Comparison of Hydrogels Prepared with Ionic-Liquid-Isolated vs Commercial Chitin and Cellulose
Bibliographic record
Abstract
Physical and/or covalently linked (chemical) hydrogels were prepared from chitin and cellulose extracted with ionic liquid from shrimp shells and wood biomass, respectively, and compared with hydrogels prepared from commercially available biopolymers, practical grade chitin, and microcrystalline cellulose. The highly porous aerogels were formed by initial dissolution of the biopolymers in NaOH/urea aqueous systems using freeze/thaw cycles, followed by thermal treatment (with or without epichlorohydrin as a cross-linker) and supercritical CO 2 drying. The ionic-liquid-extracted cellulose pulp and chitin, as well as practical grade chitin could form both stable physical and chemical hydrogels, whereas biopolymers of lower apparent molecular weight such as microcrystalline cellulose required a covalent cross-linker for hydrogel formation and commercially available pure chitin was not suitable for the preparation of hydrogels of either type. Hydrogels prepared from the ionic-liquid-extracted biopolymers exhibited properties substantially different from those made from the commercially available biopolymers. Loading of an active ingredient into the hydrogel and its subsequent release was demonstrated using indigo carmine and revealed that the release rate was controlled mainly by the biopolymer concentration of the gel network.
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) | 0.002 | 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".