Results 211 to 220 of about 1,744,597 (326)

3D‐Printed Sulfur‐Derived Polymers With Controlled Architectures for Lithium‐Sulfur Batteries

open access: yesAdvanced Functional Materials, EarlyView.
Rheology‐guided formulation design for direct ink writing enables the fabrication of 3D sulfur copolymer cathodes with controlled architectures for lithium‐sulfur batteries. The printed electrodes exhibit multiscale porosity and high sulfur utilization, delivering enhanced electrochemical performance compared to conventional cast electrodes.
Bin Ling   +7 more
wiley   +1 more source

The impact of inclusive electron ion collider data on the strong coupling determination in a global PDF fit. [PDF]

open access: yesEur Phys J C Part Fields
Harland-Lang LA   +4 more
europepmc   +1 more source

Cool Kitchen: Processing Starch and Eggshell Powder into Sustainable Coatings for Passive Daytime Cooling

open access: yesAdvanced Functional Materials, EarlyView.
A food‐grade cooling composite made from starch and recycled eggshell powder offers a scalable, ultra‐low‐cost solution for passive daytime radiative cooling. Easily prepared using basic kitchen tools, this material empowers communities, even in areas with limited infrastructure, to stay cooler during worsening summer heat waves.
Qimeng Song   +3 more
wiley   +1 more source

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

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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