Reviewing the use of ethylcellulose, methylcellulose and hypromellose in microencapsulation. Part 3: Applications for microcapsules
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This three-part review has been developed following the evaluation of literature where ethylcellulose, methylcellulose, or hypromellose was used to make microcapsules. Parts 1 and 2 of the review are published in separate papers. Part 1 covers the various materials used to formulate microcapsules, and Part 2 covers the various techniques used to make microcapsules. In the current paper, Part 3 covers the end-use applications for which microcapsules are used. Examples of applications to be covered include modified release, improved efficacy and safety, multiparticulate compression, improved processability and stability, and taste- and odor-masking. It is hoped that formulators can use Part 3 to understand the various end-use applications of microcapsules made from these encapsulating polymers. SciFinder was utilized to perform the literature search. SciFinder leverages literature databases, such as Chemical Abstracts Service Registry and Medline. A total of 379 references were identified during the review. The need for a three-part review reflects the extensive amount of literature identified concerning these three encapsulating polymers.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it