Chemical Functionalization of the TiO<sub>2</sub>(110)-(1 × 1) Surface by Deposition of Terephthalic Acid Molecules. A Density Functional Theory and Scanning Tunneling Microscopy Study
Bibliographic record
Abstract
Periodic DFT calculations were used to explore structural properties of terephthalic acid (TPA) deposited on the rutile TiO 2 (110)-(1 × 1) surface at low and high coverage. Theoretical results were compared with scanning tunneling microscopy imaging data. At low loading the TPA molecules adsorb dissociatively as discrete entities adopting a flat-lying plane-on geometry. The resultant terephthalic anion is attached to the surface by two covalent bonds between the carboxylic oxygens and the 5-fold coordinated Ti atoms with an additional stabilization due to the hydrogen bond formation with the adjacent surface hydroxyls. When the saturation coverage is achieved, a well-ordered monolayer of the vertically oriented molecules is formed. In both cases the TPA admolecules bind to the surface via carboxylic groups as terephthalic anions. Formation of dimers results from the formation of hydrogen bonds between the adjacent TPA molecules. To elucidate the reactivity of the functionalized surface, we deposited zinc formate ions on top of the compact TPA monolayer. Calculations showed that the anchoring properties of the TiO 2 /TPA system are not perturbed by the dimer formation, auguring well for its prospective application as a promising chemically functionalized surface for on-top growth of metal organic frameworks.
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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".