ESR7 – H. Trivedi

ESR 7 - Trivedi Trivedi, Harsh (ESR7)
Universität Duisburg-EssenFakultät für Ingenieurwissenschaften
Institut für Materialwissenschaft
Universitätsstraße 15
45141 Essen, Germany
Tel.: +49 201 183 2606
Fax: +49 201 183 3968
CV: CV_Trivedi.pdf
ArrProj WP2c

“Best results will come when everyone in the group does best for him/herself and the group”

- One line interpretation of ‘Nash Equilibria’


I am Harsh Trivedi, associated with ITN – Nanomotion as ESR -7. I received my Masters in technology (M.Tech.) from Indian Institute of Technology, Kanpur (IITK), with specialization in Materials Science. My background is based on strong foundation of physics with an interdisciplinary interface to Materials Science. During my course of study and project works, I have learned various aspects of Materials Science (You are welcomed to refer to my CV), that provided me the necessary realization of the importance of the subject as a doctrine. My previous engagement and a start of my experimental career, involved fabrication and characterization of oxide based multiferroic materials. Raman Spectroscopy was my principle tool for analyzing the relation between the structural nuances and the resulting ferroic properties. During this period I have learned the implications of various materials fabrication aspects on the end properties, and the importance of de-convolution of experimental artifacts.

Presently my project (WP2c), involves local probing of Magnetoelectric-Effect using (but not limited to) Scanning Probe Microscopy tools like Piezoresponse Force Microscopy (PFM) and magnetic Force Microscopy (MFM), on composite multiferroics like BaTiO3 – CoFe2O4 system, with a core-shell type of phase connectivity.

The very weak nature of the ME effect coupled with the multicomponent nature of the composite makes it inevitable to use rigorous analytical techniques to process the acquired data and extract the changes. As a result, a big part of time during my research is consumed in processing, fitting and carrying out multivariate statistics like Principle Component Analysis (PCA), Self Modelling Curve Resolution (SCMR) of the acquired data, using computational platforms like MATLAB.

In case of any further interest feel free to interact with me.