Chalcogenide microfiber-assisted silica microfiber for ultrasound detection
Bibliographic record
Abstract
An ultra-compact ultrasound sensor with a chalcogenide (ChG) microfiber and a silica microfiber is fabricated. It can detect the ultrasound wave generated by a piezoelectric transducer peaked at 3.7 MHz. The sensor shows the broadband ultrasound frequency response with a high signal-to-noise ratio (SNR), owing to the high refractive index and small Young’s modulus of ChG material. A ChG ( <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">A</mml:mi> <mml:mi mathvariant="normal">s</mml:mi> </mml:mrow> <mml:mn>2</mml:mn> </mml:msub> </mml:mrow> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:msub> <mml:mrow class="MJX-TeXAtom-ORD"> <mml:mi mathvariant="normal">S</mml:mi> <mml:mi mathvariant="normal">e</mml:mi> </mml:mrow> <mml:mn>3</mml:mn> </mml:msub> </mml:mrow> </mml:math> ) microfiber of 2 µm diameter is adhered to the surface of a silica microfiber with a diameter of 5 µm via Van der Waals force; the transmission spectrum has high contrast due to multi-mode interference in this hybrid structure. The SNR response could be up to 70 dB, especially at low frequency due to the soft ChG microfiber as a sensing unit to magnify the ultrasound signal, along with an SNR over 12 dB at ultrasound of 31.2 MHz. As a comparison, a silica taper with the same size as the ChG microfiber is also placed on the silica microfiber with a diameter of 5 µm to detect an ultrasound signal from 18 kHz to 9.4 MHz with an SNR lower than that based on the ChG microfiber up to 38.8 dB, showing the high sensitivity of the ChG microfiber.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".