Low-power noncontact photoacoustic microscope for bioimaging applications
Bibliographic record
Abstract
An inexpensive noncontact photoacoustic (PA) imaging system using a low-power continuous wave laser and a kilohertz-range microphone has been developed. The system operates in both optical and PA imaging modes and is designed to be compatible with conventional optical microscopes. Aqueous coupling fluids are not required for the detection of the PA signals; air is used as the coupling medium. The main component of the PA system is a custom designed PA imaging sensor that consists of an air-filled sample chamber and a resonator chamber that isolates a standard kilohertz frequency microphone from the input laser. A sample to be examined is placed on the glass substrate inside the chamber. A laser focused to a small spot by a 40 × objective onto the substrate enables generation of PA signals from the sample. Raster scanning the laser over the sample with micrometer-sized steps enables high-resolution PA images to be generated. A lateral resolution of 1.37 ?? ? m was achieved in this proof of concept study, which can be further improved using a higher numerical aperture objective. The application of the system was investigated on a red blood cell, with a noise-equivalent detection sensitivity of 43,887 hemoglobin molecules ( 72.88 × 10 ? 21 ?? mol or 72.88 zeptomol). The minimum pressure detectable limit of the system was 19.1 ?? ? Pa . This inexpensive, compact noncontact PA sensor is easily integrated with existing commercial optical microscopes, enabling optical and PA imaging of the same sample. Applications include forensic measurements, blood coagulation tests, and monitoring the penetration of drugs into human membrane.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".