Design and Analysis of Optimization Experiments with Internal Noise to Maximize Alignment of Carbon Nanotubes

内部噪声优化实验的设计与分析以最大化碳纳米管的排列

• 批准号: 1745714
• 负责人: Tirthankar Dasgupta
• 金额: $ 12.98万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1745714&HistoricalAwards=false
• 关键词: Design; Analysis; Optimization; Experiments; Internal

Over the past several decades, carbon nanotubes (CNT) have risen to the forefront of scientific research due to their unique electrical, mechanical and optical properties. However, transferring these properties from nanoscale materials to industrial-scale products often requires alignment (orientation in the same direction) of CNT. One of the important consequences of alignment is improved conductivity, a highly desirable property in electro-chemical water treatment and closely associated with research endeavors to improve quality of drinking water. Therefore, identification of scalable and cost-effective experimental conditions that maximize alignment of CNT is an important research problem. This is addressed in the project.The proposed research aims to establish statistical methodologies for designing and analyzing efficient experiments that determine conditions for maximizing alignment of CNT, when one or more input factors are prone to internal noise. The proposed research consists of three tasks, with particular focus on addressing the challenges arising from presence of factors with internal noise and complexity of the response surface. (i) Developing a Bayesian approach to response-surface optimization with noisy inputs. Such an approach allows the experimenter to combine data on output, controllable input, and uncontrollable input from different sources; is a natural way of incorporating expert knowledge into the analysis; and provides a natural framework for optimal design of experiments with noisy inputs. (ii) Efficient design of optimization experiments with noisy inputs. The research will focus on developing a comprehensive design strategy, which is a combination of model-free and Bayesian model-based optimal designs. The model-free design will address the challenges arising from internal noise and complex response surface. (iii) Demonstration and validation of the developed methodologies in the co-PI's lab. A series of experiments will be planned to apply the developed statistical methodology in an attempt to identify factors that trigger alignment of CNT and also to identify their optimum levels to maximize alignment. The proposed framework will allow an experimenter to effectively capture the transmission of uncertainty from input variables to output variables by combining data from different sources, and by utilizing a combination of model-free and model-based experimental designs for efficient exploration of complex response surfaces. From a material scientist's perspective, the proposed method will provide a much more accurate quantification of uncertainty, resulting in more reliable predictions about optimal process conditions as determined from laboratory experiments.

在过去的几十年里，碳纳米管（CNT）凭借其独特的电学、机械和光学特性已跻身科学研究的前沿。然而，将这些特性从纳米级材料转移到工业级产品通常需要碳纳米管的排列（同一方向的取向）。 排列的重要后果之一是提高电导率，这是电化学水处理中非常理想的特性，并且与提高饮用水质量的研究工作密切相关。因此，确定可扩展且经济有效的实验条件以最大化碳纳米管的排列是一个重要的研究问题。该项目解决了这个问题。拟议的研究旨在建立统计方法，用于设计和分析有效的实验，当一个或多个输入因素容易产生内部噪声时，确定最大化 CNT 对齐的条件。拟议的研究包括三项任务，特别侧重于解决因存在内部噪声因素和响应面复杂性而带来的挑战。 (i) 开发一种贝叶斯方法，利用噪声输入来优化响应面。这种方法允许实验者结合来自不同来源的输出、可控输入和不可控输入的数据；是将专业知识纳入分析的自然方式；并为带有噪声输入的实验的优化设计提供了一个自然的框架。 (ii) 使用噪声输入有效设计优化实验。该研究将侧重于开发综合设计策略，该策略是无模型和基于贝叶斯模型的优化设计的结合。无模型设计将解决内部噪声和复杂响应面带来的挑战。 (iii) 在联合首席研究员实验室中演示和验证已开发的方法。将计划进行一系列实验，以应用已开发的统计方法，试图确定触发 CNT 对齐的因素，并确定其最佳水平以最大化对齐。所提出的框架将允许实验者通过组合来自不同来源的数据，并利用无模型和基于模型的实验设计的组合来有效地探索复杂响应面，从而有效地捕获从输入变量到输出变量的不确定性传递。从材料科学家的角度来看，所提出的方法将提供更准确的不确定性量化，从而对实验室实验确定的最佳工艺条件进行更可靠的预测。