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CAREER:Quantifying Radiation Damage in Metals with Wigner Energy Spectral Fingerprints

CAREER:Quantifying Radiation Damage in Metals with Wigner Energy Spectral Fingerprints
职业:利用维格纳能谱指纹量化金属的辐射损伤
批准号:
1654548
负责人:
Michael Short
金额:
$64.33万
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2017
资助国家:
美国
项目状态:
已结题
起止时间:
2017-06-01 至 2022-05-31

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Non-Technical The concept of "damage" to a metal remains difficult to quantify. Metals are among our most important structural materials, providing the backbone to everything from buildings, to bridges, to nuclear reactors. If we had a universal way to measure damage, we would be able to better predict when metals would fail, measure their degradation during service, and design new metals to be both longer-lasting and more economical. The use of stored energy fingerprints is proposed as a way to quantify damage to metals from any damaging process. We focus on radiation damage as an ideal way to make all the types of defects found in metals. A two-pronged experimental and simulation approach will be used to quantify and understand these stored energy fingerprints, relating them directly to the defects created by damage. Immediate applications of this work range from reconciling the differences between ion and neutron irradiation, to predicting material property changes due to radiation damage, to verifying the historical usage of uranium enrichment centrifuges. This enhanced understanding will also be the key to lowering the barriers to its study, creating completely new opportunities for both hands-on instruction and inclusion of more underrepresented minority (URM) students into what is currently one of the least diverse fields in STEM.Technical Our ability to understand how materials respond to damage is limited by our lack of understanding about the precise populations of microstructural defects created during damage processes. Nowhere is this issue more prevalent than in the field of radiation materials science, where the lack of a measurable unit of radiation damage continues to obfuscate the quantitative mechanisms responsible for the degradation of material properties under ionizing irradiation. Were the full populations of every defect in a damaged material to be known, then its material properties could be predicted with existing structure-property relations. We propose to use stored energy fingerprints to visualize the full plethora of defects resulting from damage of any kind, particularly irradiation. We draw inspiration from a long-neglected idea stating that radiation damage should store energy like amorphization or cold work. This idea is extended to describe all forms of microstructural damage in metals, in a measurable way which reveals the defects responsible. Using time-accelerated parallel replica dynamics simulations and ultra-fast nanocalorimetric measurements, we will directly link simulated and measured stored energy releases at the mesoscale, with atomistic understanding. This will enable a posteriori measurements of stored energy fingerprints of damaged metals, revealing the atomic configurations and quantities of defects responsible. Thus we seek to provide the full picture of defects resulting from damage, demonstrate a method to measure them, explain their evolution using atomistic simulations, and create unifying theories to predict the defect structures and resultant material properties resulting from damage in metals.
期刊论文(5)
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会议论文
DOI: 10.1016/j.actamat.2019.12.058
发表时间: 2020-03
期刊: Acta Materialia
影响因子: 9.4
作者: [Miaomiao Jin;P. Cao;M. Short]
通讯作者: Miaomiao Jin;P. Cao;M. Short
DOI: 10.1073/pnas.1907317116
发表时间: 2019-09-17
期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
影响因子: 11.1
作者: [Cao, Penghui, Short, Michael P., Yip, Sidney]
通讯作者: Yip, Sidney
Measuring Effects of Radiation on Precipitates in Aluminum 7075-T6 Using Differential Scanning Calorimetry
使用差示扫描量热法测量辐射对铝 7075-T6 中沉淀物的影响
DOI: 10.1115/icone26-82457
发表时间: 2018
期刊: Proceedings of the 2018 26th International Conference on Nuclear Engineering ICONE26
影响因子: --
作者: [Connick, Rachel C., Hirst, Charles A., Cao, Penghui, So, Kangpyo, Kemp, R. Scott, Short, Michael P.]
通讯作者: Short, Michael P.
DOI: 10.1073/pnas.1708618114
发表时间: 2017-12-26
期刊: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
影响因子: 11.1
作者: [Cao, Penghui, Short, Michael P., Yip, Sidney]
通讯作者: Yip, Sidney
Artificial Intelligence Enabling Future Optimal Flexible Biogas Production for Net-Zero
  • 批准号:
    EP/Y005600/1
  • 项目类别:
    Research Grant
  • 资助金额:
    $183.04万
  • 财政年份:
    2023
  • 负责人:
    Michael Short
  • 依托单位:
海外基金