Efficient photocatalytic removal of <scp>N‐nitrosamines</scp> from amine washing wastewater using bismuth tungstate
Bibliographic record
Abstract
Abstract The most mature and practical technology to reduce industrial CO 2 emissions, the main contributor to global warming, is amine‐based post‐combustion CO 2 capture. However, this results in amine degradation products that pose a threat to human health as well as marine life. To reduce the impact on human health and marine life, identifying and treating carcinogenic and mutagenic compounds like N‐nitrosamines is extremely important. Photocatalysis, in particular, an advanced oxidation process which uses a UV light source and semiconductor catalysts is studied for the degradation of organic and inorganic pollutants. N‐Nitrosodiethylamine (NDEA) is treated with strong reactive hydroxyl radicals generated by a bismuth tungstate (Bi 2 WO 6 ) semiconductor under UV/visible irradiation. The Bi 2 WO 6 was studied both in pure form and surface‐modified forms using transition metal impregnation like Ag, Fe, Cu, and La. Various catalyst characterization techniques like Brunauer–Emmett–Teller (BET), X‐ray diffraction analysis (XRD), UV–visible, and scanning electron microscopy–energy‐dispersive X‐ray (SEM‐EDS) are used to study the surface textural and morphological properties of the catalyst. Furthermore, the effect of pH of the solution, catalyst dosing, and metal impregnation (%) on the photocatalytic degradation of NDEA is analyzed. The face centred‐central composite design (FC‐CCD) experimental design method was used through response surface methodology (RSM) and optimization studies for removal of NDEA. The quadratic model was obtained as a functional link between NDEA concentration and three operation variables for all metal impregnated Bi 2 WO 6 . The results showed that the pH of the solution was the most significant factor compared to other variables like catalyst dosing and metal impregnation. The average degradation efficiency of NDEA was 89.2% for Fe‐Bi 2 WO 6 , 87.4% Ag‐Bi 2 WO 6 , 86.9% for La‐Bi 2 WO 6 , and 85% for Cu‐Bi 2 WO 6 .
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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".