Interaction of Titanium Oxide Nanostructures with Graphene and Functionalized Graphene Nanoribbons: A DFT Study
Bibliographic record
Abstract
Graphene substrates are known to have randomly located functional groups on their surface, particularly at their edges, including carboxylate, carbonyl, epoxy, and alcohol functionalities. However, the detailed interactions of these graphene functionalities with metal oxide nanoclusters are unexplored. This work examined the interaction of titania nanostructures with both graphene and functionalized graphene nanoribbons (GNRs) using density functional theory (DFT) calculations. The interactions of TiO 2 (anatase, rutile, and molecular) with graphene were found to favor the physisorption of rutile titania. The interactions of TiO 2 with GNRs were found to considerably improve the strength of the nanostructure binding to the substrate with rutile and anatase showing similar chemisorption. Charge density maps showed the importance of the electron distribution in the interaction between titania and graphene with chemisorption sites. Valuable information on the strength of the binding energies was determined by studying the electronic structure using partial density of states (PDOS) of the TiO 2 /graphene systems at specific adsorption sites. These results show the potential for controlled and oriented growth mechanisms that have applications in next generation photovoltaic and photocatalytic devices.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".