Part 3. Theoretical study on some amino acids and their potential activity as corrosion inhibitors for mild steel in HCl
Bibliographic record
Abstract
Quantum chemical parameters, namely energy of the highest occupied molecular orbital, energy of the lowest unoccupied molecular orbital, energy gap, dipole moment, total energy, total electronic energy, core–core repulsion, ionisation potential, cosmo area, cosmo volume and other quantum descriptors [calculated from PM6, PM3, AM1, RM1 and modified neglect of diatomic overlap (MNDO) Hamiltonians], have been used to predict the corrosion inhibition potential of asparagine, aspartic acid, glutamine and glutamic acid. The results obtained indicate that the trend for the variation of the inhibition efficiencies of the compound is in the order: glutamine>asparagine>aspartic acid>glutamic acid. There is a strong agreement between some quantum chemical parameters and the experimental inhibition efficiencies. In order to establish the sites for electrophilic and nucleophilic attacks, condensed Fukui function, condensed softness and relative nucleophilicity/electrophilicity were considered. The results reveal that the sites for nucleophilic attacks in aspartic acid and glutamine are at the nitrogen atom (N5) but at the carbon atom (C3) for asparagine and glutamic acid. The sites for electrophilic attacks are at the oxygen atom (O9, for aspartic acid), carbon atom (C6, for asparagine), oxygen atom (O10, for glutamic acid) and nitrogen atom (N9, for glutamine).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".