Portable cell imprinted polymer-based microfluidic sensor for bacteria detection in real water
Bibliographic record
Abstract
• Cell imprinted polymer (CIP) microfluidic sensor for bacteria detection in water. • CIP-coated microparticles synthesized and used within the microfluidic device. • Sensor limits of detection at 8 × 10 3 CFU/mL and quantification at 6 × 10 5 CFU/mL. • Sensor specificity to E. coli confirmed using non-specific Salmonella and Sarcina. • Compared to lab-based method, sensor similarly quantified bacteria in pond water. Cell-imprinted polymer (CIP) based optical biosensors have transformed point-of-care detection. However, challenges remain in their portability and detection sensitivity, time, and cost. Herein, we present an imprinted polymer-based low-cost microfluidic device integrated into a portable enclosure that enables rapid and sensitive bacteria detection in real water. A portable 3D-printed platform was custom-designed, housing all essential detection components, i.e., pumping and fluorescent imaging units and the microfluidic sensor. CIP coated magnetic microparticles (MPs) with affinity to bacteria were manipulated inside the magnetophoretic microfluidic device at an optimized flow rate of 0.01 mL/min for bacteria capturing. Fluorescent imaging pre- and post-bacteria capture facilitated quantification of fluorescence intensity changes as bacteria were trapped by the CIP-MPs. The sensor’s dose–response curve established limits of detection (LOD) and quantification (LOQ) at 8 × 10 3 and 6 × 10 5 CFU/mL, respectively, within a dynamic range of 10 3 to 10 9 CFU/mL. It specifically detected E. coli , distinguishing it from non-specific bacteria like Salmonella and Sarcina . In real pond water tests, our sensor detected 2 × 10⁶ CFU/mL, matching a central lab’s result of 2.33 × 10⁶ CFU/mL, demonstrating its effectiveness for real-water monitoring. While further enhancements are needed for improving the specificity in complex environmental matrices and broader bacterial strain detection, the sensor’s simplicity and portability highlight its potential for practical potential.
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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) | 0.000 | 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 | 0.001 |
| 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".