Trace chemical detection plays an important role in evaluating environmental hazards as well as benign chemical sources. 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 dissertation, a low power dual-polarity ionization-based detector was developed. The detector electronics are operated with a single 9 V battery and provide concentration sensitive voltage outputs. The complete assembly and operation of the detector is detailed. The detector can achieve a system step response of ~1.6 s. We performed laboratory measurements with several ionized chemicals using both positive and negative mode. The results showed highly linear responses at trace concentrations as low as 100 ppb. Following, 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. 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 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 the distribution pipeline.

A bonding process was developed for glass-to-glass fusion bonding using Borofloat 33 wafersresulting in high bonding yield and high flexural strength. The Borofloat 33 wafers went through a two-step process with a pre-bond and high-temperature bond in a furnace. The pre-bond process included surface activation bonding using O2 plasma and N2 microwave radical activation where the glass wafers were brought into contact in a vacuum environment in an EVG 501 Wafer Bonder. The optimal hold time in the EVG 501 Wafer bonder was investigated and concluded to be a 3 h hold time. The bonding parameters in the furnace were investigated for hold time, applied force, and high bonding temperature. It was concluded that the optimal parameters for glass-to-glass Borofloat 33 wafer bonding were at 550 °C with a hold time of 1 h with 550 N of applied force. From the bonding process, we have developed a statistical model approach the quality analysis (QA) and quality control (QC) of a micro preconcentrator chip (μPC) when manufactured at scale for chemical and biochemical analysis. To test the proposed model, a medium-sized production of 118 chips were batch manufactured at the wafer level and subjected to rigorous chemical performance testing. We quantitatively report the outcomes of each manufacturing process leading up to the final chip, with some of the highlights being XY etch accuracy of ± 0.089 μm, etch depth accuracy of ± 0.645 μm, bond charge accuracy of ± 4.9 mC, electrode fabrication accuracy of ± 3.1 Ω, and thin film silicon oxide of 7.24 nm. We implemented a principal component analysis (PCA) model to score individual chip performance, and we observed the first 2 principal components represents 74.28% of chemical testing variance with 111 of 118 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 steps. In our analysis, we find the amount of sorbent mass present in the chip (VIS = 2.6) and heater and RTD resistance values (VIS = 1.1) the manufacturing parameters with the most impact on chip performance. While these were not surprising, other non-obvious latent manufacturing parameters did have quantified influence. Statistical distributions for each manufacturing step will allow future large scale production runs to be statistically sampled during production itself to perform QA/QC in a real-time environment.

Trace analysis of volatile organic compounds (VOCs) during wildfires is imperative for environmental and health risk assessment. Because air is a heterogeneous system composed of gases, liquids, and solid particles that are continuously evolving in time and space, sampling is essential for assessing combustion related chemicals in air. 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 can 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 deadliest and most destructive wildfire in California’s history - the 2018 Camp Fire. The air 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 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 concentrations of benzene and toluene were highest with average concentrations of 4.7 and 15.1 µg/m3, respectively. Total BTEX values ranged from 4 to 48.5 µg/m3 with an average value of 20.2 µg/m3. A moderate negative correlation between daily AQI and toluene, benzene and toluene/benzene ratios. were observed as well. The viability of deploying this sampling technology in future wildfires was evaluated by fixing the µPC sampler to a UAV and sampling air while flying in the presence of a controlled experimental fire. Fuel contents of the experimental fire included various woods, carpet, electronics, and biomass. Numerous fire-related compounds including BTEX and aldehydes such as octanal and nonanal were detected upon fire ignition, even at a much smaller sampling time compared to air samples taken during the 2018 Camp Fire. Analysis of the air samples taken both stationary during the 2018 Camp Fire and mobile during an experimental fire show the successful operation of our sampler in a fire environment. Mobile VOC sampling technology has the potential to monitor important VOCs in localized areas during wildfires and provide additional information on the effects that wildfires have on the ambient environment.

Portable methods for measurement and quantification of biological analytes have tremendous promise to advance non-invasive personal health monitoring devices. Exhaled breath metabolomics shares this promise and has been gaining popularity as a non-invasive technique amenable to a vast range of medical uses. Novel sampling technologies, rapid portable breath chemical analysis platforms, and miniaturized manufacturing methods have the potential to be scaled up for wide use in basic medical practice. This work presents design and implementation for collection and chemical analysis of human exhaled breath condensate. Exhaled breath condensate (EBC) samples were collected with a novel miniaturized sampler. Breath metabolite abundances were compared in healthy control subjects and asthmatic subjects. This work also investigates the relationship between breath aerosol size and EBC metabolomic content. Additionally, this work contributes to the fundamental performance of electrospray with a microfluidic chip, a technique used for liquid biochemical detection in analytical chemistry.

In this work, we describe the design, manufacturing development, and refinement of achemical detection platform designed to identify specific odorants in the natural gas industry. As the demand for reliable and sensitive volatile organic compound (VOC) detection systems is growing, our project aimed to construct multiple prototypes to enhance our detection capabilities and provide portable detection platforms. Throughout the development process across nominally identical and duplicated instruments, various failure modes were encountered, which provided insight into the design and manufacturing challenges present when designing such platforms. We conducted a post hoc root cause analysis for each failure mode, leading to a series of design modifications and solutions. This paper details these design and manufacturing challenges, the analytical methods used to diagnose and address them, and the resulting improvements in system performance. In the end, a debugging flow chart is presented to aid future researchers in solving the possible issues that could be encountered. Our findings show the complexities of bespoke chemical sensor design for unique applications and highlight the critical importance of iterative testing and problem-solving in the development of industrial detection technologies. Achieving consistency across devices is essential for optimizing device-to-device efficiency. The work presented is the first step towards ensuring uniform performance across a production run of chemically sensitive devices. In the future, a universal device calibration model will be implemented, eliminating the need to collect data from each individual device.