Cellulose Nanocrystal Gels with Tunable Mechanical Properties from Hybrid Thermal Strategies
Bibliographic record
Abstract
Gels are useful materials for drug delivery, wound dressings, tissue engineering, and 3D printing. These various applications require gels with different mechanical properties that can be easily tuned, also preferably excluding the use of chemical additives, which can be toxic or harmful to the body or environment. Here, we report a novel strategy to synthesize cellulose nanocrystal (CNC) gels with tunable mechanical properties. Sequential freeze-thaw cycling and hydrothermal treatments were applied to CNC suspensions in different orders to give a series of pristine CNC hydrogels. Freeze-drying of the hydrogels also afforded a series of lightweight CNC aerogels. The mechanical properties of the hydrogels and aerogels were studied by rheological measurements and compression strength tests, respectively. Specifically, the complex modulus of CNC hydrogels ranged from 160 to 32,000 Pa among eight different hydrogels, while Young's modulus of CNC aerogels was tuned from 0.114 to 3.98 MPa across five different aerogels. The microstructures of aerogels were also investigated by scanning electron microscopy and X-ray microtomography, which revealed remarkable differences between the materials. Solvent sorption-desorption tests showed that the reinforced networks have excellent stability over the basic CNC aerogels in ethanol, demonstrating a material enhancement from the preparation strategies we developed. Thermal conductivity and thermal stability for these materials were also investigated, and it was found that the lowest thermal conductivity was 0.030 W/m K, and all of the aerogels are generally stable below 280 °C. These characteristics also expand the potential applications of this family of CNC gels to lightweight supporting materials and thermal insulators.
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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".