Modular and reconfigurable gas chromatography / differential mobility spectrometry (GC/DMS) package for detection of volatile organic compounds (VOCs)

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, tert‑butyl 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.

Plant pest surveillance: from satellites to molecules

Plant pests and diseases impact both food security and natural ecosystems, and the impact has been accelerated in recent years due to several confounding factors. The globalisation of trade has moved pests out of natural ranges, creating damaging epidemics in new regions. Climate change has extended the range of pests and the pathogens they vector. Resistance to agrochemicals has made pathogens, pests, and weeds more difficult to control. Early detection is critical to achieve effective control, both from a biosecurity as well as an endemic pest perspective. Molecular diagnostics has revolutionised our ability to identify pests and diseases over the past two decades, but more recent technological innovations are enabling us to achieve better pest surveillance. In this review, we will explore the different technologies that are enabling this advancing capability and discuss the drivers that will shape its future deployment.

Trends and applications in plant volatile sampling and analysis

Volatile organic compounds (VOCs) released by plants serve as information and defense chemicals in mutualistic and antagonistic interactions and mitigate effects of abiotic stress. Passive and dynamic sampling techniques combined with gas chromatography-mass spectrometry analysis have become routine tools to measure emissions of VOCs and determine their various functions. More recently, knowledge of the roles of plant VOCs in the aboveground environment has led to the exploration of similar functions in the soil and rhizosphere. Moreover, VOC patterns have been recognized as sensitive and time-dependent markers of biotic and abiotic stress. This focused review addresses these developments by presenting recent progress in VOC sampling and analysis. We show advances in the use of small, inexpensive sampling devices and describe methods to monitor plant VOC emissions in the belowground environment. We further address latest trends in real-time measurements of volatilomes in plant phenotyping and most recent developments of small portable devices and VOC sensors for non-invasive VOC fingerprinting of plant disease. These technologies allow for innovative approaches to study plant VOC biology and application in agriculture.

Low-cost and open-source strategies for chemical separations

In recent years, a trend toward utilizing open access resources for laboratory research has begun. Open-source design strategies for scientific hardware rely upon the use of widely available parts, especially those that can be directly printed using additive manufacturing techniques and electronic components that can be connected to low-cost microcontrollers. Open-source software eliminates the need for expensive commercial licenses and provides the opportunity to design programs for specific needs. In this review, the impact of the "open-source movement" within the field of chemical separations is described, primarily through a comprehensive look at research in this area over the past five years. Topics that are covered include general laboratory equipment, sample preparation techniques, separations-based analysis, detection strategies, electronic system control, and software for data processing. Remaining hurdles and possible opportunities for further adoption of open-source approaches in the context of these separations-related topics are also discussed.

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.