Compact and full-range carbon dioxide sensor using photoacoustic and resonance dependent modes
Bibliographic record
Abstract
A compact and robust optical excitation photoacoustic sensor with a self-integrated laser module excitation and an optimized differential resonator was developed to achieve high sensitivity and full linear range detection of carbon dioxide (CO 2 ) based on dual modes of wavelength modulated photoacoustic spectroscopy (WMPAS) and resonant frequency tracking (RFT). The integrated laser module equipped with three lasers (a quantum cascade laser (QCL), a distributed feedback laser (DFB) and a He-Ne laser) working in a time-division multiplexing mode was used as an integrated set of spectroscopic sources for detection of the designated concentration levels of CO 2 . With the absorption photoacoustic mode, the WMPAS detection with the QCL and DFB sources was capable of CO 2 detection at concentrations below 20 %, yielding a noise equivalent concentration (NEC) as low as 240 ppt and a normalized noise equivalent absorption coefficient (NNEA) of 4.755 × 10 −10 W cm −1 /√Hz, and dynamic range as great as 11 orders of magnitude. Higher concentration detection ranges (20 %-100 %) of CO 2 were investigated using the RFT mode with an amplitude-stabilized He-Ne laser and a mechanical chopper. With the dual modes of WMPAS and RFT, the optical excitation sensor achieved full-range CO 2 detection, with an R² ≥ 0.9993 and a response time of 5 seconds. The compact and full-range CO 2 sensor combines the advantages of WMPAS and RFT and offers a solution for high sensitivity, linearity and full-range CO 2 detection.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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 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".