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PFI (ERI): Screening and Development of Doped Mixed Metal Oxides for Resistance-Heated Thermal Energy Storage

PFI (ERI): Screening and Development of Doped Mixed Metal Oxides for Resistance-Heated Thermal Energy Storage
PFI (ERI):用于电阻加热热能存储的掺杂混合金属氧化物的筛选和开发
批准号:
2138456
负责人:
Stephen Lam
金额:
$19.99万
依托单位国家:
美国
项目类别:
Standard Grant
财政年份:
2022
资助国家:
美国
项目状态:
未结题
起止时间:
2022-05-01 至 2025-04-30

项目摘要

项目成果

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中文摘要
翻译
这个创新伙伴关系(工程研究启动)(PFI(ERI))项目的更广泛的影响/商业潜力是使材料开发工作产生的多功能,低成本的热能储存技术成为可能。直接电阻加热式热能储存(DRH-ETS)技术储存来自电网的电力,这些电力既可以直接用于高温工业热量,也可以有效地转换回电力。这直接解决了全球脱碳的挑战,即(1)平衡可再生能源高度渗透的电网的供需,以及(2)减少工业对化石燃料的依赖,以产生高温工业热量。于二零二一年,水泥及炼钢等重工业应用燃烧化石燃料以产生热量占全球总排放量的10%。然而,工业热输入脱碳的选择仍然非常有限,大部分都是大规模成本高昂的。利用间接金属加热元件的传统热能储存技术已被认为是最具成本效益的加热选择之一,但无法实现足够高的温度以适应大多数碳密集型工艺。DRH-ETS将通过用电直接加热导热储热材料来克服这些局限性。拟议项目将筛选和开发优化DRH-ETS系统性能的材料。虽然耐火氧化物材料具有良好的抗氧化性并且可以掺杂以获得导电性,但大多数简单的二元氧化物不能满足所需的化学、热和电性能的组合。因此,对于DRH-ETS应用可行的已知体掺杂剂材料组合的数量极其有限。在这里,混合金属氧化物是有前途的,但大多数高阶混合物的相关性质还不容易知道。为了应对这一挑战,该项目将对材料进行计算筛选,以获得大温度范围内的单相稳定性、小电阻率温度导数和掺杂性。筛选将采用热化学建模,电子能带结构计算和从头缺陷分析的组合,以从大的多组分组成空间中进行向下选择。这将导致在掺杂的多元金属氧化物,这将通知系统的设计和操作的组成-结构-性能关系的更好的理解。该筛选将与制造和测试相结合,以识别和开发新的候选材料,以在DRH-ETS系统中实现低成本和最佳性能。该奖项反映了NSF的法定使命,并通过使用该基金会的知识价值进行评估,被认为值得支持和更广泛的影响审查标准。
英文摘要
The broader impact/commercial potential of this Partnerships for Innovation (Engineering Research Initiation) (PFI (ERI)) project is the enabling of versatile, low-cost thermal energy storage technologies that will result from the materials development work. Direct resistance-heated thermal energy storage (DRH-ETS) technology stores electricity from the grid, which can either be directly utilized for high-temperature industrial heat or efficiently converted back to electricity. This directly addresses the global decarbonization challenges of (1) balancing supply and demand on an electrical grid with a high penetration of renewables, and (2) reducing industrial dependence of fossil fuels for producing high-temperature industrial heat. In 2021, combustion of fossil fuels for the production of heat in heavy industrial applications such as cement and steel-making accounted for 10% of all global emissions. Yet, the options for decarbonizing industrial heat inputs remain very limited, with most being cost-prohibitive at large scale. Traditional thermal energy storage technologies utilizing indirect metallic heating elements have been recognized as among the most cost-effective heating options but cannot achieve high enough temperatures to suit most carbon-intensive processes. DRH-ETS will overcome these limitations by using electricity to directly heat conductive thermal storage materials.The proposed project will screen and develop materials that optimize the performance of DRH-ETS systems. While refractory oxide materials have good oxidation resistance and can be doped for electrical conductivity, most simple binary oxides are unable to satisfy the combination of required chemical, thermal, and electrical properties. As such, the number of known bulk-dopant material combinations that would be feasible for DRH-ETS applications is extremely limited. Here, mixed metal oxides are promising, but the relevant properties of most higher order mixtures are not readily known. To address this challenge, this project will perform computational screening of materials for single-phase stability over a large temperature range, small resistivity temperature derivative, and dopability. The screening will employ a combination of thermochemical modeling, electronic band structure calculations, and ab initio defect analysis to downselect from large multicomponent compositional spaces. This will result in improved understanding of composition-structure-property relationships in doped multinary metal oxides, which will inform system design and operation. This screening will be combined with fabrication and testing to identify and develop new candidate materials for achieving low cost and optimal performance in a DRH-ETS system.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(1)
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会议论文
High-Throughput Prediction of Molten Salt Mixture Density with Supervised Machine Learning
利用监督机器学习高通量预测熔盐混合物密度
DOI: --
发表时间: 2023
期刊: Transactions of the American Nuclear Society
影响因子: --
作者: [Julián Barra Otondo, Shayan Shahbazi]
通讯作者: Julián Barra Otondo, Shayan Shahbazi
国内基金
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