Fabrication of thermo-responsive multicore microcapsules using a facile extrusion process
Bibliographic record
Abstract
A process employing extrusion was used to produce multicore microcapsules composed of multiple beads. The inner beads were made from κ-carrageenan (κ-c), a thermo-responsive linear sulphated polymer whose gelling temperature ranges at 40-60 °C, depending on the concentration of κ-c polymer and the amount of potassium chloride used for gelation. The resulting beads were then enveloped by chitosan through gelation with sodium triphosphate. The pesticide ammonium glufosinate was encapsulated in the κ-c/chitosan multicore microcapsules for demonstration of controlled release of the encapsulant. It was found that in response to an external stimulus, such as elevated temperature or solar simulation, the microcapsules exhibit the gradual release of encapsulated pesticide molecules from multicore microcapsules, compared with beads only. This process of making multicore microcapsules can be extended to other polymer pairs based on applications. This work is relevant to agriculture, where the controlled-release of the pesticides or fertilizers could be triggered by the sun and/or temperature changes, thus extending the residual period of the chemicals as well as decreasing the extent of pollution by leaching of abundant chemicals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".