Portable chemical detection platform for on-site monitoring of odorant levels in natural gas.

The adequate odorization of natural gas is critical to identify gas leaks and to reduce accidents. To ensure odorization, natural gas utility companies collect samples to be processed at core facilities or a trained human technician smells a diluted natural gas sample. In this work, we report a detection platform that addresses the lack of mobile solutions capable of providing quantitative analysis of mercaptans, a class of compounds used to odorize natural gas. Detailed description of the platform hardware and software components is provided. Designed to be portable, the platform hardware facilitates extraction of mercaptans from natural gas, separation of individual mercaptan species, and quantification of odorant concentration, with results reported at point-of-sampling. The software was developed to accommodate skilled users as well as minimally trained operators. Detection and quantification of six commonly used mercaptan compounds (ethyl mercaptan, dimethyl sulfide, n-propylmercaptan, isopropyl mercaptan, tert­butyl mercaptan, and tetrahydrothiophene) at typical odorizing concentrations of 0.1-5 ppm was performed using the device. We demonstrate the potential of this technology to ensure natural gas odorizing concentrations throughout distribution systems.

Controlled air exchange rate method to evaluate reduction of volatile organic compounds by indoor air cleaners.

Air cleaning technologies are needed to reduce indoor concentrations and exposure to volatile organic compounds (VOCs). Currently, air cleaning technologies lack an accepted test standard to evaluate their VOC removal performance. A protocol to evaluate the VOC removal performance of air cleaning devices was developed and piloted with two devices. This method injects a VOC mixture and carbon dioxide into a test chamber, supplies outdoor air at a standard building ventilation rate, periodically measures the VOC concentrations in the chamber using solid phase microextraction-gas chromatography-mass spectrometry over a 3-h decay period, and compares the decay rate of VOCs to carbon dioxide to measure the VOC removal air cleaning performance. The method was demonstrated with both a hydroxyl radical generator and an activated carbon air cleaner. It was shown that the activated carbon air cleaner device tested had a clean air delivery rate an order of magnitude greater than the hydroxyl radical generator device (72.10 vs 6.32 m3/h).

Non-destructive method to classify walnut kernel freshness from volatile organic compound (VOC) emissions using gas chromatography-differential mobility spectrometry (GC-DMS) and machine learning analysis.

Analysis of volatile organic compounds (VOCs) can be an effective strategy to inspect the quality of horticultural commodities and following their degradation. In this work, we report that VOCs emitted by walnuts can be studied using gas chromatography-differential mobility spectrometry (GC-DMS), and those GC-DMS data can be analyzed to predict the rancidity of walnuts, i.e., classify walnuts into grades of freshness. Walnut kernels were assigned a class n depending on their level of freshness as determined by a peroxide assay. VOC samples were analyzed using GC-DMS. From these VOC data, a partial least square regression (PLSR) model provided a freshness prediction value m, which corresponded to the rancid class n when m=n±0.5. The PLSR model had an accuracy of 80% to predict walnut grade and demonstrated a minimal root mean squared error of 0.42 for the m response variables (representative of walnut grade) with the GC-DMS data. We also conducted gas chromatography-mass spectrometry (GC-MS) experiments to identify volatiles that emerged or were enhanced with more rancid walnuts. The findings of the GC-MS study of walnut VOCs align excellently with the GC-DMS study. Based on our results, we conclude that a GC-DMS device deployed with a pre-trained machine learning model can be a very effective device for classifying walnut grades in the industry.

Machine learning and signal processing assisted differential mobility spectrometry (DMS) data analysis for chemical identification.

Differential mobility spectrometry (DMS)-based detectors are being widely studied to detect chemical warfare agents, explosives, chemicals, drugs and analyze volatile organic compounds (VOCs). The dispersion plots from DMS devices are complex to effectively analyze through visual inspection. In the current work, we adopted machine learning to differentiate pure chemicals and identify chemicals in a mixture. In particular, we observed the convolutional neural network algorithm exhibits excellent accuracy in differentiating chemicals in their pure forms while also identifying chemicals in a mixture. In addition, we propose and validate the magnitude-squared coherence (msc) between the DMS data of known chemical composition and that of an unknown sample can be sufficient to inspect the chemical composition of the unknown sample. We have shown that the msc-based chemical identification requires the least amount of experimental data as opposed to the machine learning approach.

An environmental air sampler to evaluate personal exposure to volatile organic compounds.

A micro fabricated chip-based wearable air sampler was used to monitor the personnel exposure of volatile chemical concentrations in microenvironments. Six teenagers participated in this study and 14 volatile organic compounds (VOCs) including naphthalene, 3-decen-1-ol, hexanal, nonanal, methyl salicylate and limonene gave the highest abundance during routine daily activity. VOC exposure associated with daily activities and the location showed strong agreements with two of the participant's results. One of these subjects had the highest exposure to methyl salicylate that was supported by the use of a topical analgesic balm containing this compound. Environmental based air quality monitoring followed by the personnel exposure studies provided additional evidence associated to the main locations where the participants traveled. Toluene concentrations observed at a gas station were exceptionally high, with the highest amount observed at 1213.1 ng m-3. One subject had the highest exposure to toluene and the GPS data showed clear evidence of activities neighboring a gas station. This study shows that this wearable air sampler has potential applications including hazardous VOC exposure monitoring in occupational hazard assessment for certain professions, for example in industries that involve direct handling of petroleum products.

Peak detection and random forests classification software for gas chromatography/differential mobility spectrometry (GC/DMS) data.

Gas Chromatography/Differential Mobility Spectrometry (GC/DMS) is an effective tool to discern volatile chemicals. The process of correlating GC/DMS data outputs to chemical identities requires time and effort from trained chemists due to lack of commercially available software and the lack of appropriate libraries. This paper describes the coupling of computer vision techniques to develop models for peak detection and can align chemical signatures across datasets. The result is an automatically generated peak table that provides integrated peak areas for the inputted samples. The software was tested against a simulated dataset, whereby the number of detected features highly correlated to the number of actual features (r2 = 0.95). This software has also been developed to include random forests, a discriminant analysis technique that generates prediction models for application to unknown samples with different chemical signatures. In an example dataset described herein, the model achieves 3% classification error with 12 trees and 0% classification error with 48 trees. The number of trees can be optimized based on the computational resources available. We expect the public release of this software can provide other GC/DMS researchers with a tool for automated featured extraction and discriminant analysis capabilities.

Machine Vision Methods, Natural Language Processing, and Machine Learning Algorithms for Automated Dispersion Plot Analysis and Chemical Identification from Complex Mixtures.

Gas-phase trace chemical detection techniques such as ion mobility spectrometry (IMS) and differential mobility spectrometry (DMS) can be used in many settings, such as evaluating the health condition of patients or detecting explosives at airports. These devices separate chemical compounds in a mixture and provide information to identify specific chemical species of interest. Further, these types of devices operate well in both controlled lab environments and in-field applications. Frequently, the commercial versions of these devices are highly tailored for niche applications (e.g., explosives detection) because of the difficulty involved in reconfiguring instrumentation hardware and data analysis software algorithms. In order for researchers to quickly adapt these tools for new purposes and broader panels of chemical targets, it is critical to develop new algorithms and methods for generating libraries of these sensor responses. Microelectromechanical system (MEMS) technology has been used to fabricate DMS devices that miniaturize the platforms for easier deployment; however, concurrent advances in advanced data analytics are lagging. DMS generates complex three-dimensional dispersion plots for both positive and negative ions in a mixture. Although simple spectra of single chemicals are straightforward to interpret (both visually and via algorithms), it is exceedingly challenging to interpret dispersion plots from complex mixtures with many chemical constituents. This study uses image processing and computer vision steps to automatically identify features from DMS dispersion plots. We used the bag-of-words approach adapted from natural language processing and information retrieval to cluster and organize these features. Finally, a support vector machine (SVM) learning algorithm was trained using these features in order to detect and classify specific compounds in these represented conceptualized data outputs. Using this approach, we successfully maintain a high level of correct chemical identification, even when a gas mixture increases in complexity with interfering chemicals present.

Wearable Environmental Monitor To Quantify Personal Ambient Volatile Organic Compound Exposures.

Air pollution can cause acute and chronic health problems. It has many components, and one component of interest is volatile organic compounds (VOCs). While the outdoor environment may have regulations regarding exposure limits, the indoor environment is often unregulated and VOCs often appear in greater concentrations in the indoor environment. Therefore, it is equally critical to monitor both the indoor and outdoor environments for ambient chemical levels that an individual person is exposed to. While a number of different chemical detectors exist, most lack the ability to provide portable monitoring. We have developed a portable and wearable sampler that collects environmental VOCs in a person's immediate "exposure envelope" onto custom micro-preconcentrator chips for later benchtop analysis. The system also records ambient temperature and humidity and the GPS location during sampling, and the chip cartridges can be used in sequence over time to complete a profile of individual chemical exposure over the course of hours/days/weeks/months. The system can be programmed to accumulate sample for various times with varying periodicity. We first tested our sampler in the laboratory by completing calibration curves and testing saturation times for various common chemicals. The sampler was also tested in the field by collecting both indoor and outdoor personal exposure samples. Additionally under IRB approval, a teenaged volunteer wore the sampler for 5 days during which it sampled periodically throughout a 12 h period each day and the volunteer replaced the micro-preconcentrator chip each day.

High Asymmetric Longitudinal Field Ion Mobility Spectrometry Device for Low Power Mobile Chemical Separation and Detection.

We have developed a novel chemical sensing technique termed high asymmetric longitudinal field ion mobility spectrometry (HALF-IMS), which allows separation of ions based on mobility differences in high and low electric fields. Our device is microfabricated, has a miniature format, and uses exceptionally low power due to the lack of RF separation fields normally associated with ion mobility spectrometry (IMS) or differential mobility spectrometry (DMS). It operates at room temperature and atmospheric pressure. This HALF-IMS chip contains a microscale drift cell where spatially varying electric field regions of high and low strengths are generated by direct current (DC) applied to the electrodes that are physically placed to cause ionic separation as the ionized chemical flows along the drift cell. Power and complexity are reduced at the chip and system levels by reducing the voltage magnitude and using DC-powered electronics. A testing platform utilizing an ultraviolet (UV) photoionization source was used with custom electronic circuit boards to interface with the chip and provide data inputs and outputs. Precise control of the electrode voltages allowed filtering of the passage of the ion of interest through the drift cell, and ionic current was measured at the detector. The device was tested by scanning of electrode voltages and obtaining ion peaks for methyl salicylate, naphthalene, benzene, and 2-butanone. The current experimental setup was capable of detecting as low as ∼80 ppb of methyl salicylate and naphthalene. The use of benzene as a dopant with 2-butanone allowed one to see two ion peaks, corresponding to benzene and 2-butanone.

Tandem ion mobility spectrometry at ambient pressure and field decomposition of mobility selected ions of explosives and interferences.

A tandem ion mobility spectrometer at ambient pressure included a thermal desorption inlet, two drift regions, dual ion shutters, and a wire grid assembly in the second drift region. An ion swarm could be mobility isolated in the first drift region using synchronized dual ion shutters and decomposed in a wire grid assembly using electric fields of 1.80 × 104 V cm-1 (118 Td) from a 1.8 MHz sinusoidal waveform. Mobility selected ions that underwent field induced decomposition were NO3-, from PETN·Cl- and NG·Cl-, and NO2- from RDX·Cl-. The extent of decomposition ranged from 60 to 90%, depending on gas temperature, field strength, and ion identity, introducing additional controls to improve selectivity in trace determination of explosives. Ion transmission through the wire grid assembly ranged from 80 to >95% with losses increasing for increased field strength. Studies with pairs of explosives and interfering substances demonstrated decisive detection of explosives and portend reduced rates of false positive using tandem ion mobility spectrometers with a reactive stage.

Automated chemical identification and library building using dispersion plots for differential mobility spectrometry.

Differential mobility spectrometry (DMS) based detectors require rapid data analysis capabilities, embedded into the devices to achieve the optimum detection capabiites as portable trace chemical detectors. Automated algorithm-based DMS dispersion plot data analysis method was applied for the first time to pre-process and separate 3-dimentional (3-D) DMS dispersion data. We previously demonstrated our AnalyzeIMS (AIMS) software was capable of analyzing complex gas chromatography differential mobility spectrometry (GC-DMS) data sets. In our present work, the AIMS software was able to easliy separate DMS dispersion data sets of five chemicals that are important in detection of volatile organic compounds (VOCs): 2-butanone, 2-propanone, ethyl acetate, methanol and ethanol. Identification of chemicals from mixtures, separation of chemicals from a mixture and prediction capability of the software were all tested. These automated algorithms may have potential applications in separation of chemicals (or ion peaks) from other 3-D data obtained by hybrid analytical devices such as mass spectrometry (MS). New algorithm developments are included as future considerations to improve the current numerical approaches to fingerprint chemicals (ions) from a significantly complicated dispersion plot. Comprehensive peak identifcation by DMS-MS, variations of the DMS data due to chemical concentration, gas phase ion chemistry, temperature and pressure of the drift gas are considered in future algorithm improvements.

Dissociation Enthalpies of Chloride Adducts of Nitrate and Nitrite Explosives Determined by Ion Mobility Spectrometry.

The kinetics for thermal dissociations of the chloride adducts of the nitrate explosives 1,3-dinitroglycerin (1,3-NG), 1,2-dinitroglycerin (1,2-NG), the nitrite explosive 3,4-dinitrotoluene (3,4-DNT), and the explosive taggant 2,3-dimethyl-2,3-dinitrobutane (DMNB) have been studied by atmospheric pressure ion mobility spectrometry. Both 1,3-NG·Cl(-) and1,2-NG·Cl(-) decompose in a gas-phase SN2 reaction in which Cl(-) displaces NO3(-) while 3,4-DNT·Cl(-) and DMNB·Cl(-) decompose by loss of Cl(-). The determined activation energy (kJ mol(-1)) and pre-exponential factor (s(-1)) values for the dissociations respectively are 1,3-NG·Cl(-), 86 ± 2 and 2.2 × 10(12); 1,2-NG·Cl(-), 97 ± 2 and 3.5 × 10(12); 3,4-DNT·Cl(-), 81 ± 2 and 4.8 × 10(13); and DMNB·Cl(-), 68 ± 2 and 9.7 × 10(11). Calculations by density functional theory show the structures of the nitrate ester adducts involve three hydrogen bonds: one from the hydroxyl group and the other two from the two nitrated carbons. The relative Cl(-) dissociation energies of the nitrates together with the previously reported smaller value for glycerol trinitrate and the calculated highest value for glycerol 1-mononitrate are explicable in terms of the number of hydroxyl hydrogen bond participants. The theoretical enthalpy changes for the nitrate ester displacement reactions are in agreement with those derived from the experimental activation energies but considerably higher for the nitro compounds.

Decomposition kinetics of nitroglycerine·Cl(-)(g) in air at ambient pressure with a tandem ion mobility spectrometer.

A dual-shutter ion mobility spectrometer operating at atmospheric pressure and interfaced to a gas chromatograph for sample introduction has been used to study the reaction of Cl(-) with explosives. Of particular interest was an investigation of the formation of NO3(-) from the reaction of the Cl(-) with nitroglycerin (NG). The adduct NG·Cl(-) together with NO3(-) and NG·NO3(-) compose the mobility spectrum. Over the temperature range 111 to 122 °C, NG·NO3(-) is stable, but NG·Cl(-) decomposes to NO3(-) and 1,2-dinitro-3-chloropropane (DNClP). The activation energy and pre-exponential factor for this first order decomposition are 80 ± 3 kJ mol(-1) and 1.3 × 10(12) s(-1), respectively. Ab initio calculation shows that the reaction is a substitution reaction occurring over a two well potential energy profile with stable ion-molecule complexes NG·Cl(-) and DNClP·NO3(-).