Enhanced centrifuge-based approach to powder characterization
Abstract
Many types of manufacturing processes involve powders and are affected by powder behavior. It is highly desirable to implement tools that allow the behavior of bulk powder to be predicted based on the behavior of only small quantities of powder. Such descriptions can enable engineers to significantly improve the performance of powder processing and formulation steps. In this work, an enhancement of the centrifuge technique is proposed as a means of powder characterization. This enhanced method uses specially designed substrates with hemispherical indentations within the centrifuge. The method was tested using simulations of the momentum balance at the substrate surface. Initial simulations were performed with an ideal powder containing smooth, spherical particles distributed on substrates designed with indentations. The van der Waals adhesion between the powder, whose size distribution was based on an experimentally-determined distribution from a commercial silica powder, and the indentations was calculated and compared to the removal force created in the centrifuge. This provided a way to relate the powder size distribution to the rotational speed required for particle removal for various indentation sizes. Due to the distinct form of the data from these simulations, the cumulative size distribution of the powder and the Hamaker constant for the system were be extracted. After establishing adhesion force characterization for an ideal powder, the same proof-of-concept procedure was followed for a more realistic system with a simulated rough powder modeled as spheres with sinusoidal protrusions and intrusions around the surface. From these simulations, it was discovered that an equivalent powder of smooth spherical particles could be used to describe the adhesion behavior of the rough spherical powder by establishing a size-dependent ‘effective’ Hamaker constant distribution. This development made it possible to describe the surface roughness effects of the entire powder through one adjustable parameter that was linked to the size distribution. It is important to note that when the engineered substrates (hemispherical indentations) were applied, it was possible to extract both powder size distribution and effective Hamaker constant information from the simulated centrifuge adhesion experiments. Experimental validation of the simulated technique was performed with a silica powder dispersed onto a stainless steel substrate with no engineered surface features. Though the proof-of-concept work was accomplished for indented substrates, non-ideal, relatively flat (non-indented) substrates were used experimentally to demonstrate that the technique can be extended to this case. The experimental data was then used within the newly developed simulation procedure to show its application to real systems. In the absence of engineered features on the substrates, it was necessary to specify the size distribution of the powder as an input to the simulator. With this information, it was possible to extract an effective Hamaker constant distribution and when the effective Hamaker constant distribution was applied in conjunction with the size distribution, the observed adhesion force distribution was described precisely. An equation was developed that related the normalized effective Hamaker constants (normalized by the particle diameter) to the particle diameter was formulated from the effective Hamaker constant distribution. It was shown, by application of the equation, that the adhesion behavior of an ideal (smooth, spherical) powder with an experimentally-validated, effective Hamaker constant distribution could be used to effectively represent that of a realistic powder. Thus, the roughness effects and size variations of a real powder are captured in this one distributed parameter (effective Hamaker constant distribution) which provides a substantial improvement to the existing technique. This can lead to better optimization of powder processing by enhancing powder behavior models.
Degree
Ph.D.
Advisors
Beaudoin, Purdue University.
Subject Area
Chemical engineering|Pharmacy sciences|Materials science
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