Preferential Adsorption of TiO<sub>2</sub> Nanostructures on Functionalized Single-Walled Carbon Nanotubes: A DFT Study
Bibliographic record
Abstract
Abstract The mechanism of attachment of nanocrystals (NCs) to curved carbonaceous species such as graphene nanoribbons and carbon nanotubes (CNTs) is of current scientific interest. In addition, we have observed anisotropic growth patterns of titania NCs from carbonaceous materials, for which there is no theoretical explanation. In this work, we use density functional theory (DFT) calculations for calculating the energy of adsorption of titania nanostructures to both armchair metallic and zigzag semiconducting single-walled carbon nanotubes (SWCNTs) in their pure and functionalized forms. Several adsorption sites are considered including top, bridge, and hollow sites for pure SWCNTs, while for functionalized SWCNTs epoxy, alcohol, and carboxylate are examined. Results from binding energy calculations were found to predict favorable adsorption of TiO2 NCs on the chemical adsorption sites of functionalized SWCNTs compared to the physical adsorption sites of pure SWCNTs. The rotation of anatase and rutile titania species on the physical adsorption sites showed interesting behavior particularly regarding binding strength and growth direction predictions. Partial density of states (PDOS) calculations examined the electronic structure of the assemblies. Charge density maps showed the importance of chemisorption sites for interactions between titania structures and SWCNTs. Electronic local potentials showed the difference in binding strengths for anatase titania on SWCNT physical adsorption sites. These results provide new theoretical evidence for controlled and oriented growth mechanisms on curved carbon-based substrates that have applications in various emerging applications from photovoltaic devices to nanomedicine.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".