Control of Alq<sub>3</sub> Wetting Layer Thickness via Substrate Surface Functionalization
Bibliographic record
Abstract
The effects of substrate surface energy and vapor deposition rate on the initial growth of porous columnar tris(8-hydroxyquinoline)aluminum (Alq3) nanostructures were investigated. Alq3 nanostructures thermally evaporated onto as-supplied Si substrates bearing an oxide were observed to form a solid wetting layer, likely caused by an interfacial energy mismatch between the substrate and Alq3. Wetting layer thickness control is important for potential optoelectronic applications. A dramatic decrease in wetting layer thickness was achieved by depositing Alq3 onto alkyltrichlorosilane-derivatized Si/oxide substrates. Similar effects were noted with increasing deposition rates. These two effects enable tailoring of the wetting layer thickness.
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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 | 0.001 | 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".