Cellulose Nanofibrils as a Damping Material for the Production of Highly Crystalline Nanosized Zeolite Y via Ball Milling
Bibliographic record
Abstract
Nanosized zeolite Y is used in various applications from catalysis in petroleum refining to nanofillers in water treatment membranes. Ball milling is a potential and fast technique to decrease the particle size of zeolite Y to the nano range. However, this technique is associated with a significant loss of crystallinity. Therefore, in this study, we investigate the effect of adding biodegradable and recyclable cellulose nanofibrils (CNFs) to zeolite Y in a wet ball milling approach. CNFs are added to shield the zeolite Y particles from harsh milling conditions due to their high surface area, mechanical strength, and water gel-like format. Different zeolite Y to CNFs ratios were studied and compared to optimize the ball milling process. The results showed that the optimal zeolite Y to CNFs ratio is 1:1 to produce a median particle size diameter of 100 nm and crystallinity index of 32%. The size reduction process provided accessibility to the zeolite pores and as a result increased their adsorption capacity. The adsorption capacity of ball-milled particles for methylene blue increased to 29.26 mg/g compared to 10.66 mg/g of the pristine Zeolite. These results demonstrate the potential of using CNF in protecting zeolite Y particles and possibly other micro particles during ball milling.
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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".