Metal Microwires Functionalized with Cell-Imprinted Polymer for Capturing Bacteria in Water
Bibliographic record
Abstract
Molecularly imprinted polymers (MIPs) and cell-imprinter polymers (CIPs) have emerged as synthetic recognition elements in biomimetic sensors. In this paper, we have conducted a parametric study to optimize a bulk polymerization methodology for uniform functionalization of stainless steel microwires (MWs) with CIPs comprising single to fourplex combinations of functional monomers (FMs). MWs are widely used in biosensors, and their functionalization with single-FM MIPs has been demonstrated. Complex MIPs comprising multiple FMs have shown enhanced selectivity toward microorganisms, but their coating on MWs has yet to be shown. Moreover, imprinting microorganisms into these coatings has not been reported. In our studies, solvent, FM, cross-linker-to-FM ratio, polymerization temperature, and time were found to significantly influence the thickness and uniformity of CIP coatings on MWs. Reproducible CIP coatings with a thickness of 2.2 ± 0.4 μm, imprinted with E. coli OP50 as the template, were achieved. E. coli rebinding assays demonstrated a 76 ± 10% capture efficiency in a suspension with an initial bacteria count of 10 4 CFU/mL, using a 3 cm long CIP-MW with an optimized fourplex CIP composition, while the capture efficiency obtained by using a single-monomer CIP composition was 30 ± 5%. Our results indicated a higher binding capacity of fourplex CIP-MWs to target bacteria, while nonsignificant binding was obtained using single-monomer CIP-MWs. The addition of N -vinylpyrrolidone significantly increased the binding performance due to its hydrophobic–hydrophilic functional groups interacting with counterparts on the surface of bacterial cells. The developed CIP-MWs can be integrated with microfluidic sensing systems as low-cost and stable working electrodes for future transduction of CIP-target binding events to an electrical read-out in CIP-based electrochemical biomimetic sensors.
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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 | 0.000 | 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".