Nanocellulose-lysozyme colloidal gels via electrostatic complexation
Bibliographic record
Abstract
Biohybrid colloids were fabricated based on electrostatic complexation between anionic TEMPO-oxidized cellulose nanofibrils (TO-CNF) and cationic hen egg white lysozyme (HEWL). By altering the loading of HEWL, physical colloidal complexes can be obtained at a relatively low concentration of TO-CNF (0.1 wt%). At neutral pH, increasing the HEWL loading induces an increase in charge screening, as probed by zeta-potential, resulting in enhanced TO-CNF aggregation and colloidal gel formation. Systematic rheological testing shows that mechanical reinforcement of the prepared biohybrid gels is easily achieved by increasing the loading of HEWL. However, due to the relatively weak nature of electrostatic complexation, the formed colloidal gels exhibit partial destruction when subjected to cyclic shear stresses. Still, they resist thermo-cycling up to 90 °C. Finally, the pH responsiveness of the colloidal complex gels was demonstrated by adjusting pH to above and below the isoelectric point of HEWL, representing a facile mechanism to tune the gelation of TO-CNF/HEWL complexes. This work highlights the potential of using electrostatic complexation between HEWL and TO-CNF to form hybrid colloids, and demonstrates the tunability of the colloidal morphology and rheology by adjusting the ratio between the two components and the pH.
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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) | 0.001 | 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 | 0.001 | 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".