Solvent Free Glycolysis of Plastic Waste as Green Corrosion Inhibitor for Carbon Steel in Sulfuric Acid
Bibliographic record
Abstract
Solvent free efficient green glycolysis of Polyethylene terephthalate waste, PET, in this respect, PET waste was subjected to depolymerization with propylene glycol in the presence of manganese acetate as a catalyst, the product is Bis-(3-hydroxy-propyl)-terephthalate, BHPT, as non-ionic surfactant was separated, characterized by FT-IR and 1HNMR, BHPT was evaluated as corrosion inhibitor for steel in the presence of 1.0 M sulfuric acid by chemical and electrochemical methods, the corrosion inhibition efficiency increase with inhibitor concentration and decrease with temperatures, the galvanostatic polarization curves indicate that the BHPT, compound act as mixed inhibitor, the impedance of the inhibited solution has increased with the increase in the concentration of the inhibitor. the inhibition due to adsorption of the inhibitor molecules on the steel surface, the adsorption obey Langmuir adsorption isotherm. Thermodynamic parameters were computed and discussed, Scanning electron microscope (SEM) was used to study the surface morphology of steel in absence and presence of the used inhibitor, hydrogen evolution techniques was used to evaluate the inhibition efficiency of the BHPT inhibitor compound. the data obtained from different techniques are in good agreement to each other and show the BHPT, surfactant derived from PET, waste considered as good corrosion inhibitor for steel in sulfuric acid.
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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".