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

合作研究：多组分纳米颗粒均质化学成核的详细化学动力学模型

• 批准号: 0500320
• 负责人: Linda Broadbelt
• 金额: --
• 依托单位: Northwestern University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-04-01 至 2010-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0500320&HistoricalAwards=false
• 关键词: Collaborative; Research; Detailed; Chemical; Kinetic

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.

摘要：Mark T.Swihart和Linda Broadbelt研究所：纽约州立大学布法罗分校和西北大学建议编号：0500249和0500320标题：纳米颗粒均匀化学成核的详细化学动力学模型智力优点：微粒污染是半导体加工中产量损失的主要原因。随着集成电路变得更小，以及改进的洁净室技术消除了颗粒物的外部来源，加工环境中颗粒物的均匀成核正在迅速成为颗粒物污染源的最重要来源。在大多数情况下，这些颗粒是通过本项目中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。如果要控制颗粒的形成，基本了解化学成核过程是很重要的。这种同样的理解有助于设计纳米粒子和纳米结构材料的气溶胶合成方法，这些纳米粒子和纳米结构材料是纳米科学和工程的基石。在机理水平上找到了模拟化学成核的最详细和最有信息量的方法。在这种方法中，粒子成核是由一个基本化学反应网络来描述的，其速率可以与参与物种的性质有关。该项目继续了PIS在这一主题上的合作，他们应用了两种互补的方法--自动反应机理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)开发了确定物种唯一性和识别复杂多环团簇中环的改进算法，(2)对硅氢团簇进行了广泛的量子化学计算并将结果推广为基团加法方案，(3)开发了选择生成反应路径的改进方法，并应用这些方法来确定颗粒成核的关键团簇尺寸和硅纳米颗粒成核的关键反应路径，(4)应用动力学蒙特卡罗模拟来确定团簇生长概率和路径，以及(5)构建了一个框架，将详细的化学反应机理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尺寸分布。从最近的工作中，他们确定了在这个问题上继续研究的最重要的领域：(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)改进了将这些分子纳入确定性和KMC模拟，以及(3)将这些详细的成核模型与气溶胶动力学模型联系起来，提供了实验上可获得的量，如颗粒浓度和粒度分布。广泛的影响：本科生，包括传统上代表不足的群体的成员，将有机会通过布法罗纳米结构半导体的REU站点参与这个项目和相关工作，Swihart是Pi，并通过其他有针对性的计划，如麦克奈尔学者计划、路易斯·斯托克斯少数群体参与联盟(LS-AMP)计划和大学科技入门(C-STEP)计划。该项目的例子将被用于西北大学布罗德贝特的应用分子建模课程，其中将增加一个关于动力学蒙特卡罗模拟的新课程模块，以及斯威哈特在纽约州立大学布法罗分校的气溶胶科学与技术课程，这是一门新课程，于2003年春季作为一门专题课程教授，并于2005年春季永久加入课程。这两门课程都吸引了研究生和本科生，扩大了该项目对教育的影响。