📣 Help Shape the Future of UKRI's Gateway to Research (GtR)

We're improving UKRI's Gateway to Research and are seeking your input! If you would be interested in being interviewed about the improvements we're making and to have your say about how we can make GtR more user-friendly, impactful, and effective for the Research and Innovation community, please email gateway@ukri.org.

Analyzing drop coalescence in microfluidic devices with a deep learning generative model. (2023)

Abstract

No abstract provided

Bibliographic Information

Digital Object Identifier: http://dx.doi.org/10.1039/d2cp05975d

PubMed Identifier: 37232111

Publication URI: http://europepmc.org/abstract/MED/37232111

Type: Journal Article/Review

Volume: 25

Parent Publication: Physical chemistry chemical physics : PCCP

Issue: 23

ISSN: 1463-9076