High-Frequency Ultrasound Sensing From Multimode Coupling in CO<sub>2</sub>-Written Long-Period Fiber Gratings
Bibliographic record
Abstract
High-frequency (> 50 MHz) ultrasound sensing requires the detection of subtle, rapid perturbations, often a small fraction of the acoustic wavelength, which can be much smaller than the optical wavelength. This leads to the associated phase shift due to ultrasound modulation to being too small to be detected using a telecom fiber-based interferometer. Structured fiber-based sensors can overcome these challenges by detecting locally induced deformations in the fiber structure inside the core. We propose a novel approach using CO2-written long-period fiber gratings (LPFGs), where randomly distributed micro-deformities act as amplitude gratings, eliminating the need for phase detection. Unlike commonly used UV light inscribed LPFGs, where the inscription pitch is uniform in the fiber between the periods, the wavelength of the CO2laser falls in the absorption band of SiO2. This causes thermal stress-induced deformations in the fiber, leading to the formation of randomly spaced Fabry-Perot (FP) cavities in the micrometer range, as demonstrated by the spatial frequency spectrum (inverse fast Fourier transform (IFFT)). The higher-order modes in CO2-written LPFGs and tilted LPFGs enhance the sensitivity to high-frequency ultrasound waves. This sensitivity arises from the broadband frequency resonance condition spanning 1 to 80 MHz in randomly spaced deformation-formed FP cavities. By analyzing the transmission and spatial frequency spectra of CO2-and UV-written LPFGs, where the latter fails to respond to ultrasound signals beyond 10MHz, we establish a framework for practical high-sensitivity ultrasound sensing.
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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) | 0.000 | 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 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".