Growing TiO<sub>2</sub>nanowires on the surface of graphene sheets in supercritical CO<sub>2</sub>: characterization and photoefficiency
Bibliographic record
Abstract
Tremendous interest exists towards synthesizing nanoassemblies for dye-sensitized solar cells (DSSCs) using earth-abundant and -friendly materials with green synthetic approaches. In this work, high surface area TiO(2) nanowire arrays were grown on the surface of functionalized graphene sheets (FGSs) containing -COOH functionalities acting as a template by using a sol-gel method in the green solvent, supercritical carbon dioxide (scCO(2)). The effect of scCO(2) pressure (1500, 3000 and 5000 psi), temperature (40, 60 and 80 °C), acetic acid/titanium isopropoxide monomer ratios (HAc/TIP = 2, 4 and 6), functionalized graphene sheets (FGSs)/TIP weight ratios (1:20, 1:40 and 1:60 w/w) and solvents (EtOH, hexane) were investigated. Increasing the HAc/TIPweight ratio from 4 to 6 in scCO(2) resulted in increasing the TiO(2) nanowire diameter from 10 to 40 nm. Raman and high resolution XPS showed the interaction of TiO(2) with the -COOH groups on the surface of the graphene sheets, indicating that graphene acted as a template for polycondensation growth. UV-vis diffuse reflectance and photoluminescence spectroscopy showed a reduction in titania's bandgap and also a significant reduction in electron-hole recombination compared to bare TiO(2) nanowires. Photocurrent measurements showed that the TiO(2)nanowire/graphene composites prepared in scCO(2) gave a 5× enhancement in photoefficiency compared to bare TiO(2) nanowires.
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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) | 0.000 | 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 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".