ANTI-CORROSION COATINGS BASED ON RECYCLED POLYPROPYLENE AND FILLERS
Bibliographic record
Abstract
The article deals with the issues of obtaining anti-corrosion compositions for corrosion protection of oil pipelines. The purpose of this study was to develop effective compositions of anti-corrosion coatings based on recycled polypropylene (PPrc), cotton soap stock, vegetable filler - guzapay; mineral filler wollastonite and montmorillonite (MMT). The proposed process of chemical interaction of the initial compounds in the prescription modification of the soap stock, in the extruder. The authors obtained a new composite of the following composition (wt. %: vegetable filler - guzapaya - 35; mineral filler - wollastonite: MMT (1: 1) - 5; sevilen - 8; soap stock - 30; PPrc - the rest. The composite composition has high performance indicators on the following positions: breaking stress in bending increased by 15-17%; shear strength increased by 10-12%; tensile strength increased by 6-8%; indentation hardness under a given load of the ball increased by 15- 20% impact strength increased by 15-17%, shrinkage during casting decreased by 30-35%. It to protect against corrosion of the main oil pipeline. This composition of the composite is selected on the basis of available and relatively inexpensive components, polyethylene rich, vegetable and mineral fillers.
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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".