Electrochemical Sensing of Lead in Drinking Water Using Copper Foil Bonded with Polymer
Bibliographic record
Abstract
Levels of lead (Pb) in tap water that are well below established guidelines are now considered harmful, so the detection of sub-parts-per-billion (ppb) Pb levels is crucial. In this work, we developed a two-step, facile, and inexpensive fabrication approach that involves direct bonding of copper (Cu) and liquid crystal polymer (LCP) followed by polyester resin printing for masking onto Cu/LCP to fabricate Cu thin-film-based Pb sensors. The oxygen plasma-treated surfaces resulted in strongly bonded Cu/LCP with a high peel strength of 500 N/m due to the highly hydrophilic nature of both surfaces. The bonded specimen can withstand wet etching of the electrode and can address delamination of the electrode for prolonged use in application environments. The Cu-foil-based electrochemical sensor showed sensitivity of ~11 nA/ppb/cm2 and a limit of detection (LOD) of 0.2 ppb (0.2 µg/L) Pb ions in water. The sensor required only 30 s and a 100 µL sample to detect Pb. To date, this is the most rapid detection of Pb performed using an all-Cu-based sensor. The selectivity test of Cu to Pb with interferences from cadmium and zinc showed that their peaks were separated by a few hundred millivolts. This approach has strong potential towards realizing low-cost, highly reliable integrated water quality monitoring 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".