Collaborative Research: Detailed Chemical Kinetic Modeling of the Homogeneous Chemical Nucleation of Multicomponent Nanoparticles

摘要：pi: Mark T. Swihart和Linda broadbelt机构：纽约州立大学布法罗分校和西北大学提案号：0500249和0500320标题：纳米颗粒均匀化学成核的详细化学动力学建模知识优点：颗粒污染是半导体加工中产量损失的主要原因。随着集成电路变得越来越小，随着改进的洁净室技术消除了颗粒的外部来源，加工环境中颗粒的均匀成核正迅速成为颗粒污染的最重要来源。在大多数情况下，这些颗粒是通过本项目将考虑的化学成核过程产生的，而不是通过纯材料的过饱和蒸汽的冷凝产生的。如果要控制粒子的形成，对化学成核过程的基本了解是很重要的。同样的理解可以帮助设计纳米粒子和纳米结构材料的气溶胶合成方法，这些材料是纳米级科学和工程的基石。对化学成核进行建模的最详细和信息丰富的方法是在机制层面上发现的。在这种方法中，粒子成核是由基本化学反应的网络来描述的，其速率可以与参与物质的性质有关。该项目延续了pi在该主题上的合作，他们应用了两种互补的方法，自动反应机制生成和动力学蒙特卡罗（KMC）模拟，以发展对硅纳米颗粒成核的机理理解。在最近的工作中，他们(1)开发了用于确定物种独特性和识别复杂多环簇中的环的改进算法，(2)对硅-氢簇进行了广泛的量子化学计算，并将结果推广为群可加性方案，(3)开发了用于选择性生成反应途径的改进方法。(4)应用动力学蒙特卡罗模拟方法确定颗粒成核的临界簇大小和硅纳米颗粒成核的关键反应途径；(5)构建了一个框架，将详细的化学反应机制与反应流和气溶胶动力学模拟联系起来，可以预测颗粒浓度和大小分布。从最近的工作中，他们已经确定了继续研究这个问题的最重要的领域：(1)改进了对多环硅氢分子和具有多个官能团的硅氢分子的化学描述，(2)改进了将这些分子结合到确定性和KMC模拟中，(3)将这些成核的详细模型与气溶胶动力学模型联系起来，这些模型提供了实验可获得的数量，如颗粒浓度和大小分布的结果。更广泛的影响：本科生，包括传统上代表性不足的群体的成员，将有机会通过布法罗的纳米结构半导体REU网站参与该项目和相关工作，Swihart是该网站的PI，以及通过其他目标项目，如麦克奈尔学者项目、路易斯斯托克斯少数民族参与联盟（LS-AMP）项目和大学科学技术进入（C-STEP）项目。该项目的实例将用于西北大学Broadbelt的应用分子建模课程，该课程将增加一个新的动力学蒙特卡罗模拟课程模块，以及纽约州立大学布法罗分校Swihart的气溶胶科学与技术课程，这是一门新课程，2003年春季作为专题课程教授，并将于2005年春季永久添加到课程中。这两门课程都吸引了研究生和本科生，扩大了这个项目对教育的影响。

ABSTRACTPI: Mark T. Swihart and Linda BroadbeltInstitution: SUNY Buffalo and Northwestern UniversityProposal Number: 0500249 and 0500320Title: Detailed Chemical Kinetic Modeling of the Homogeneous Chemical Nucleation of NanoparticlesIntellectual Merit: Particulate contamination is a leading cause of yield loss in semiconductor processing. As integrated circuits become smaller, and as improved cleanroom technology eliminates external sources of particles, homogeneous nucleation of particles within the processing environment is rapidly becoming the most important source of particulate contamination. In most cases, these particles are generated via the chemical nucleation processes that will be considered in this project, rather than by condensation of the supersaturated vapor of a pure material. Fundamental understanding of the chemical nucleation process is important if one is to control particle formation. This same understanding can help in design methods for aerosol synthesis of nanoparticles and nanostructured materials that are the building blocks of nanoscale science and engineering. The most detailed and informative approach to modeling chemical nucleation is found at the mechanistic level. In this approach, particle nucleation is described by a network of elementary chemical reactions whose rates can be related to properties of the participating species. This project continues the PIs' collaboration on this topic in which they have applied two complementary methodologies, automated reaction mechanism generation and kinetic Monte Carlo (KMC) simulation, to the development of mechanistic understanding of silicon nanoparticle nucleation. In recent work, they have (1) developed improved algorithms for determining species uniqueness and identifying rings in complex polycyclic clusters, (2) carried out extensive quantum chemical calculations on silicon-hydrogen clusters and generalized the results as a group additivity scheme, (3) developed improved methods for selective generation of reaction pathways, and applied these methods to identify the critical cluster size for particle nucleation and key reaction pathways for silicon nanoparticle nucleation, (4) applied kinetic Monte Carlo simulation to identify cluster growth probabilities and pathways, and (5) constructed a framework for linking detailed chemical reaction mechanisms to reacting flow and aerosol dynamics simulations that can predict particle concentrations and size distributions. From this recent work they have identified the most important areas for continued research on this problem as (1) improved descriptions of the chemistry of polycyclic silicon hydrogen molecules and silicon-hydrogen molecules with multiple functional groups, (2) improved incorporation of such molecules into both deterministic and KMC simulations, and (3) linking of these detailed models of nucleation to aerosol dynamics models that provide results for experimentally accessible quantities like particle concentration and size distribution.Broader Impacts: Undergraduates, including members of traditionally underrepresented groups, will have opportunities to participate in this project and related work through an REU site on nanostructured seminconductors in Buffalo, for which Swihart is the PI, and through additional targeted programs such as the McNair Scholars program, the Louis Stokes Alliance for Minority Participation (LS-AMP) program, and the Collegiate Science and Technology Entry (C-STEP) program. Examples from this project will be used in Broadbelt's Applied Molecular Modeling course at Northwestern, for which a new course module on kinetic Monte Carlo simulations will be added, and in Swihart's Aerosol Science and Technology course at SUNY Buffalo, which is a new offering, taught as a special topics course in spring 2003, and being permanently added to the curriculum in spring 2005. Both of these courses attract both graduate and undergraduate students, broadening the impact of this project on education.