A capacitively coupled contactless conductivity detector with micellar electrokinetic chromatography for the detection of ethanol in milk
Bibliographic record
Abstract
The consumption of alcohol during breastfeeding can lead to ethanol-contaminated breastmilk, and when consumed by infants, can cause reduced intake of milk and improper development. Health organizations provide oversimplified and vague guidelines to breastfeeding women regarding alcohol consumption. Commercialized products exist to enumerate the presence of ethanol in breastmilk but are underdeveloped. Laboratory separation and detection methods offer accurate and sensitive results, but require off-site testing, expensive equipment, and skilled personnel. This thesis addresses the lack of point-of-care sensors to detect ethanol in breastmilk by developing a microfluidic sensor that uses capacitively coupled contactless conductivity detection and micellar electrokinetic chromatography to separate and detect ethanol in bovine skim milk as a preliminary step towards detecting ethanol in breastmilk. A custom-built capacitively coupled contactless conductivity microfluidic sensor is developed and the effects of shielding techniques and electrode spacing against the signal-to-noise ratio are optimized. The optimal results are achieved with a ground line shielding technique and 500 μm electrode spacing. For ethanol separation, micellar electrokinetic chromatography, a mode of microchip capillary electrophoresis, is employed with a 20 mM citrate, 30 mM sodium dodecyl sulfate, and 4.3% 0.1 M sodium hydroxide (v/v) background electrolyte solution. Ethanol is detected in three Canadian milk brands in five minutes average and a limit of detection of 0.97% (v/v) is achieved. The developed microfluidic sensor serves as a promising approach to the on-site testing of ethanol in breastmilk using laboratory grade separation and detection methods on a miniaturized scale.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".