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 1000 μGC runs that analyzed various chemical mixtures.

A chemical recognition algorithm is an integral part of any autonomous microscale gas chromatography (µGC) system for automated chemical analysis. For a multi-detector µGC system, the chemical analysis must account for the retention time of each chemical analyte as well as the relative response of each detector to each analyte, i.e., the detector response pattern (DRP). In contrast to the common approaches of heuristically using principal component analysis and machine learning, this paper reports a rule-based automated chemical recognition algorithm for a multi-cell, multi-detector µGC system, in which the DRP is related to theoretical principles; consequently, this algorithm only requires a small amount of calibration data but not extensive training data. For processing both the retention time and the raw DRP, the algorithm applies rules based on expert knowledge to compare the detected peaks; these rules are located in a customized software library. Additionally, the algorithm provides special handling for chromatogram peaks with a small signal-to-noise ratio. It also provides separate special handling for asymmetrical peaks that may result from surface adsorptive analytes. This work also describes an experimental evaluation in which the algorithm used the relative response of two complementary types of capacitive detectors as well as a photoionization detector that were incorporated into the µGC system of interest. In these tests, which were performed on chromatograms with 21–31 peaks for each detector, the true positive rate was 96.3%, the true negative rate was 94.1%, the false positive rate was 5.9%, and the false negative rate was 3.7%. The results demonstrated that the algorithm can support µGC systems for automated chemical screening and early warning applications.