Detection of trace arsenic in drinking water: challenges and opportunities for microfluidics
Bibliographic record
Abstract
Arsenic contamination of drinking water is a major global problem, with contamination in Bangladesh deemed most serious. Although the current World Health Organisation (WHO) maximum contamination limit (MCL) for arsenic in drinking water is 10 μg L−1, due to practical and economic constraints, the standard limit in Bangladesh and many other developing nations is 50 μg L−1. We propose that an ideal arsenic sensor, designed for routine monitoring, will have five essential qualities: sensitivity and selectivity for arsenic; speed and reliability; portability and robustness; reduced health and environmental risks; and affordability and ease of use for local technicians. It is our opinion that many of these characteristics can be accentuated by microfluidic systems. We describe candidate colorimetric, electrochemical, biological, electrophoretic, surface-sensing, and spectroscopic methods for arsenic detection; and comment on their potential for portable microfluidic adaptation. We also describe existing developments in the literature towards the ultimate creation of microfluidic total analysis systems (μTASs) for arsenic detection. The fundamental purpose of this review is to highlight the need for better portable arsenic contamination detection, and describe how microfluidic technology may be developed to address this need.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".