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Mechanistic prediction of powder flowability through Bond number: Model calibration and estimation of flow enhancement after dry coating
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DOI：10.1016/j.ijpharm.2026.126902.png)
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Abstract
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The granular Bond number, estimated using Chen's multi-asperity particle adhesion model, has been shown as a basis for powder flowability prediction for both as-received and dry-coated powders. Here, the prediction model calibrated solely using as-received powders is tested by estimating the expected flowability enhancement after dry coating with two different nano-silicas. Prediction accuracy for 9 APIs and 18 excipients is compared for three characteristic particle size parameters for the Bond number; median particle size (d50), Sauter mean diameter (d3,2), and the size class-dependent (SCD) metric utilizing entire particle size distribution (PSD). A new sigmoid-function based flowability prediction model is proposed and fitted for as-received powders. Both d3,2 and SCD lead to better predictive accuracy with narrower prediction intervals as compared to d50, suggesting d3,2 captures the same surface-area-dependent PSD effects as SCD without added computational burden. The model could accurately predict flow category for all but one as-received powder. The model calibrated through as-received uncoated powders can predict enhancement of one to three flow categories for most dry coated powders with high accuracy. Notable exceptions are materials with SSA above similar to 2 m(2)/g whose experimental SSA deviates significantly from PSD-estimated theoretical SSA (high Delta SSA), indicating surface roughness or adherent debris that restricts dry coating effectiveness. Materials with high SSA but low Delta SSA, where high surface area is simply a consequence of fine particle size, respond well to dry coating as predicted. In summary, the proposed unifying model can be calibrated using small samples of as-received powders to estimate expected flowability improvements after dry coating and, importantly, to identify dry coated powders likely to underperform.
Keywords:
Bond number
Flowability prediction
Mechanistic predictions
Dry coating effectiveness
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