What Could Go Wrong? Promoting Success by Planning for Failure in Label-Free Biosensor Assay Development
Bibliographic record
Abstract
Abstract Label-free biosensors offer powerful platforms for detecting molecular interactions, but developing robust assays on these systems presents several challenges due to the complexity of the testing systems and intersection of disciplines. In this work, we describe a lightweight, 3-step experimental workflow supporting troubleshooting and root cause analysis that we developed during the design and optimization of silicon photonic biosensor assays in an academic research setting. Because such environments often lack the resources and formal quality-management infrastructure available in industry, our approach emphasizes practicality and ease of adoption. Drawing from our own assay failures and successes, we identify common failure modes, propose structured troubleshooting workflows, and provide case studies illustrating the application of established frameworks, including the Five Whys, the Plan-Do-Check-Act cycle, Open-Narrow-Close and the Ishikawa (fishbone) diagram. By applying this framework to our research, we increased our assay yield by a factor of 2, from ~45% to ~90%. This paper aims to support other teams engaged in label-free assay development by equipping them with practical tools and experimental best practices. Highlights We doubled assay yield from ~45% to ~90% by planning for failure, not just success. Tailored troubleshooting frameworks help pinpoint and solve various assay failures. Applying innovative problem-solving to workflows accelerates biosensor development. Systematic troubleshooting improves assay development across a range of lab settings. A 3-step workflow highlighting what could go wrong supports continuous improvement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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 teacher head, 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".