Exploring the Synthesis and Properties of Hybrid Perovskites with a "Gas" Atom, or Molecule, as a Structural Component

探索以“气体”原子或分子作为结构成分的杂化钙钛矿的合成和性能

• 批准号: 2002739
• 负责人: Angus Wilkinson
• 金额: $ 44.68万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2002739&HistoricalAwards=false
• 关键词: Exploring; Synthesis; Properties; Hybrid; Perovskites

Part 1: Non-Technical SummaryA new family of solid materials that contain gas atoms or molecules as part of their structure is being synthesized and characterized in this project. This work takes fundamental understanding of functional solids in a new direction and also opens opportunities for new technologies. The materials under study can potentially trap large amounts of gas at room temperature and pressure, and then release the gas on warming. For a given volume, they can contain more gas than the high pressure storage tanks typically used to contain hydrogen, helium and other industrially important gases. Efficient and safe gas storage is one of the major barriers to using hydrogen as a fuel. The new gas-containing materials also have potential for gas separation and purification processes that are of importance to the nuclear industry and national defense. Computer simulations, which make use of a machine learning approach, support and guide the experimental work. The training of graduate and undergraduate students in a variety of techniques that are of wide utility in the development of new functional materials is integrated into the project, which is supported by the Solid State and Materials Chemistry program within the Division of Materials Research. Parts 2: Technical SummaryThis project explores the preparation and properties of perovskites where a small gas molecule or atom, for example molecular hydrogen, neon or helium, is a structural component of the material. While there is vast body of prior work on perovskites, due to their technological significance across a wide range of applications, there is almost nothing known about gas-containing perovskites. Materials of this type can contain the gas at much higher densities than found in conventional high pressure gas storage tanks, and the small pores within their structures may enable isotope separation by quantum sieving effects. Synthetic approaches include three distinct strategies for preparing new materials in different compositional families. High pressure diffraction and Raman spectroscopy, along with gas release measurements from recovered samples, provide information on the formation and properties of the materials. In this project, supported by the Solid State and Materials Chemistry program within the Division of Materials Research, experiment is complemented by advanced computational tools, so that computation both explains experimental results and guides the preparation of new materials. Monte Carlo and molecular dynamics simulations, based on force fields derived from density functional theory calculations using a deep-learning convolution neural network approach, provide an opportunity to realistically capture the behavior of the materials. The training of graduate and undergraduate students in a variety of synthetic, computational and materials characterization techniques is integrated into the work.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.

本项目正在合成和表征一类结构中含有气体原子或分子的新型固体材料。这项工作将功能固体的基本认识推向了一个新的方向，也为新技术的发展开辟了机会。所研究的材料可能在室温和常压下捕获大量气体，然后在变暖时释放气体。在给定的体积下，它们可以比通常用于储存氢、氦和其他工业重要气体的高压储罐容纳更多的气体。高效和安全的气体储存是使用氢作为燃料的主要障碍之一。这种新型含气材料在气体分离和净化过程中也具有潜力，对核工业和国防具有重要意义。计算机模拟，利用机器学习的方法，支持和指导实验工作。该项目由材料研究部的固态与材料化学项目提供支持，旨在培养研究生和本科生在开发新功能材料方面广泛应用的各种技术。本项目探讨钙钛矿的制备和性能，其中小气体分子或原子，例如分子氢，氖或氦，是材料的结构成分。虽然之前有大量关于钙钛矿的研究，但由于它们在广泛应用中的技术意义，人们对含气钙钛矿几乎一无所知。与传统的高压储气罐相比，这种类型的材料可以以更高的密度容纳气体，并且其结构中的小孔隙可以通过量子筛分效应实现同位素分离。合成方法包括三种不同的策略来制备不同成分族的新材料。高压衍射和拉曼光谱，以及回收样品的气体释放测量，提供了有关材料形成和性质的信息。本项目由材料研究部固态与材料化学专业支持，实验辅以先进的计算工具，计算既能解释实验结果，又能指导新材料的制备。蒙特卡罗和分子动力学模拟，基于使用深度学习卷积神经网络方法的密度泛函理论计算得出的力场，提供了一个真实捕捉材料行为的机会。研究生和本科生在各种合成、计算和材料表征技术方面的培训被整合到工作中。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。