A novel strategy for controllable electrofabrication of molecularly imprinted polymer biosensors utilizing embedded Prussian blue nanoparticles
Bibliographic record
Abstract
The reproducibility of ultrasensitive biosensors is vital for clinical research, scalable manufacturing, commercialization, and reliable clinical decision-making, as batch-to-batch variations introduce significant uncertainty. However, most biosensors lack robust quality control (QC) measures. This study introduces an innovative QC strategy to produce highly reproducible molecularly imprinted polymer (MIP) biosensors by leveraging real-time data from the electrofabrication process. Prussian Blue nanoparticles (PB NPs) embedded within the MIP structure enable precise monitoring of surface properties, conductivity, MIP film thickness, and template extraction efficiency. The QC strategy utilizes variations in the current intensity of PB NPs during fabrication to implement real-time, non-destructive QC protocols at critical fabrication stages, minimizing measurement variability and ensuring consistency. This approach was validated by fabricating MIP biosensors for detecting agmatine metabolite and glial fibrillary acidic protein (GFAP) in phosphate-buffered saline (PBS). The QC strategy reduced relative standard deviation (RSD) by 79% for agmatine (RSD = 2.05% QC, RSD = 9.68% control) and 87% for GFAP (RSD = 1.44% QC, RSD = 11.67% control). Moreover, quality-controlled biosensors achieved success rates of 45% for agmatine and 36% for GFAP detection, significantly outperforming bare screen-printed electrodes. This work marks a significant advancement in biosensor development by integrating robust QC protocols directly into the fabrication process. By embedding PB NPs and monitoring electrochemical signals in real-time, this strategy delivers an unprecedented level of reproducibility, scalability, and reliability for MIP biosensors, addressing critical challenges in point-of-care diagnostics and commercial applications.
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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) | 0.000 | 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".