Interfacial Charge Transfer between Phenyl-Capped Aniline Tetramer Films and Iron Oxide Surfaces
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Redox-active organic compounds have been studied as corrosion inhibitors for steel. Even though it is clear that chemical interactions at the organic–metal oxide interface are behind this inhibitive process, the detailed mechanism is not yet fully understood. Using phenyl-capped aniline tetramer (PCAT), we have elucidated the interactions at the interface with iron oxide. We demonstrate the partial reduction of fully oxidized PCAT and the partial oxidation of fully reduced PCAT upon interaction with iron oxide. X-ray photoelectron spectroscopy reveals the appearance of charged nitrogen structures in PCAT deposited on hematite. Iron oxide films in contact with reduced PCAT show a higher conductance due to the introduction of defects, resulting in n-doping. In contrast, the iron oxide film in contact with oxidized PCAT shows a lower conductance, indicating that defects in the film are removed via oxidation, thus reducing the doping level. This is consistent with accepted models for corrosion inhibition, in which PCAT assists in the formation of a passive oxide film. These results are indicative of interfacial charge transfer between PCAT and iron oxide. The extent of the charge transfer and the direction of redox processes depend on the oxidation state of the molecules, enabling the construction of redox-active devices, including sensors and switches.
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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".