Robustness of Large-Area Suspended Graphene under Interaction with Intense Laser.

Y. Kuramitsu,T. Minami，H. Kiriyama,Y. Fukuda

Scientific reports（2022）SCI 3区

Cited 14|Views76

Abstract

Graphene is known as an atomically thin, transparent, highly electrically and thermally conductive, light-weight, and the strongest 2D material. We investigate disruptive application of graphene as a target of laser-driven ion acceleration. We develop large-area suspended graphene (LSG) and by transferring graphene layer by layer we control the thickness with precision down to a single atomic layer. Direct irradiations of the LSG targets generate MeV protons and carbons from sub-relativistic to relativistic laser intensities from low contrast to high contrast conditions without plasma mirror, evidently showing the durability of graphene.

Translated text

Key words

Laser-produced plasmas,Photonic devices,Two-dimensional materials,Science,Humanities and Social Sciences,multidisciplinary

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