Polymer Coating over Solid Particles with <i>In Situ</i> Thermal Curing
Bibliographic record
Abstract
Solid particles coated by cross-linked polymer layers find applications in many areas, including hydraulic fracturing operations. In this study, a hot-melt resin coating process for solid particles is developed and optimized for hydraulic fracturing applications. Phenolic resin is used to coat the particles above its melting point and is subsequently cured in situ by hexamethylenetetramine (HMTA). The coating quality is then characterized by the surface morphology, acid solubility, and crush resistance of the resin-coated particles. The effects of various operating parameters on the coating performance are systematically studied. Among them, temperature is shown to play an especially important role. The coating process involves intricate coupling between resin rheology, HMTA mass transfer, and curing kinetics, all of which are profoundly influenced by temperature. Different constant temperature levels as well as controlled temperature ramps are investigated, and the results show a complex dependence. Higher temperature leads to stronger coating layers with better barrier properties, whereas lower temperature is preferable for better surface morphology. These two trends can be partially reconciled with ramping temperature profiles; the improvement is, however, eventually limited by the rate of heat transfer. This study not only provides insight into the physical and chemical processes underlying the resin coating operation, it also demonstrates a generalizable strategy suitable for various particle coating processes.
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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) | 0.001 | 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".