Investigation of the Performance of Fumed Silica as Flow Additive in Polyester Powder Coatings
Bibliographic record
Abstract
Fumed silica is one of the most commonly used flow additives in the powder coating industry. To investigate the influence of the properties of fumed silica on powder coatings, three different types of fumed silica, Aerosil R812, R972, and R8200, were selected and introduced to an ultra-high-gloss powder paint by the dry-blending method with preset mixing conditions and times. Their effect on the powder flowability, coating application related properties and film properties were carefully studied. The angle of repose (AOR) and bed expansion height data, which represent the semi-dynamic and dynamic flowability of powders respectively, show a strong flowability enhancement for the powders with additives, and R812 exhibits the best performance compared to 8200 and R972, mainly due to its high hydrophobicity and specific surface area. For the ultra-high-gloss powder paint, all the flow additives cause slight gloss reductions, surface roughness increase and a significant effect on the distinctness of image (DOI). The addition of R972 is beneficial to the transfer efficiency of powders compared with the other two, while the additives impose only a minor influence in the Faraday cage effect. The melting and curing dynamics, i.e., gel time, and inclined plate flow, are not affected by the flow additives.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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".