A Density Functional Theory Study to Analyze the Inhibition Potential of Some Antidepressant Molecules on Metal Corrosion in Acidic Media
Bibliographic record
Abstract
The use of therapeutic molecules in corrosion inhibition field contributes to environment preservation. In this context that this current work applies quantum chemical method to study the interaction between some metals and four compounds with antidepression effect. Which compounds are clomipramine, imipramine, amoxapine and iproniazide. The inhibition properties of these compounds in acidic media were evaluated by density functional theory (DFT) with B3LYP functional in 6-31G(d,p) basis set. It was proved that these compounds have a strong electron donating and accepting capacity. This capacity is influenced by their substituents. The low energy gap (DE) values obtained denote that these molecules are highly reactive and can form coordination bonds for the establishment of a barrier on metal surface that could reduce corrosion process. Reactivity sites prediction were carried out by Fukui functions ( fk+, fk- ) and dual descriptor (∆fkr) ,it appears that the centers of nucleophilic attacks are in general nitrogen (N) atoms whereas the centers of electrophilic attacks are only carbon atoms (C).
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".