Preparation of Microcapsules Containing Benzoyl Peroxide Initiator with Gelatin-Gum Arabic/Polyurea-Formaldehyde Shell and Evaluating Their Storage Stability
Bibliographic record
Abstract
This work involves the optimized preparation and characterization of microcapsules which contain benzoyl peroxide (BPO) dispersed in dibutyl phthalate (DBP) with gelatin-gum arabic (Gel-GA)/polyurea-formaldehyde (PUF) shell. The microcapsules were prepared in two steps using complex coacervation and in situ polymerization techniques, respectively, at various mixing speeds and different core:shell ratios. The scanning electron microscopy (SEM), optical microscopy, and Fourier transform infrared (FTIR) spectroscopy were used for characterization of prepared microcapsules. The resultant microcapsules were spherical with average diameters about 120-200 μm, had no intercapsule bonding, and had thicknesses of 0.7-1.5 μm. The results revealed high core content loading, 82-89 wt % for microcapsules prepared at various mixing speeds. The differential scanning calorimetry analysis (DSC) indicated that the encapsulated BPO was not influenced by the encapsulation process and maintained its activity. Moreover, with a compact and double Gel-GA/PUF shell, the microcapsules were stable, and no leakage of core material in an acrylate-based resin and toluene as an organic solvent was recorded. The resultant microcapsules have the potential of usage in industries such as self-healing systems and structural adhesives where the impermeability of microcapsules is an important factor.
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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".