Viscosity‐concentration relationships for nanodispersions based on glass transition point
Bibliographic record
Abstract
ABSTRACT The theoretical background relevant to modelling of the viscous behaviour of nanodispersions (nanosuspensions and nanoemulsions) is discussed. Using free‐volume arguments, a simple model is developed to describe the viscous behaviour of nanodispersions. A large pool (16 sets) of available experimental data on the zero‐shear relative viscosity of nanodispersions is correlated on the basis of the effective volume fraction of solvated nanoparticles/nanodroplets. It is found that the experimental data can be successfully correlated using the volume fraction of solvated nanoparticles/nanodroplets only below the glass transition volume fraction of solvated nanoparticles/nanodroplets. The glass transition volume fraction of solvated nanoparticles/nanodroplets is about 0.58, which is in agreement with the value found in the literature. The experimental relative viscosity data of all sixteen sets of nanoemulsions and nanosuspensions collapse on to a single curve which is well described by the proposed model provided that the volume fraction of solvated nanoparticles/nanodroplets is below the glass transition value of 0.58. Above the glass transition volume fraction of 0.58, the correlation of relative viscosity on the basis of solvated volume fraction is not successful. Above the glass transition point, the nanodispersion is in an arrested (jammed) state and is expected to possess yield‐stress. Thus the concept of zero‐shear viscosity itself becomes unreliable and meaningless above the glass transition point.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| 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 | 0.000 |
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".