Results 171 to 180 of about 988,957 (292)

Excited states investigated [PDF]

Nature Photonics, 2012
openaire   +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

Similarity Constrained CC2: Toward Efficient Coupled Cluster Nonadiabatic Dynamics among Excited States. [PDF]

J Chem Theory Comput
Stoll L, Angelico S, Kjønstad EF, Koch H.   +3 more
europepmc   +1 more source

Supraparticles Composed of Graphitic Carbon Nitride Nanoparticles and Silica‐Supported Horseradish Peroxidase as Customizable Hybrid Catalysts for Photo‐Biocatalytic Cascade Reactions in Continuous Flow

Advanced Functional Materials, EarlyView.
Herein presented supraparticles combine the nanoparticulate photocatalyst graphitic carbon nitride with the enzyme horseradish peroxidase, which is immobilized on silica nanoparticles. In an optimized compatibility range, both catalysts operate effectively within the hybrid supraparticles and catalyze a cascade reaction consisting of the photocatalytic
Bettina Herbig, Egzon Cermjani, Doris Hanselmann, Johannes Prieschl, Angelika Schmitt, Michael S. Moritz, Christian Papp, Greta Nölke, Stefano Di Fiore, Christoph Deckers, Thomas H. Rehm, Susanne Wintzheimer   +11 more
wiley   +1 more source

Computing Excited States of Molecules Using Normalizing Flows. [PDF]

J Chem Theory Comput
Saleh Y, Fernández Corral Á, Vogt E, Iske A, Küpper J, Yachmenev A.   +5 more
europepmc   +1 more source

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

Advanced 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, Lei Wu, Dong Zhang, Linxiao Wu, Graca Ferreira Vaz Pinto Mendes, Wanqiao J. Tu, Heping Ma, Toby J. Proctor, Colin R. Butler, Thomas Kjærgaard, Mark W. Lowdell, Martin A. Birchall, Robert E. Hynds, Richard Hewitt, Wenhui Song   +14 more
wiley   +1 more source

Nature of Anti-Dissipative High-Energy Excited States in Quaterpyridine-Bridged Ruthenium Complexes. [PDF]

Angew Chem Int Ed Engl
Chantry N, Cotic A, De Kreijger S, Di Forti R, Elias B, Troian-Gautier L, Cadranel A.   +6 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

Evaluation of Approximate Fourth-Order N-Electron Valence Perturbation Theory (NEVPT4(SD)) for the Excited States of Organic Molecules. [PDF]

J Chem Theory Comput
Kempfer EM, Sivalingam K, Neese F.
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