Photothermal characterization of biodiesel and petroleum diesel fuels—A review and perspective
Bibliographic record
Abstract
Alternative fuels hold considerable promise as substitutes for petroleum diesel fuel. As such, biodiesel is a promising renewable fuel that has been developed and tested by a number of research groups. Quality control of this transportation fuel is of great significance to its commercialization. Conventional chromatographic and spectroscopic analytical methods are most commonly used for biodiesel characterization, in some cases yielding information detail beyond that needed for the determination of biodiesel quality. By contrast, less common methods, such as photothermal techniques, are well suited to characterize a wide range of transportation fuels. The complexities of photothermal and chemical analytical techniques are roughly similar, as are the costs. Photothermal methods are based on spectroscopic and thermophysical properties of the sample, an advantage with respect to ordinary chromatography and spectroscopy techniques. Furthermore, some photothermal techniques can be adapted for remote signal detection, which can be used for in situ analysis in fuel production for inline biodiesel quality inspection and control. Therefore, an overview and outlook of the photothermal characterization is of considerable interest. In this paper, the applications of photothermal techniques in the characterization of biodiesel, petroleum diesel fuels, and their blends are reviewed. The review includes thermophysical properties and correlations for fuels, determination of blend levels, and biodiesel stability investigations. After the review, discussion and perspective are presented for future improvement of photothermal characterization and industrial applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".