High-Sensitivity Micro-Gas Chromatograph-Photoionization Detector for Trace Vapor Detection

Identification of Different Classes of VOCs Based on Optical Emission Spectra Using a Dielectric Barrier Helium Plasma Coupled with a Mini Spectrometer

In this study, a micro helium dielectric barrier discharge (μHDBD) plasma device fabricated using 3D printing and molding techniques was coupled with a mini spectrometer to detect and identify different classes of volatile organic compounds (VOCs) using optical emission spectrometry (OES). We tested 11 VOCs belonging to three different classes (straight-chain alkanes, aromatics, and polar organic compounds). Our results clearly demonstrate that the optical emission spectra of different classes of VOCs show clear differences, and therefore, can be used for identification. Additionally, the emission spectra of VOCs with a similar structure (such as n-pentane, n-hexane, n-heptane, n-octane, and n-nonane) have similar optical emission spectrum shape. Acetone and ethanol also have similar emission wavelengths, but they show different line intensities for the same concentrations. We also found that the side-chain group of aromatics will also affect the emission spectra even though they have a similar structure (all have a benzene ring). Moreover, our μHDBD-OES system can also identify multiple compounds in VOC mixtures. Our work also demonstrates the possibility of identifying different classes of VOCs by the OES shape.

1D-Guided Differential Rescaling of a Contour Plot in Comprehensive 2D Gas Chromatography

A 1D-guided differential rescaling algorithm for a contour plot is developed based on our recently proposed comprehensive two-dimensional gas chromatography (GC × GC) system with a first-dimensional (1D) detector added. Chromatograms obtained from 1D and second-dimensional (2D) detectors are both incorporated during the data processing. As compared to the conventional contour plot methods using only 2D data, our algorithm can significantly improve precision and consistency of GC × GC results in terms of retention times, peak widths, and peak areas or volumes, regardless of modulation time selection, modulation phase shift fluctuations, and modulation duty cycle. The peak identification, quantification, and capacity can therefore be enhanced. Furthermore, the 1D-guided differential rescaling method is shown to better handle the coelution and missing peak issues often encountered in the conventional methods. Finally, the new method exhibits high versatility in 1D and 2D detector selection, which greatly broadens GC × GC utility. Our method can easily be adapted to other two-dimensional chromatography systems that have direct access to 1D chromatograms.

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.

Data-Driven Approach to Modeling Microfabricated Chemical Sensor Manufacturing

We have developed a statistical model-based approach to the quality analysis (QA) and quality control (QC) of a gas micro pre-concentrator chip (μPC) performance when manufactured at scale for chemical and biochemical analysis of volatile organic compounds (VOCs). To test the proposed model, a medium-sized university-led production batch of 30 wafers of chips were subjected to rigorous chemical performance testing. We quantitatively report the outcomes of each manufacturing process step leading to the final functional chemical sensor chip. We implemented a principal component analysis (PCA) model to score individual chip chemical performance, and we observed that the first two principal components represent 74.28% of chemical testing variance with 111 of 118 viable chips falling into the 95% confidence interval. Chemical performance scores and chip manufacturing data were analyzed using a multivariate regression model to determine the most influential manufacturing parameters and steps. In our analysis, we find the amount of sorbent mass present in the chip (variable importance score = 2.6) and heater and the RTD resistance values (variable importance score = 1.1) to be the manufacturing parameters with the greatest impact on chemical performance. Other non-obvious latent manufacturing parameters also had quantified influence. Statistical distributions for each manufacturing step will allow future large-scale production runs to be statistically sampled during production to perform QA/QC in a real-time environment. We report this study as the first data-driven, model-based production of a microfabricated chemical sensor.

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.

Analytical Tools and Methods for Explosive Analysis in Forensics: A Critical Review

This review summarizes (i) compositions and types of improvised explosive devices; (ii) the process of collection, extraction and analysis of explosive evidence encountered in explosive and related cases; (iii) inter-comparison of analytical techniques; (iv) the challenges and prospects of explosive detection technology. The highlights of this study include extensive information regarding the National & International standards specified by USEPA, ASTM, and so on, for explosives detection. The holistic development of analytical tools for explosive analysis ranging from conventional methods to advanced analytical tools is also covered in this article. The most important aspect of this review is to make forensic scientists familiar with the challenges during explosive analysis and the steps to avoid them. The problems during analysis can be analyte-based, that is, interferences due to matrix or added molding/stabilizing agents, trace amount of parent explosives in post-blast samples and many more. Others are techniques-based challenges viz. specificity, selectivity, and sensitivity of the technique. Thus, it has become a primary concern to adopt rapid, field deployable, and highly sensitive techniques.

Colorimetric Signal Readout for the Detection of Volatile Organic Compounds Using a Printable Glass-Based Dielectric Barrier Discharge-Type Helium Plasma Detector

In this paper, we report on a printable glass-based manufacturing method and a new proof-of-concept colorimetric signal readout scheme for a dielectric barrier discharge (DBD)-type helium plasma photoionization detector. The sensor consists of a millimeter-sized glass chamber manufactured using a printable glass suspension. Plasma inside the chip is generated using a custom-built power supply (900 V and 83.6 kHz), and the detector uses ∼5 W of power. Our new detection scheme is based on detecting the change in the color of plasma after the introduction of target gases. The change in color is first captured by a smartphone camera as a video output. The recorded video is then processed and converted to an image light intensity vs retention time plot (gas chromatogram) using three standard color space models (red, green, blue (RGB), hue, saturation, lightness (HSL), and hue, saturation, value (HSV)) with RGB performing the best among the three models. We successfully detected three different categories of volatile organic compounds using our new detection scheme and a 30-m-long gas chromatography column: (1) straight-chain alkanes (n-pentane, n-hexane, n-heptane, n-octane, and n-nonane), (2) aromatics (benzene, toluene, and ethylbenzene), and (3) polar compounds (acetone, ethanol, and dichloromethane). The best limit of detection of 10 ng was achieved for benzene at room temperature. Additionally, the device showed excellent performance for different types of sample mixtures consisting of three and five compounds. Our new detector readout method combined with our ability to print complex glass structures provides a new research avenue to analyze complex gas mixtures and their components.

Ultrathin Silica Integration for Enhancing Reliability of Microfluidic Photoionization Detectors

Microfluidic photoionization detectors (μPIDs) based on silicon chips can rapidly and sensitively detect volatile compounds. However, the applications of μPID are limited by the manual assembly process using glue, which may outgas and clog the fluidic channel, and by the short lifetime of the vacuum ultraviolet (VUV) lamps (especially, argon lamps). Here, we developed a gold-gold cold welding-based microfabrication process to integrate ultrathin (10 nm) silica into μPID. The silica coating enables direct bonding of the VUV window to silicon under amicable conditions and works as a moisture and plasma exposure barrier for VUV windows that are susceptible to hygroscopicity and solarization. Detailed characterization of the silica coating was conducted, showing that the 10 nm silica coating allows 40-80% VUV transmission from 8.5 to 11.5 eV. It is further shown that the silica-protected μPID maintained 90% of its original sensitivity after 2200 h of exposure to ambient (dew point = 8.0 ± 1.8 °C), compared to 39% without silica. Furthermore, argon plasma inside an argon VUV lamp was identified as the dominant degradation source for the LiF window with color centers formation in UV-vis and VUV transmission spectra. Ultrathin silica was then also demonstrated effective in protecting the LiF from argon plasma exposure. Lastly, thermal annealing was found to bleach the color centers and restore VUV transmission of degraded LiF windows effectively, which will lead to future development of a new type of VUV lamp and the corresponding μPID (and PID in general) that can be mass produced with a high yield, a longer lifetime, and better regenerability.

On-Site Detection of Volatile Organic Compounds (VOCs)

Volatile organic compounds (VOCs) are of interest in many different fields. Among them are food and fragrance analysis, environmental and atmospheric research, industrial applications, security or medical and life science. In the past, the characterization of these compounds was mostly performed via sample collection and off-site analysis with gas chromatography coupled to mass spectrometry (GC-MS) as the gold standard. While powerful, this method also has several drawbacks such as being slow, expensive, and demanding on the user. For decades, intense research has been dedicated to find methods for fast VOC analysis on-site with time and spatial resolution. We present the working principles of the most important, utilized, and researched technologies for this purpose and highlight important publications from the last five years. In this overview, non-selective gas sensors, electronic noses, spectroscopic methods, miniaturized gas chromatography, ion mobility spectrometry and direct injection mass spectrometry are covered. The advantages and limitations of the different methods are compared. Finally, we give our outlook into the future progression of this field of research.

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

Microscale gas chromatographs (μGCs) promise in-field analysis of volatile organic compounds (VOCs) in environmental and industrial monitoring, healthcare, and homeland security applications. As a step toward addressing challenges with performance and manufacturability, this study reports a highly integrated monolithic chip implementing a multisensing progressive cellular architecture. This architecture incorporates three μGC cells that are customized for different ranges of analyte volatility; each cell includes a preconcentrator and separation column, two complementary capacitive detectors, and a photoionization detector (PID). An on-chip carrier gas filter scrubs ambient air for the analysis. The monolithic chip, with all 16 components, is 40.3 × 55.7 mm² in footprint. To accommodate surface adsorptive and low-volatility analytes, the architecture eliminates the commonly used inlet valve, eliminating the need for chemically inactive surfaces in the valves and pumps, allowing the use of standard parts. Representative analysis is demonstrated from a nonpolar 14-analyte mixture, a polar 12-analyte mixture, and a 3-phosphonate ester mixture, covering a wide vapor pressure range (0.005-68.5 kPa) and dielectric constant range (1.8-23.2). The three types of detectors show highly complementary responses. Quantitative analysis is shown in the tens to hundreds ppb range. With 200 mL samples, the projected detection limits reach 0.12-4.7 ppb. Limited tests performed at 80% humidity showed that the analytes with vapor pressures <12 kPa were unaffected. A typical full run takes 28 min and consumes 2.3 kJ energy for the fluidic elements (excluding electronics). By eliminating chip-to-chip fluidic interconnections and requiring just one custom-fabricated element, this work presents a path toward high-performance and highly manufacturable μGCs.

Portable comprehensive two-dimensional micro-gas chromatography using an integrated flow-restricted pneumatic modulator

Two-dimensional (2D) gas chromatography (GC) provides enhanced vapor separation capabilities in contrast to conventional one-dimensional GC and is useful for the analysis of highly complex chemical samples. We developed a microfabricated flow-restricted pneumatic modulator (FRPM) for portable comprehensive 2D micro-GC (μGC), which enables rapid 2D injection and separation without compromising the 1D separation speed and eluent peak profiles. 2D injection characteristics such as injection peak width and peak height were fully characterized by using flow-through micro-photoionization detectors (μPIDs) at the FRPM inlet and outlet. A 2D injection peak width of ~25 ms could be achieved with a 2D/1D flow rate ratio over 10. The FRPM was further integrated with a 0.5-m long 2D μcolumn on the same chip, and its performance was characterized. Finally, we developed an automated portable comprehensive 2D μGC consisting of a 10 m OV-1 1D μcolumn, an integrated FRPM with a built-in 0.5 m polyethylene glycol 2D μcolumn, and two μPIDs. Rapid separation of 40 volatile organic compounds in ~5 min was demonstrated. A hybrid 2D contour plot was constructed by using both 1D and 2D chromatograms obtained with the two μPIDs at the end of the 1D and 2D μcolumns, which was enabled by the presence of the flow resistor in the FRPM.

Stress-Induced Volatile Emissions and Signalling in Inter-Plant Communication

The sessile plant has developed mechanisms to survive the “rough and tumble” of its natural surroundings, aided by its evolved innate immune system. Precise perception and rapid response to stress stimuli confer a fitness edge to the plant against its competitors, guaranteeing greater chances of survival and productivity. Plants can “eavesdrop” on volatile chemical cues from their stressed neighbours and have adapted to use these airborne signals to prepare for impending danger without having to experience the actual stress themselves. The role of volatile organic compounds (VOCs) in plant–plant communication has gained significant attention over the past decade, particularly with regard to the potential of VOCs to prime non-stressed plants for more robust defence responses to future stress challenges. The ecological relevance of such interactions under various environmental stresses has been much debated, and there is a nascent understanding of the mechanisms involved. This review discusses the significance of VOC-mediated inter-plant interactions under both biotic and abiotic stresses and highlights the potential to manipulate outcomes in agricultural systems for sustainable crop protection via enhanced defence. The need to integrate physiological, biochemical, and molecular approaches in understanding the underlying mechanisms and signalling pathways involved in volatile signalling is emphasised.

Wearable Plant Sensor for In Situ Monitoring of Volatile Organic Compound Emissions from Crops

Methanol is a major volatile organic compound (VOC) emitted from plants. Methanol emission reflects indirect plant defense against insects, promotes cell-to-cell communication, and adapts plants to various environmental stresses. This paper reports a wearable plant sensor that can monitor methanol emission directly on the leaf of a plant under field conditions with low cost, high portability, and easy installation and use. The sensor technology eliminates the need for complex sampling, expensive instruments, and skilled operators for conventional gas chromatography-mass spectrometry. The sensor uses a composite of conducting polymer microcrystallites and platinum nanoparticles (PtNPs). The conducting poly(2-amino-1,3,4-thiadiazole) or poly(ATD) provides a high electrocatalytic activity with redox behavior. The modification of poly(ATD) with catalytic PtNPs enables efficient electrochemical oxidation of methanol at a specific potential. The advantages of poly(ATD) and PtNPs are synergized for high sensitivity and selectivity of the sensor for detecting methanol emissions with a sub-ppm limit of detection. Further, the infusion of a polymer electrolyte into the porous electrode of the sensor enables an all-solid-state VOC sensor. The sensor is integrated into a miniature gas collection chamber and capped with a hydrophobic gas diffusion membrane to minimize the influence of environmental humidity on the sensor performance. The sensor is installed on the leaf surface. In situ detection shows a difference in methanol emission between the lower and upper leaves of greenhouse maize plants. Further, under field conditions, the sensor reveals a noticeable difference in methanol emission concentration between two genotypes (Mo17 and B73 inbred lines) of maize plants. Therefore, the sensor will provide a promising new means of directly monitoring volatile emission of plants, which is a physiological phenotype as a function of genes and environment.