Fe doped TiO 2 nanofibers on the surface of graphene sheets for photovoltaics applications
Bibliographic record
Abstract
Highly ordered, visible light driven TiO2 nanowire arrays doped with Fe photocatalysts were grown on the surface of functionalized graphene sheets (FGSs) using a sol-gel method with titanium isopropoxide (TIP) monomer, acetic acid (HAc) as the polycondensation agent and iron chloride in the green solvent, supercritical carbon dioxide (scCO 2 ). The morphology of the synthesized materials was studied by SEM and TEM, which showed uniform formation of Fe doped TiO 2 nanofibers on the surface of graphene sheets, which acted as a template for nanowire growth through surface -COOH functionalities. Increasing Fe content in the nanowires did not change the morphology significantly. Optical properties of the synthesized composites were examined by UV spectroscopy which showed a significant reduction in band gap with increasing Fe content, i.e. 2.25 eV at 0.6% Fe. The enhancement of the optical properties of synthesized materials was confirmed by photocurrent measurement. The optimum sample containing 0.6% Fe doped TiO 2 on the graphene sheets increased the power conversation efficiency by 6-fold in comparison to TiO 2 alone.
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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) | 0.001 | 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".