Experimental methods in chemical engineering: Karl Fischer titration
Bibliographic record
Abstract
Abstract Karl Fischer titration (KF) measures the water content in organic and inorganic compounds. Water reacts with a titrant solution containing iodine and a sulphurous monoalkyl ester to produce a monoalkyl sulphate ester and HI. In volumetric titrators, a burette injects the titrant directly into a cell while coulometric titrators generate titrant in situ. Regardless of the volume of the sample, volumetric titration is limited to 100 mg, while coulometric titration is best suited for . KF is fast (), considered accurate (), and precise (). However, factors such as humidity, non‐standard analytes (hexane, isopropanol, ethylene glycol), tritrant composition, and temperature compromise its repeatability: the % sample standard deviation, (), varied from 2% to 60% (). This high variability is, in part, due to the experimental design that included samples and conditions ill‐suited for the technique. However, even for the tests for which KF is suitable, . Certain compounds react with the titrant, which increases the variability. The main sources of error affecting the analysis are ambient moisture, pH, solvent, sample handling, and titration speed. As of 2023, the Web of Science has indexed 1332 articles with Karl Fischer as a key word (Topic), but more than 3600 mentioning the technique (All fields). A bibliometric analysis classifies these contributions into five clusters: spectroscopy, stability, temperature, solubility, and mixtures.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 0.039 |
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".