Williamson-hall Analysis in Estimation of Crystallite Size and Lattice Strain in Bi1.34Fe0.66Nb1.34O6.35 Prepared by the Sol-Gel Method

S. Devesa,A. P. Rooney,M. P. Graca,D. Cooper,L. C. Costa

Materials Science and Engineering B-advanced Functional Solid-state Materials（2021）SCI 3区

Univ Aveiro | CEA LETI Minatec

Cited 62|Views30

Abstract

Bi1.34Fe0.66Nb1.34O6.35 powders were prepared by the sol-gel method, through the citrate route, and heat treated at 500 degrees C. The obtained samples were characterized by X-ray diffraction analysis and by high resolution scanning transmission electron microscopy. An X-ray energy dispersive analysis was also performed. The average crystallite size and lattice strain were studied using the Scherrer's formula and the Williamson Hall (W-H) analysis, assuming Uniform Deformation Model (UDM), Uniform Deformation Stress Model (UDSM) and Uniform Deformation Energy Density Model (UDEDM). The obtained results revealed that the mean crystallite size and lattice strain estimated from the different analysis were highly correlated.

Translated text

Key words

Williamson-hall analysis,X-ray diffraction,Transmission electron microscopy,Crystal structure,Raman spectroscopy,Sol-gel

Bibtex

AI Read Science

AI Summary

AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.

Example

Background

Key content

Introduction

Methods

Results

Related work

Fund

Key content

Pretraining has recently greatly promoted the development of natural language processing (NLP)
We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance

Try using models to generate summary,it takes about 60s

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper