Gelation of crystalline nanocellulose in the presence of hydroxyethyl cellulose
Bibliographic record
Abstract
Abstract In this work, hydroxyethyl cellulose (HEC) was used to induce gelation of electrosterically stabilized cellulose nanocrystal (ECNC) and common cellulose nanocrystal (CNC) suspensions. The main goals were to shift the gelation point to low concentrations of nanoparticles and clarify the role of interactions between ECNCs in contrast to CNCs. The rheological properties of CNC suspensions were investigated in the presence of HEC with or without CaCl2 while ECNC suspensions would be only mixed with HEC since the addition of salt would not alter the viscoelastic properties of the whole system. The structure build‐up kinetics as well as the viscoelastic properties of the suspensions were compared. CaCl2 was used to induce gelation of CNC suspensions at a solid content as low as 0.2 g/g in the presence of HEC. ECNC suspensions were less sensitive to HEC since the best result obtained for inducing gelation was achieved at a concentration of 4 g/g. All the results presented are explained by the adsorption of HEC on the nanoparticles, which was determined by viscometry. High adsorption level of HEC chains imparted CNCs better colloidal stability in the presence of CaCl2 as compared to pristine CNCs, whereas it did not affect the ECNC colloidal stability.
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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".