Effect of the Buffer Layers on the Performance of Fe(Se, Te) Films Fabricated on IBAD Metal Templates by Pulsed Laser Deposition

Mou Shaojing,Ye Jiachao,Zhu Rongji,Liu Linfei,Li Yijie

JOURNAL OF SUPERCONDUCTIVITY AND NOVEL MAGNETISM（2023）

Cited 2|Views11

Abstract

In the development of Fe(Se, Te) films, the substrate is thought to be a consequential factor in the growth and characteristics of the films. In this study, Fe(Se, Te) films were grown on ion beam-assisted deposition flexible metallic templates with MgO, CeO 2 , LaMnO 3 , and SrTiO 3 as buffer layers by pulsed laser deposition to explore the influence of buffer layers. The surface morphology, crystalline structure, chemical component, and superconductivity of the films were investigated. All the films have a smooth surface and a highly biaxial texture structure and present point and surface pinning centers with the dominance of surface pinning. Although the lattice constant of Fe(Se, Te) film is not significantly related to the lattice mismatch between the buffer layer and the Fe(Se, Te) film, the crystallinity and the critical current density J c of the film are negatively correlated with the lattice mismatch. The critical temperature T c declines as a -axis length increases. In-plane compressive strain and crystallinity are the main variables impacting superconductivity. In addition, the Fe(Se, Te) film grown on CeO 2 had the best superconducting properties with T_c^0 =16.4 K and T_c^onset =16.8 K. The self-field J c was 2.26 MA/cm 2 at 4.2 K and can maintain 10 5 A/cm 2 at 9 T. The upper critical field can reach 53 T allowing the applications of Fe(Se, Te) coated conductors in high field.

Translated text

Key words

Fe(Se, Te),Pulsed laser deposition,IBAD,Buffer layers

求助PDF

上传PDF

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

Upload PDF to Generate Summary

Must-Reading Tree

Example

Generate MRT to find the research sequence of this paper