Quantitative Determination of Moisture in Lubricants by Fourier Transform Infrared Spectroscopy
Bibliographic record
Abstract
This paper describes the development of a practical Fourier transform infrared (FT-IR) method for the determination of moisture in lubricants through the combined use of signal transduction and differential spectroscopy to circumvent matrix effects. The acid-catalyzed stoichiometric reaction of 2,2-dimethoxypropane (DMP) with moisture to produce acetone was used to provide IR signals proportional to the amount of moisture present in oils. Calibration standards were prepared by spiking polyalphaolefin (PAO) gravimetrically with water using dioxane as a carrier. For FT-IR analysis, standards and samples were diluted with acidified isooctane and then split, with one aliquot treated with DMP and the other with a blank reagent. The spectra of the two aliquots were collected, and a differential spectrum was obtained so as to ratio out the invariant spectral contributions from the sample. Quantitation for moisture was based on measurement of the peak height of the nu(C=O) absorption of acetone at 1717 cm(-1), yielding a standard error of calibration of approximately 40 ppm H2O. The method was validated by standard addition of water in dioxane to PAO containing added base as well as to new and used oils. In all cases the method responded quantitatively to standard addition, the average standard error of prediction being approximately 80 ppm, with the results showing only a minor dependence on the oil formulation. From an analytical perspective, the FT-IR method is both more reproducible and more accurate than Karl Fischer methods and has advantages in terms of environmental considerations, sample size, and speed of analysis as well as the variety of oil types that can be handled. Signal transduction/differential spectroscopy may have broader utility as an alternative means for the determination of low levels of moisture in complex matrices.
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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 | 0.001 | 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".