Highly Integrated μGC Based on a Multisensing Progressive Cellular Architecture with a Valveless Sample Inlet

SiNx/SiO2-Based Fabry-Perot Interferometer on Sapphire for Near-UV Optical Gas Sensing of Formaldehyde in Air

Fabry-Perot interferometers (FPIs), comprising foundry-compatible dielectric thin films on sapphire wafer substrates, were investigated for possible use in chemical sensing. Specifically, structures comprising two vertically stacked distributed Bragg reflectors (DBRs), with the lower DBR between a sapphire substrate and a silicon-oxide (SiO2) resonator layer and the other DBR on top of this resonator layer, were investigated for operation in the near-ultraviolet (near-UV) range. The DBRs are composed of a stack of nitride-rich silicon-nitride (SiNx) layers for the higher index and SiO2 layers for the lower index. An exemplary application would be formaldehyde detection at sub-ppm concentrations in air, using UV absorption spectroscopy in the 300-360 nm band, while providing spectral selectivity against the main interfering gases, notably NO2 and O3. Although SiNx thin films are conventionally used only for visible and near-infrared optical wavelengths (above 450 nm) because of high absorbance at lower wavelengths, this work shows that nitride-rich SiNx is suitable for near-UV wavelengths. The interplay between spectral absorbance, transmittance and reflectance in a FPI is presented in a comparative study between one FPI design using stoichiometric material (Si3N4) and two designs based on N-rich compositions, SiN1.39 and SiN1.49. Spectral measurements confirm that if the design accounts for phase penetration depth, sufficient performance can be achieved with the SiN1.49-based FPI design for gas absorption spectroscopy in near-UV, with peak transmission at 330 nm of 64%, a free spectral range (FSR) of 20 nm and a full-width half-magnitude spectral resolution (FWHM) of 2 nm.

Automatic peak detection algorithm based on continuous wavelet transform for complex chromatograms from multi-detector micro-scale gas chromatographs

Peak detection for chromatograms, including the detection of peak retention times, peak start locations, and peak end locations, is an important processing step for extracting peak information that is used for chemical recognition. Compared to benchtop gas chromatographs, the chromatograms generated by microscale gas chromatographs (µGCs) often contain higher noise levels, peak overlap, peak asymmetry, and both positive and negative chromatographic peaks, increasing the challenges for peak detection. This paper reports an automatic peak detection algorithm based on continuous wavelet transform (CWT) for chromatograms generated by multi-detector µGCs. The relationship between chemical retention time and peak width is leveraged to differentiate chromatographic peaks from noise and baseline drift. Special features in the CWT coefficients are leveraged to detect peak overlap and asymmetry. For certain detectors that may generate positive and negative chromatographic peaks, the peaks cannot be independently detected reliably, but the peak information can be well extracted using peak information generated by other in-line single-polarity detectors. The implemented algorithm provided a true positive rate of 97.2 % and false discovery rate of 7.8 % for chromatograms generated by a µGC with three integrated detectors, two capacitive and one photoionization. The chromatograms included complex scenarios with positive and negative chromatographic peaks, up to five consecutive overlapping peaks, peak asymmetry factor up to 24, and signal-to-noise ratios spanning 9-2800.

Control Software Design for a Multisensing Multicellular Microscale Gas Chromatography System

Microscale gas chromatography (μGC) systems are miniaturized instruments that typically incorporate one or several microfabricated fluidic elements; such systems are generally well suited for the automated sampling and analysis of gas-phase chemicals. Advanced μGC systems may incorporate more than 15 elements and operate these elements in different coordinated sequences to execute complex operations. In particular, the control software must manage the sampling and analysis operations of the μGC system in a time-sensitive manner; while operating multiple control loops, it must also manage error conditions, data acquisition, and user interactions when necessary. To address these challenges, this work describes the investigation of multithreaded control software and its evaluation with a representative μGC system. The μGC system is based on a progressive cellular architecture that uses multiple μGC cells to efficiently broaden the range of chemical analytes, with each cell incorporating multiple detectors. Implemented in Python language version 3.7.3 and executed by an embedded single-board computer, the control software enables the concurrent control of heaters, pumps, and valves while also gathering data from thermistors, pressure sensors, capacitive detectors, and photoionization detectors. A graphical user interface (UI) that operates on a laptop provides visualization of control parameters in real time. In experimental evaluations, the control software provided successful operation and readout for all the components, including eight sets of thermistors and heaters that form temperature feedback loops, two sets of pressure sensors and tunable gas pumps that form pressure head feedback loops, six capacitive detectors, three photoionization detectors, six valves, and an additional fixed-flow gas pump. A typical run analyzing 18 chemicals is presented. Although the operating system does not guarantee real-time operation, the relative standard deviations of the control loop timings were <0.5%. The control software successfully supported >1000 μGC runs that analyzed various chemical mixtures.