Vibrational Spectroscopy and Thermophysical Properties of Ultralow Sulfur Diesel—Alternative Fuel Binary Blends
Bibliographic record
Abstract
Four alternative fuels (AF) were blended with ultralow sulfur diesel (ULSD) at five different proportions (10, 20, 30, 50, and 100 vol % AF) to create 20 binary mixtures in this work. Two renewable jet AFs and two renewable diesel AFs were investigated. Interactions between the components in the mixtures were analyzed by means of spectroscopy (Raman, near-infrared), thermophysical (thermal diffusivity, thermo-optic coefficient), and physical (density) techniques. Correlations among the data were investigated using principal component analysis and partial least-squares regression. Trends in Raman intensities and band positions as well as thermophysical properties showed that the AF/ULSD blends resembled two-component mixtures despite the known complexities of the constituents. Specifically, spectra combined according to the percentages of the components in each mixture; thermophysical and physical properties exhibited similar behavior. The spectra showed strong correlations with all three physical properties, creating the possibility for predicting the properties of similar AF/ULSD mixtures. These properties are governed by the chemical compositions of the fuels.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".