Synthesis, Structure and Magnetism of RTa7O19 (R = Pr, Sm, Eu, Gd, Dy, Ho) with Perfect Triangular Lattice

Cited 3|Views10

Abstract

We present the synthesis, structure and magnetic properties of a hitherto unstudied family of rare-earth based RTa7O19 (R = Pr, Sm, Eu, Gd, Dy, Ho) compounds with space groupP6¯2c. The structural analysis reveals that magnetic R ions form layers of ideal two-dimensional triangular lattices in the ab-plane separated by several nonmagnetic polyhedra layers along the c-axis. The magnetic susceptibility and magnetization measurements indicate the absence of long-range magnetic order and field-induced magnetic transitions in all the studied compounds. The details about the magnetism strongly depend on the type of R ions. The presence of geometrical frustration is discussed. Thus, RTa7O19 provides a new platform for exploring the frustrated magnetism.

Translated text

Key words

Rare earth tantalates,Triangular lattice,Geometric magnetic frustration

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