Isolation and Identification of Six Difunctional Ethyl Esters from Bio-oil and Their Special Mass Spectral Fragmentation Pathways
Bibliographic record
Abstract
Three difunctional ethyl esters (DFEEs), including ethyl 16-hydroxyhexadecanoate, ethyl ( Z )-18-hydroxyoctadec-9-enoate, and diethyl ( Z )-octadec-9-ene-1,18-dioate, were isolated as nearly pure compounds from sawdust-derived bio-oil. Their structures were tentatively identified by gas chromatography/mass spectrometry (GC/MS) and high-resolution mass spectrometry (HR-MS). Combination transesterification reaction of DFEEs to difunctional methyl esters (DFMEs) and determination of the GC/MS retention time differences of DFEEs and corresponding DFMEs is a simple method to determine the ester group number of unknown DFEEs. GC/MS results indicate that the fragment ions at mass-to-charge ratios ( m / z ) of 88 and 98 are typical ions for these DFEEs. The former ion is formed via well-known McLafferty rearrangement, whereas the formation pathway of the ion at m / z 98 is unreported before. HR-MS results show that the fragment ion at m / z 98 should be a cyclohexanone radical ion, which is possibly formed via a novel rearrangement initiated by remote hydrogen rearrangement. Besides long-chain DFEEs, long-chain difunctional carboxylic acids (including dicarboxylic acids, ω-hydroxy acids, diacid monomethyl ester, and diacid monoethyl ester) and DFMEs (including diacid dimethyl ester and ω-hydroxy methyl ester) are also typical of the fragment ion at m / z 98. Hence, it is a common characteristic to form the rearrangement ion at m / z 98 for long-chain difunctional carboxylic acids, DFEEs, and DFMEs, and this special mass spectral rearrangement could facilitate the identification of these classes of compounds. Using this rearrangement rule of DFEEs, three ω-hydroxy ethyl esters, including ethyl 22-hydroxydocosanoate, ethyl 24-hydroxytetracosanoate, and ethyl 26-hydroxyhexacosanoate, were tentatively identified by GC/MS.
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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 | 0.000 |
| 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".