Implementing All-Weather Photocatalysis of Exhaust Fumes Based on the g-C3N4/TiO2/SrAl2O4: Eu2+, Dy3+ Ternary Composite Coating
Bibliographic record
Abstract
This study examines the use of SrAl2O4: Eu2+, Dy3+ long-afterglow materials doped into g-C3N4/TiO2 coatings for photodegradation. The prepared sample was tested for the purification of automotive exhaust fumes, with the optimal mass ratio of g-C3N4/TiO2 and SrAl2O4: Eu2+, Dy3+ determined to be 1:1. Characterization tests, including XRD, FT-IR, XPS, and TG-DSC, were conducted to evaluate the microstructure and properties of the samples. Under poor lighting conditions, g-C3N4/TiO2 reduced CH and NOx by 59 ppm and 13 ppm within 4 h, respectively, while g-C3N4/TiO2/SrAl2O4: Eu2+, Dy3+ decreased CH and NOx by 98ppm and 34ppm, respectively, resulting in a significant improvement in degradation efficiency. The addition of long-afterglow materials significantly improves the efficiency of photocatalysts in purifying exhaust fumes in low-light environments, providing potential value for all-weather exhaust treatment in the future.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".