Potential of NMR Spectroscopy in the Characterization of Nonconventional Oils
Bibliographic record
Abstract
NMR spectroscopy was applied for the characterization of two biomass based pyrolysis oil samples. The samples were extracted in various solvents and the extracts were investigated by both 1H and 13C NMR spectroscopy. Subsequent evaluation of the integrated analytical data revealed chemical information regarding semiquantitative estimation of various functional groups. This information could not have been obtained readily from the individual spectroscopic techniques. Semiquantitative estimation of the various functional groups allowed a comparison of the extraction efficiency of these groups in various solvents. The method is based on the premise that although the number of individual molecular species in pyrolysis oil liquid is large, most of these species are composed of a limited number of functional groups. The methodology provided information on the concentration of chemical functionalities that are potentially useful for synthetic modifications and may help to guide the use of pyrolysis oil as a chemical feedstock. The approach described is expected to be generally applicable to complex mixture of hydrocarbon oils such as bio-oils, oil sands bitumen, and coal pyrolysis oils.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".