N- and C-Modified TiO2 Nanotube Arrays: Enhanced Photoelectrochemical Properties and Effect of Nanotubes Length on Photoconversion Efficiency
Bibliographic record
Abstract
In this investigation, a new, facile, low cost and environmental-friendly method was introduced to fabricate N- and C-modified TiO2 nanotube arrays by immersing the as-anodized TiO2 nanotube arrays (TNTAs) in a urea aqueous solution with mechanical agitation for a short time and keeping the TNTAs immersed in the solution for 6 h at room temperature. Then, the TNTAs were annealed at different temperatures. The produced N-, C-modified TNTAs were characterized using FESEM, EDX, XRD, XPS, UV-Vis diffuse reflectance spectra. Modified optical properties with narrow band gap energy, Eg, of 2.65 eV was obtained after annealing the modified TNTAs at 550 °C. Modified TNTAs showed enhanced photoelectochemical performance. Photoconversion efficiency (PCE) was increased from 4.35% for pristine (unmodified) TNTAs to 5.18% for modified TNTAs, an increase of 19%. Effect of nanotubes length of modified TNTAs on photoelectrochemical performance was also studied. Photocurrent density and PCE were increased by increasing nanotube length with a maximum PCE of 6.38% for nanotube length of 55 µm. This high PCE value was attributed to: band gap reduction due to C- and N-modification of TNTAs surface, increased surface area of long TNTAs compared with short TNTAs, investigated in previous studies.
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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".