Near‐field scanning optical microscopy probes: a comparison of pulled and double‐etched bent NSOM probes for fluorescence imaging of biological samples
Bibliographic record
Abstract
Bent near-field optical probes for biological applications have been fabricated using a combination of a two-step chemical etching method and focused ion beam milling to create a well-defined aperture. The transmission efficiencies have been evaluated as a function of laser wavelength (lambda) and aperture size (D) for both large and small core fibres. The probe transmission behaviour follows a (D/lambda)3 relationship. The double-etched probes are compared to pulled probes fabricated from highly GeO2-doped dispersion compensating fibre and a standard single-mode optical fibre. The transmission efficiencies of both types of pulled probes are approximately two orders of magnitude lower than double-etched probes with similar aperture sizes. To demonstrate the utility of the various probes, their imaging performance has been evaluated for samples of polymer beads and phase-separated phospholipid monolayers of dipalmitoylphosphatidylcholine or cholesterol/phosphatidylcholine/sphingomyelin mixtures. Both pulled and double-etched probes are suitable for fluorescence imaging of polymer spheres. However, pulled probes are rapidly damaged at the higher input laser intensities required for fluorescence imaging of monolayer samples doped with < 1% of a fluorescent dye-labelled lipid. The images obtained with the double-etched probes show excellent spatial resolution and signal/noise, illustrating the potential of such probes for imaging of biological samples.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".