Analytical characterization of choline chloride in oilfield process waters and commercial samples by capillary electrophoresis
Bibliographic record
Abstract
In this study, a rapid and sensitive method using capillary electrophoresis with indirect UV detection and minimal sample pretreatment was developed and evaluated to analyze choline chloride in oilfield process water samples. To improve peak resolution and separation of choline chloride from other cations present in the samples, the addition of a cationic visualization agent in the background electrolyte, imidazole, and a complexing agent, 18-crown-6, were introduced. Other factors affecting separation and sensitivity were also investigated. Under optimized conditions, choline chloride was baseline separated (<7 min) from common cationic adulterants in commercial choline chloride products with a peak resolution of >1.5 between adjacent peaks. The limits of detection (signal-to-noise ratio = 3) and quantitation (signal-to-noise ratio = 10) were 14.7 and 48.9 mg L−1, respectively. The peak area and migration time’s intraday and interday precision (percent RSD) were all <15%, and the recoveries ranged from 79.4% to 115.2% at different spiking levels. Finally, statistical (Student’s t-test) comparison of the choline chloride content data of oilfield process water samples from the proposed capillary electrophoresis (CE) method compare favourably with traditional methods such as liquid chromatography – mass spectrometry (LC–MS) and Reinecke salt gravimetry.
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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) | 0.000 | 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 | 0.000 |
| 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 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".