Bibliographic record
Abstract
The hydrophobicity of polymer surfaces limits their applications in many areas such as for use as biomaterials and in membrane filtration. One solution to this problem is to modify the polymer surface by ozonation. Ozonation introduces peroxide groups on polymer surface, which can initiate graft polymerization of monomers with hydrophilic groups, and thus improves the hydrophilicity of the polymer surfaces. The concentration of peroxide groups formed can be used to indicate the effectiveness of ozonation process. In this study, the low cost polyethylene film was selected as a model polymer film to conduct the investigation. Ozonation treatment was carried out in both gaseous and aqueous phases, to study the contribution of hydroxyl radical in the generation of peroxide group. Results revealed that aqueous ozonation generated slightly less peroxide than gaseous ozonation. However, the addition of soluble catalyst, copper (II) sulfate, to the aqueous ozonation resulted in 18% more peroxide concentration than that yielded by gaseous ozonation. Further investigation indicated that 0.05 g/L copper (II) sulfate was the optimal catalyst dose, and the optimal pH was approximately 5.60. A 19% reduction in tensile strength of the film was observed after 120 minutes of catalytic ozonation. Upon addition of a radical scavenger, tert-butyl alcohol (TBA), a decrease of 12% in the peroxide concentration was observed for catalytic ozonation with 0.1 mol/L TBA. This decrease indicated that both ozone and hydroxyl radical contributed to the peroxide generation in catalytic ozonation. A reaction mechanism for aqueous ozonation of polyethylene was proposed in this study by combining the reaction mechanism for gaseous ozonation of polyethylene and the decomposition mechanism of ozone in water. The experimental data found in this study verified the exponential function obtained for peroxide concentration. This verification was obtained for various ozonation time and dose ranging from 15 – 120 minutes and 1.0 – 3.0 wt%, respectively.
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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".