Automated Acid Content Determination in Lubricants by FTIR Spectroscopy as an Alternative to Acid Number Determination
Bibliographic record
Abstract
Abstract A new instrumental method for the quantitative measurement of acid content (AC) in mineral-based lubricants was devised by employing FTIR spectroscopy, with AC serving as an alternative to traditional acid number (AN) measures commonly made to assess lubricant quality. The method involves the addition of an oil-immiscible ethanolic solution of the base sodium hydrogen cyanamide (NaHN–CN) to the lubricant to extract and react with the acids present. After separation of the phases, the FTIR spectrum of the ethanol layer is recorded, and a differential spectrum is generated by subtracting out the spectrum of the reagent solution. AC is determined by measuring the absorbance of NaHN–CN at 2109 cm−1 (νCN) in the differential spectrum, which is proportional to the extent to which the reagent has been consumed by reaction with acidic constituents in the oil. Calibration standards were prepared by direct addition of oleic acid to the NaHN–CN/ethanol solution, and a calibration equation for the determination of AC was obtained by a quadratic fit of the concentration data to the FTIR νCN absorbance data. The equivalent response of the νCN band to strong inorganic acids and oleic acid demonstrated that NaHN–CN, a somewhat weaker base than KOH, fully ionizes organic acids. Comparison between FTIR AC values and titrimetric AN values (obtained by ASTM D664-89) for a set of used oils spanning an AN range of 0.3–5 mg KOH/g showed a reasonably good linear relationship (R=0.985), with the FTIR method generally producing lower values. This tendency was attributed to the presence of weakly acidic species, which would be less extensively ionized by NaHN–CN than by KOH. Implementation of the FTIR AC method on an autosampler-equipped spectrometer allows for the automated analysis of up to 120 preprepared samples/h, representing a significant increase in analytical throughput relative to traditional titrimetric procedures as well as substantive reductions in consumables and waste oil.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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".