Rheology of Suspensions of Solid Particles in Liquids Thickened by Starch Nanoparticles
Bibliographic record
Abstract
The rheology of suspensions of solid particles in aqueous matrix liquids thickened by starch nanoparticles (SNP) was investigated. The SNP concentration varied from 9.89 to 34.60 wt% based on the aqueous matrix phase. The solids concentration of suspensions varied from 0 to 47 wt% (0 to 56 vol%). The suspensions at any given SNP concentration were generally Newtonian at low solids concentrations. At high solids concentrations, the suspensions were non-Newtonian shear-thinning. With the increase in the SNP concentration, the suspensions become non-Newtonian at a lower solids concentration. The rheological behavior of non-Newtonian suspensions could be described adequately with a power-law model. The consistency index of the suspension increased with the increase in solids concentration of the suspension at any given SNP concentration. The flow behavior index of suspensions was well below unity at high solids concentrations, indicating non-Newtonian shear-thinning behavior. The value of the flow behavior index decreased with the increase in solids concentration indicating an enhancement of shear-thinning in suspensions. The experimental viscosity and consistency data for Newtonian and non-Newtonian suspensions showed good agreement with the predictions of the Pal viscosity model for suspensions.
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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 | 0.001 |
| 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".