Breathable Microgel Colloidosome: Gas-Switchable Microcapsules with O<sub>2</sub> and CO<sub>2</sub> Tunable Shell Permeability for Hierarchical Size-Selective Control Release
Bibliographic record
Abstract
Microcapsules enabling precise delivery and controlled release are highly desirable. However, it is still challenging to control the release profile by regulating the microcapsule shell permeability. In this work, gas-switchable microgel colloidosome (MGC) with oxygen (O 2 ) and carbon dioxide (CO 2 ) dual gas-tunable shell permeability has been developed and tested for control release of water-soluble cargo molecules, based on the size exclusion mechanism. The O 2 and CO 2 dual gas-switchable poly(2-(diethylamino)ethyl methacrylate- co -2,3,4,5,6-pentafluorostyrene), P(DEA- co -FS), microgels having surface modified with amino group (−NH 2 ) were synthesized and used to stabilize oil-in-water (O/W) Pickering emulsions. The oil-soluble poly(propylene glycol) diglycidyl ether (PPGDGE) was added as an intermicrogel cross-linker. The cross-linking between adjacent microgel particles at the water–oil interface was achieved through the amine–epoxy reaction of PPGDGE with the amine groups at the particle surface. Fluorescent-labeled dextran model cargo molecules of 10 kDa (D 1 ) and 2000 kDa (D 2 ) were uploaded under CO 2 treatment and locked inside the MGC with N 2 treatment. The O 2 and CO 2 dual-gas switchable properties offered the MGC with tunable shell permeability, which allowed the hierarchical release of D 1 and D 2 based on size exclusive mechanism. This work provides a robust method for preparation of gas-switchable microcapsules with tunable permeability and size-exclusive hierarchical release profile, promising for multiple ingredient controllable release, separation, and reaction.
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 | 0.000 |
| 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".