Quantum chemical studies and molecular modeling of the effect of polyethylene glycol as corrosion inhibitors of an aluminum surface
Bibliographic record
Abstract
It was shown that polyethylene glycol molecules act as good corrosion inhibitors. Quantum chemical simulation showed the effect of molecular weight on studying polymers with repeating units 1, 2, 3, 4, and 5 and also polymers with high molecular weights with repeating units 10, 20, and 30. Using the density functional theory at the B3LYP/6-31G+(d,p) basis set level, ab initio calculations using the HF/6-31G+(d,p) and semi-empirical PM3 methods were performed on polyethylene glycol to investigate the correlation between its molecular structure and the corresponding inhibition efficiency. The calculated parameters include the highest occupied molecular orbital, the lowest unoccupied molecular orbital, the separation energy (ΔE), the dipole moment (μ), the softness (σ), the fraction of electrons transferred from the inhibitor to the metal surface (ΔN), the electronegativity (χ), the hardness (η), and the total negative charge. Furthermore, the adsorption energies of the inhibitors with the aluminum (1 1 1) surface were studied using the molecular dynamic method. A good correlation between the theoretical data and the experimental results was found.
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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".