Sustainable and Robust Cellulose‐Based Core–Shell Hydrogels Recycled from Waste Cotton Fabrics as High‐Performance Food Coolants
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
Abstract Ideal temperature condition is one of the essential determinants that critically impact the quality of food products. Conventional water‐based ice cubes present challenges from meltwater being breeding grounds for microorganisms and heightening the risk for cross‐contamination. Hereby, the presented cellulose‐based hydrogels crosslinked by epichlorohydrin are dip‐coated with alginate/calcium chloride to form a core–shell structure for achieving the critical benchmarks of an ideal food coolant with limited meltwater production, high‐water retention capacity, and high mechanical strength. The structures and properties of the hydrogels before and after freeze–thaw cycles are characterized by scanning electron microscopy, compressive test, water retention test, and differential scanning calorimetry. All formulated hydrogels demonstrate promising compressive strength, latent heat of fusion, and water retention properties. Notably, the C2A10Cl hydrogel exhibits a maximum compressive strength of 144.7 kPa and high latent heat of fusion of 272.5 J g –1 , which is better than previously reported sustainable hydrogel coolants. Furthermore, comparison studies reveal that the cellulose‐based hydrogels demonstrate a similar thawing pattern to conventional ice cubes but without the generation of any meltwater. The temperature of blueberries can be cooled down from 22 to 3.9 °C in 32 min by the hydrogels and in 26 min by ice cubes, respectively.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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