Results 231 to 240 of about 606,579 (281)

Modeling trap dynamics in oxide-engineered heterostructure TFETs for breast cancer detection. [PDF]

Sci Rep
Ghosh R, Saha P.
europepmc   +1 more source

Smart, Bio‐Inspired Polymers and Bio‐Based Molecules Modified by Zwitterionic Motifs to Design Next‐Generation Materials for Medical Applications

Advanced Functional Materials, EarlyView.
Bio‐based and (semi‐)synthetic zwitterion‐modified novel materials and fully synthetic next‐generation alternatives show the importance of material design for different biomedical applications. The zwitterionic character affects the physiochemical behavior of the material and deepens the understanding of chemical interaction mechanisms within the ...
Theresa M. Lutz, Jonas De Breuck, Sahar Salehi, Meike N. Leiske   +3 more
wiley   +1 more source

Insights into the operational stability of wide-bandgap perovskite and tandem solar cells under rapid thermal cycling. [PDF]

Nat Commun
Sun K, Guo R, Zhou Q, Fang L, Jiang X, Wegener SA, Liang Y, Li Z, Liang S, Schwartzkopf M, Aydin E, Koyiloth Vayalil S, Roth SV, Paetzold UW, Müller-Buschbaum P.   +14 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

Examination of kinetic components (pressure and time) of spark plasma sintering on the mechanical properties of WC/W FGM. [PDF]

Sci Rep
Vadeghani MA, Shafyei A, Najafabadi RA, Atapour M.   +3 more
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

Lactiplantibacillus plantarum GUANKE ameliorates influenza a virus-induced inflammation and lung barrier dysfunction through enhancing mitophagy and improving oxidative phosphorylation. [PDF]

Cell Mol Life Sci
Lu S, Yue K, He S, Mi J, Yang T, Yang Y, Ma H, Ren Z, Ren L, Xu J.   +9 more
europepmc   +1 more source