📣 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.

Maximizing Information: A Machine Learning Approach for Analysis of Complex Nanoscale Electromechanical Behavior in Defect-Rich PZT Films. (2021)

First Author: Zhang F

Attributed to: Quantitative Hall Voltage mapping at conducting Ferroelectric domain walls: A novel approach to extracting conduction mechanisms on the nanoscale funded by EPSRC

Abstract

No abstract provided

Bibliographic Information

Digital Object Identifier: http://dx.doi.org/10.1002/smtd.202100552

PubMed Identifier: 34928037

Publication URI: http://europepmc.org/abstract/MED/34928037

Type: Journal Article/Review

Volume: 5

Parent Publication: Small methods

Issue: 12

ISSN: 2366-9608