<p>To facilitate indoor air quality (IAQ) monitoring, the research described herein develops and implements methods for the electronic integration of two types of gas sensor, each functionalized with a polymer blend tailored for CO₂ detection. A highly sensitive and tunable electronic chemiresistive sensor interface was developed and experimentally validated. This device achieved analog-to-digital conversion (ADC) through a pulse width modulated (PWM) signal, temporary data storage with an efficient data buffering system, and noise reduction and signal amplification utilizing an instrumentation amplifier integrator circuit. These techniques can used beyond CO₂-specific applications to compensate for certain undesirable chemiresistive sensor characteristics, such as low response magnitude and signal noise. Additionally, resonant mass sensing circuitry was combined with an on-chip field programmable gate array (FPGA) implemented frequency counter. Hz-level resolution was achieved with an Alorium Snō FPGA board and a Verilog data acquisition and communication program. This device can monitor up to 16 sensor channels simultaneously and has a straightforward interface with a controllable output. Furthermore, the functionality of each integrated sensor was experimentally validated. With additional work, these integrated designs have the potential to be inexpensive, low-power, highly sensitive devices that are suitable for practical use in IAQ monitoring applications.</p>

<p>Volatile organic compounds (VOCs) are endogenous byproducts of metabolic pathways that can be altered by a disease or condition, leading to an associated and unique VOC profile or signature. Current methodologies for VOC detection include canines, gas chromatography-mass spectrometry (GC-MS), and electronic nose (eNose). Some of the challenges for canines and GC-MS are cost-effectiveness, extensive training, expensive instrumentation. On the other hand, a significant downfall of the eNose is low selectivity. This thesis proposes to design a breathalyzer using chemiresistive gas sensors that detects VOCs from human breath, and subsequently create an interface to process and deliver the results via Bluetooth Low Energy (BLE). Breath samples were collected from patients with hypoglycemia, COVID-19, and healthy controls for both. Samples were processed, analyzed using GC-MS and probed through statistical analysis. A panel of 6 VOC biomarkers distinguished between hypoglycemia (HYPO) and Normal samples with a training AUC of 0.98 and a testing AUC of 0.93. For COVID-19, a panel of 3 VOC biomarkers distinguished between COVID-19 positive symptomatic (COVID-19) and healthy Control samples with a training area under the curve (AUC) of receiver operating characteristic (ROC) of 1.0 and cross-validation (CV) AUC of 0.99. The model was validated with COVID-19 Recovery samples. The discovery of these biomarkers enables the development of selective gas sensors to detect the VOCs. </p><p><br></p><p>Polyethylenimine-ether functionalized gold nanoparticle (PEI-EGNP) gas sensors were designed and fabricated in the lab and metal oxide (MOX) semiconductor gas sensors were obtained from Nanoz (Chip 1: SnO₂ and Chip 2: WO₃). These sensors were tested at different relative humidity (RH) levels, and VOC concentrations. Contact angle which measures hydrophobicity, was 84° and the thickness of the PEI-EGNP coating was 11 µ m. The PEI-EGNP sensor response at RH 85% had a signal 10x higher than at RH 0%. Optimization of the MOX sensor was performed by changing the heater voltage and concentration of VOCs. At RH 85% and heater voltage of 2500 mV, the performance of the sensors increased. Chip 2 had higher sensitivity towards VOCs especially for one of the VOC biomarkers identified for COVID-19. PCA distinguished VOC biomarkers of HYPO, COVID-19, and healthy human breath using the Nanoz. A sensor interface was created to integrate the PEI-EGNP sensors with the printed circuit board (PCB) and Bluno Nano to perform machine learning. The sensor interface can currently process and make decisions from the data whether the breath is HYPO (-) or Normal (+). This data is then sent via BLE to the Hypo Alert app to display the decision.</p>