Microfluidic electrochemical sensor with lead ion-imprinted polymer membrane for selective trace lead detection in water
Bibliographic record
Abstract
Lead contamination in water remains a critical global concern due to its toxicity and persistence. We present a microfluidic sensor that integrates a stand-alone lead ion-imprinted polymer (Pb-IIP) membrane for selective lead detection. Synthesized in situ with Pb(II) as the template, methacrylic acid as the monomer, and 8-hydroxyquinoline as the ligand, the membrane acts as a selective affinity element embedded between non-functionalized electrodes. Leveraging imprinted cavity sites and the Pb-IIP's chemical affinity, the integrated sensor demonstrated heightened lead ion sensitivity, achieving a detection limit of 7.3 ppb and consistent quantification up to 100 ppm. Detection responses were 6.1-fold higher than the membrane-less sensor, 1.5-fold higher than the MAA-based non-imprinted polymer (NIP) sensor, and 13-fold higher than the acrylamide (AAM)-based NIP sensor. The Pb-IIP membrane also differentiated Pb(II) from Cd(II) and Zn(II), with Pb(II) responses being 12.5 % to 69.4 % higher than other ions. Validation with unfiltered municipal tap water yielded recoveries between 96.6 % and 109.0 %, with relative standard deviations below 7 %. These results demonstrate regulatory-relevant detection performance using a low-cost, easily fabricated platform suitable for future adaptation to portable, field-deployable systems.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".