Measurement Of Refractive Indices Of Ternary Mixtures Using Digital Interferometry And Multi-Wavelength Abbemat Refractometer
Bibliographic record
Abstract
Abstract Knowing the liquid mixtures properties are significantly important in scientific experimentation and technological development. Thermal diffusion in mixtures plays a crucial role in both nature and technology. The temperature and concentration coefficients of refractive indices so called the Contrast factors contribute to study in various fields including, crude oil experiments (SCCO) and the distribution of crude oil components. The Abbemat Refractometer and Mach-Zehnder Interferometer technique has been proven a precise, highly accurate, and non-intrusive method for measuring the refractive index of a transparent medium. Refractive indices for three ternary mixtures containing three hydrocarbon compositions and their pure components of Tetrtahydronaphtalenene (THN), Isobutylbenzen (IBB), and Dodecane (C12), used mainly in gasoline, were experimentally measured using both the Mach-Zehnder Interferometer and a multi-wavelength Abbemat refractometer. Temperature and concentration coefficients of refractive indices, or contrast factors, as well as their individual correlation to calculate refractive indices have been presented in this research. The experimental measurements were correlated with a wide range of temperatures and wavelengths over a broad range of compositions. The experimental measurements of the refractive indices were compared with those estimated by applying several mixing rules: Lorentz-Lorenz, Gladstone-Dale, Arago-Biot, Eykman, Wiener, Newton, and Oster predictive equations. The experimental values of refractive indices are in substantial agreement with the values obtained by L-L, G-D, and A-B equations, and excepting values obtained by Oster and Newton equations. The temperature, concentration and wavelength dependence of refractive index in mixtures agrees with published data. A comparison with available literature and mixing rules shows that new correlations can predict the experimental data with deviations of less than 0.001.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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 | 0.001 |
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".