Novel synthesis method of oxygen vacancy <scp>WO<sub>3</sub></scp> and its photocatalytic performance for degradation of rhodamine B
Bibliographic record
Abstract
Abstract BACKGROUND Photocatalytic degradation of organic pollution as a ‘green’ treatment technology has been a broad concern in water treatment. This work proposed a simple method for introducing oxygen vacancies into WO3 and enhancing the degradation of WO3 for rhodamine B under simulated solar light. RESULTS In this work, titanium power was used as an oxygen absorber to introduce oxygen vacancies into WO3. The degradation of organic dyes by semiconductor materials is significantly improved by the presence of oxygen vacancies. After 4 h of simulated sunlight irradiation, 100% degradation efficiency of rhodamine B by WO3 with vacancies was achieved, compared to about 60% by WO3. Combined with liquid chromatography–mass spectrometry and total organic carbon analysis results, we speculated the degradation path of rhodamine B degraded by WO3‐2. CONCLUSION There exist three reasons for the enhanced photocatalytic performance of oxygen‐containing vacancy WO3: (i) the introduction of vacancies generates an energy level of the donor that can reduce the band gap; (ii) the presence of oxygen vacancies can effectively prevent the electron–hole complexation process; and (iii) oxygen vacancies increase the adsorption capacity of the catalyst. It is expected that this method of oxygen vacancy introduction can be extended to other semiconductor systems to achieve higher performance in pollutant degradation and water remediation. © 2023 Society of Chemical Industry (SCI).
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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".