Analysis of Base Content in In-Service Oils by Fourier Transform Infrared Spectroscopy
Bibliographic record
Abstract
An automated FTIR method for the determination of the base content (BC(pKa)) of oils at rates of > 120 samples/h has been developed. The method uses a 5% solution of trifluoroacetic acid in 1-propanol (TFA/P) added to heptane-diluted oil to react with the base present and measures the ν(COO(-)) absorption of the TFA anion produced, with calibrations devised by gravimetrically adding 1-methylimidazole to a heptane-TFA/P mixture. To minimize spectral interferences, all spectra are transformed to 2(nd) derivative spectra using a gap-segment algorithm. Any solvent displacement effects resulting from sample miscibility are spectrally accounted for by measurement of the changes in the 1-propanol overtone band at 1936 cm(-1). A variety of oils were analyzed for BC(0.5), expressed as mEq base/g oil as well as converted to base number (BN) units (mg KOH/g oil) to facilitate direct comparison with ASTM D2896 and ASTM D974 results for the same samples. Linear relationships were obtained between FTIR and D2896 and D974, with the ASTM methods producing higher BN values by factors of ~1.5 and ~1.3, respectively. Thus, the FTIR BC method correlates well with ASTM potentiometric procedures and, with its much higher throughput, promises to be a useful alternative means of rapidly determining reserve alkalinity in commercial oil condition monitoring laboratories.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".