A new proposed approach to estimate the thermodiffusion coefficients for linear chain hydrocarbon binary mixtures
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Bibliographic record
Abstract
Thermodiffusion behaviors in nonassociating mixtures have an important role in separation processes of the oil industry. The variations of composition and temperature may either lessen or enhance the separation in mixtures. A new model regarding the prediction of thermodiffusion coefficients for linear chain hydrocarbon binary mixtures using the thermodynamics of irreversible process is proposed. The model predicts the net amount of heat transported based on available volume for each molecule. This newly proposed model combined with the perturbed chain statistical associating fluid theory equation of state has been applied to predict thermodiffusion coefficients for binary hydrocarbon mixtures of C(10)-nC(i) (i=5,6,7,15,16,17,18), C(12)-nC(i) (i=5,6,7,8,9), and C(18)-nC(i) (i=5,6,7,8,9,12). Comparisons of the calculated theoretical results with the experimental data show good performance of the proposed model. In particular, this model which is based on the kinetic approaches has been found to be the most reliable and represents a significant improvement over the earlier models.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 it