The versatile synthesis method for hierarchical micro‐ and mesoporous zeolite: An embedded nanocarbon cluster approach
Bibliographic record
Abstract
Abstract In this work, we are reporting for the first time the synthesis of hierarchical micro‐ and mesoporous zeolite using silica–carbon (SiO2/C) composites prepared by pyrolysis of carbonaceous gases in the presence of silica gel. The pyrolysis effectively yielded carbon deposited onto the raw silica material. The obtained SiO2/C composites were utilised as a bifunctional material, mesoporous template and silica source, for the zeolite synthesis. Tetrapropylammonium hydroxide (TPAOH) was used as a microporous template. The combination of the obtained composites and the TPAOH for the hydrothermal synthesis resulted in the formation of hierarchical micro‐ and mesoporous ZSM‐5. The results from the SEM, TEM, and N2 adsorption/desorption isotherms, and 27Al MAS NMR characterisations of the synthesised samples obtained after the removal of the templates confirmed the successful formation of the micro‐ and mesoporous zeolites. The mesoporosity of the zeolites could be controlled by adjusting the carbon content in the SiO2/C composites while the carbon content could be controlled by varying the deposition time and the concentration of the carbonaceous gases used. This controllable and efficient synthesis method is considered to be a promising method for creating hierarchical micro‐ and mesoporous zeolites. © 2011 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) | 0.001 | 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".