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CAREER: studying superconductivity and ferromagnetism in 2D material heterostructures with flat energy band

CAREER: studying superconductivity and ferromagnetism in 2D material heterostructures with flat energy band
职业:研究具有平坦能带的二维材料异质结构中的超导性和铁磁性
批准号:
2143384
负责人:
Jia Li
金额:
$70.0万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-02-15 至 2027-01-31

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中文摘要
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NON-TECHNICAL DESCRIPTION: Two-dimensional (2D) materials are materials made from single layers of atoms. Different 2D materials can be assembled into a designer structure to realize new properties and novel phenomena such as superconductivity and magnetism. This project utilizes a special design of 2D structure to study how electronic interaction can influence the stability of these novel phenomena, such as superconductivity and magnetism, in the quantum regime. Studying these phenomena on the nanometer scale is central to the advancement of quantum science, which promises to reshape the nature of technological innovation and has profound influence on national security and global economic balance. By training graduate and undergraduate students in device fabrication on a nanometer scale and exposing them to cutting edge research in studying quantum phenomena, this research and outreach program inspires and educates future scientists and engineers who can take on the leadership role and pioneer innovations in the field of quantum computational technology. The outreach effort includes collaboration with the Sci-Toon team to produce cartoon videos to expose the general public to the research activities. This project is jointly funded by the Division of Materials Research (DMR) and the Established Program to Stimulate Competitive Research (EPSCoR).TECHNICAL DESCRIPTION: Graphene moiré systems provides a fascinating material platform to study quantum phenomena, such as superconductivity and ferromagnetism, and investigate their interplay in a 2D confinement. This project utilizes specially designed 2D material heterostructure, which is usually referred to as the double-layer structure, to perform Coulomb screening and thermodynamic measurements on various moiré systems. A main objective is to understand the role of Coulomb correlation in stabilizing the superconducting and ferromagnetic phases. The role of Coulomb interaction offers important constraints to understand the pairing mechanism and order parameter symmetry of the superconducting phase, which has become an outstanding open question. This project also explores a new flavor of moiré physics using the proximity effect, which are shown to transform the ground state order of 2D moiré systems. By controlling the strength of Coulomb interaction through screening, the proposed effort directly studies the influence of the proximity effect and aims to establish a new method to engineer physical properties of 2D material structures. This project is jointly funded by the Division of Materials Research (DMR) and the Established Program to Stimulate Competitive Research (EPSCoR).This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
期刊论文(2)
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会议论文
DOI: 10.1038/s41567-023-02060-0
发表时间: 2022-06
期刊: Nature Physics
影响因子: 19.6
作者: [E. Morissette;Jiangxiazi Lin;Song Liu;D. Rhodes;K. Watanabe;T. Taniguchi;J. Hone;M. Scheurer-]
通讯作者: E. Morissette;Jiangxiazi Lin;Song Liu;D. Rhodes;K. Watanabe;T. Taniguchi;J. Hone;M. Scheurer-
DOI: 10.1038/s41567-022-01700-1
发表时间: 2022-08-15
期刊: NATURE PHYSICS
影响因子: 19.6
作者: [Lin, Jiang-Xiazi, Siriviboon, Phum, Li, J. I. A.]
通讯作者: Li, J. I. A.
RII Track-4:NSF: Resistively-Detected Electron Spin Resonance in Multilayer Graphene
  • 批准号:
    2327206
  • 项目类别:
    Standard Grant
  • 资助金额:
    $30.0万
  • 财政年份:
    2024
  • 负责人:
    Jia Li
  • 依托单位:
CIF: Small: Interpretable Machine Learning based on Deep Neural Networks: A Source Coding Perspective
Cluster Analysis for High-Dimensional and Multi-Source Data
EAGER-DynamicData: Generative Statistical Modeling for Dynamic and Distributed Data
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