Surface Mobility and Nucleation of a Molecular Switch: Tetraaniline on Hematite
Bibliographic record
Abstract
Understanding the dynamics of organic thin film formation is crucial to quality control in organic electronics and smart coatings. We have studied the nucleation and growth of the reduced and the oxidized states of phenyl-capped aniline tetramer (PCAT) deposited on hematite(1000) surfaces by physical vapor deposition. The fully reduced PCAT molecules form 2D islands on the surface, whereas the fully oxidized molecules form 3D islands. Through scaled island size distribution, it was found that the critical island sizes, i, for the reduced and oxidized molecules are i = 4 and 5, respectively. From the dependence of the island density on substrate temperature, the activation energies for the diffusion of the molecules away from the critical cluster were calculated to be 1.30 and 0.55 eV, respectively. At low temperatures, the reduced and the oxidized PCAT molecules form compact islands on the surface. At higher temperatures, the reduced islands become dendritic, whereas the oxidized islands become slightly dendritic. The attempt frequencies for surface diffusion of the reduced and the oxidized islands were estimated to be about 5 × 1025 and 8 × 1011 s–1, respectively. The former value is in line with the high degree of surface wetting by the reduced PCAT, whereas the latter value shows the higher degree of intermolecular interaction in the fully oxidized PCAT and the low degree of its interaction with the iron oxide surface. Interconversion between oxidized and reduced islands through exposure to a reducing environment, and its impact on island morphology was examined. We also found that the presence of Fe2+ defects on the hematite surface did not impact the nucleation and growth of the molecular islands, likely due to a discrepancy in time scale. This study elucidates the interactions between an oligoaniline-based molecular switch (PCAT) and hematite surfaces as a function of molecular oxidation state, with applications in molecular electronics, chemical sensors, and smart coatings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".