Rheology of nanocellulose-rich aqueous suspensions: A Review
Bibliographic record
Abstract
The flow characteristics of dilute aqueous suspensions of cellulose nanocrystals (CNC), nanofibrillated cellulose (NFC), and related products in dilute aqueous suspensions could be of great importance for many emerging applications. This review article considers publications dealing with the rheology of nanocellulose aqueous suspensions in the absence of matrix materials. In other words, the focus is on systems in which the cellulosic particles themselves – dependent on their morphology and the interactive forces between them – largely govern the observed rheological effects. Substantial progress in understanding rheological phenomena is evident in the large volume of recent publications dealing with such issues including the effects of flow history, stratification of solid and fluid layers during testing, entanglement of nanocellulose particles, and the variation of inter-particle forces by changing the pH or salt concentrations, among other factors. Better quantification of particle shape and particle-to-particle interactions may provide advances in future understanding. Despite the very complex morphology of highly fibrillated cellulosic nanomaterials, progress is being made in understanding their rheology, which supports their usage in applications such as coating, thickening, and 3D printing.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".