Standardized High-Performance Liquid Chromatography to Replace Conventional Methods for Determination of Saturate, Aromatic, Resin, and Asphaltene (SARA) Fractions
Bibliographic record
Abstract
One of the main approaches for compositional analysis of crude oils is SARA fractionation in which the sample is separated into saturate, aromatic, resin, and asphaltene fractions based on their polarity. A fully automated standardized SARA analysis for bitumen and heavy crudes has been developed and optimized using three commercial columns packed with different stationary phases based on the combination of adsorption and partition chromatography. The system is equipped with automated six-, eight-, and ten-port switching valves that control the flow direction. In this analytical technique, a bitumen (or heavy oil) sample diluted in toluene is swept through the column by pentane as the primary carrier phase. The sample is separated into four fractions by selective retention through interactions with the solvent mobile phases and the column stationary phases. The poly(tetrafluoroethylene) (PTFE) column filters asphaltenes, ZORBAX CN absorbs resins, and ZORBAX RX-SIL retains aromatics. Three samples of bitumen and heavy oils were fractionated to their SARA fractions by the developed method. Consistent results were obtained, proving the applicability of the new analytical technique to a wide range of crude oil samples. In addition, the performance of the developed SARA high-performance liquid chromatography (HPLC) method was compared with the conventional method, which demonstrates that it is more efficient, cost-effective, and consistent.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".