Hetero-aggregation of nanoparticles in advective environments

平流环境中纳米颗粒的异质聚集

• 批准号: 462463789
• 负责人: Professor Dr. Andreas Kronenburg
• 金额: --
• 依托单位: Institut für Technische Verbrennung (ITV)
• 依托单位国家: 德国
• 项目类别: Priority Programmes
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/462463789?language=en
• 关键词: Hetero; aggregation; nanoparticles; advective; environments

Mixing and aggregation are key processes in the pharmaceutical and chemical industries, and process control can markedly influence the products’ characteristics. If multiple materials are used, the number of hetero-contacts, i.e. the number of contacts between particles of the different components, are crucial for the product’s properties. It is likely that this number can be controlled by the sequence of mixing, and the relative strength of mixing at the macroscale (that brings the components together) and at the microscale (that leads to collision and contact formation) will be decisive for the final product quality. While collision dynamics for small particles that are not influence by advective flow are well understood and can be modelled with decent accuracy, there is no validated approach for a statistical description of hetero-aggregation where particle sizes are far from being monodisperse and where initial inhomogeneities in particle distribution have a large effect on the final aggregate structure and quality. The proposed work intends to address issues related to aggregation of a two-component system with variable primary particle sizes ranging from about 5 to 200 nm. Langevin dynamics (LD) will be conducted to generate aggregates and their size distributions for a variety of process conditions. LD provide a suitable tool for such study as every single primary particle will be tracked and no assumptions will be made on the resulting aggregate structure. The simulations allow for a characterization of the hetero-aggregates including their quality (number of hetero-contacts) and for an analysis of collision frequencies as function of the aggregate characteristics and flow conditions. Conventional models for collision frequencies are not likely to hold, and deep learning methods for closure of the population balance equation is suggested instead.

混合和凝聚是制药和化工行业的关键过程，过程控制对产品的特性有显著影响。如果使用多种材料，异质接触的数量，即不同组分颗粒之间的接触数量，对产品的性能至关重要。这个数字很可能可以通过混合的顺序来控制，并且在宏观尺度（将组分聚集在一起）和微观尺度（导致碰撞和接触形成）上的混合的相对强度将对最终产品质量起决定性作用。虽然不受平流影响的小颗粒的碰撞动力学已经很好地理解，并且可以以相当的精度进行建模，但对于粒径远非单分散且颗粒分布的初始不均匀性对最终聚集体结构和质量有很大影响的异质聚集，没有经过验证的统计描述方法。提出的工作旨在解决与双组分系统的聚集有关的问题，该系统具有可变的主要粒径范围从约5到200纳米。Langevin动力学（LD）将用于生成各种工艺条件下的聚集体及其尺寸分布。LD为这种研究提供了一个合适的工具，因为每一个单一的初级颗粒都将被跟踪，并且不会对所得的骨料结构做出任何假设。模拟允许对异质聚集体进行表征，包括其质量（异质接触数），并分析碰撞频率作为聚集体特性和流动条件的函数。传统的碰撞频率模型不太可能成立，建议使用深度学习方法来关闭人口平衡方程。