A new methodology for optical sensing and identification using optical-disc drives
Bibliographic record
Abstract
Optical disc drives are inexpensive, readily available, and use highly sophisticated optoelectronic components which can be adapted for sensing. One limitation of using compact discs (CDs) and optical disk drives for sensing of analytes placed on a CD is the fluctuations in the voltage signal from the disk drive generated while reading the data on the CD. In this study, we develop a simple, low-cost strategy for sensing and identification using CDs and optical disk drives that spectrally separates contributions to the voltage signal caused by an analyte intentionally placed onto the CD and that caused by the underlying data on the CD. Analytes are printed onto a CD surface with fixed spatial periodicity. As the laser beam in an optical disk drive scans over the section of the CD containing the analyte pattern, the intensity of the laser beam incident onto the photodiode integrated into the disk drive is modulated at a frequency dependent on the spatial periodicity of the analyte pattern and the speed of the optical disk drive motor. Fourier transformation of the voltage signal from the optical disk drive yields peaks in the frequency spectrum with amplitudes and locations that enable analyte sensing and identification, respectively. We study the influence of analyte area coverage, pattern periodicity, and CD rotational frequency on the peaks in the frequency spectrum associated with the patterned analyte. We apply this technique to discriminate differently-coloured analytes, perform trigger-free detection of multiple analytes distributed on a single CD, and detect at least two different, overlapped analyte patterns on a single CD. The extension of this technique for sensing and identification of colorimetric chemical reagents is discussed. Future work will focus on adapting this technique to perform measurements at multiple wavelengths and streamlining the data collection and processing.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".