Photocatalytic degradation of <scp> <i>N</i> </scp> ‐nitrosodiethylamine from carbon capture plants using tungsten trioxide‐based catalysts: Parametric and optimization study
Bibliographic record
Abstract
Abstract This study reports the synthesis and characterization of tungsten trioxide‐based catalysts for the photocatalytic degradation of N ‐nitrosodiethylamine (NDEA) in wastewater. Tungsten trioxide (WO 3 ) was synthesized using the thermal treatment method (TTM) and hard template replication method (HTRM) and impregnated with lanthanum (La), iron (Fe), chromium (Cr), and silver (Ag) to enhance its light absorption ability. The synthesized catalysts were characterized using various techniques, including UV‐Vis spectroscopy, Brunauer–Emmett–Teller (BET), Barrett–Joyner surface area and porosity, X‐ray diffraction (XRD), and thermogravimetric analysis (TGA). The experimental design approach used a response surface methodology (RSM) with a face‐centered central composite design (FCCCD) technique. Catalyst loading (%), pH of solution, and catalyst concentration (g/L) were chosen as design factors, with the response variable being NDEA degradation efficiency (%). The obtained data were analyzed, and a quadratic model was chosen as the best fit with statistically significant model terms as observed from the analysis of variance (ANOVA). 3D interaction plots were generated to explain the effect of the various interaction terms of the quadratic model. The results showed that the pH of the solution was the major factor affecting NDEA degradation efficiency. The mean degradation efficiency of NDEA was 93.03% for Fe/WO 3 , 88.90% for Ag/WO 3 , 86.48% for La/WO 3 , and 84.03% for Cr/WO 3 . These findings demonstrate the potential of tungsten trioxide‐based catalysts impregnated with various metals for the effective treatment of NDEA in wastewater through heterogeneous photocatalysis.
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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".