Resonating with replicability: factors shaping assay yield and variability in microfluidics-integrated silicon photonic biosensors
Bibliographic record
Abstract
The integration of biosensors and microfluidics has facilitated the development of compact analytical devices capable of performing automated and information-rich detection of myriad targets, both in the lab and at the point of need. However, optimization of microfluidics-integrated biosensor systems and replicability challenges present roadblocks in validation and commercialization. Understanding factors contributing to yield and replicability in biosensor performance is key to the development of biosensor optimization frameworks and technology translation beyond the research setting. Hence, for the first time, we present a detailed analysis of factors affecting performance, assay yield, and intra- and inter-assay replicability in microfluidics-integrated silicon photonic (SiP) evanescent-field microring resonator biosensors. Strategies for mitigating bubbles—a major operational hurdle and contributor to instability and variability in microfluidics-integrated biosensors—are analyzed to improve assay yield. Effective bubble mitigation is demonstrated by combining microfluidic device degassing, plasma treatment, and microchannel pre-wetting with surfactant solution. Both intrinsic and analyte-detection performance metrics and their replicability are quantified for the first time for sub-wavelength grating-based SiP biosensors, highlighting a path to further optimization. Lastly, the effects of sensor functionalization on analyte detection performance and replicability are evaluated. We compare polydopamine-vs. Protein A-mediated bioreceptor immobilization chemistries and spotting-vs. flow-mediated bioreceptor patterning approaches. We find that simple polydopamine-mediated, spotting-based functionalization improves spike protein (1 µg/mL) detection signal by 8.2× and 5.8× compared to polydopamine/flow and Protein A/flow approaches, respectively, and yields an inter-assay coefficient of variability below the standard 20% threshold for immunoassay validation. Overall, this work proposes a practical framework through which evanescent-field SiP biosensors, and microfluidics-integrated biosensors more generally, can be characterized, compared, and optimized to facilitate efficient biosensor development.
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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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".