Adsorption of organic molecules on the TiO2(011) surface: STM study
Bibliographic record
Abstract
High resolution scanning tunneling microscopy has been applied to investigate adsorption and self-assembly of large organic molecules on the TiO(2)(011) surface. The (011) face of the rutile titania has been rarely examined in this context. With respect to possible industrial applications of rutile, quite often in a powder form, knowledge on behavior of organic molecules on that face is required. In the presented study we fill in the gap and report on experiments focused on the self-assembly of organic nanostructures on the TiO(2)(011) surface. We use three different kinds of organic molecules of potential interest in various applications, namely, PTCDA and CuPc representing flat, planar stacking species, and Violet Landers specially designed for new applications in molecular electronics. In order to reach a complete picture of molecular behavior, extended studies with different surface coverage ranging from single molecule up to 2 monolayer (ML) thick films are performed. Our results show that the adsorption behavior is significantly different from previously observed for widely used metallic templates. Creation of highly ordered molecular lines, quasi-ordered wetting layers, controlled geometrical reorientation upon thermal treatment, existence of specific adsorption geometries, and prospects for tip-induced molecule ordering and manipulation provide better understanding and add new phenomena to the knowledge on the (011) face of rutile titania.
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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) | 0.002 | 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".