CAREER: Computational Design and Optimization of Operationally Robust Crystal Nucleating Materials via Surface Nano-Patterning

职业：通过表面纳米图案化计算设计和优化操作稳健的晶体成核材料

基本信息

批准号：
1751971
负责人：
Amir Haji-Akbari
金额：
$ 50万
依托单位：
Yale University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-04-01 至 2024-03-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1751971&HistoricalAwards=false
关键词：
CAREER Computational Design Optimization Operationally

项目摘要

Single crystalline materials are in high demand for a variety of applications in the electronics, solar energy conversion and aerospace industries. Large-scale growth of single crystals typically requires conducting crystallization under conditions at which the nucleation rate is slow enough to prevent the formation of polycrystalline particles. This process is time consuming and expensive. The proposed research is aimed at using advanced computational techniques to study the use of engineered surfaces, called crystal nucleating agents (CNAs), for accelerating the nucleation and growth of single crystals. The proposed studies aim to develop fundamental theories that can be used for optimizing crystal growth of a variety of materials.The main objective of this project is to develop a fundamental understanding of the effects of nanoscale patterning of a CNA surface on crystal nucleation and to identify operating conditions leading to reduced sensitivity of the nucleation rate to changes in process variables over a wide range of operating conditions within the nucleation-limited regime. This previously unknown deviation from the classical picture of heterogeneous nucleation is interesting to explore and understand from a theoretical perspective. The proposed research will computationally explore how different features of a nano-patterned CNA surface affect the sensitivity of nucleation rate to temperature and concentration in the nucleation-limited regime, and use that knowledge to design optimal CNA surfaces that offer the smallest sensitivity of rate to operating conditions at specific nucleation rates. The insights obtained from such theoretical and computational investigations can result in design rules that can guide experimental efforts to design operationally robust CNA materials. Integration of research and education will be focused towards developing a new course on Computational Materials Science for graduate and undergraduate students. Outreach efforts will involve public presentations and programming workshops targeting underrepresented minority students to motivate them to pursue career paths in STEM fields. The proposed project also involves the development of an advanced sampling software package that will be publicly shared with researchers that study rare events in a wide variety of physical, chemical and biological systems.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.

单晶材料对电子，太阳能转换和航空航天行业的各种应用的需求很高。单晶的大规模生长通常需要在成核速率足够慢以防止形成多晶颗粒的条件下进行结晶。这个过程耗时且昂贵。拟议的研究旨在使用先进的计算技术研究工程表面的使用，称为晶体成核剂（CNA），以加速单晶的成核和生长。拟议的研究旨在开发可用于优化各种材料的晶体生长的基本理论。该项目的主要目的是对CNA表面对晶体成核的纳米级构图的影响发展基本理解，并确定在范围内构成范围内核敏感性降低的敏感性降低的操作条件。从理论角度来看，这种以前未知的偏离异质成核的经典偏差很有趣。拟议的研究将在计算上探讨纳米图案的CNA表面的不同特征如何影响成核限制限制的核心对温度和浓度的敏感性，并使用该知识来设计最佳的CNA表面，从而在特定的成核速率下为操作条件提供最小的速率敏感性。从这种理论和计算调查中获得的见解可能会导致设计规则，可以指导实验性工作以设计运行稳健的CNA材料。研究和教育的整合将集中于为研究生和本科生开发有关计算材料科学的新课程。推广工作将涉及公开演讲和编程研讨会，以代表人数不足的少数族裔学生激励他们在STEM领域的职业道路。拟议的项目还涉及开发先进的采样软件包，该软件包将与研究人员公开共享，这些研究人员将在各种物理，化学和生物系统中研究罕见事件。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子优点和更广泛的审查标准通过评估来获得支持的。