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CDI-Type I: Collaborative Research: Cyber Enabled Engineering of Particle Based Materials and Devices using Energy Landscapes

CDI-Type I: Collaborative Research: Cyber Enabled Engineering of Particle Based Materials and Devices using Energy Landscapes
CDI-I 型:协作研究:利用能源景观对基于粒子的材料和设备进行网络工程
批准号:
0835549
负责人:
Michael Bevan
金额:
$29.41万
依托单位:
依托单位国家:
美国
项目类别:
Continuing Grant
财政年份:
2008
资助国家:
美国
项目状态:
已结题
起止时间:
2008-09-15 至 2012-08-31

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中文摘要
翻译
胶体(纳米到微尺度)粒子在材料和器件结构中的自组装和定向组装是一种具有广泛技术影响的新兴范例。然而,创建具有可接受的小缺陷水平的目标结构的能力仍然缺乏;系统很容易动态地陷入不希望的无序状态或缺陷丰富的状态。我们的研究将协同结合数字显微成像技术和自由能计算方法的最新进展来解决这个问题。新兴的显微成像工具,包括共聚焦和全内反射等技术,为胶体组装过程提供了前所未有的高分辨率、实时、三维可视化。关键是挖掘这些实验中产生的大量数字数据,以确定成功的粒子组装途径。传统上,理论家们使用能量景观(EL)范式来解决这类问题,其中一个将数据从高维构型空间(N阶,系统中的粒子数量)映射到低维关键描述符集合中的EL。这个EL是与感兴趣的过程的工程直接相关的数量;它包含了量化平衡状态和它们之间的转换速率的信息(峰、谷、鞍)。在这项研究中,我们将(1)开发一种数字光学显微镜实验和基于粒子的模拟之间的紧密耦合方法,以比较测量和预测的ELs,以及(2)使用所得的ELs来设计(控制、优化)两个应用:光子晶体的自组装和电子纳米线器件的操作。通常被称为胶体的大小在纳米到微米量级的颗粒浸泡在流体中,可以作为有趣的新产品的基石。例子包括光子带隙材料(例如,用光而不是电子操作的计算机)和动态可重构纳米线(例如,用于可调谐射频设备)。在一定条件下,胶体粒子会自发地组装成这种有用的材料,或者通过施加外部刺激,如电场或温度梯度,诱导它们这样做。然而,创建具有可接受的低缺陷水平的所需结构的途径还没有得到很好的理解。本研究将现代数字成像技术与统计力学领域的理论工具相结合,对装配过程进行测量、量化和控制。我们将开发低缺陷结构颗粒材料的工程途径知识,这些材料可以在未来的制造过程中系统地实施。此外,我们将利用实验(如图像、视频)、模拟(如效果图、动画)和分析(如动态、多维图)中生成的丰富视觉数据,为各级学生(k -研究生)和向公众推广的项目提供直观的教育体验。
英文摘要
The self- and directed- assembly of colloidal (nano- to micro- scale) particles into structures within materials and devices is an emerging paradigm with wide-ranging technological impact. However, the ability to create a target structure with an acceptably small level of defects is still lacking; systems too easily become dynamically arrested in undesired disordered, or defect-rich, states. Our research will synergistically combine recent advances in digital microscopic imaging techniques and free energy calculation methods to address this problem. Emerging microscopic imaging tools, including techniques such as confocal and total internal reflectance, provide unprecedented high-resolution, real-time, three-dimensional visualizations of colloidal assembly processes. The key is to mine the tremendous amount of digital data produced in these experiments to identify successful pathways to particle assembly. Theoreticians have traditionally approached such problems using the energy landscape (EL) paradigm, wherein one maps data from the high-dimensional configuration space (of order N, the number of particles in the system) to an EL in a low-dimensional set of key descriptors. This EL is the quantity of direct relevance to engineering of the process of interest; it contains the information (peaks, valleys, saddles) to quantify the equilibrium states and transition rates between them. In this research we will (1) develop a close coupling methodology between digital optical microscopy experiments and particle-based simulation to compare measured and predicted ELs, and (2) use the resulting ELs to engineer (design, control, optimize) two applications: the self-assembly of photonic crystals and operation of electronic nanowire devices.Particles with sizes on the order of nanometers to micrometers immersed in fluid, commonly called colloids, can serve as the building blocks of interesting new products. Examples include photonic band gap materials (e.g. for computers that operate with light instead of electrons) and dynamically reconfigurable nanowires (e.g. for tunable RF devices). Under certain conditions the colloidal particles will spontaneously assemble into such useful materials, or they can be induced to do so through the application of external stimuli such as electric fields or temperature gradients. However, the pathways to creating desired structures with acceptably low levels of defects are not well understood. This research employs a combination of modern digital imaging techniques and theoretical tools from the field of statistical mechanics to measure, quantify, and control the assembly process. We will develop knowledge of engineered pathways to low-defect structured particulate materials that can be systematically implemented in the manufacturing processes of the future. Furthermore, we will use the rich visual data generated in experiments (e.g. images, videos), simulations (e.g. renderings, animations), and analyses (e.g. dynamic, multi-dimensional plots) to provide intuitive educational experiences for students at all levels (K-postgraduate) and in outreach programs to the general public.
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