Results 201 to 210 of about 889,604 (280)

Band Alignment in In‐Oxo Metal Porphyrin SURMOF Heterojunctions

open access: yesAdvanced Functional Materials, EarlyView.
Porphyrin core metalation in indium‑oxo SURMOFs enables systematic tuning of band edge positions without altering the crystal structure. First‑principles calculations reveal type‑I and type‑II heterostructures as well as multi‑junction energy cascades, establishing a modular strategy for exciton funneling and charge separation in optoelectronic ...
Puja Singhvi, Nina Vankova, Thomas Heine
wiley   +1 more source

Photoswitching Conduction in Framework Materials

open access: yesAdvanced Functional Materials, EarlyView.
This mini‐review summarizes recent advances in state‐of‐the‐art proton and electron conduction in framework materials that can be remotely and reversibly switched on and off by light. It discusses the various photoswitching conduction mechanisms and the strategies employed to enhance photoswitched conductivity.
Helmy Pacheco Hernandez   +4 more
wiley   +1 more source

Magnetic Control of Chiral Hybridized Phonon Magnetic Moments in Ferrimagnets Fe2‐xZnxMo3O8

open access: yesAdvanced Functional Materials, EarlyView.
Helicity‐resolved magneto‐Raman spectroscopy reveals magnetic control of chiral phonon magnetic moments in polar ferrimagnet (ZnxFe2−xMo3O₈). Large spontaneous zero‐field phonon splittings, selective phonon–magnon coupling, and asymmetric Zeeman responses demonstrate that phonon chirality is governed by magnon‐phonon coupling and magnetization.
Youngsu Choi   +8 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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