A Portable Analytical System for Colorimetric Detection of Metal Ions in Water
Bibliographic record
Abstract
Heavy metal ions released into various water environments have severe impact on both human beings and aqueous environments, and excess amount of lead and aluminum ions pose high risks to human health and could cause life-threatened diseases. The existence of metal ions in drinking water contributes most to the daily intake by humans, and thus it urges to develop a rapid, low-cost and sensitive method for detection of heavy metal ions. In this research, we develop a portable analytical system for metal ion detection in water by combining a powerful gold nanoparticle (AuNP) based colorimetric method with lab-on-a-chip technology. We utilize single-step assays involving surface functionalized AuNPs for colorimetric detection of lead (Pb2+) and aluminum (Al3+) ions in water with low limit of detection (LOD) and high sensitivity. We demonstrate that this portable system provides LODs of 30 ppb for Pb2+ and 89 ppb for Al3+, both comparable to bench-top analytical spectrometers. The system permits metal ion detections in a more economical and convenient fashion, and is particularly useful for water quality monitoring in remote and/or resource-poor settings.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".