Zirconia, Titania, Silica, and Carbon Columns for the Group-Type Characterization of Heavy Gas Oils Using High Temperature Normal Phase Liquid Chromatography
Bibliographic record
Abstract
The hydrocarbon group-type composition of fossil fuels affects their performance and environmental properties. Normal phase high performance liquid chromatography (NPLC) is commonly used to determine the proportion of saturates, mono-, di-, tri-, and polyaromatics in fuel and gas oil samples. It is often difficult to achieve sufficient resolution between group-types using conventional normal phase columns and conditions. This incomplete resolution can negatively impact the accuracy of the group-type analysis. This paper investigates the use of titania, zirconia, carbon, silica, and aminopropyl bonded silica columns to improve group-type resolution using high temperature NPLC. Better group-type selectivity and faster separations were obtained by increasing column temperatures from 35 to 200 °C. A high resolution hydrocarbon group-type analysis method was developed using titania and silica columns with valve-switching and dual gradients to analyze three heavy gas oils (boiling range > 350 °C). A titania column at a high flow rate of 5.0 mL min –1 yielded separations in only 3 min.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".