Elimination of Toluene Vapours using Natural Zeolite Treated by Copper Oxide
Bibliographic record
Abstract
Background: Volatile organic compounds (VOCs) are the most frequent air contaminants which are produced due to industrial processes such as production of chemicals, petrochemical industries and their related industries. These compounds are harmful (even in low concentrations) not only for the environment but also for the human health. One method to control these contaminants is using catalytic beds. In this study we used modified natural Zeolite (Clinoptilolite) to eliminate toluene vapours. Methods: In this study, at first natural Zeolite were modified with cooper ions and calsined thermally. Then, the effect of variables such as toluene concentration, reactor temperature, and flow rate on toluene vapours elimination were surveyed by using a tubular stainless steel reactor and modified. Results: It was found that cooper oxide, as a catalyst, led to the considerable decrease in combustion temperature of toluene vapours. Of course, increasing the space velocity (or flow rate) and toluene concentration led to decreasing Zeolite bed efficiency. Conclusion: Regarding the presence of many natural storage of Clinoptilolite in Iran, it seems that its modification with cooper oxide could be a good catalyst for eliminating of VOCs in air.
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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".