Results 171 to 180 of about 81,939 (297)

Direct Evidence of Topological Dirac Fermions in a Low Carrier Density Correlated 5d Oxide

open access: yesAdvanced Functional Materials, EarlyView.
The 5d oxide BiRe2O6 is discovered as a low‐carrier‐density topological semimetal hosting symmetry‐protected Dirac fermions stabilized by nonsymmorphic symmetries. Angle‐resolved photoemission spectroscopy, quantum oscillations, and magnetotransport measurements reveal gapless Dirac cones, quasi‐2D Fermi surfaces, high carrier mobility, and a field ...
Premakumar Yanda   +11 more
wiley   +1 more source

Do ESG Frameworks Capture Corporate Health Impacts? An Analysis of the Food and Beverage Industry. [PDF]

open access: yesInt J Environ Res Public Health
Burgess R   +9 more
europepmc   +1 more source

Backbone Heterojunction Photocatalysts for Efficient Sacrificial Hydrogen Production

open access: yesAdvanced 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   +11 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

The carbon footprint of tourism businesses in Pavlodar region (Kazakhstan): Baseline assessment and decarbonization hot spots. [PDF]

open access: yesPLoS One
Yessimova D   +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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