Results 181 to 190 of about 2,258,629 (292)

Fermi Surface Nesting and Anomalous Hall Effect in Magnetically Frustrated Mn2PdIn

Advanced Functional Materials, EarlyView.
Mn2PdIn, a frustrated inverse Heusler alloy, showing electronic‐structure driven anomalous Hall effect with Weyl crossings, Fermi‐surface nesting and near‐zero magnetization ideal for low‐magnetization spintronics. Abstract Noncollinear magnets with near‐zero net magnetization and nontrivial bulk electronic topology hold significant promise for ...
Afsar Ahmed, Arnab Bhattacharya, Prashant Singh, Ajay Kumar, Tukai Singha, Anis Biswas, Yaroslav Mudryk, I. Das   +7 more
wiley   +1 more source

Miniature Nanomesh Mechano‐Acoustic Sensor with Wide Linear Dynamic Range, Broad Bandwidth, and Flat Frequency Response

Advanced Functional Materials, EarlyView.
A miniaturized mechano‐acoustic sensor is developed using an electrospun PVDF nanomesh as the diaphragm in a capacitive sensor structure. Unlike conventional nanomesh‐based sensors, it achieves high linear sensitivity, a broad and flat frequency response, and a compact form factor.
Jeng‐Hun Lee, Kang Hyuk Cho, Seojin Yun, Siyoung Lee, Woongji Kim, Yunsik Kim, Wonkyu Moon, Yoonyoung Chung, Kilwon Cho   +8 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

Hybrid Revascularization Approach Using Robot-Assisted Bilateral Internal Mammary Artery Grafting. [PDF]

Braz J Cardiovasc Surg
Issa HMN, Matar L, Shuster A, Athayde G, Ferreira L, Bucek M, Romanelli P, Centenaro DF, Hornestam JF, Issa AMN, Glineur D.   +10 more
europepmc   +1 more source

Inactivating SARS‐CoV‐2 Virus with MOF‐Composites as Smart Face Masks

Advanced Functional Materials, EarlyView.
In situ preparation and functionalization of MOF@Cotton fabrics as smart face masks for the immobilization of proteins and inactivation viruses, such as SARS‐CoV‐2. Abstract The significant impact of the SARS‐CoV‐2 (COVID‐19) pandemic outbreak on people's lives has highlighted the urgent need for effective personal protective equipment.
Romy Ettlinger, Aamod V. Desai, Hau Lam Jody Cheong, Louise L. Major, Terry K. Smith, Aaron B. Naden, Jane Hilton, Catherine S. Adamson, A. Robert Armstrong, Russell E. Morris   +9 more
wiley   +1 more source

Overcoming quadratic hardware scaling for a fully connected digital oscillatory neural network. [PDF]

Front Neurosci
Haverkort BF, Todri-Sanial A.
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

Pragmatic Models for Detection of Hypertension Using Ballistocardiograph Signals and Machine Learning. [PDF]

Bioengineering (Basel)
Prabhakar SK, Won DO.
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

Guide to Hybrid Work

, 2023
Hyrkkänen, Ursula, Karppi, Marion, Granberg, Anu   +3 more
openaire   +2 more sources