Iron-loaded amine/thiol functionalized polyester fibers with high catalytic activities: a comparative study
Bibliographic record
Abstract
Dispersion of iron nanoparticles (Fe-NPs) was achieved on polyester fabrics (PET) before and after the incorporation of dendrimers (PAMAM), 3-(aminopropyl) triethoxysilane (APTES) or thioglycerol (SH). The catalytic activity of the resulting materials (PET-Fe, PET-PAMAM-Fe, PET-APTES-Fe and PET-SH-FE) was comparatively investigated in the degradation of 4-nitrophenol (4-NP) and methylene-blue (MB). Full characterization through diverse instrumental methods allowed correlating the type of the organic moiety incorporated with the Fe content, catalytic properties and stability. The highest 4-NP degradation yield reached 99.6% in 12 min for PET-SH-Fe. The catalytic activity was explained in terms of reactant interaction with Fe-NPs. The 1st order reaction kinetics and pseudo-1st order adsorption kinetics provide evidence of the key role of reactant adsorption. These findings allow envisaging the preparation of fiber-based catalysts for potential uses in environmental and green chemistry.
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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".