Results 241 to 250 of about 10,265,072 (333)

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

open access: yesAdvanced 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

Exploring Dipolar Dynamics and Ionic Transport in Metal‐Organic Frameworks: Experimental and Theoretical Insights

open access: yesAdvanced Functional Materials, EarlyView.
In this study, the interplay of dipolar dynamics and ionic charge transport in MOF compounds is investigated. Synthesizing the novel structure CFA‐25 with integrated freely rotating dipolar groups, local and macroscopic effects, including interactions with Cs cations are explored.
Ralph Freund   +6 more
wiley   +1 more source

Advancing Electronic Application of Coordination Solids: Enhancing Electron Transport and Device Integration via Surface‐Mounted MOFs (SURMOFs)

open access: yesAdvanced Functional Materials, EarlyView.
The layer‐by‐layer (LbL) assembly of coordination solids, enabled by the surface‐mounted metal‐organic framework (SURMOF) platform, is on the cusp of generating the organic counterpart of the epitaxy of inorganics. The programmable and sequential SURMOF protocol, optimized by machine learning (ML), is suited for accessing high‐quality thin films of ...
Zhengtao Xu   +2 more
wiley   +1 more source

Flexibility and Dynamicity Enhances and Controls Supramolecular Self‐Assembly of Zinc(II) Metallogels

open access: yesAdvanced Functional Materials, EarlyView.
Zinc(II) coordination complexes with tunable aryloxy‐imine ligands exhibit controllable supramolecular self‐assembly into hierarchical fibrous structures. Coordination‐driven stacking, not π–π interactions, enables gelation, dynamic assembly/disassembly, and enhanced nanomechanical properties.
Merlin R. Stühler   +10 more
wiley   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

open access: yesAdvanced 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   +7 more
wiley   +1 more source

Home - About - Disclaimer - Privacy