An Electro-Thermal Actuation Method for Resonance Vibration of a Miniaturized Optical-Fiber Scanner for Future Scanning Fiber Endoscope Design
Bibliographic record
Abstract
Medical professionals increasingly rely on endoscopes to carry out many minimally invasive procedures on patients to safely examine, diagnose, and treat a large variety of conditions. However, their insertion tube diameter dictates which passages of the body they can be inserted into and, consequently, what organs they can access. For inaccessible areas and organs, patients often undergo invasive and risky procedures—diagnostic confirmation of peripheral lung nodules via transthoracic needle biopsy is one example from oncology. Hence, this work sets out to present an optical-fiber scanner for a scanning fiber endoscope design that has an insertion tube diameter of about 0.5 mm, small enough to be inserted into the smallest airways of the lung. To attain this goal, a novel approach based on resonance thermal excitation of a single-mode 0.01-mm-diameter fiber-optic cantilever oscillating at 2–4 kHz is proposed. The small size of the electro-thermal actuator enables miniaturization of the insertion tube. Lateral free-end deflection of the cantilever is used as a benchmark for evaluating performance. Experimental results show that the cantilever can achieve over 0.2 mm of displacement at its free end. The experimental results also support finite element simulation models which can be used for future design iterations of the endoscope.
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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".