Experimental and Calculational Study on Effects of Flow Additive on Flowability of Fine Coating Particles
Bibliographic record
Abstract
A method of encapsulation of inorganic additives with organic materials was developed to improve the fine power flowability and film quality for powder coating. The flowability tests angle of repose (AOR) and avalanche angle (AVA) were conducted for the coating samples to characterize the effectiveness of the encapsulated additives on group C fine powder flowability. The results show that both AOR and AVA are significantly affected by the encapsulating materials, the encapsulating material weight percentage, as well as the total loading ratios of additives added in fine powders. Polyester shows the best performance on the modification of the additive due to the high similarity to host powder coating. AOR/AVA first decreases and then decreases with the encapsulating material weight. An optimum percentage exists at approximately 10%. A similar trend is observed with the additive loading ratio, and the minimal AOR/AVA is obtained at additive loading ratios between 0.5% and 0.8%. The effective surface area coefficient (η) was introduced to improve the adhesion force model to determine the optimum additive loading ratio for various host particle and additive particle sizes, which agrees well with the experimental results.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".