Synergizing nanocomposites of CdSe/TiO<sub>2</sub> nanotubes for improved photoelectrochemical activity via thermal treatment
Bibliographic record
Abstract
In this work, we show the effect of the thermal treatment temperature on the photoelectrochemical (PEC) activity of CdSe/TiO2 nanocomposites. TiO2 nanotubes (NTs) were synthesized by anodization and the nanocomposites were obtained by depositing CdSe clusters via magnetron sputtering. A two-step thermal treatment was performed: heating the TiO2 NTs at different temperatures prior to CdSe deposition and further heating the CdSe/TiO2 nanocomposites. The nanocomposites were characterized by Rutherford backscattering spectroscopy (RBS), scanning electron microscopy (SEM), scanning transmission electron microscopy (STEM), high-resolution transmission electron microscopy (HRTEM), energy dispersive X-ray spectroscopy (EDX), X-ray diffraction (XRD), UV-Vis spectrophotometry, and electrochemical impedance spectroscopy (EIS). To compare the PEC performance of the CdSe/TiO2 nanocomposites and pristine TiO2 NTs, linear sweep voltammetry (LSV) curves were obtained under visible light and under 1 sun illumination. It was observed that CdSe incorporation into the TiO2 template enhances the visible light absorbance thereby improving the PEC performance of the nanocomposites. We have found that the optical, structural and PEC properties of the CdSe/TiO2 nanocomposites are dependent on the thermal treatment temperature of the TiO2 nanotubular substrate, prior to CdSe deposition. Moreover, a three-fold improvement in photocurrent was observed upon further thermal treatment of the obtained nanocomposite.
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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".