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.

该项目的重点是开发一个多尺度模型，以预测大气处理过程中烟尘颗粒的演变。碳烟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，通过重组，其性质会发生变化。在模拟烟尘的大气过程方面取得的进展将有助于减少烟尘在大气中的寿命的不确定性，也将改善烟尘对空气质量、人类健康和气候影响的评估。这一努力将解决有关烟尘聚集体变化的起源和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的连接处的冷凝物引起的应力，以及（2）将各个单体保持在一起的力。将开发一个离散元法（DEM）模型，其中碳烟重组是由来自冷凝物的生长或收缩层的应力驱动的，并由单体之间的颈部抵抗。DEM模型将使用原子级模拟和实验室实验进行参数化。将使用质量迁移率测量、电子显微镜和原子力显微镜进行实验，以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灰老化机制：痕量气体化学物质的冷凝形成涂层，以及随后对涂层烟灰颗粒的云处理。该项目将在丰富的跨学科环境中为研究生和本科生以及年轻研究人员提供跨学科培训。该奖项反映了NSF的法定使命，并通过评估被认为值得支持使用基金会的智力价值和更广泛的影响审查标准。