Fabrication of optical fiber probes for scanning near-field optical microscopy
Bibliographic record
Abstract
Many areas of cell biology have remained unexplored due to the limitations of conventional optical microscopy for image structures smaller than the diffraction limit of light. Scanning near-field optical microscopy (SNOM) is an emerging technique which allows sub-diffraction limit optical resolution and hence access to nanoscale structures such as those in biological cells. Since the success of a scanning probe microscopy techniques depend on the tip, we present a method for the fabrication of SNOM aperture probes and antenna probes capable of high resolution imaging of delicate liquid based samples such as neurons. The procedure includes thinning and tapering optical fibers by chemical etching, followed by the deposition of a thin aluminium film, and micromaching using focused ion beam (FIB). Tip geometry, antenna resonances, excitation conditions, and field localizations have been examined. The probes have a resonant frequency of 18-28 kHz, a Q of 200-400 in air and up to 100 in water, and a spring constant of on the orders of 140 N/m. Successful optical and topographical imaging up to a resolution of 250 nm was achieved with the probes. Together with adjustments in equipment and instrumentation, the probe’s optical and mechanical properties allow for a low imaging force and high tip sensitivity which are necessary conditions for the imaging of biological cells.
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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 | 0.001 |
| 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 | 0.001 | 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".