Porous Silicon MEMS Infrared Filters for Micromechanical Photothermal Spectroscopy
Bibliographic record
Abstract
Introduction Spectroscopy of gases is of great importance for the defense and security industries [1]. Optical spectroscopy has the following advantages over other methods: high resolution, high sensitivity, and ability to detect gas mixtures [2]. We propose a system for high-resolution photothermal spectroscopy of gases based on a MEMS spectrometer and a MEMS tunable filter, as shown in Fig. 1. This presentation details the progress on development of the two main components of the system. Photothermal Spectrometer We have developed and demonstrated a MEMS-based photothermal spectroscopy system based on optical probing of mechanical bridge deformation [3]. Briefly, when the wavelength of a tunable infrared source corresponds to a rotational or vibrational resonance of an analyte molecule in the sorbent material, the radiation is absorbed, which induces heating and bending of the microstructure. High sensitivity sorbent materials and interferometric readout techniques result in ppb detection levels throughout the 2.5 μm to 14 μm wavelength range. MEMS Tunable Filter In the demonstration of photothermal spectrometer, the source of illumination was a widely-tunable MIRAN source. Integration into a portable system would require a miniaturized source of light in the 3-12 μm range. Many wide-band sources are available, such as a glow bar or a wide-spectrum LED. We report progress towards realization of a MEMS filter based on porous silicon components that provides narrow-band tunability for such sources. The ability to create complicated optical structures with thin films of almost arbitrary index of refraction [4], combined with low dispersion across the entire 3-12 μm wavelength range, makes porous silicon an attractive material for realization of optical filters in the mid-wave and long-wave infrared. A proof-of-principle Fabry-Perot etalon with 95 nm FWHM passband at 8 μm is shown in Figure 3. Porosity and index of refraction gradients in multiple layer optical systems based on porous silicon are discussed, with relevance to optical component design. Three main components of the tunable filter are realized with porous silicon: highly reflective distributed Bragg reflectors, anti-reflective layers, and thermal bimorphs. Progress in fabrication of the tunable filter and its integration with a source and a photothermal spectrometer into a complete system for gas sensing is reported. References [1] “Chemical and Biological Point Sensors for Homeland Defense”, A. J. Sedlacek III; R. Colton; T. Vo-Dinh, editors, Proc. SPIE 5269, Providence, RI, October 27, 2003 [2] J. T. Robinson, L. Chen, and M. Lipson, Opt. Express 16, 4296 (2008). [3] Stievater, T. H., N. A. Papanicolaou, R. Bass, W. S. Rabinovich, and A. R. McGill, "Micromechanical Photothermal Spectroscopy of Trace Gases Using Functionalized Polymers", Optics Letters, vol. 37, issue 12, 2012. [4] B. H. King and M.l J. Sailor, "Medium-wavelength infrared gas sensing with electrochemically fabricated porous silicon optical rugate filters", J. Nanophoton. 5(1), 051510
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.005 | 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".