Challenges and Opportunities for Capillary Based Biofunctionalization of Microcantilever Arrays
Bibliographic record
Abstract
Microcantilever as sensing platform has been proven to be extremely sensitive for investigating various biochemical events. Despite of its rapid and label-free detection, obtaining molecular finger print for simultaneous detection of biomarkers is limited due to the difficulty in simultaneous functionalization of cantilevers. Though capillary based functionalization has been considered as one of the best method for simultaneous functionalization, it is limited by alignment of different sized capillary arrays. In the present work, we demonstrated the challenges and opportunities available for capillary based functionalization of microcantilever arrays and address the challenges by proposing an innovative capillary functionalization using on-chip V-grooves connected to flexible capillaries. Cantilever array chip comprising 8 cantilevers spaced at 250 μm distance is simultaneously functionalized with different biomolecules by inserting into dimensional–matched V-groove arrays. We demonstrated that a simple on-chip V-groove capillary functionalization is an easy, effective and highly repeatable by performing a standard sandwich immunoassay for detecting various cardiac markers such as myoglobin and troponin T.
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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 | 0.002 | 0.002 |
| 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) | 0.001 | 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".