Results 231 to 240 of about 569,076 (288)

A Bespoke Programmable Interpenetrating Elastomer Network Composite Laryngeal Stent for Expedited Paediatric Laryngotracheal Reconstruction

open access: yesAdvanced Functional Materials, EarlyView.
A programmable interpenetrating double‐network architecture, created via 3D‐TIPS printing and resin infusion, synergistically combines thermoplastic and thermosetting elastomers to balance structural rigidity and surface softness—crucial for paediatric laryngeal stents.
Elizabeth F. Maughan   +14 more
wiley   +1 more source

National Guide on Nutrition Care and Support for People Living With HIV/AIDS\ud [PDF]

open access: yes, 2003
Abdallah., Fatma   +20 more
core  

MOFs and COFs in Electronics: Bridging the Gap between Intrinsic Properties and Measured Performance

open access: yesAdvanced Functional Materials, EarlyView.
Metal‐organic frameworks (MOFs) and covalent organic frameworks (COFs) hold promise for advanced electronics. However, discrepancies in reported electrical conductivities highlight the importance of measurement methodologies. This review explores intrinsic charge transport mechanisms and extrinsic factors influencing performance, and critically ...
Jonas F. Pöhls, R. Thomas Weitz
wiley   +1 more source

Defects Dynamic in Photo‐Excited CeO2 and their Influence on CO2 Photoreduction

open access: yesAdvanced Functional Materials, EarlyView.
X‐ray photoelectron spectroscopy study under light excitation is presented to track the defect dynamic (Ce4+ to Ce3+) in CeO2. Surface enhanced Raman spectroscopy confirmed the key role of Ce3+ states in controlling charge and energy transfer across the CeO2‐dye molecule interface.
Rambabu Yalavarthi   +3 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

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