Ultra‐trace analysis of furanic compounds in transformer/rectifier oils with water extraction and high‐performance liquid chromatography
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
A novel approach for the determination of parts-per-billion level of 5-hydroxymethyl-2-furaldehyde, furfuryl alcohol, furfural, 2-furyl methyl ketone, and 5-methylfurfural in transformer or rectifier oils has been successfully innovated and implemented. Various extraction methods including solid-phase extraction, liquid-liquid extraction using methanol, acetonitrile, and water were studied. Water was by far the most efficient solvent for use as an extraction medium. Separation of the analytes was conducted using a 4.6 mm × 250 mm × 3.5 μm Agilent Zorbax column while detection and quantitation were conducted with a variable wavelength UV detector. Detection limits of all furans were at 1 ppb v/v with linear ranges range from 5 to 1000 ppb v/v with correlation coefficients of 0.997 or better. A relative standard deviation of at most 2.4% at 1000 ppb v/v and 7.3% at 5 ppb v/v and a recovery from 43% to 90% depending on the analyte monitored were obtained. The method was purposely designed to be environmental friendly with water as an extraction medium. Also, the method uses 80% water and 20% acetonitrile with a mere 0.2 mL/min of acetonitrile in an acetonitrile/water mixture as mobile phase. The analytical technique has been demonstrated to be highly reliable with low cost of ownership, suitable for deployment in quality control labs or in regions where available analytical resources and solvents are difficult to procure.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it