Results 171 to 180 of about 767,891 (268)

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

Emergent Spin‐Glass Behavior in an Iron(II)‐Based Metal–Organic Framework Glass

open access: yesAdvanced Functional Materials, EarlyView.
A one‐pot, solvent‐free synthesis yields an Fe2+‐based metal‐organic framework (MOF) glass featuring a continuous random network structure. The material exhibits spin‐glass freezing at 14 K, driven by topological‐disorder and short‐range magnetic frustration, showcasing the potential of MOF glasses as a plattform for cooperative magnetic phenomena in ...
Chinmoy Das   +8 more
wiley   +1 more source

An All‐Optical Driven Bio‐Photovoltaic Interface for Active Control of Live Cells

open access: yesAdvanced Functional Materials, EarlyView.
Bio‐photovoltaic Interface (BIO‐PV‐I) for live cell manipulation is presented. BIO‐PV‐I can be activated non‐invasively and remotely to control the spatial motility, adhesion, and morphology of cells adhering to it. BIO‐PV‐I uses a patterned light‐induced electric potential in iron‐doped lithium niobate crystals whose light‐driven and reversible nature,
Lisa Miccio   +8 more
wiley   +1 more source

From Single Atoms to Nanoparticles: Pathways Toward Efficient and Durable Pt/TiO2 Photocatalysts

open access: yesAdvanced Functional Materials, EarlyView.
Platinum single atoms on TiO2 nanosheets evolve into clusters and nanoparticles under ethanol photoreforming and thermal treatments. By controlling deposition and post‐treatments, particle size and location on specific facets are modulated. The study reveals how stability pathways determine efficiency, guiding the design of more durable photocatalysts.
Juan José Delgado   +6 more
wiley   +1 more source

What is the learning curve of the "outside the cage" robotic approach?-a prospective single-center study. [PDF]

open access: yesJ Thorac Dis
Streit A   +5 more
europepmc   +1 more source

Predicting Atomic Charges in MOFs by Topological Charge Equilibration

open access: yesAdvanced Functional Materials, EarlyView.
An atomic charge prediction method is presented that is able to accurately reproduce ab‐initio‐derived reference charges for a large number of metal–organic frameworks. Based on a topological charge equilibration scheme, static charges that fulfill overall neutrality are quickly generated.
Babak Farhadi Jahromi   +2 more
wiley   +1 more source

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