On-chip nanohole array based sensing: a review
Why this work is in the frame
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Bibliographic record
Abstract
The integration of nanohole array based plasmonic sensors into microfluidic systems has enabled the emergence of platforms with unique capabilities and a diversified palette of applications. Recent advances in fabrication techniques together with novel implementation schemes have influenced the progress of these optofluidic platforms. Here, we review the advances that nanohole array based sensors have experienced since they were first merged with microfluidics. We examine established and new fabrication methodologies that have enabled both the fabrication of nanohole arrays with improved optical attributes and a reduction in manufacturing costs. The achievements of several platforms developed to date and the significant benefits obtained from operating the nanoholes as nanochannels are also reviewed herein. Finally, we discuss future opportunities for on-chip nanohole array sensors by outlining potential applications and the use of the abilities of the nanostructures beyond the optical context.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.010 |
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 it