Granular hydrogels with tunable properties prepared from gum Arabic and protein microgels
Bibliographic record
Abstract
Granular hydrogels have emerged as a new class of materials for 3D printing, tissue engineering, and food applications due to their extrudability, porosity, and modularity. This work introduces a convenient method to prepare granular hydrogel with tunable properties by modulating the interaction between gum Arabic (GA) and whey protein isolate (WPI) microgels. As the concentration of GA increased, the appearance of the hydrogel changed from fluid liquid to moldable solid, and the microstructure changed from a macro-porous structure with thin walls to a dense structure formed by the accumulation of spherical particles. At a GA concentration of 0.5 %, the hydrogels remained fluid. Granular hydrogels containing 1.0 % GA showed mechanical properties similar to those of tofu (compressive strength: 10.8 ± 0.5 kPa, Young's modulus: 16.7 ± 0.4 kPa), while granular hydrogels containing 1.5 % GA showed mechanical properties similar to those of hawthorn sticks and sausages (compressive strength: 300.4 ± 5.8 kPa; Young's modulus: 200.5 ± 3.4 kPa). The hydrogel with 2.0 % GA was similar to hawthorn sticks, with satisfactory bite resistance and elasticity. Such tunability has led to various application potentials in the food industry to meet consumer demand for healthy, nutritious, and diverse textures.
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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".