Identification of plant-based spilled oils using direct analysis in real-time–time-of-flight mass spectrometry with hydrophobic paper sampling
Bibliographic record
Abstract
Spilled plant-based oils behave very differently in comparison to petroleum oils and require different clean-up measures. They do not evaporate, disperse, dissolve, or emulsify to a significant degree but can polymerize and form an impermeable cap on sediment, smothering benthic media and resulting in an immediate impact on the wildlife community. The current study explored the application of rapid up-to-date direct analysis in real time (DART) with high-resolution mass spectrometry for plant-based oil typing. The study introduced a new concept of using hydrophobic paper to collect and analyze oil samples, thus minimizing sample preparation and expenses. Application of this technique showed its ability to speedily distinguish plant-based from petroleum-based oils. A microcosm experiment exposing plant-based oil samples to weathering processes for comparison with petroleum-based oils demonstrated the ability of the method to classify weathered oil samples and identify their source oil. It was observed that canola and peanut oil were the most resistant to weathering processes. The developed DART-TOFMS method was shown to be accurate for short-term weathered oil spills up to between 12 and 26 days of exposure. The developed method performed identification in less than a day compared to the established multi-day method for oil spill forensics requiring careful sample collection in glass containers, time-consuming laboratory clean-up, lengthy gas chromatography sequences, and careful integration including integration of retention time markers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".