Prediction and Experimental Measurement of Refractive Index in Ternary Hydrocarbon Mixtures
Bibliographic record
Abstract
The physical properties of hydrocarbon mixtures are of great importance in the field of science and technology. Knowing the refractive index of multicomponent liquid mixtures is essential in order to characterize these systems; however, there is a limited amount of experimental data regarding their optical properties. The present study provides precise experimental measurements of the refractive indices of three hydrocarbon compounds: 1,2,3,4-tetrahydronaphthalene (THN), isobutylbenzene (IBB), and dodecane ( n C 12 ). Sixty-three compositions (36 ternaries +27 binaries) were prepared and investigated along with their three pure components using a wide range of concentrations, temperatures and wavelengths. The refractive indices were measured using the multiwavelength Abbemat refractometer. The experimental data were then used to develop and validate new mathematical correlations which can be used to predict the refractive index of ternary mixtures as a function of concentration, temperature, and wavelength. There was a strong correlation between the experimental data and the predictive values with average residual values of ± 1.55·10 –3 . This study also investigated the relative validity of the experimental measurements of the refractive indices with theoretically estimated values using mixing rules and data from the literature. The experimental values were in substantial agreement with the predictive equation values with deviations of ± 2.50·10 –3 .
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".