O/W Pickering Emulsion Templated Organo-hydrogels with Enhanced Mechanical Strength and Energy Storage Capacity
Why this work is in the frame
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Bibliographic record
Abstract
The increased quantities of fat in plants could allow the cells to inhibit the growth of ice and thus prevent the damages of their tissue structure in winter. In view of the structural buildup of freezing tolerance mechanism, here we presented a facile way of employing O/W Pickering emulsion as a template to produce the freestanding organo-hydrogels with increased mechanical stability and energy storage capacity. The oil droplets stabilized by cellulose nanofibrils were dispersed in the alginate polymer network that cross-linked with Ca2+, which resulted in homogeneous and closely packed microstructures. The prepared organo-hydrogels could maintain original gel structure under frozen conditions and had extraordinary mechanical performance. It could endure compressive stress up to 35 KPa (at 50% strain) and the elastic modulus was around 72 KPa, while the solid content of polysaccharides was only about 0.75%. By using our comprehensive strategy, organo-hydrogels with higher volumes of oil phase exhibited an enhanced cold storage capacity. For alginate hydrogel, it took 8 min when the temperature rose from 0 to 5 °C, while for the organo-hydrogel with oil volume of 30%, it took about 24 min. After 34 min, the inner temperature of alginate hydrogel was close to 25 °C, and about 70 min were needed for the temperature of organo-hydrogel to reach 25 °C. This kind of gel materials with complementary heteronetworks not only will have potential applications in cold chain logistics, but also can be applied in other fields with unusual functions.
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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) | 0.000 | 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 it