Bibliographic record
Abstract
Colloidal gels of cellulose nanocrystals (CNCs) were prepared using three different strategies by manipulating their colloidal stability. In the first approach, colloidal gels were prepared by hydrothermally treating CNC suspensions. Desulfation of the CNCs at high temperature appears to be responsible for the gelation of the CNCs, giving highly porous networks. In the second approach, a carbon dioxide-switchable (CO₂-switchable) hydrogel was prepared by adding imidazole to a suspension of CNCs. Sparging of CO₂ through the imidazole-containing CNC suspension led to gelation of the CNCs, which could be reversed by subsequent sparging with nitrogen gas (N₂) to form a low-viscosity CNC suspension. The gelation process and the properties of the hydrogels were investigated by rheology, zeta potential, pH, and conductivity measurements, and the gels were found to have tunable mechanical properties. This work describes a straightforward way to obtain switchable CNC hydrogels without the need to functionalize CNCs or add strong acids or bases. These CO₂-responsive CNC hydrogels have potential applications in stimuli-responsive adsorbents, filters, and flocculants. Lastly, physical colloidal gels were prepared by freeze-thaw (FT) cycling of CNC suspensions. The aggregation of CNCs was driven by the physical confinement of CNCs between growing ice crystal domains. FT cycling was employed to form larger aggregates of CNCs without changing the surface chemistry or ionic strength of the suspensions. Gelation of CNC suspensions by FT cycling was demonstrated in water and other polar solvents. The mechanical and structural properties of the gels were investigated using rheometry, electron microscopy, X-ray diffraction and dynamic light scattering. It was found that the rheology could be tuned by varying the freezing time, the number of FT cycles, and concentration of CNCs in suspension. Considering the wide natural abundance and biocompatibility of CNCs, these approaches to CNC-based hydrogels are attractive for producing materials that can be used in drug delivery, insulating materials, and as tissue scaffolds.
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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) | 0.002 | 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".