Detecting the Spin-Polarization of Edge States in Graphene Nanoribbons

Jens Brede,Nestor Merino-Díez,Alejandro Berdonces-Layunta,Sofía Sanz,Amelia Domínguez-Celorrio,Jorge Lobo-Checa,Manuel Vilas-Varela,Diego Peña,Thomas Frederiksen,José I. Pascual,Dimas G. de Oteyza,David Serrate

NATURE COMMUNICATIONS（2023）

Donostia International Physics Center | Instituto de Nanociencia y Materiales de Aragón (INMA) | Universidade de Santiago de Compostela | Ikerbasque

Cited 2|Views1

Abstract

Low dimensional carbon-based materials can show intrinsic magnetism associated to p-electrons in open-shell π -conjugated systems. Chemical design provides atomically precise control of the π -electron cloud, which makes them promising for nanoscale magnetic devices. However, direct verification of their spatially resolved spin-moment remains elusive. Here, we report the spin-polarization of chiral graphene nanoribbons (one-dimensional strips of graphene with alternating zig-zag and arm-chair boundaries), obtained by means of spin-polarized scanning tunnelling microscopy. We extract the energy-dependent spin-moment distribution of spatially extended edge states with π -orbital character, thus beyond localized magnetic moments at radical or defective carbon sites. Guided by mean-field Hubbard calculations, we demonstrate that electron correlations are responsible for the spin-splitting of the electronic structure. Our versatile platform utilizes a ferromagnetic substrate that stabilizes the organic magnetic moments against thermal and quantum fluctuations, while being fully compatible with on-surface synthesis of the rapidly growing class of nanographenes.

Translated text

Key words

Nanoribbons

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

Data Disclaimer

The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn

Chat Paper

【要点】：本文通过自旋极化扫描隧道显微镜技术研究了手性石墨烯纳米带的边缘态自旋极化，揭示了电子相关作用导致电子结构的自旋分裂现象。

【方法】：利用自旋极化扫描隧道显微镜对石墨烯纳米带的边缘态进行成像，结合平均场Hubbard模型计算分析电子相关作用。

【实验】：实验在铁磁基底上进行，使用自旋极化扫描隧道显微镜技术对边缘态的自旋极化进行了测量，数据集名称未在文中提及，但实验结果显示了边缘态的π轨道特性自旋矩分布。

去 AI 文献库对话