Fast, Efficient, Catalyst-Free Epoxidation of Butyl Rubber Using Oxone/Acetone for Improved Filler Dispersion
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Incorporation of a polar filler such as silica into a nonpolar rubber matrix is challenging and energy consuming due to their large difference in polarity. Epoxidation of carbon–carbon double bonds in unsaturated rubber, especially for rubber with low unsaturation such as butyl rubber, is an effective method to introduce polar functional groups to the rubber macromolecules for better filler dispersion. Although different epoxidation reagents including hydrogen peroxide (H 2 O 2 ), peracid, and meta-chloroperoxybenzoic acid (mCPBA) have been previously reported, these reagents have different drawbacks. In this article, a metal-free epoxidation reagent, dimethyl dioxirane (DMDO), generated from acetone and Oxone is explored for efficient epoxidation of rubber with low unsaturation. The effects of the addition manner of the reactant Oxone and buffer sodium bicarbonate (NaHCO 3 ) and reaction temperature on the epoxide formation are studied. Compared to peracid, a faster and more efficient epoxidation without the generation of a ring-opened product is achieved when DMDO is used as the epoxidation reagent. Furthermore, it is found that the epoxidation using DMDO is not sensitive to the water concentration in the rubber solution up to 20 wt %. The addition of quaternary ammonium salt as a phase transfer catalyst not only improves the conversion but also further increases the water tolerance to 25 wt %. The reaction conditions for preparation of epoxidized butyl rubber with different percentages of epoxide group are optimized by Design of Experiments (DoE). At the end, improved dispersion of silica in the matrix of epoxidized butyl rubber is achieved, as revealed by the rubber process analyzer (RPA) and atomic force microscopy (AFM).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".