Effect of silylating agents on the superhydrophobic and self-cleaning properties of siloxane/polydimethylsiloxane nanocomposite coatings on cellulosic fabric filters for oil–water separation
Bibliographic record
Abstract
Si) chemical groups that allowed for the formation of nanosilica with Si-O-Si linkages needed to foster stable coatings. After characterization and testing, these coated fabrics demonstrated varying responses to harsh solvents and thermal conditions. Both sets of coated fabrics exhibited unique capacities for self-cleaning and oil-water separation as superhydrophobic filters due to (a) their low surface energy silylated hybrid polysiloxane chemical groups, (b) their highly reduced surface wettability and (c) nanopatterned surface morphologies. In this study, coated superhydrophobic cotton fabrics revealed a higher static aqueous contact angle of more than 150° and sliding hysteresis angle of less than 5°. Coated fabrics with 30 mg TMOS/10 mg HMDS (CMF3) and 30 mg HMDS/10 mg TMOS (CTF3) exhibited optimal superhydrophobicity. Both fabrics also retained percentage separation efficiencies over 90% for both chloroform-water and toluene-water mixtures. However, CTF3 displayed with a recorded separation efficiency less than 90° after five filtration cycles.
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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".