Decomposition of 2‐chlorophenol, 4‐chlorophenol and 2,4,6‐trichlorophenol by catalytic oxidation over cobalt and nickel impregnated SBA‐15
Bibliographic record
Abstract
Abstract This work describes the use of Co(II) and Ni(II) impregnated SBA‐15 as catalysts for the oxidative degradation of a few persistent chlorinated phenols in an aqueous medium: 2‐chlorophenol (2‐CP), 4‐chlorophenol (4‐CP) and 2,4,6‐trichlorophenol (2,4,6‐TCP). The catalysts were characterised in terms of their crystallographic features, surface topography, functional groups, thermal stability, etc. The oxidation reactions were carried out using the reaction time, concentration of chlorophenol, amount of catalyst and pH of the reaction mixture as the process variables with or without hydrogen peroxide as the chemical oxidising agent. The conversion achieved with Co/SBA‐15 for 2‐CP, 4‐CP and 2,4,6‐TCP was respectively 84.7%, 78.4% and 64.8% with H2O2 and 86.3%, 80.2% and 70.3% in the absence of H2O2. The conversion with Ni/SBA‐15 also at 353 K for 2‐CP, 4‐CP and 2,4,6‐TCP was, respectively, 82.3%, 81.9% and 64.0% at 5 h with H2O2 and 89.5%, 82.9% and 65.6% without H2O2. The reactions followed pseudo‐first‐order kinetics. The leachability study indicated that the catalysts release very little Co and Ni to water. Therefore, the possibility of water contamination through metal leaching was almost negligible. Oxidative degradation was confirmed by measuring the total organic carbon. © 2012 Canadian Society for Chemical Engineering
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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".