Bi0.33Zr2(PO4)3, a Negative Thermal Expansion Material with a Nasicon-type Structure.

Cited 0|Views5

Abstract

A new negative thermal expansion ceramic material, Bi0.33Zr2(PO4)3, with a sodium zirconium phosphate structure, has been investigated and the results are presented. A three-dimensional structural framework is formed by the interconnection of ZrO6 octahedra and PO4 tetrahedra through their vertices. The occupation of a Bi ion in a type I site, located between two ZrO6 octahedra along ZrO6 < PO4 > ZrO6 ribbons induced the structure to crystallize in a trigonal system with a P3̄c1 space group. The average thermal expansion coefficients between 300 and 1073 K are found to be αa = -12.99 × 10-6 K-1, αc = -26.67 × 10-6 K-1, and αv = -52.25 × 10-6 K-1. The large negative thermal expansion is highly associated with the peak shift towards a higher angle in temperature variable X-ray diffraction patterns, coupled rotation and distortion of ZrO6 octahedra and PO4 tetrahedra, and elongation or shrinkage in the Zr-O-P bond angles. Dilatometer results authenticate the negative thermal expansion behavior. The average bulk thermal expansion is registered as -0.73 × 10-6 K-1.

Translated text

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