinel composites through tailored redox reactions and cation exchange. Comparison of the MgO–Fe2O3, steel–(MgO+Cr2O3), and steel–(MgO+Fe2O3) systems shows how oxygen activity and interface chemistry govern spinel formation, phase ...
Mahnaz Mehdizadehlima, Christian Schimpf, Stefan Martin, Olga Fabrichnaya, David Rafaja   +4 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

The use of public spatial databases in risk analysis: A US-oriented tutorial. [PDF]

Risk Anal
Greenberg MR, Schneider D, Cox LA.
europepmc   +1 more source

Direct Metal Deposition of Graphene–Ti28Nb35.4Zr Matrix Composites With Enhanced Mechanical, Corrosion, and Biocompatibility Properties for Bone Implants

Advanced Engineering Materials, EarlyView.
Graphene nanoplatelet (0.1 wt.%) reinforcement significantly enhances the performance of β Ti‐28Nb‐35.4Zr alloy. Grain refinement, reduced water contact angle, and improved surface characteristics promote osteoblast adhesion and complete surface coverage after 7 days.
Khurram Munir, Jixing Lin, Yuncang Li, Li Zhu, Paul F. A. Wright, Cuie Wen   +5 more
wiley   +1 more source

An Experimental High‐Throughput Approach for the Screening of Hard Magnet Materials

Advanced Engineering Materials, EarlyView.
An entire workflow for the high‐throughput characterization and analysis of compositionally graded magnetic films is presented. Characterization protocols, data management tools and data analysis approaches are illustrated with test case Sm(Fe, V)12 based films.
William Rigaut, Pierre Le Berre, Hamdi Jaballah, Lukas Fink, Victor Poline, Richard Haettel, Stéphane Grenier, Gilbert Chahine, Ewen Bellec, Guillaume Buatier, Wilfried Hortschitz, Santa Pile, Thomas Schrefl, Nils Blanc, Yuan Hong, Thibaut Devillers, Nora M. Dempsey   +16 more
wiley   +1 more source

Segmentation Methodologies for the Construction of Hyperspectral Cell Nuclei Databases in Histopathology. [PDF]

Bioengineering (Basel)
Rosa-Olmeda G, Hiller-Vallina S, Villa M, Segura-Collar B, Gargini R, Chavarrías M.   +5 more
europepmc   +1 more source

Near‐Infrared Emitting Lanthanide Catecholate Giant Single Crystals – Morphology Control and Photon Down‐Conversion

Advanced Functional Materials, EarlyView.
Controlled syntheses of lanthanide coordination polymers based on the dihydroxybenzoquinone (DHBQ) organic linker afforded large single crystals of Ln‐DHBQ CPs (Ln = Yb, Nd). A novel structural variant of Yb‐DHBQ is identified by means of single crystal diffraction analysis.
Marina I. Schönherr, Alexander Biewald, Andre Mähringer, Thaddäus J. Koller, Peter Mayer, Markus Döblinger, Achim Hartschuh, Dana D. Medina   +7 more
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

The 2026 Nucleic Acids Research database issue and the online molecular biology database collection. [PDF]

Nucleic Acids Res
Rigden DJ, Fernández XM.
europepmc   +1 more source