Characteristic and discrimination of unconventional oil samples by two‐dimensional extraction combined with concentration‐resolved fluorescence spectroscopy
Bibliographic record
Abstract
Abstract Fluorescence detection of petroleum related samples has excellent application value for unconventional oil exploration and petrochemical detection. In this paper, based on the concentration‐resolved fluorescence spectra, the synchronous fluorescence spectra were used in the range of 3.0 × 10−3 g/L to 5.0 g/L for different kinds of petroleum samples and petrochemical products with n‐hexane and isopropanol as extractants. A red shift of the fluorescence spectra with an increase in the concentration was found under both extractants. The solubility of n‐hexane on crude oil was stronger while that of isopropanol was better for aromatic compounds. The results show that the effect of different extracting agents on the spectra can be used to acquire more information about aromatics components in petroleum related samples, which should be carefully considered in the process of quantitative and qualitative analysis of crude oil samples by fluorescence spectra. It is also shown that the combined extraction of different extractants could enhance the analytical ability of the fluorescence spectrometry, and the rapid, non‐contact measurement is promising for in‐field use.
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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 | 0.001 | 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".