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

Autor: Zhang F; School of Physics, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland.; Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland., Williams KN; School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332-0405, USA., Edwards D; School of Physics, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland.; Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland., Naden AB; University of St Andrews School of Chemistry, Purdie Building, St Andrews, Fife, KY16 9ST, United Kingdom., Yao Y; School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332-0405, USA., Neumayer SM; School of Physics, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland.; Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland., Kumar A; Centre for Nanostructured Media, School of Mathematics and Physics, Queen's University Belfast, Belfast, BT7 1NN, United Kingdom., Rodriguez BJ; School of Physics, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland.; Conway Institute of Biomolecular and Biomedical Research, University College Dublin, Belfield, Dublin, D04 V1W8, Ireland., Bassiri-Gharb N; School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, GA, 30332-0405, USA.; G.W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332-0405, USA.
Jazyk: angličtina
Zdroj: Small methods [Small Methods] 2021 Dec; Vol. 5 (12), pp. e2100552. Date of Electronic Publication: 2021 Oct 22.
DOI: 10.1002/smtd.202100552
Abstrakt: Scanning Probe Microscopy (SPM) based techniques probe material properties over microscale regions with nanoscale resolution, ultimately resulting in investigation of mesoscale functionalities. Among SPM techniques, piezoresponse force microscopy (PFM) is a highly effective tool in exploring polarization switching in ferroelectric materials. However, its signal is also sensitive to sample-dependent electrostatic and chemo-electromechanical changes. Literature reports have often concentrated on the evaluation of the Off-field piezoresponse, compared to On-field piezoresponse, based on the latter's increased sensitivity to non-ferroelectric contributions. Using machine learning approaches incorporating both Off- and On-field piezoresponse response as well as Off-field resonance frequency to maximize information, switching piezoresponse in a defect-rich Pb(Zr,Ti)O 3 thin film is investigated. As expected, one major contributor to the piezoresponse is mostly ferroelectric, coupled with electrostatic phenomena during On-field measurements. A second component is electrostatic in nature, while a third component is likely due to a superposition of multiple non-ferroelectric processes. The proposed approach will enable deeper understanding of switching phenomena in weakly ferroelectric samples and materials with large chemo-electromechanical response.
(© 2021 The Authors. Small Methods published by Wiley-VCH GmbH.)
Databáze: MEDLINE
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