Results 211 to 220 of about 148,742 (290)

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

Quantum technologies and geopolitics: comparing parliamentary rhetoric. [PDF]

open access: yesEPJ Quantum Technol
Suter V, Pöhlmann G, Ma C, Meckel M.
europepmc   +1 more source

Positive‐Tone Nanolithography of Antimony Trisulfide with Femtosecond Laser Wet‐Etching

open access: yesAdvanced Functional Materials, EarlyView.
A butyldithiocarbamic acid (BDCA) etchant is used to fabricate various micro‐ and nanoscale structures on amorphous antimony trisulfide (a‐Sb2S3) thin film via femtosecond laser etching. Numerical analysis and experimental results elucidate the patterning mechanism on gold (reflective) and quartz (transmissive) substrates.
Abhrodeep Dey   +12 more
wiley   +1 more source

Electroactive Metal–Organic Frameworks for Electrocatalysis

open access: yesAdvanced Functional Materials, EarlyView.
Electrocatalysis is crucial in sustainable energy conversion as it enables efficient chemical transformations. The review discusses how metal–organic frameworks can revolutionize this field by offering tailorable structures and active site tunability, enabling efficient and selective electrocatalytic processes.
Irena Senkovska   +7 more
wiley   +1 more source

Impact of Additional Monitoring on Pharmacovigilance and Pharmacists' Role: A Scoping Review. [PDF]

open access: yesPharmacoepidemiol Drug Saf
Aizpurua-Arruti X   +7 more
europepmc   +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

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