Degradation of wastewater from carbon capture plants using metal‐impregnated <scp> TiO <sub>2</sub> </scp> photocatalyst
Bibliographic record
Abstract
Abstract Amine‐based carbon capture (ABCC) is an advanced and cost‐effective technology used to reduce the effects of climate change by capturing emitted from different sources. Although it has been demonstrated commercially, amine degradation poses a significant threat to humans and aquatic life. Amine degradation produces a wide variety of complex products such as nitrosamines and some organic acids. Some of these products are carcinogenic and mutagenic in nature and have demonstrated acute toxicity for laboratory animals. In order to mitigate the adverse impact of these compounds on human health and aquatic life, heterogeneous photocatalysis, an advanced oxidation process which can degrade a wide variety of chemical species with potent reactive hydroxyl radicals, was considered for the degradation of these compounds. The photocatalytic degradation of N ‐nitrosodiethylamine (NDEA), acetic acid, and formic acid were tested using TiO 2 and metal‐impregnated TiO 2 catalysts such as Fe, Co, Ni, and Cu. Various techniques, such as thermogravimetric analysis (TGA), UV–Vis, X‐ray diffraction (XRD), Brunauer–Emmett–Teller (BET), scanning electron microscopy (SEM), and X‐ray fluorescence (XRF) were used to characterize these catalysts. The operational parameters for the photocatalytic degradation process were chosen as solution pH, catalyst dose, and metal impregnation percentage (imp%), and they were optimized using response surface methodology (RSM). pH was found as an important factor, and its impact on the photocatalytic degradation efficiency was more significant than the other operational parameters. The average degradation efficiency of the compounds reached 93.1% for Fe‐TiO 2 , 92.08% for Co‐TiO 2 , 89.09% for Ni‐TiO 2 , 88.81% for Cu‐TiO 2 , and 86.3% for TiO 2 at the optimum conditions.
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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 | 0.001 | 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".