Results 131 to 140 of about 1,296,730 (297)

Language laboratory teaching [PDF]

The Vocational Aspect of Education, 1964
openaire   +1 more source

NFDI MatWerk Ontology (MWO): A BFO‐Compliant Ontology for Research Data Management in Materials Science and Engineering

Advanced Engineering Materials, EarlyView.
This article presents the NFDI‐MatWerk Ontology (MWO), a Basic Formal Ontology‐based framework for interoperable research data management in materials science and engineering (MSE). Covering consortium structures, research data management resources, services, and instruments, MWO enables semantic integration, Findable, Accessible, Interoperable, and ...
Hossein Beygi Nasrabadi, Ebrahim Norouzi, Kostiantyn Hubaiev, Jörg Waitelonis, Harald Sack   +4 more
wiley   +1 more source

The Language Laboratory in Finland [PDF]

, 1973
Lindqvist, Pirkko
core   +2 more sources

Removing Homocoupling Defects in Alkoxy/Alkyl‐PBTTT Enhances Polymer:Fullerene Co‐Crystal Formation and Stability

Advanced Functional Materials, EarlyView.
PBTTT‐OR‐R, a C14‐alkoxy/alkyl‐PBTTT polymer derivative, is of substantial interest for optoelectronics due to its specific fullerene intercalation behavior and enhanced charge‐transfer absorption. Comparing this polymer with (S) and without (O) homocoupling defects reveals that PBTTT‐OR‐R(O) forms stable co‐crystals with PC61BM, while PBTTT‐OR‐R(S ...
Zhen Liu, Jochen Vanderspikken, Paola Mantegazza, Stefania Moro, Mouna Hamid, Sigurd Mertens, Niko Van den Brande, Laurence Lutsen, Vincent Lemaur, David Beljonne, Giovanni Costantini, Koen Vandewal, Bruno Van Mele, Wouter Maes, Bart Goderis   +14 more
wiley   +1 more source

The Language Laboratory and Foreign Language Teaching Training [PDF]

, 1970
Meder, Zoltan
core   +2 more sources

Positive‐Tone Nanolithography of Antimony Trisulfide with Femtosecond Laser Wet‐Etching

Advanced Functional Materials, EarlyView.
A butyldithiocarbamic acid (BDCA) etchant is used to fabricate various micro‐ and nanoscale structures on amorphous antimony trisulfide (a‐Sb2S3) thin film via femtosecond laser etching. Numerical analysis and experimental results elucidate the patterning mechanism on gold (reflective) and quartz (transmissive) substrates.
Abhrodeep Dey, Uwe Hübner, Albane Benardais, Xiaofei Wu, Andrea Dellith, Jan Dellith, Torsten Wieduwilt, Henrik Schneidewind, Markus A Schmidt, Virginie Nazabal, Volker Deckert, Jer‐Shing Huang, Wei Wang   +12 more
wiley   +1 more source

What do ESL Students need from the Language Laboratory? [PDF]

, 1990
Castro, Pamela
core   +2 more sources

Electroactive Metal–Organic Frameworks for Electrocatalysis

Advanced Functional Materials, EarlyView.
Electrocatalysis is crucial in sustainable energy conversion as it enables efficient chemical transformations. The review discusses how metal–organic frameworks can revolutionize this field by offering tailorable structures and active site tunability, enabling efficient and selective electrocatalytic processes.
Irena Senkovska, Yutong Luo, Ruyan Zhao, Jianjun Zhang, Mingchao Wang, Xinliang Feng, Eike Brunner, Stefan Kaskel   +7 more
wiley   +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

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