Collaborative Research: Nanostructure Growth Process Modeling and Optimal Experimental Strategies for Repeatable Fabrication of Nanostructures for Application in Photovoltaics

合作研究：纳米结构生长过程建模和可重复制造光伏应用纳米结构的最佳实验策略

• 批准号: 1000720
• 负责人: Tirthankar Dasgupta
• 金额: $ 16万
• 依托单位: Harvard University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-08-15 至 2013-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1000720&HistoricalAwards=false
• 关键词: Collaborative; Research; Nanostructure; Growth; Process

The research objective of this award is to establish statistics-transformed nanostructure growth process models and efficient experimental strategies for improving process repeatability in the fabrication of nanostructures for the application in photovoltaic cells. To achieve repeatable fabrication of photovoltaic cells with respect to yield (productivity) and uniformity (quality), it is essential to identify and optimize the growth conditions rooted on predictive process models. These models will capture the mechanisms of nanostructure growth under process uncertainties. Since most of the current growth kinetics models are deterministic, the research will first devise statistics-transformed nanostructure growth process models that account for uncertainties. Based on the process model, optimal experimental strategies will be established for model estimation and validation with a high degree of precision under cost and time constraints. The methodology will be validated through controlled growth of nanowires and fabrication of photovoltaic cells.Successful completion of this research will lead to new tools and methods for improving process repeatability and yield in nanomanufacturing, particularly in the large scale fabrication of photovaic cells. Reducing cost in photovoltaics gives prospect of achieving highly efficient and low-cost solar energy conversion, increasing the utilization of clean and renewable energy, and creating green job opportunities. This truly interdisciplinary project will promote training of a new breed of workforce excelling at nanomanufacturing process modeling and optimization and contributing to the sustainable growth of US economy. The educational goal will be achieved through (1) creating interdisciplinary nanomanufacturing curricular materials, (2) enhancing the existing research and education collaborations between University of Southern California and Harvard University, and (3) involving women/minority students through REU (Research Experience for Undergraduates) program. The generated knowledge will be broadly disseminated through leading journals, conferences, websites, and collaborators.

该奖项的研究目标是建立统计转换的纳米结构生长过程模型和有效的实验策略，以提高用于光伏电池的纳米结构制造过程的可重复性。为了实现光伏电池在产量（生产率）和均匀性（质量）方面的可重复制造，基于预测过程模型确定和优化生长条件至关重要。这些模型将捕捉在工艺不确定性下纳米结构生长的机制。由于目前大多数生长动力学模型都是确定性的，因此该研究将首先设计统计转换的纳米结构生长过程模型，以考虑不确定性。在过程模型的基础上，建立最优实验策略，在成本和时间约束下进行高精度的模型估计和验证。该方法将通过控制纳米线的生长和光伏电池的制造来验证。这项研究的成功完成将带来新的工具和方法，以提高纳米制造过程的可重复性和产量，特别是在光电池的大规模制造中。降低光伏发电的成本为实现高效率和低成本的太阳能转换、增加清洁和可再生能源的利用以及创造绿色就业机会提供了前景。这个真正的跨学科项目将促进培养擅长纳米制造过程建模和优化的新型劳动力，并为美国经济的可持续增长做出贡献。教育目标将通过(1)创建跨学科纳米制造课程材料，(2)加强南加州大学和哈佛大学之间现有的研究和教育合作，以及(3)通过REU（本科生研究经验）计划吸引女性/少数民族学生来实现。所产生的知识将通过主要期刊、会议、网站和合作者广泛传播。