Preparation and deodorization behavior of copper-modified mesostructured MCM-41 for the treatment of malodorous gas in sewage plants
Bibliographic record
Abstract
ABSTRACT To find efficient adsorption material for the treatment of malodorous gas in sewage plants, in this study, pure silicon Mobil composition of matter-41 (MCM-41) with cobblestone morphology, a member of the family of mesoporous molecular sieve, was used. Herein, copper-modified MCM-41 (Cu-MCM-41) was prepared by hydrothermal method for H 2 S adsorption. The experimental results showed that when the water-to-silicon ratio (nH 2 O:nTEOS) was 493:1, stirring time was 15 min, and modification amount of copper reached 30%; the H 2 S adsorption time on Cu-MCM-41 was close to 90 min and the theoretical sulfur capacity could reach 34 mg g –1 , which is much higher than the adsorption performance of pure nano-copper oxide. After copper modification, the specific surface area of the sample decreased, and countless small nano CuO particles could be observed in the cobblestone particles. Moreover, these particles were found to be evenly dispersed on the surface and pores of MCM-41, which facilitated the adsorption reaction with H 2 S, and improved the utilization rate of the active component nano CuO. The adsorbed H 2 S mainly existed in the form of CuS, Cu 2 S, and CuSO 4 on the surface of the material, and redox reaction occurred during the adsorption process. This research provides an effective approach for the preparation of new material for the treatment of sulfur-containing malodorous gas.
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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".