Photoacoustic and Fiber-Optic Interferometer Spectroscopic Method for Simultaneous Detection of Multiple Trace Gases
Bibliographic record
Abstract
Dissolved gases in transformer oil are reliable indicators of operating conditions and fault types. Additionally, ambient water vapor can seriously affect the accuracy of photoacoustic dissolved gas analysis systems. Therefore, there is an urgent need for the development of the simultaneous detection of dissolved gases and water. A high-sensitivity, multiple-gas sensing system was developed by combining a differential photoacoustic cell and a water vapor fiber-optic sensor. The acoustic properties of the designed differential photoacoustic cell were analyzed through simulation and experimental validation for the differential and longitudinal modes, and the frequency difference between excitation and nonexcitation optical paths in the longitudinal mode was leveraged, achieving an amplitude response comparable to that of the differential mode. C 2 H 2, CH 4, and CO measurements were performed at three resonance frequencies using two DFB and a QCL source. To monitor H 2 O concentration and evaluate its effect on photoacoustic detection, a fiber-optic Fabry–Perot interferometer was developed using self-assembled microspheres with high specific surface area, single-mode optical fibers, and concentric tapered capillary tubes. Water vapor adsorption on the microspheres altered the refractive index, and cavity-length demodulation was employed to analyze the interference spectra to obtain the water vapor concentration. The water optical sensor showed high sensitivity of ∼112 pm/% for H 2 O detection. Experimental results demonstrated that the dual-mode multicomponent gas sensor can achieve detection limits of 1.15, 241.07, and 367.32 ppb for CO, C 2 H 2, and CH 4, respectively, with corresponding normalized equivalent noise absorption coefficients of 1.53 × 10 –8 cm –1 ·W·Hz –1/2, 4.56 × 10 –9 cm –1 ·W· Hz –1/2, and 3.75 × 10 –9 cm –1 ·W·Hz –1/2 .
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".