Cluster/Polarized Continuum Models for Density Functional Theory Investigations of Benzimidazole Corrosion Inhibitors at Metal/Solution Interface
Bibliographic record
Abstract
The molecular behavior of some benzimidazole (C7H6N2) derivatives as corrosion inhibitors of iron in hydrochloric acid (HCl) solution was investigated quantum electrochemically via the inhibitors’ chemical potential (μ), molecular softness (σ), and the extent of charge transfer from the inhibitor to the metal (ΔN). These quantities were obtained from density functional theory (DFT) calculations for three models, i.e., isolated inhibitors (in vacuo), inhibitors in solution, and, finally, inhibitors in electrical double layer (EDL). In these models, the effects of solvent, substrate, and electric field were considered using a polarized continuum model, iron Fe13(9,4) cluster, and a finite-normal-homogeneous electric field. The investigations show that at the metal/solution interphase, the desolvation of benzimidazole takes place more easily than in the bulk of solution. Moreover, as the molecule enters EDL, an abrupt increase in μ and σ is observed. The calculations of interaction energies show that among the various possible adsorption modes of the inhibitor molecule on the iron surface, the vertical adsorption via a nitrogen lone pair is predominant. Finally, a relatively good correlation is observed between inhibitor efficiency and individual quantities of μ, σ, and ΔN. Moreover, it is observed that these correlations are improved as the model changes from a simple-ideal form (isolated inhibitor) to a more sophisticated-realistic one (inhibitors at metal/solution interphase).
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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".