Viscosity Studies of Aqueous Solutions of Hafnium Oxide Particles and Polystyrene Nanospheres
Bibliographic record
Abstract
Nanoparticle colloidal system rheology has long been researched, without many concrete conclusions. Literature has been devoted to the viscosity and shear properties of these systems since Einstein’s PhD thesis. However, most models are based on molecular dynamics which are not necessarily applicable to real systems, and most real systems are modeled by empiricism. This report looks to unify these approaches through rheological testing and mathematical analysis in order to achieve several goals using a system composed of hafnium oxide particles suspended in water. The first goal is to have a viscosity model that fits not only empirical data, but also the relevant theory and first principles. By employing the modern techniques of a rhoemeter-on-a-chip to nano-scale particles, the limitations of traditional rheometry are bypassed. The molecular dynamics approaches are converted to zero-shear and infinite-shear viscosities which can be applied to traditional models. A modern model was then derived, applied to new data, and agreement was found to a satisfactory degree. No significant change in viscosity with shear rate was found experimentally or analytically. Traditional research is done with spherical particles, such as polystyrene nanopsheres, as which we are approximating hafnium oxide (HfOx) to be. Polystyrene nanospheres are nominally spherical and commercially available at relatively inexpensive costs. Actual spherical data was required for appropriate comparison, and the findings show that the spherical particles have distinct properties.
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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 | 0.001 |
| 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) | 0.000 | 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".