Sonochemical post-synthesis modification of Y zeolite with iron species
Bibliographic record
Abstract
Faujasite is a zeolite that, when in a Y structure, is widely used as a catalyst to produce fuel by fluid catalytic cracking (FCC) and is recently being investigated for applications in sustainable processes. Hence, there is an increased interest in modifying Y zeolites with metals such as Fe to improve and optimize their catalytic properties. Several post-synthesis methods such as chemical vapor deposition, impregnation and ion exchange have been explored for the introduction of Fe. Nevertheless, these currently developed methods can be time-consuming and unable to selectively tune the Fe species. Therefore, this study explores the use of ultrasonic irradiation in a liquid state modification of a Fe-exchanged Y zeolite (FeY) in a buffer solution with different pH values (5 and 9) and oxidation states of Fe ions (ferrous and ferric) for the introduction of Fe species. At the tested ultrasonic modification conditions, the zeolitic structure was preserved with slight Si/Al ratio and pore structure adjustments in correlation with the introduced Fe species (ions, hydroxides and oxides). Setting the sonochemical method as a potential path for the search of reaction conditions for the design and fabrication of Fe-modified zeolitic materials with tuned properties for a wide range of applications. • Modification of zeolite Y with Fe species can be achieved under ultrasonic irradiation in a buffer solution. • Sono-assisted Fe modification of zeolite Y with potential for tuning the Si/Al ratio, pore structure and Fe species. • Influence of pH and oxidation state of Fe ions on a sonochemically Fe modified zeolite Y.
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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".