Efficiency of Methods for Karl Fischer Determination of Water in Oils Based on Oven Evaporation and Azeotropic Distillation
Bibliographic record
Abstract
The efficiency of azeotropic distillation and oven evaporation techniques for trace determination of water in oils has recently been questioned by the National Institute of Standards and Technology (NIST), on the basis of measurements of the residual water found after the extraction step. The results were obtained by volumetric Karl Fischer (KF) titration in a medium containing a large excess of chloroform (> or = 65%), a proposed prerequisite to ensure complete release of water from the oil matrix. In this work, the extent of this residual water was studied by means of a direct zero-current potentiometric technique using a KF medium containing more than 80% chloroform, which is well above the concentration recommended by NIST. A procedure is described that makes it possible to correct the results for dilution errors as well as for chemical interference effects caused by the oil matrix. The corrected values were found to be in the range of 0.6-1.5 ppm, which should be compared with the 12-34 ppm (uncorrected values) reported by NIST for the same oils. From this, it is concluded that the volumetric KF method used by NIST gives results that are much too high.
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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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".