Quantitative condition monitoring of lubricating oils by Fourier transform infrared (FTIR) spectroscopy
Why this work is in the frame
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Bibliographic record
Abstract
Three new quantitative Fourier transform infrared (FTIR) spectroscopic methods were developed to measure key lubricant condition monitoring parameters; total acid number (TAN), total base number (TBN), and moisture (H2O). All methods employ a common sample-handling accessory and are based on the addition of specific reagents designed to react stoichiometrically with target species in oils, with quantification being carried out using differential FTIR spectroscopy. The combined use of a stoichiometric reaction and differential spectroscopy overcomes the need for a reference oil, which has traditionally hindered quantitative analysis of lubricants by FTIR spectroscopy. Potassium hydroxide, trifluoroacetic acid (TFA) and 2,2-dimethoxypropane (DMP) were the stoichiometric reagents used to develop the FTIR TAN, TBN and H2 O methods, respectively. Calibrations were developed using either peak height measurements or partial least squares (PLS) regression and the methods were validated using standard addition techniques, as the ASTM (American Society of Testing and Materials) standard methods were not sufficiently reproducible to make valid comparisons. Validation of the methods indicated that the TAN, TBN and H2O methods had accuracies of +/-0.095 mg KOH/g, +/-0.5 mg KOH/g and +/-32ppm respectively and corresponding reproducibilities of +/-0.05 mg KOH/g, +/-0.17 mg KOH/g and +/-22 ppm. The TAN, TBN and H2O methods were implemented on a Continuous Oil Analysis and Treatment (COATRTM) System, integrating instrumentation, software and sample handling so as to provide packaged, user and environmentally friendly analytical methods that are alternatives to conventional ASTM wet chemical methods.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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