Copper and chromium oxide nanocomposite catalysts for simultaneous elimination of CO and oxygenate VOCs in toxic gas emissions
Bibliographic record
Abstract
Copper and chromium oxide nanocomposite materials with different Cu/Cr ratios were prepared by impregnation of a conventional silica support with toluene solutions of the corresponding acetylacetonate precursors and characterized by X-ray diffraction, Fourier transform infrared spectroscopy, UV–vis spectroscopy, X-ray photoelectron spectroscopy, and temperature-programmed reduction–thermogravimetric methods. Their catalytic behaviour in the total oxidation of CO and different oxygenate VOCs (methanol, dimethyl ether, and ethyl acetate) was tested. The relation between the phase composition and catalytic properties of the materials was also analyzed. It was established that materials with a 1:2 CuO/Cr2O3 molar ratio exhibit high catalytic activity for dimethyl ether oxidation, those with 2:1 and 1:1 ratios are suitable for CO and methanol oxidation, respectively, and both of the latter, but at much higher temperatures, are good for ethyl acetate elimination. A catalyst with an optimal composition of 1:1 (mol/mol) CuO/Cr2O3, working in a reactor with three temperature zones of 490–500 K, 570–580 K, and 620–630 K, is proposed for the effective simultaneous elimination of CO, methanol, ethyl acetate, and dimethyl ether in toxic emissions.
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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 | 0.001 | 0.000 |
| 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) | 0.000 | 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".