Sustainable and Robust Cellulose‐Based Core–Shell Hydrogels Recycled from Waste Cotton Fabrics as High‐Performance Food Coolants
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.
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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) | 0.001 | 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".