MSPA-MPS: Experimental design for achieving consistent and high yield in the controlled synthesis of nanostructures

纳米结构由于其新颖的物理、化学和生物特性，是纳米科学和纳米技术的基石。为了满足大规模、可控和设计合成纳米结构的需要，系统地寻找可重复、大批量和可控形貌合成所需纳米结构的实验条件至关重要。本项目旨在广泛应用统计设计来实现上述目标。纳米结构合成中遇到的问题是现有实验设计技术无法解决的挑战。因此，本研究的主要目标是开发和应用新的实验设计技术，以找到在时间和成本限制下生长纯和高质量纳米结构的最佳和稳健的加工条件。基于两组在过去两年中开展的合作，研究人员研究了一种称为最小能量设计的新技术，该技术适用于受控纳米结构合成的指导实验。这种技术的一个独特之处在于它起源于统计理论和物理基本定律的结合。它有可能在统计和纳米材料研究中变得重要。所提出的方法将通过在氧化锌（ZnO）和硒化镉（CdSe）两种不同材料中生长的纳米结构的合成来发展和验证。具体来说，重点是纳米线的合成。纳米线有可能影响从电子学、光子学、光电子学到生命科学和医疗保健的许多领域。特别是，ZnO和CdSe纳米线在制造破路纳米器件方面具有巨大的潜力。因此，利用所提出的技术可以在纳米线的合成中实现的改进可能会对纳米技术产生重大影响。合成ZnO和CdSe纳米线的进展为开展新的研究项目提供了良好的平台。在这个研究项目中，纳米材料生产的产量、质量、纯度和稳健性问题都可以通过应用最小能量设计的不同变体来解决，这是为适应与纳米结构生长相关的特定现象而量身定制的。这可能是统计设计与纳米材料合成相结合的早期实例，从而为纳米材料制造开辟了一条新的道路。从科学上讲，本研究有助于对不同纳米结构的生长机制和动力学的基本理解。在工业上，这项研究可以导致具有定制所需特征的纳米结构的商业化供应。通过应用独特的新型ZnO纳米结构作为纳米器件的构建模块，本项目还将推进微传感器系统，提高其灵敏度、稳定性、选择性、功耗和响应速度。这些进步可能对能源、国土安全和环境监测产生巨大影响。从统计学的角度来看，最小能量算法是一种新颖的方法，可以快速生成与模型无关的设计。开拓?没有可观察到的纳米结构形态的区域，允许探索复杂的响应面，并可用于顺序实验。由于它起源于物理定律，它应该对广泛的科学研究人员具有吸引力和可理解性。

Nanostructures, by virtue of their novel physical, chemical and biological properties, are building blocks in nanoscience and nanotechnology. To meet the needs of large scale, controlled and designed synthesis of nanostructures, it is critical to systematically find experimental conditions under which the desired nanostructures are synthesized reproducibly, at large quantity and with controlled morphology. This project aims at an extensive application of statistical design for achieving the above goal. The problems encountered in the synthesis of nanostructures pose challenges that cannot be solved by existing experimental design techniques. Therefore, the primary objective of this research is to develop and apply novel experimental design techniques in order to find optimum and robust processing conditions for growing pure and high-quality nanostructures under time and cost constraints. Based on the collaboration carried out in the last two years between the two groups, the investigators study a novel technique called minimum energy design that is suitable for guiding experiments in controlled nanostructure synthesis. A unique feature of this technique lies in the fact that it originates from a combination of statistical theory and fundamental laws of physics. It has the potential to become important in both statistical and nanomaterial research. The proposed methods will be developed and validated using the synthesis of nanostructures grown in two different materials: Zinc Oxide (ZnO) and Cadmium Selenide (CdSe). Specifically, the focus is on the synthesis of nanowires. Nanowires have the potential to impact numerous areas ranging from electronics, photonics, optoelectronics to life sciences and health care. In particular, ZnO and CdSe nanowires have tremendous potential of being used in the manufacturing of path breaking nanodevices. Thus the improvement that can be achieved in the synthesis of nanowires by using the proposed technique may create a significant impact in nanotechnology. The progress already made in synthesizing ZnO and CdSe nanowires provides a sound platform to launch the new research project.With this research project, the issues of yield, quality, purity and robustness of nanomaterial production can all be resolved by applying different variants of the minimum-energy design, which is tailor-made to suit the specific phenomena associated with the growth of nanostructures. This is likely to be an early instance of the integration of statistical design with nanomaterial synthesis, thus opening a new path for nanomaterials manufacturing. Scientifically, this research can facilitate fundamental understanding about the growth mechanisms and kinetics of different nanostructures. Industrially, this research can lead to a commercialized supply of nanostructure with custom desired features. By applying the unique and novel ZnO nanostructures as nanodevice building blocks, this project will also advance micro sensor systems and improve their sensitivity, stability, selectivity, power consumption, and response speed. These advancements can have huge impacts on energy, homeland security and environmental monitoring. From the statistical point of view, the minimum energy algorithm is a novel approach to generate designs that are model independent, can quickly ?carve out? regions with no observable nanostructure morphology, allow for the exploration of complex response surfaces, and can be used for sequential experimentation. Owing to its origination from physical laws, it should be appealing and comprehensible to a broad spectrum of scientific researchers.