Dynamics of a piezoelectric tuning fork/optical fiber assembly in a near-field scanning optical microscope
Bibliographic record
Abstract
Factors leading to a decrease in the resonance quality (the Q factor) of quartz microtuning fork/optical fiber assemblies used as sensing elements in near-field scanning optical microscopes were considered using a simple elastomechanical analysis. Experiments to test the predictions of the analysis were carried out and strategies for recovering high Q factors were proposed and tested. Three major factors affecting the magnitude of the Q factor are discussed. The first is the stiffness imparted to the tine of the microtuning fork by the optical fiber attached to it; the second is the location of the attachment point of the fiber along the tine; the third is the resonant vibrational excitation of the fiber tip which acts as an energy dissipative channel. For tapping mode operation using a standard 125 μm diameter fiber, the large longitudinal stiffness of the fiber results in a dramatic Q-factor degradation. This effect can be overcome by reducing the diameter of the fiber cladding, d, and by slightly bending the fiber. Under these conditions, bending rather than longitudinal stretching dominates the fiber dynamics. The effective bending force constant for a thinned fiber is predicted to be proportional to d4. A sharp upturn in the Q factor is observed for d⩽25 μm, consistent with this prediction. The effective stiffness and mass of the fiber are also expected to scale approximately as x3, where x is the distance from the point of attachment of the fiber to the fork’s base. Hence, the Q factor can be improved further by attaching the fiber closer to the tuning fork’s base. Vibrational coupling between the tuning fork and the probe tip can result in a substantial Q-factor degradation for tips of a certain size. By taking these insights into consideration, we were able to construct tapping mode tuning fork/optical fiber assemblies with Q factors of up to 9000.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".