New Experimental Data and Reference Models for the Viscosity and Density of Squalane
Bibliographic record
Abstract
Empirical models for the density and the viscosity of squalane (C 30 H 62; 2,6,10,15,19,23-hexamethyltetracosane) have been developed based on an exhaustive review of the data available in the literature and new experimental density and viscosity measurements carried out as a part of this work. The literature review shows there is a substantial lack of density and viscosity data at high temperature (373 to 473) K and high pressure conditions (pressures up to 200 MPa). These gaps were addressed with new experimental measurements carried out at temperatures of (338 to 473) K and at pressures of (1 to 202.1) MPa. The new data were utilized in the model development to improve the density and viscosity calculation of squalane at all conditions including high temperatures and high pressures. The model presented in this work reproduces the best squalane density and viscosity data available based on a new combined outlier and regression algorithm. The combination of the empirical models and the regression approach resulted in models which could reproduce the experimental density data with average absolute percent deviation of 0.04 %, bias of 0.000 %, standard deviation of 0.05 %, and maximum absolute percent deviation of 0.14 % and reproduce the experimental viscosity data with average absolute percent deviation of 1.4 %, bias of 0.02 %, standard deviation of 1.8 %, and maximum absolute percent deviation of 4.9 % over a wide range of temperatures and pressures. On the basis of the data set used in the model regression (without outliers), the density model is limited to the pressure and temperature ranges of (0.1 to 202.1) MPa and (273 to 525) K, whereas the viscosity model is limited to the pressure and temperature ranges of (0.1 to 467.0) MPa and (273 to 473) K. These models can be used to calibrate laboratory densitometers and viscometers at relevant high-temperature, high-pressure conditions.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".