Microfluidic Sensor Based on Cell-Imprinted Polymer-Coated Microwires for Conductometric Detection of Bacteria in Water
Bibliographic record
Abstract
The rapid, inexpensive, and on-site detection of bacterial contaminants using highly sensitive and specific microfluidic sensors is attracting substantial attention in water quality monitoring applications. Cell-imprinted polymers (CIPs) have emerged as robust, cost-effective, and versatile recognition materials with selective binding sites for capturing whole bacteria. However, electrochemical transduction of the binding event to a measurable signal within a microfluidic device to develop easy-to-use, compact, portable, durable, and affordable sensors remains a challenge. For this paper, we employed CIP-functionalized microwires (CIP-MWs) with an affinity towards E. coli and integrated them into a low-cost microfluidic sensor to measure the conductometric transduction of CIP–bacteria binding events. The sensor comprised two CIP-MWs suspended perpendicularly to a PDMS microchannel. The inter-wire electrical resistance of the microchannel was measured before, during, and after exposure of CIP-MWs to bacteria. A decline in the inter-wire resistance of the sensor after 30 min of incubation with bacteria was detected. Resistance change normalization and the subsequent analysis of the sensor’s dose-response curve between 0 to 109 CFU/mL bacteria revealed the limits of detection and quantification of 2.1 × 105 CFU/mL and 7.3 × 105 CFU/mL, respectively. The dynamic range of the sensor was 104 to 107 CFU/mL where the bacteria counts were statistically distinguishable from each other. A linear fit in this range resulted in a sensitivity of 7.35 μS per CFU/mL. Experiments using competing Sarcina or Listeria cells showed specificity of the sensor towards the imprinted E. coli cells. The reported CIP-MW-based conductometric microfluidic sensor can provide a cost-effective, durable, portable, and real-time solution for the detection of pathogens in water.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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 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".