Determination of sulfate and chloride ions in highly saline oilfield water by capillary electrophoresis using bilayer-coated capillaries and indirect absorption detection
Bibliographic record
Abstract
Analysis of highly saline oilfield waters for anions presents challenges. Traditional analytical techniques used for such analysis tend to suffer from both poor sensitivity and selectivity due to the high concentrations of salt present in the samples. A capillary electrophoresis method was developed for the simultaneous determination of chloride and sulfate anions which is relevant to the oilfield analysis industry and of economic value. Due to the extremely high concentrations of chloride in highly saline oilfield waters, it is difficult to achieve baseline electrophoretic separation necessary for accurate quantitation. By using a capillary with a noncovalently bound bilayer coating using Polybrene, a cationic polymer and sodium dodecyl sulfate (SDS), an anionic surfactant and a buffer consisting of 50 mM TRIS, 30 mM SDS, 5% methanol and 26 mM chromium trioxide (CrO3) at pH 6.7, baseline separation (Rs > 1.5) of chloride and sulfate was achieved. To mimic possible oilfield water samples, model water solutions of 5%, 10%, 15% and 20% chloride containing low ppm sulfate were prepared and successfully analysed using the method developed. In addition, the method was applied to determine chloride and sulfate anions in highly saline oilfield water samples. The accuracy of the method developed was verified by analysing NIST certified standards of chloride and sulfate. The results obtained for chloride and sulfate with the indirect CE-UV method were in close agreement (94–100% accuracy; <2.5% relative standard deviations) with those of the certified standard analysed by ion chromatography.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".