A PHOTONIC GAS SENSOR FOR THE MID-INFRARED
Bibliographic record
Abstract
The mid-infrared (MIR) contains the strong absorption signatures of many molecules, such as Methane and Carbon Dioxide, that are of extreme interest in real-world sensing applications. The miniaturization of spectroscopic sensing equipment made possible by engineered silicon photonics has the potential to revolutionize the way we conduct emissions sensing in the MIR.\nNanophotonic devices have greatly benefited from telecommunication technology in the near infrared (NIR) region. The industry has reached a level of maturity where high volume production of integrated circuitry can be done at low cost. Advances in materials engineering have shown that silicon based photonic devices can support optical propagation in the MIR past 8 microns with losses approaching those of the telecommunications band \\footnote{R. Shankar, I. Bulu, M. Loncar, \\textit{Applied Physics Letters}, \\textbf{102}, 051108, 2013.} making the region attractive for nanoscale sensor development.\nAbsorption sensing with photonic devices has been demonstrated in silicon on sapphire \\footnote{C. Smith, R. Shankar, M. Laderer, M. Frish, M. Loncar, M. Allen, \\textit{Optics Express}, \\textbf{23} 5491, 2015.}, silicon nitride \\footnote{C. Ranacher, C. Consani, N. Vollert, A. Tortschanoff, M. Bergmeister, T. Grille, B. Jakoby, \\textit{IEEE Photonics Journal}, \\textbf{10} 2018.}, and metal assisted silicon on insulator \\footnote{C. Ranacher, C. Consani,, A. Tortschanoff, R. Jannesari, M. Bergmeister, T. Grille, B. Jakoby, \\textit{Sensors and Actuators A: Physical}, \\textbf{277}, 117, 2018.} platforms, among others. These methodologies have demonstrated the ability to sense analyte concentrations as low as 5000 ppmv (parts per million by volume), which is the workplace limit in many North American constituencies.\n\nWe present our current state of research on the development of a high-quality factor MIR silicon-on-sapphire (SOS) photonic gas sensor for use in lab-on-a-chip sensing applications. An optical parametric oscillator (OPO) will be used as a MIR source to pump a grating coupled SOS ring cavity immersed in a controlled CO$_2$ environment. The cavity will be geometrically engineered to allow for high sensitivity spectroscopy of the CO$_2$ fundamental at 2350 \\wn via absorption of the cavity evanescent field. Design and optimization is conducted through the use of COMSOL Multiphysics and Lumerical software suites.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".