Determination of Acid Number and Base Number in Lubricants by Fourier Transform Infrared Spectroscopy
Bibliographic record
Abstract
This paper describes the development of practical Fourier transform infrared (FT-IR) methods for the determination of acid number (AN) and base number (BN) in lubricants through the combined use of signal transduction via stoichiometric reactions and differential spectroscopy to circumvent matrix effects. Trifluoroacetic acid and potassium phthalimide were used as stoichiometric reactants to provide infrared (IR) signals proportional to the basic and acidic constituents present in oils. Samples were initially diluted with 1-propanol, then split, with one half treated with the stoichiometric reactant and the other half with a blank reagent, their spectra collected, and a differential spectrum obtained to ratio out the invariant spectral contributions from the sample. Quantitation for AN and BN was based on measurement of the peak height of the v(C = O) or v(COO) absorptions, respectively, of the products of the corresponding stoichiometric reactions, yielding a standard error of calibration of < 0.1 mg KOH/g oil. The AN/BN FT-IR methods were validated by the analysis of a wide range of new and used oils supplied by third parties, which had been analyzed by ASTM methods. Good correlations were obtained between the chemical and FT-IR methods, indicating that the measures are on the whole comparable. From a practical perspective, these new FT-IR methods have significant advantages over ASTM titrimetric methods in terms of environmental considerations, sample size, and speed of analysis, as well as the variety of oil types that can be handled. FT-IR analysis combining stoichiometric signal transduction with differential spectroscopy may be of wider utility as an alternative to titration in the determination of acid or basic constituents in complex nonaqueous systems.
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".