📣 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.

Machine learning to empower electrohydrodynamic processing. (2022)

First Author: Wang F

Attributed to: EPSRC IRC in Targeted Delivery for Hard-to-Treat Cancers funded by EPSRC

Abstract

No abstract provided

Bibliographic Information

Digital Object Identifier: http://dx.doi.org/10.1016/j.msec.2021.112553

PubMed Identifier: 35148867

Publication URI: http://europepmc.org/abstract/MED/35148867

Type: Journal Article/Review

Volume: 132

Parent Publication: Materials science & engineering. C, Materials for biological applications

ISSN: 0928-4931