The sol–gel transition of ultra-low solid content TEMPO-cellulose nanofibril/mixed-linkage β-glucan bionanocomposite gels
Bibliographic record
Abstract
We present the preparation, morphological analysis, and rheological characterization of ultra-low solid content gels prepared by physically cross-linking TEMPO-oxidized cellulose nanofibrils (TEMPO-CNF) with the soluble plant-cell-wall polysaccharide, mixed-linkage β-glucan (MLG). Of particular note, gel formation was rapidly induced by very small amounts of MLG (e.g. 0.125% w/v) at extremely low TEMPO-CNF concentration (0.05% w/v), which independently were otherwise fluid and thus easily handled. Rheology of these bionanocomposite gel systems as a function of MLG and TEMPO-CNF concentrations revealed that the critical gel concentration of MLG and TEMPO-CNF followed a power-law relation of the concentration of the other component. Surprisingly, these systems also exhibited an additional transition to thick gels at high TEMPO-CNF and MLG concentrations that was visible only at low frequencies. Cryogenic scanning electron microscopy (cryo-SEM) imaging of admixture solutions and gels revealed increased network crowding with increasing MLG amounts. The data are consistent with the hypothesis that non-covalent cellulose-MLG interactions, analogous to those occurring within plant cell walls, drive gel formation. The ability to tune gel physical properties simply by controlling CNF (a promising forest bioproduct) and MLG (a readily available agricultural polysaccharide) fractions at very low solid and polymer content opens new possibilities for material applications in diverse industries.
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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".