Cell Imprinted Polymers Integrated with Microfluidic Biosensors for Electrical and Electrochemical Detection of Bacteria in Water.
Bibliographic record
Abstract
There is a growing demand for sensors that enable rapid, cost-effective, and laboratory-free detection of microorganisms in clinical, food and environmental samples. Traditional methods are slow, expensive, and require specialized personnel. Biosensors offer a promising alternative but face challenges like instability, high cost, short lifespan, and complex synthesis of the biorecognition elements. Molecularly imprinted polymers (MIPs) provide a more robust, cost-effective solution by embedding the target analyte's imprint into a polymer matrix. While MIPs are effective for small molecules, designing them for biological cells is more complex due to their structural diversity. Noncovalent interactions, preferred in synthesizing cell-imprinted polymers (CIPs), enable easier binding and dissociation. Selecting suitable functional monomers is crucial, as their interactions with cell surface molecules determine imprinting success. However, the effects of CIP composition on the bacterial capture efficiency remain unexplored. Furthermore, integrating CIPs into microfluidic and electrochemical sensing platforms is vital for portable, real-time detection systems. This research aimed to improve the understanding of the CIPs’ effectiveness in capturing bacteria to develop effective bacteria-sensing platforms using microfluidic devices. In Objective 1, we optimized a polymerization methodology for uniform functionalization of stainless steel microwires reproducible CIP coatings, imprinted with E. coli as the template. In Objective 2, we assessed E. coli rebinding performance which demonstrated 76±5 % uptake efficiency with the optimized composition. In Objective 3, we integrated CIPs into a conductometric-based microfluidic sensor. Resistance changes normalization and subsequent analysis of the dose-response curve revealed a dynamic range of 10^4 to 10^7 CFU/mL, with a limit of detection (LOD) of 2.1×10^5 CFU/mL. Specificity experiments demonstrated the specificity of the sensor towards imprinted E. coli cells. Further improvements were made by modifying the sensor design to a three-electrode configuration and employing electrochemical impedance spectroscopy (EIS). The charge-transfer resistance changes normalization and the subsequent analysis revealed an enhanced LOD of 2× 10^2 CFU/mL, with a broader dynamic range of 10^2 to 10^7 CFU/mL. The proposed sensor has the potential to offer a cost-effective, durable, portable, and real-time solution for the detection of waterborne pathogens. Future impacts include enhancing bacterial detection in environmental monitoring, food safety, and healthcare.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".