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.

A Metabolomics Assay to Diagnose Citrus Huanglongbing Disease and to Aid in Assessment of Treatments to Prevent or Cure Infection.

Citrus greening disease, or Huanglongbing (HLB), has devastated citrus crops globally in recent years. The causal bacterium, 'Candidatus Liberibacter asiaticus', presents a sampling issue for qPCR diagnostics and results in a high false negative rate. In this work, we compared six metabolomics assays to identify HLB-infected citrus trees from leaf tissue extracted from 30 control and 30 HLB-infected trees. A liquid chromatography-mass spectrometry-based assay was most accurate. A final partial least squares-discriminant analysis (PLS-DA) model was trained and validated on 690 leaf samples with corresponding qPCR measures from three citrus varieties (Rio Red grapefruit, Hamlin sweet orange, and Valencia sweet orange) from orchards in Florida and Texas. Trees were naturally infected with HLB transmitted by the insect vector Diaphorina citri. In a randomized validation set, the assay was 99.9% accurate to classify diseased from nondiseased samples. This model was applied to samples from trees receiving plant defense-inducer compounds or biological treatments to prevent or cure HLB infection. From two trials, HLB-related metabolite abundances and PLS-DA scores were tracked longitudinally and compared with those of control trees. We demonstrate how our assay can assess tree health and the efficacy of HLB treatments and conclude that no trialed treatment was efficacious.

Stable electrospray signal on a microfabricated glass chip with three-dimensional open edge and tiered depth geometries.

This paper presents the microfabrication and performance of a three-dimensional electrospray ionization (ESI) emitter tip made from glass, which achieves stable current signals important for chemical analysis. Our novel microfabrication process and custom-built signal conditioning hardware provides the advantage of providing accurate features and steady signals. The fabrication process relies on standard microfabrication techniques (i.e., deposition, photolithography, and wet etching). This fabrication method involves the novel application of two layers of positive and negative photoresists in addition to Parafilm® wax tape. Open edge and tiered depth details were successfully created from a multilayer planar mask. This is a benefit for integrated miniaturized and microfluidic systems that often require micro features for their functionality but relatively large millimeter size features for their physical periphery. We demonstrate the fundamental performance of electrospray with this microfluidic chip. The emitter tip was fixed on a linear axis stage with high resolution (10 µm) to finely control the tip distance from a metal counter electrode plate. A custom printed circuit board system was built to safely control four voltages applied to the microchip ports from a single high voltage power supply. To readily form the electrospray, non-aqueous solvents were used for their low viscosity and a constant voltage of +2.7 kV was applied to the sheath electrospray microchannel. The liquid being sprayed was 80/20 (v/v) methanol/acetonitrile with 0.1% acetic acid in the sheath microchannel and with ammonium acetate (10-40 mM) in its remaining microchannels. The electrospray signal was measured in response to varying the distance (1.4 to 1.6 mm) between the electrospray emitter tip and a metal counter electrode plate in addition to the varying concentration of the background electrolyte, ammonium acetate. Stable and repeatable electrospray signal showed linear relationships with emitter tip distance and concentration (r2≥0.95).

Predicting Influenza and Rhinovirus Infections in Airway Cells Utilizing Volatile Emissions.

BACKGROUND: Respiratory viral infections are common and potentially devastating to patients with underlying lung disease. Diagnosing viral infections often requires invasive sampling, and interpretation often requires specialized laboratory equipment. Here, we test the hypothesis that a breath test could diagnose influenza and rhinovirus infections using an in vitro model of the human airway. METHODS: Cultured primary human tracheobronchial epithelial cells were infected with either influenza A H1N1 or rhinovirus 1B and compared with healthy control cells. Headspace volatile metabolite measurements of cell cultures were made at 12-hour time points postinfection using a thermal desorption-gas chromatography-mass spectrometry method. RESULTS: Based on 54 compounds, statistical models distinguished volatile organic compound profiles of influenza- and rhinovirus-infected cells from healthy counterparts. Area under the curve values were 0.94 for influenza, 0.90 for rhinovirus, and 0.75 for controls. Regression analysis predicted how many hours prior cells became infected with a root mean square error of 6.35 hours for influenza- and 3.32 hours for rhinovirus-infected cells. CONCLUSIONS: Volatile biomarkers released by bronchial epithelial cells could not only be used to diagnose whether cells were infected, but also the timing of infection. Our model supports the hypothesis that a breath test could serve to diagnose viral infections.

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.

Analysis of Volatile Profiles for Tracking Asymptomatic Infections of Phytophthora ramorum and Other Pathogens in Rhododendron.

Phytophthora ramorum is an invasive, broad host range pathogen that causes ramorum blight and sudden oak death in forest landscapes of western North America. In commercial nurseries, asymptomatic infections of nursery stock by P. ramorum and other Phytophthora species create unacceptable risk and complicate inspection and certification programs designed to prevent introduction and spread of these pathogens. In this study, we continue development of a volatile organic compound (VOC)-based test for detecting asymptomatic infections of P. ramorum in Rhododendron sp. We confirmed detection of P. ramorum from volatiles collected from asymptomatic root-inoculated Rhododendron plants in a nursery setting, finding that the VOC profile of infected plants is detectably different from that of healthy plants, when measured from both ambient VOC emissions and VOCs extracted from leaf material. Predicting infection status was successful from ambient volatiles, which had a mean area under the curve (AUC) value of 0.71 ± 0.17, derived from corresponding receiver operating characteristic curves from an extreme gradient boosting discriminant analysis. This finding compares with that of extracted leaf volatiles, which resulted in a lower AUC value of 0.51 ± 0.21. In a growth chamber, we contrasted volatile profiles of asymptomatic Rhododendron plants having roots infected with one of three pathogens: P. ramorum, P. cactorum, and Rhizoctonia solani. Each pathogen induced unique and measurable changes, but generally the infections reduced volatile emissions until 17 weeks after inoculation, when emissions trended upward relative to those of mock-inoculated controls. Forty-five compounds had significant differences compared with mock-inoculated controls in at least one host-pathogen combination.

Environmental sampling of volatile organic compounds during the 2018 Camp Fire in Northern California.

Trace analysis of volatile organic compounds (VOCs) during wildfires is imperative for environmental and health risk assessment. The use of gas sampling devices mounted on unmanned aerial vehicles (UAVs) to chemically sample air during wildfires is of great interest because these devices move freely about their environment, allowing for more representative air samples and the ability to sample areas dangerous or unreachable by humans. This work presents chemical data from air samples obtained in Davis, CA during the most destructive wildfire in California's history - the 2018 Camp Fire - as well as the deployment of our sampling device during a controlled experimental fire while fixed to a UAV. The sampling mechanism was an in-house manufactured micro-gas preconcentrator (µPC) embedded onto a compact battery-operated sampler that was returned to the laboratory for chemical analysis. Compounds commonly observed in wildfires were detected during the Camp Fire using gas chromatography mass spectrometry (GC-MS), including BTEX (benzene, toluene, ethylbenzene, m+p-xylene, and o-xylene), benzaldehyde, 1,4-dichlorobenzene, naphthalene, 1,2,3-trimethylbenzene and 1-ethyl-3-methylbenzene. Concentrations of BTEX were calculated and we observed that benzene and toluene were highest with average concentrations of 4.7 and 15.1 µg/m3, respectively. Numerous fire-related compounds including BTEX and aldehydes such as octanal and nonanal were detected upon experimental fire ignition, even at a much smaller sampling time compared to samples taken during the Camp Fire. Analysis of the air samples taken both stationary during the Camp Fire and mobile during an experimental fire show the successful operation of our sampler in a fire environment.

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.

Progress Toward an Attract-and-Kill Device for Asian Citrus Psyllid (Hemiptera: Liviidae) Using Volatile Signatures of Citrus Infected With Huanglongbing as the Attractant.

Asian citrus psyllid, Diaphorina citri (Kuwayama), preferentially orient toward citrus hosts infected with the phytopathogenic bacterium, Candidatus liberibacter asiaticus (CLas) the agent of citrus greening (Huanglongbing, HLB), compared to uninfected counterparts. We investigated whether this preference for the odors of infected plants could be useful for the development of an attract-and-kill (AK) device for D. citri. Twenty-nine blends of volatile organic compounds derived from the odor of citrus infected with CLas were tested in laboratory olfactometer tests, and two blends were also assessed under field conditions. A seven component blend of tricosane: geranial: methyl salicylate: geranyl acetone: linalool: phenylacetaldehyde: (E)-ß-ocimene in a 0.40: 0.06: 0.08: 0.29: 0.08: 0.06: 0.03 ratio released from a proprietary slow-release matrix attracted twice more D. citri to yellow sticky traps compared with blank control traps. The attractive blend was subsequently co-formulated with spinosad insecticide into a slow-release matrix to create a prototype AK formulation against D. citri. This formulation effectively reduced the population density of D. citri up to 84% as measured with tap counts when deployed at a density of eight 2.5 g dollops per tree as compared with untreated controls in small plot field trials conducted in citrus orchards. Psyllid populations were not statistically affected at a deployment rate of four dollops per tree. Our results indicate that an AK formulation incorporating spinosad and a volatile blend signature of citrus greening into a slow-release matrix may be useful to suppress D. citri populations.

Wearable Sensor System to Monitor Physical Activity and the Physiological Effects of Heat Exposure.

Mobile health monitoring via non-invasive wearable sensors is poised to advance telehealth for older adults and other vulnerable populations. Extreme heat and other environmental conditions raise serious health challenges that warrant monitoring of real-time physiological data as people go about their normal activities. Mobile systems could be beneficial for many communities, including elite athletes, military special forces, and at-home geriatric monitoring. While some commercial monitors exist, they are bulky, require reconfiguration, and do not fit seamlessly as a simple wearable device. We designed, prototyped and tested an integrated sensor platform that records heart rate, oxygen saturation, physical activity levels, skin temperature, and galvanic skin response. The device uses a small microcontroller to integrate the measurements and store data directly on the device for up to 48+ h. continuously. The device was compared to clinical standards for calibration and performance benchmarking. We found that our system compared favorably with clinical measures, such as fingertip pulse oximetry and infrared thermometry, with high accuracy and correlation. Our novel platform would facilitate an individualized approach to care, particularly those whose access to healthcare facilities is limited. The platform also can be used as a research tool to study physiological responses to a variety of environmental conditions, such as extreme heat, and can be customized to incorporate new sensors to explore other lines of inquiry.

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.

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.

SPME-based mobile field device for active sampling of volatiles.

Monitoring plant volatile organic compound (VOC) profiles can reveal information regarding the health state of the plant, such as whether it is nutrient stressed or diseased. Typically, plant VOC sampling uses sampling enclosures. Enclosures require time and equipment which are not easily adapted to high throughput sampling in field environments. We have developed a new, easily assembled active sampling device using solid phase microextraction (SPME) that uses a commercial off the shelf (COTS) hand vacuum base to provide rapid and easy mobile plant VOC collection. Calibration curves for three representative plant VOCs (α-pinene, limonene, and ocimene) were developed to verify device functionality and enable the quantification of field-samples from a Meyer lemon tree. We saw that the active sampling allowed us to measure and quantify this chemical in an orchard setting. This device has the potential to be used for VOC sampling as a preliminary diagnostic in precision agriculture applications due to its ease of manufacturing, availability, and low cost of the COTS hand vacuum module.

Portable combination of Fourier transform infrared spectroscopy and differential mobility spectrometry for advanced vapor phase analysis.

Designing mobile devices for the analysis of complex sample mixtures containing a variety of analytes at different concentrations across a large dynamic range remains a challenging task in many analytical scenarios. To meet this challenge, a compact hybrid analytical platform has been developed combining Fourier transform infrared spectroscopy based on substrate-integrated hollow waveguides (iHWG-FTIR) with gas chromatography coupled differential mobility spectrometry (GC-DMS). Due to the complementarity of these techniques regarding analyte type and concentration, their combination provides a promising tool for the detection of complex samples containing a broad range of molecules at different concentrations. To date, the combination of infrared spectroscopy and ion mobility techniques remains expensive and bound to a laboratory utilizing e.g. IMS as prefilter or IR as ionization source. In the present study, a cost-efficient and portable solution has been developed and characterized representing the first truly hyphenated IR-DMS system. As a model analyte mixture, 5 ppm isopropylmercaptan (IPM) in methane (CH4) were diluted, and the concentration-dependent DMS signal of IPM along with the concentration-dependent IR signal of CH4 were recorded for all three hybrid IR-DMS systems. While guiding the sample through the iHWG-FTIR or the GC-DMS first did not affect the obtained signals, optimizing the IR data acquisition parameters did benefit the analytical results.

Effects of Phytophthora ramorum on volatile organic compound emissions of Rhododendron using gas chromatography-mass spectrometry.

Phytophthora ramorum is an invasive and devastating plant pathogen that causes sudden oak death in coastal forests in the western United States and ramorum blight in nursery ornamentals and native plants in various landscapes. As a broad host-range quarantine pest that can be asymptomatic in some hosts, P. ramorum presents significant challenges for regulatory efforts to detect and contain it, particularly in commercial nurseries. As part of a program to develop new detection methods for cryptic infections in nursery stock, we compared volatile emissions of P. ramorum-inoculated and noninoculated Rhododendron plants using three gas chromatography-mass spectrometry methods. The first used a branch enclosure combined with headspace sorptive extraction to measure plant volatiles in situ. Seventy-eight compounds were found in the general Rhododendron profile. The volatile profile of inoculated but asymptomatic plants (121 days post-inoculation) was distinguishable from the profile of the noninoculated controls. Three compounds were less abundant in inoculated Rhododendron plants relative to noninoculated and mock-inoculated control plants. A second method employed stir bar sorptive extraction to measure volatiles in vitro from leaf extractions in methanol; 114 volatiles were found in the overall profile with 30 compounds less abundant and one compound more abundant in inoculated Rhododendron plants relative to mock-inoculated plants. At 128 days post-inoculation, plants were asymptomatic and similar in appearance to the noninoculated controls, but their chemical profiles were different. In a third technique, volatiles from water runoff from the soil of potted healthy and inoculated Rhododendron plants were compared. Runoff from the inoculated plants contained four unique volatile compounds that never appeared in the runoff from mock-inoculated plants. These three volatile detection techniques could lead to innovative approaches that augment detection and diagnosis of P. ramorum and oomycete pathogens in nurseries and other settings. Graphical abstract Detection of volatile signatures may aid in discriminating healthy vs. infected but asymptomatic plants in nursery and greenhouse facilities.

Noninvasive Respiratory Metabolite Analysis Associated with Clinical Disease in Cetaceans: A Deepwater Horizon Oil Spill Study.

Health assessments of wild cetaceans can be challenging due to the difficulty of gaining access to conventional diagnostic matrices of blood, serum and others. While the noninvasive detection of metabolites in exhaled breath could potentially help to address this problem, there exists a knowledge gap regarding associations between known disease states and breath metabolite profiles in cetaceans. This technology was applied to the largest marine oil spill in U.S. history (The 2010 Deepwater Horizon oil spill in the Gulf of Mexico). An accurate analysis was performed to test for associations between the exhaled breath metabolome and sonographic lung abnormalities as well as hematological, serum biochemical, and endocrine hormone parameters. Importantly, metabolites consistent with chronic inflammation, such as products of lung epithelial cellular breakdown and arachidonic acid cascade metabolites were associated with sonographic evidence of lung consolidation. Exhaled breath condensate (EBC) metabolite profiles also correlated with serum hormone concentrations (cortisol and aldosterone), hepatobiliary enzyme levels, white blood cell counts, and iron homeostasis. The correlations among breath metabolites and conventional health measures suggest potential application of breath sampling for remotely assessing health of wild cetaceans. This methodology may hold promise for large cetaceans in the wild for which routine collection of blood and respiratory anomalies are not currently feasible.

Exhaled breath condensate methods adapted from human studies using longitudinal metabolomics for predicting early health alterations in dolphins.

Monitoring health conditions is essential to detect early asymptomatic stages of a disease. To achieve this, blood, urine and breath samples are commonly used as a routine clinical diagnostic. These samples offer the opportunity to detect specific metabolites related to diseases and provide a better understanding of their development. Although blood samples are commonly used routinely to monitor health, the implementation of a relatively noninvasive technique, such as exhaled breath condensate (EBC) analysis, may further benefit the well-being of both humans and other animals. EBC analysis can be used to track possible physical or biochemical alterations caused by common diseases of the bottlenose dolphin (Tursiops truncatus), such as infections or inflammatory-mediated processes. We have used an untargeted metabolomic method with liquid chromatography-mass spectrometry analysis of EBC samples to determine biomarkers related to disease development. In this study, five dolphins under human care were followed up for 1 year. We collected paired blood, physical examination information, and EBC samples. We then statistically correlated this information to predict specific health alterations. Three dolphins provided promising case study information about biomarkers related to cutaneous infections, respiratory infections, dental disease, or hormonal changes (pregnancy). The use of complementary liquid chromatography platforms, with hydrophilic interaction chromatography and reverse-phased columns, allowed us to detect a wide spectrum of EBC biomarker compounds that could be related to these health alterations. Moreover, these two analytical techniques not only provided complementary metabolite information but in both cases they also provided promising diagnostic information for these health conditions. Graphical abstract Collection of the exhaled condensed breath from a bottlenose dolphin from U.S. Navy Marine Mammal Program (MMP).

PHOTOLITHOGRAPHY-FREE LASER-PATTERNED HF ACID-RESISTANT CHROMIUM-POLYIMIDE MASK FOR RAPID FABRICATION OF MICROFLUIDIC SYSTEMS IN GLASS.

Excellent chemical and physical properties of glass, over a range of operating conditions, make it a preferred material for chemical detection systems in analytical chemistry, biology, and the environmental sciences. However, it is often compromised with SU8, PDMS, or Parylene materials due to the sophisticated mask preparation requirements for wet etching of glass. Here, we report our efforts toward developing a photolithography-free laser-patterned hydrofluoric acid-resistant chromium-polyimide tape mask for rapid prototyping of microfluidic systems in glass. The patterns are defined in masking layer with a diode-pumped solid-state laser. Minimum feature size is limited to the diameter of the laser beam, 30 µm; minimum spacing between features is limited by the thermal shrinkage and adhesive contact of the polyimide tape to 40 µm. The patterned glass substrates are etched in 49% hydrofluoric acid at ambient temperature with soft agitation (in time increments, up to 60 min duration). In spite of the simplicity, our method demonstrates comparable results to the other current more sophisticated masking methods in terms of the etched depth (up to 300 µm in borosilicate glass), feature under etch ratio in isotropic etch (~1.36), and low mask hole density. The method demonstrates high yield and reliability. To our knowledge, this method is the first proposed technique for rapid prototyping of microfluidic systems in glass with such high performance parameters. The proposed method of fabrication can potentially be implemented in research institutions without access to a standard clean-room facility.

Identification of fungal metabolites from inside Gallus gallus domesticus eggshells by non-invasively detecting volatile organic compounds (VOCs).

The natural porosity of eggshells allows hen eggs to become contaminated with microbes from the nesting material and environment. Those microorganisms can later proliferate due to the humid ambient conditions while stored in refrigerators, causing a potential health hazard to the consumer. The microbes' volatile organic compounds (mVOCs) are released by both fungi and bacteria. We studied mVOCs produced by aging eggs likely contaminated by fungi and fresh eggs using the non-invasive detection method of gas-phase sampling of volatiles followed by gas chromatography/mass spectrometry (GC/MS) analysis. Two different fungal species (Cladosporium macrocarpum and Botrytis cinerea) and two different bacteria species (Stenotrophomas rhizophila and Pseudomonas argentinensis) were identified inside the studied eggs. Two compounds believed to originate from the fungi themselves were identified. One fungus-specific compound was found in both egg and the fungi: trichloromethane. Graphical abstract Trichloromethane is a potential biomarker of fungal contamination of eggs.

Detection of Huanglongbing disease using differential mobility spectrometry.

The viability of the multibillion dollar global citrus industry is threatened by the "green menace", citrus greening disease (Huanglongbing, HLB), caused by the bacterial pathogen Candidatus Liberibacter. The long asymptomatic stage of HLB makes it challenging to detect emerging regional infections early to limit disease spread. We have established a novel method of disease detection based on chemical analysis of released volatile organic compounds (VOCs) that emanate from infected trees. We found that the biomarkers "fingerprint" is specific to the causal pathogen and could be interpreted using analytical methods such as gas chromatography/mass spectrometry (GC/MS) and gas chromatography/differential mobility spectrometry (GC/DMS). This VOC-based disease detection method has a high accuracy of ∼90% throughout the year, approaching 100% under optimal testing conditions, even at very early stages of infection where other methods are not adequate. Detecting early infection based on VOCs precedes visual symptoms and DNA-based detection techniques (real-time polymerase chain reaction, RT-PCR) and can be performed at a substantially lower cost and with rapid field deployment.