<i>In-situ</i> co-doping of sputter-deposited TiO2:WN films for the development of photoanodes intended for visible-light electro-photocatalytic degradation of emerging pollutants
Bibliographic record
Abstract
We report on the magnetron sputtering deposition of in-situ codoped TiO2:WN films intended for electro-photocatalytic (EPC) applications under solar irradiation. By varying the RF-magnetron sputtering deposition parameters, we were able to tune the in-situ incorporation of both N and W dopants in the TiO2 films over a wide concentration range (i.e., 0–9 at. % for N and 0–3 at. % for W). X-ray photoelectron spectroscopy analysis revealed that both dopants are mostly of a substitutional nature. The analysis of the UV-Vis transmission spectra of the films confirmed that the optical bandgap of both TiO2:N and TiO2:WN films can be significantly narrowed (from 3.2 eV for undoped-TiO2 down to ∼2.3 eV for the doped ones) by tuning their dopant concentrations. We were thus able to pinpoint an optimal window for both dopants (N and W) where the TiO2:WN films exhibit the narrowest bandgap. Moreover, the optimal codoping conditions greatly reduce the recombination defect state density compared to the monodoped TiO2:N films. These electronically passivated TiO2:WN films are shown to be highly effective for the EPC degradation of atrazine (pesticide pollutant) under sunlight irradiation (93% atrazine degraded after only 30 min of EPC treatment). Indeed, the optimally codoped TiO2:WN photoanodes were found to be more efficient than both the undoped-TiO2 and equally photosensitized TiO2:N photoanodes (by ∼70% and ∼25%, respectively) under AM1.5 irradiation.
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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) | 0.001 | 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".