Glancing angle deposited nanostructured film Fabry-Perot etalons for optical detection of ultrasound
Bibliographic record
Abstract
In this paper a new class of optical Fabry-Perot-based ultrasound detectors using low acoustic impedance glancing angle deposited (GLAD) films is demonstrated. GLAD is a single-step physical vapor-deposition (PVD) technique used to fabricate porous nanostructured thin films. Using titanium dioxide (TiO(2)), a transparent semiconductor with a high refractive index (n = 2.4), the GLAD technique can be employed to fabricate samples with tailored nano-porosity, refractive index periodicities, and high Q-factor reflectance spectra. The average acoustic impedance of the porous films is lower than bulk materials which will improve acoustic coupling, especially for high acoustic frequencies. For this work, two filters with high reflection in the C-band range and high transparency in the visible range (~80%) using GLAD films were fabricated. A 23 µm Parylene C layer was sandwiched between these two GLAD films in order to form a GLAD Fabry Perot Interferometer (GLAD-FPI). A high speed tunable continuous wavelength C-band laser was focused at the FPI and the reflection was measured using a high speed photodiode. The ultrasound pressure modulated the optical thickness of the FPI and hence its reflectivity. The fabricated sensor was tested using a 10 MHz unfocused transducer. The ultrasound transducer was calibrated using a hydrophone. The minimum detectable acoustic pressure was measured as 80 ± 20 Pa and the -3dB bandwidth was measured to be 18 MHz. This ultra-sensitive sensor can be an alternative to piezoelectric ultrasound transducers for any techniques in which ultrasound waves need to be detected including ultrasonic and photoacoustic imaging modalities. We demonstrate our GLAD-FPI for photoacoustic signal detection in optical-resolution photoacoustic microscopy (OR-PAM). To the best of our knowledge, this is the first time that a FPI fabricated using the GLAD method has been used for ultra-sensitive ultrasound detection.
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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) | 0.000 | 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 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".