Rheological investigation of complex micro and nanofibrillated cellulose (MNFC) suspensions: Discussion of flow curves and gel stability
Bibliographic record
Abstract
Micro and nanofibrillated cellulose in aqueous suspension presents many challenges when considering its use, for example, in forming nanocomposites. The inclusion of filler particles either as extender or as functional additive allows the range of strength and deformation properties to be extended. These properties, however, are linked in many cases to the rheological properties of the raw material mix. Interactions under dynamic shear or under controlled stress at low amplitude reveal the potential to generate functional interactions, not only between the cellulose components themselves but also between the cellulose and polymer additives, as well as surface modified pigment fillers. Examples are given demonstrating the action of adding cellulosic polymer in the form of carboxymethyl cellulose (CMC) to micro and nanofibrillated cellulose (MNFC). Rheological studies show how these combinations with CMC, added either in free form or preadsorbed onto calcium carbonate filler particles, lead to a variety of responses. Dispersability of the MNFC is increased by the use of free CMC polymer addition, and the usually expected flocculating action on added filler is seen not to occur. Alternatively, the preadsorbed CMC on the calcium carbonate pigment filler leads to an interaction between the fibrillar cellulose and the surface modified calcium carbonate pigment filler, to which incorporation of cationic polymer leads to a reduction of interaction, provided theaddition level does not exceed the isoelectric point of the mix. The observations are viewed in the context of a combination of proposed physical contact dynamics in the form of disordered and ordered alignment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".