Fluorescence in sub-10 nm channels with an optical enhancement layer
Bibliographic record
Abstract
Fluorescence microscopy uniquely enables physical and biological research in micro- and nanofluidic systems. However, in channels with depths below 10 nm, the limited number of fluorophores results in fluorescence intensity below the detection limit of optical microscopes. To overcome this barrier, we applied Fabry-Pérot interference to enhance fluorescence intensity with a silicon nitride layer below the sub-10 nm channel. A silicon nitride layer of suitable thickness can selectively enhance both absorption and emission wavelengths, leading to a fluorescent signal that is enhanced 20-fold and readily imaged with traditional microscopes. To demonstrate this method, we studied the mass transport of a binary solution of ethanol and Rhodamin B in 8 nm nanochannels. The large molecular size of Rhodamin B (∼1.8 nm) relative to the channel depth results in both separation and reduced diffusivity, deviating from behavior at larger scales. This method extends the widely available suite of fluorescence analysis tools and infrastructure to unprecedented sub-10 nm scale with relevance to a wide variety of biomolecular interactions.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".