Results 161 to 170 of about 179,499 (335)

Dielectrophoretic Manipulation of Particles for Use in Microfluidic Devices

, 1999
Robin Miles, Phil Belgrader, Kerry Bettencourt, Julie Hamilton, Shanavaz Nasarabadi   +4 more
openalex   +2 more sources

Microfluidic Synthesis of Channel‐Rich Pd‐Cu Alloy Nanodendrites for Efficient Electrocatalytic CO2 Reduction to Formate

Advanced Functional Materials, EarlyView.
A microfluidic system enables the rapid, room‐temperature fabrication of channel‐rich Pd‐Cu alloy nanodendrites with tunable composition, uniform morphology, and finely branched internal structures. The resulting catalysts exhibit over 90% formate selectivity across a broad potential window, along with excellent CO tolerance and enhanced long‐term ...
Xintong Huang, Xude Yu, Hengyuan Liu, Shunkai Xia, Yongqi Jia, Zhuo Chen, Jianhong Xu, Younan Xia   +7 more
wiley   +1 more source

Integrating Microfluidic Systems and NMR Spectroscopy: Preliminary Results

, 1998
Jonathan D. Trumbull, Ian Glasgow, David J. Beebe, Richard L. Magin   +3 more
openalex   +1 more source

Harnessing Non‐Covalent Protein–Protein Interaction Domains for Production of Biocatalytic Materials Systems

Advanced Functional Materials, EarlyView.
Non‐covalent protein–protein interactions mediated by SH3, PDZ, or GBD domains enable the self‐assembly of stable and biocatalytically active hydrogel materials. These soft materials can be processed into monodisperse foams that, once dried, exhibit enhanced mechanical stability and activity and are easily integrated into microstructured flow ...
Julian S. Hertel, Maria Alessandra Martini, Marius Stoeckle, Annika J. Weber, Kersten S. Rabe, Christof M. Niemeyer   +5 more
wiley   +1 more source

Integrated optical measurement system for fluorescence spectroscopy in microfluidic channels [PDF]

, 2001
Jörg Hübner, Klaus Bo Mogensen, Anders M. Jørgensen, Peter Friis, Pieter Telleman, Jörg P. Kutter   +5 more
openalex   +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

Flexible Methods for Microfluidics [PDF]

, 2001
George M. Whitesides, Abraham D. Stroock
openalex   +1 more source

Wafer‐Scale Integration of α‐quartz Thin Films toward Super High Frequency Piezoelectric bioNEMS for Chikungunya Virus Detection

Advanced Functional Materials, EarlyView.
Epitaxial piezoelectric α‐quartz/Si BioNEMS sensors, made using soft chemistry, effectively detect the Chikungunya virus. They have a mass sensitivity of 205 pg Hz−1 in liquid and can detect the virus at a limit of 9 ng mL−1. This development enables high‐frequency mass devices for point‐of‐care testing in healthcare and other electronic applications ...
Raissa Rathar, Sijie Ding, David Sanchez‐Fuentes, Claire Jolly, Charlotte André‐Arpin, Rudy Desgarceaux, Nicolas Guillaume, Gustavo Ardila, Ricardo Garcia‐Bermejo, Jaume Gazquez, Sergi Plana‐Ruiz, Laura Picas, Adrian Carretero‐Genevrier   +12 more
wiley   +1 more source

Separation of DNA in Microfluidic Systems

, 2000
H. G. Craighead, Jun P. Han, Steve Turner   +2 more
openalex   +1 more source

Using In Situ TEM to Understand the Surfaces of Electrocatalysts at Reaction Conditions: Single‐Atoms to Nanoparticles

Advanced Functional Materials, EarlyView.
This review summarizes recent advances in closed‐cell in situ TEM strategies for accurate determination of the activity and stability of single‐atom catalyst systems during operation. Operando conditions causing dynamic changes of SAC systems are highlighted and we explain why ensemble average‐based optical techniques may benefit from the technological
Martin Ek, Soshan Cheong, Julie Cairney, Richard D. Tilley, Ingemar Persson   +4 more
wiley   +1 more source