(77d) An Immiscible Fluids Approach for Correctly Predicting Agglomerate Dynamics during Particle ALD | AIChE

(77d) An Immiscible Fluids Approach for Correctly Predicting Agglomerate Dynamics during Particle ALD


Hartig, J. - Presenter, University of Colorado Boulder
Weimer, A., University Of Colorado
Fine particle agglomeration can occur when running particle atomic layer deposition (Particle ALD) in fluidized beds. The fine powders frequently used in Particle ALD tend to effectively fluidize as larger aggregates due to high interparticle forces, potentially blocking surface sites and inhibiting surface coating uniformity. By modeling the agglomeration process during coating, steps can be taken to facilitate agglomerate shedding/breakup and mixing, thereby enhancing surface coating uniformity. However, current models of gas-solid flows which preserve the gas-solid interface, an important component for modeling ALD, have several limitations when incorporating agglomeration. Many of these approaches fail to address agglomerate size distributions or the dynamic formation and shedding/breakup process of fluidized agglomerates, shortcomings which remain a significant challenge to properly modeling fluidized bed Particle ALD. In this work, we propose an alternative modeling approach which naturally accounts for the dynamic nature of fluidized agglomerates by treating the fluidizing gas and particles as two immiscible (non-interpenetrating) fluids. Agglomerates are modeled using dynamic “bubbles” whose interior consists of many primary particles from the solids phase. The position, shape and formation/breakup of these agglomerate “bubbles” are allowed to change with time as dictated by the corresponding transport equations. With this model, we can investigate the formation and shedding/breakup of agglomerates without prior knowledge of the agglomerate size characteristics. This study provides some preliminary agglomerate size distribution results from fluidized bed Particle ALD simulations and compares these results to experimental data from previous literature studies.