4.8 Article

Discovery of Salt Hydrates for Thermal Energy Storage

期刊

JOURNAL OF THE AMERICAN CHEMICAL SOCIETY
卷 144, 期 47, 页码 21617-21627

出版社

AMER CHEMICAL SOC
DOI: 10.1021/jacs.2c08993

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资金

  1. J. Robert Beyster Computational Innovation Graduate Fellows program
  2. University of Michigan Energy Institute-Undergraduate Research Opportunities Program (UROP) summer fellowship
  3. Automotive Research Center (ARC)
  4. University of Michigan Graham Sustainability Institute's Carbon Neutrality Acceleration Program
  5. U.S. Army DEVCOM GVSC
  6. [W56HZV-19-2-0001]

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This study predicts the energy densities, turning temperatures, and thermodynamic stabilities of a class of potential thermal energy storage materials through high-throughput density functional theory calculations. Several stable TES materials with superior performance are identified among many salt hydrates and demonstrated at the system level. Machine learning models are developed for salt hydrate thermodynamics to provide design guidelines for maximizing energy density.
Thermal energy storage (TES) has the potential to improve the efficiency of many applications but has not been widely deployed. The viability of a TES system depends upon the performance of its underlying storage material; improving the energy density of TES materials is an important step in accelerating the adoption of TES systems. For applications in thermochemical energy storage, salt hydrates are a promising class of materials due to their relatively high energy densities and their reversibility. Despite their promise, relatively few salt hydrates have been characterized, presenting the possibility that new hydrate compositions with superior properties may exist. Here, the energy densities, turning temperatures, and thermodynamic stabilities of 5292 hypothetical salt hydrates are predicted using high-throughput density functional theory calculations. The hydrates of several metal fluorides, including CaF2, VF2, and CoF3, are identified as stable TES materials with class-leading energy densities and operating temperatures suitable for use in domestic heating and intermediate-temperature applications. The promising performance of these materials is demonstrated at the system level by parameterizing an operating model of a solar thermal TES system with data from the new hydrates. Finally, machine learning models for salt hydrate thermodynamics are developed and used to identify design guidelines for maximizing the energy density. In total, the new materials and design rules reported here are expected to nurture the implementation of TES systems.

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