Document Type
Dissertation
Date of Award
12-31-2020
Degree Name
Doctor of Philosophy in Chemical Engineering - (Ph.D.)
Department
Chemical and Materials Engineering
First Advisor
Rajesh N. Dave
Second Advisor
Ecevit Atalay Bilgili
Third Advisor
Edward L. Dreyzin
Fourth Advisor
Gennady Gor
Fifth Advisor
Steven Chan
Abstract
Loose powder packing and flowability are both important bulk properties to consider in relation to pharmaceutical tablet and capsule manufacturing. These properties as well as cohesive powder agglomeration tendency impact the efficiency of various processes such as feeding, blending, conveying, and compaction, and could have an adverse effect on final drug product. Pharmaceutical powders are often milled to fine sizes (<100 gm) for dissolution improvements via increased available surface area. However, such particle size reductions lead to inadequate bulk powder properties due to high cohesion among the individual particles. Recent work has demonstrated the ability of nano-silica dry coating techniques to significantly decrease interparticle forces without changes in particle size. In this work, the ability to predict properties such as powder bed porosity, flowability, and particle agglomeration both before and after nano-silica dry coating is examined, along with assessment of the extent of property enhancements after dry coating. Previous studies have utilized empirical approaches to build property prediction models. However, such approaches lack physical fundamentals which dictate bulk powder behavior and would be unable to account for the effects of dry powder coating. Incorporation of physics can be done via computation of the granular Bond number, as is done in this study for a large dataset of commonly used pharmaceutical powders, with wide variations in particle size, shape, density, and surface roughness. Predictive capability of current Bond number models is investigated, reasons for outliers are elucidated, and amendments to improve prediction is attempted. Overall, the aim of this work is to expand generalizability of Bond number models to a wider variety of particles, and extend its use from single powders to multi-component mixtures. The major outcome of this work is the development of a predictive framework that is expected to simplify pharmaceutical dry powder formulation development, reduce required experimental testing, and provide quantifiable estimates to aid processing route decision making.
Recommended Citation
Kunnath, Kuriakose T., "Predicting bulk-scale properties of pharmaceutical powders from their particle-scale dimensionless measures" (2020). Dissertations. 1826.
https://digitalcommons.njit.edu/dissertations/1826
