Nature of the Interaction of <i>N</i>,<i>N</i>′-Diphenyl-1,4-phenylenediamine with Iron Oxide Surfaces
Bibliographic record
Abstract
Abstract Redox-active polymers and small molecules are of great interest in coatings, such as corrosion inhibitors for steel and other metals. In this work the interaction of the redox-active phenyl-capped aniline dimer (N,N′-diphenyl-1,4-phenylenediamine, DPPD) with iron oxide surfaces was investigated with the aim to understand the corrosion inhibition and self-healing properties of polyaniline and aniline oligomers on iron oxide surfaces. Raman, mid-IR, and visible spectroscopies all show that reduced DPPD transforms into the semiquinone form by interacting with α-Fe2O3. Thermal gravimetric analysis (TGA) was used to quantify the strength of these interactions, clearly within the chemisorption range. TGA analysis, mid-IR spectroscopy, and atomic force microscopy showed the DPPD molecules to be standing on their edge on the surface and changing their orientation to standing on end upon initiation of multilayer formation. DPPD─and hence other reduced oligoanilines or polyaniline─are therefore shown to strongly interact with iron oxide surfaces through hydrogen bonding and charge transfer to the surface. A full understanding of coatings will ultimately require the study of all oxidation states and their surface interactions. Here we provide the most detailed understanding to date of the reduced state as a first step.
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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".