A Multiscale Model for Restructuring of Atmospheric Soot Particles

大气烟灰颗粒重构的多尺度模型

• 批准号: 2222104
• 负责人: Alexei Khalizov
• 金额: $ 61.96万
• 依托单位: New Jersey Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-11-01 至 2025-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2222104&HistoricalAwards=false
• 关键词: Multiscale; Model; Restructuring; Atmospheric; Soot

This project is focused on the development of a multiscale model to predict the evolution of soot particles during atmospheric processing. The nature of soot particles can change after emission through restructuring when they come in contact with trace gas chemicals and water vapor in the atmosphere. Advances in modeling the atmospheric processing of soot will help to reduce uncertainties in the atmospheric lifetime of soot and also will improve the assessment of the impacts of soot on air quality, human health, and climate.This effort will address outstanding questions on the origin and magnitude of changes to soot aggregates, including: (1) the stress induced by condensate located in junctions between monomers and monomer branches, and (2) the forces holding together the individual monomers. A Discrete Element Method (DEM) model will be developed in which soot restructuring is driven by stresses from a growing or shrinking layer of condensate and resisted by necks between monomers. The DEM model will be parameterized using atomic-level simulations and lab experiments. Experiments will be conducted using mass-mobility measurements, electron microscopy, and atomic force microscopy to obtain the required neck parameters (number and size distribution, strength, and atomic structure). Two major soot aging mechanisms will be considered: the condensation of trace-gas chemicals to form coatings, and the subsequent cloud processing of coated soot particles.This project will provide interdisciplinary training for graduate and undergraduate students and young researchers in a rich interdisciplinary environment.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

本项目的重点是建立一个多尺度模型来预测烟尘颗粒在大气处理过程中的演变。烟灰颗粒在排放后与大气中的微量气体、化学物质和水蒸气接触后，会发生性质的改变。烟尘的大气过程模拟的进展将有助于减少烟尘在大气寿命中的不确定性，也将改进烟尘对空气质量、人类健康和气候影响的评估。这项工作将解决关于烟尘聚集体的起源和变化幅度的悬而未决的问题，包括：(1)由位于单体和单体分支之间连接处的冷凝水引起的应力，以及(2)将单个单体结合在一起的力。将开发一种离散元法（DEM）模型，在该模型中，煤烟重组是由来自增长或收缩的冷凝层的应力驱动的，并由单体之间的颈部抵抗。DEM模型将通过原子级模拟和实验室实验进行参数化。实验将使用质量迁移率测量、电子显微镜和原子力显微镜来获得所需的颈部参数（数量和尺寸分布、强度和原子结构）。将考虑两种主要的烟尘老化机制：微量气体化学物质凝结形成涂层，以及随后涂层烟尘颗粒的云处理。该项目将在丰富的跨学科环境中为研究生和本科生以及青年研究人员提供跨学科培训。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。