Continuous progress in science, technology, and clean environmental regulations for energy requires low-power chip scale devices in sensing applications. Conventional trace gas sensing in the midinfrared region is highly sensitive. However, it requires a complex optomechanical setup that may not be suitable for wide-area deployments. This paper shows the development of new waveguide materials for near and mid-infrared silicon photonics ranging from 0.7 to 10 mm. These include amorphous semiconductors like Chalcogenide Glasses (ChGs) of Germanium-Selenium-Silicon (Ge-Se-S) elements with different compositions. UV-Vis measurements show the optical energy gap between 1.6 eV with high Se concentration to 3.8 eV, where Se is replaced by S in the compositions. ATRFTIR measurements show a high transmission spectrum ranging from 4000 to 400 cm-1. We show the optical properties of such thin film materials in the broadband range of mid-infrared, suitable for fabricating waveguides and micro-resonator cavities for on-chip sensing applications.
Mid-infrared sensing in the broadband spectral region of 5 – 11 𝜇m is suitable for detecting and quantifying multiple trace species. However, the challenge in detection is precise discrimination due to the broad linewidth of molecular transitions of species like methane, nitrous oxide, and other volatile organic compounds. In addition, isotopic transitions are generally weaker, with significant overlap with the neighboring abundant molecular transitions. This paper shows broadband detection of multiple species using an external cavity laser operation in 6 to 11 𝜇m spectral region. We use a combination of Savitzy-Golay filtering and machine learning-based classification to discern weaker rotational vibrational transitions. The proposed scheme is used to denoise and discriminate molecular transition in mid-infrared absorption spectroscopy. We show that an optimized S-G framework can be used by choosing a selected frame length determined by the adaptive learning outcome with low loss. We show that an ML-based adaptive SV filter can effectively suppress mod-hop (or any other instrumental-related effects and drifts). This is achieved by appropriately training the absorption spectroscopy signals with a calibrated reference in a (gaussian or thermal) noisy environment.
Several environmental trace gas species and toxic chemicals or warfare simulants have fingerprint spectral signatures in the mid-infrared region of the spectrum. For instance, methane, nitrous oxide, and water vapor are critical greenhouse gases relevant for environmental sensing. In contrast, Sarin is one of the most lethal warfare agents that is a highly toxic synthetic chemical organophosphorus compound, which is of interest in defense and security sensing applications. Due to complex chemical structure and significant absorption and collision cross-section, the molecular linewidths of such chemicals can cover a broad range of spectral widths in the mid-infrared region. Detection of such molecules in the mid-infrared region is sensitive, which requires broadly tunable sources and appropriate spectral resolution in detection schemes. We show a rapid detection methodology of atmospheric bands of trace gases in the 7 μm to 8 μm region, which also coincides with the fingerprints region of several hazardous chemicals. Methane absorbs strongly in the wavelength range of 3 μm to 8 μm, and nitrous oxide has absorption from 5 μm to 8 μm. We use molecular rotational-vibrational transitions of carbon and nitrogen trace species to demonstrate well-resolved peaks in the spectral region of 6.88 μm to 7.6 μm for detection. The detection was performed by a continuous wave multiplexed quantum cascade laser source capable of an ultra-wide tuning range from 6.88 μm to 11.05 μm.
It is estimated that about 60 percent of total global methane emissions are thought to be from anthropogenic sources and about 40 percent from natural sources. Anthropogenic sources encompass a wide range of human activities, including food and energy production and waste disposal. Livestock (through fermentation processes in their digestive system that generates methane and manure management), rice cultivation, landfills, and sewage account for 55-57 percent of global anthropogenic emissions. This paper investigates methane emissions from agricultural land-use and livestock (e.g., poultry and cattle) farming practices in Delaware. Laser-based point sensing can provide a higher spatial and temporal resolution that can complement satellite observations to identify individual sources and broader geographical areas. A detailed understanding of their sources and sinks is necessary to model emissions profile accurately. This paper shows field measurements of methane using mid-IR laser-based sensors and validation with satellite data. We conducted our field deployment locally in the Delaware, Kent, and Sussex county regions focusing on high methane emitting areas. We used the TROPOspheric Monitoring Instrument (TROPOMI) methane satellite data to get a unified emissions map of methane production in Delaware by comparing our ground-based measurements with the satellite data. Furthermore, we examined the satellite data for long-term methane emissions trends to quantify 2020 average methane emissions.
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