A portable lab-on-a-chip system for gold-nanoparticle-based colorimetric detection of metal ions in water
Bibliographic record
Abstract
Heavy metal ions released into various water systems have a severe impact on the environment and human beings, and excess exposure to toxic metal ions through drinking water poses high risks to human health and causes life-threatening diseases. Thus, there is high demand for the development of a rapid, low-cost, and sensitive method for detection of metal ions in water. We present a portable analytical system for colorimetric detection of lead (Pb(2+)) and aluminum (Al(3+)) ions in water based on gold nanoparticle probes and lab-on-a-chip instrumentation. The colorimetric detection of metal ions is conducted via single-step assays with low limits of detection (LODs) and high selectivity. We design a custom-made microwell plate and a handheld colorimetric reader for implementing the assays and quantifying the signal readout. The calibration experiments demonstrate that this portable system provides LODs of 30 ppb for Pb(2+) and 89 ppb for Al(3+), both comparable to bench-top analytical spectrometers. It promises an effective platform for metal ion analysis in a more economical and convenient way, which is particularly useful for water quality monitoring in field and 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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 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".