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: University of Massachusetts Lowell
• 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
• 起止时间: 2022-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2138456&HistoricalAwards=false
• 关键词: PFI; ERI; Screening; Development; Doped

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.

该创新伙伴关系（工程研究启动）（PFI (ERI)）项目更广泛的影响/商业潜力是通过材料开发工作实现多功能、低成本热能存储技术。直接电阻加热热能存储（DRH-ETS）技术存储来自电网的电力，这些电力可以直接用于高温工业热量或有效地转换回电力。这直接解决了全球脱碳挑战：（1）通过可再生能源的高渗透率平衡电网的供需，以及（2）减少工业对生产高温工业热量的化石燃料的依赖。到 2021 年，水泥和炼钢等重工业应用中用于产生热量的化石燃料燃烧占全球排放总量的 10%。然而，工业热输入脱碳的选择仍然非常有限，大多数大规模成本高昂。利用间接金属加热元件的传统热能存储技术已被认为是最具成本效益的加热选择之一，但无法达到足够高的温度来适应大多数碳密集型工艺。 DRH-ETS 将通过使用电力直接加热导热储热材料来克服这些限制。拟议项目将筛选和开发可优化 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通过掺杂来提高导电性，但大多数简单的二元氧化物无法满足所需的化学、热和电性能的组合。因此，可用于 DRH-ETS 应用的已知体掺杂材料组合的数量极其有限。在这里，混合金属氧化物很有前景，但大多数高阶混合物的相关性质尚不清楚。为了应对这一挑战，该项目将对材料进行计算筛选，以实现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算缺陷分析的组合来从大型多组分组成空间中进行向下选择。这将提高对掺杂多元金属氧化物的成分-结构-性能关系的理解，从而为系统设计和操作提供信息。该筛选将与制造和测试相结合，以确定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，并通过使用基金会的智力价值进行评估，被认为值得支持以及更广泛的影响审查标准。

项目成果

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

• 发表时间: 2023
• 期刊: Transactions of the American Nuclear Society
• 作者: Julián Barra Otondo, Shayan Shahbazi