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.

电子、太阳能转换和航空航天工业的各种应用对单晶材料的需求量很大。单晶的大规模生长通常需要在成核速率足够慢以防止多晶颗粒形成的条件下进行结晶。这个过程既耗时又昂贵。拟议的研究旨在利用先进的计算技术来研究工程表面（称为晶体成核剂（CNA））的使用，以加速单晶的成核和生长。拟议的研究旨在发展可用于优化各种材料的晶体生长的基础理论。该项目的主要目标是对 CNA 表面纳米级图案化对晶体成核的影响有一个基本的了解，并确定在成核限制范围内的各种操作条件下，操作条件导致成核速率对过程变量变化的敏感性降低。从理论角度探索和理解这种以前未知的与异质成核经典图景的偏差是很有趣的。拟议的研究将通过计算探索纳米图案 CNA 表面的不同特征如何影响成核速率对成核限制区域中的温度和浓度的敏感性，并利用该知识设计最佳 CNA 表面，以提供最小的成核速率敏感性。特定成核率下的操作条件。从此类理论和计算研究中获得的见解可以产生设计规则，从而指导实验工作来设计操作稳健的 CNA 材料。研究和教育的整合将侧重于为研究生和本科生开发一门计算材料科学新课程。外展工作将包括针对代表性不足的少数族裔学生的公开演讲和编程研讨会，以激励他们追求 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，该软件包将与研究各种物理、化学和生物系统中罕见事件的研究人员公开共享。该奖项反映了 NSF 的法定使命，并被认为值得通过以下方式获得支持：使用基金会的智力价值和更广泛的影响审查标准进行评估。