Sulfuric Acid Highly Sensitive Detection at Different Concentrations Using Photonic Crystal Fiber Sensor
Bibliographic record
Abstract
This paper presents a new PCF sensor developed explicitly for accurately detecting sulfuric acid.A complete vector finite element technique (FEM) in COMSOL Multiphysics was used to evaluate the optical parameters of the sensor, including its refractive index, power fraction, relative sensitivity, confinement loss, and effective area.The sensor demonstrated excellent sensitivity and minimal confinement loss during testing with different concentrations of aqueous sulfuric acid ranging from 0% to 40%.The PCF sensor demonstrated outstanding performance at a wavelength of 1.5 μm, with the incident light's polarization in the x-direction, which enhances evanescent field interaction by confining nearly all optical power within an enlarged air core, boosting sensitivity to analytes compared to the y-polarized mode.The test's sensitivity was high when exposed to different concentrations of sulfuric acid, ranging from 0% to 40%.The sensitivities recorded were 99.55%, 99.72%, 99.82%, 99.90%, and 99.97% for acid concentrations of 0%, 10%, 20%, 30%, and 40% respectively.In addition, the sensor's losses due to confinement were measured and recorded.The recorded values for the different concentrations were 3.04×10 -11 dB/m, 4.65×10 -11 dB/m, 6.99×10 -11 dB/m, 8.52×10 -11 dB/m, and 9.65×10 -11 dB/m.The PCF sensor exhibits exceptional sensitivity and dependability in detecting sulfuric acid over various concentrations, rendering it a valuable tool for environmental monitoring, industrial safety, and health preservation.
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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 | 0.000 |
| Research integrity | 0.001 | 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 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".