Results 231 to 240 of about 342,935 (312)

Molecular phylogeny reveals a new species of ghost electric knifefish Porotergus Ellis 1912 (Gymnotiformes: Apteronotidae), from the Amazon basin. [PDF]

J Fish Biol
Mendonça MB, Peixoto LAW, Chamon CC, Akama A, de Santana CD.   +4 more
europepmc   +1 more source

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

Advanced 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, Haoran Wang, Pan Yang, Rishav Agrawal, Alex R. Neale, Laurence J. Hardwick, Tom Hasell, Esther García‐Tuñón   +7 more
wiley   +1 more source

A new genus and three new species of Lycoperdaceae (Agaricales) from Southern China revealed by molecular phylogeny and taxonomy. [PDF]

MycoKeys
Yang X, Duan S, Li M, Li D, Yang R, Zhang S, Xu T, Li W, Zhou H, Zhao C.   +9 more
europepmc   +1 more source

Backbone Heterojunction Photocatalysts for Efficient Sacrificial Hydrogen Production

Advanced Functional Materials, EarlyView.
Herein, a ‘single‐component’ organic semiconductor photocatalyst is presented in which a molecular donor is bonded to a polymer acceptor. The resultant material demonstrates exceptional photocatalytic activity for hydrogen evolution in aqueous triethylamine with an outstanding external quantum efficiency of 38% at 420 nm.
Richard J. Lyons, Rhys J. Bourhill, Ewan McQueen, Sam D. Harding, Krzysztof Pawlak, Thomas Fellowes, Harry W. Capps, Alexander J. Cowan, Andrew I. Cooper, Adrian M. Gardner, Martijn A. Zwijnenburg, Reiner Sebastian Sprick   +11 more
wiley   +1 more source

Molecular Phylogeny of the Family Cordulegastridae (Odonata) Worldwide. [PDF]

Insects
Schneider T, Vierstraete A, Kosterin OE, Ikemeyer D, Hu FS, Novelo-Gutiérrez R, Kompier T, Everett L, Müller O, Dumont HJ.   +9 more
europepmc   +1 more source

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

Advanced 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, Evelyn Kubacki, Alexander Berger, Markus Retsch   +3 more
wiley   +1 more source

Using active learning methodologies to teach sequence analysis and molecular phylogeny. [PDF]

Biochem Mol Biol Educ
Ortiz Martín I, Del Espino Pérez Á, García Luque E, Viguera Mínguez E.   +3 more
europepmc   +1 more source

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

Advanced 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

Revision of Isoglaridacris (Cestoda: Caryophyllidea), parasites of suckers (Catostomidae) in North America: molecular phylogeny and description of four new species. [PDF]

Sci Rep
Uhrovič D, Pantoja C, Kudlai O, Scholz T.   +3 more
europepmc   +1 more source

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced 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, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source