Characterisation and applications of microcapsules obtained by interfacial polycondensation
Bibliographic record
Abstract
This review highlights the materials, mechanisms and applications of microencapsulation by interfacial polycondensation in different areas. This technology entraps active ingredients inside microcapsules/microspheres, having an average diameter ranging from nanosize to several 100 µ. Polycondensation reactions take place at the boundary of two phases to form the shells of microcapsules or matrix microspheres. The emulsion can be classified into three types: water-in-oil, oil-in-water and oil-in-oil. According to the hydrophilic-lipophilic property of core phase, different active substances, such as proteins, enzymes, insecticides, herbicides, vitamins, catalysts, drugs, essential oils, dyes and phase change materials, have been successfully incorporated into different microcapsules/microspheres. Based on the shell-forming materials, this technology is capable of preparing polyamine, polyurea, polyurethane, polythiourea, polyester, polyepoxide, polyacrylamide and polysiloxane microcapsules. Over the past two decades, microcapsules prepared by interfacial polycondensation have been widely used in carbonless paper, cosmetics, pharmacy, agriculture, energy storage/transfer, thermal insulation/regulation and information and magnetic recording.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".