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Browsing by Subject "Volatile organic compounds"
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Item Cross-Selectivity Enhancement of Poly(vinylidene fluoride-hexafluoropropylene)-Based Sensor Arrays for Detecting Acetone and Ethanol(MDPI, 2017-03-15) Daneshkhah, Ali; Shrestha, Sudhir; Siegel, Amanda; Varahramyan, Kody; Agarwal, Mangilal; Electrical and Computer Engineering, School of Engineering and TechnologyTwo methods for cross-selectivity enhancement of porous poly(vinylidene fluoride-hexafluoropropylene) (PVDF-HFP)/carbon black (CB) composite-based resistive sensors are provided. The sensors are tested with acetone and ethanol in the presence of humid air. Cross-selectivity is enhanced using two different methods to modify the basic response of the PVDF-HFP/CB sensing platform. In method I, the adsorption properties of PVDF-HFP/CB are altered by adding a polyethylene oxide (PEO) layer or by treating with infrared (IR). In method II, the effects of the interaction of acetone and ethanol are enhanced by adding diethylene carbonate (DEC) or PEO dispersed in DEC (PEO/DEC) to the film. The results suggest the approaches used in method I alter the composite ability to adsorb acetone and ethanol, while in method II, they alter the transduction characteristics of the composite. Using these approaches, sensor relative response to acetone was increased by 89% compared with the PVDF-HFP/CB untreated film, whereas sensor relative response to ethanol could be decreased by 57% or increased by 197%. Not only do these results demonstrate facile methods for increasing sensitivity of PVDF-HFP/CB film, used in parallel they demonstrate a roadmap for enhancing system cross-selectivity that can be applied to separate units on an array. Fabrication methods, experimental procedures and results are presented and discussed.Item Development of a Sensor System for Rapid Detection of Volatile Organic Compounds in Biomedical Applications(2021-12) Angarita Rivera, Paula Andrea; Agarwal, Mangilal; Dalir, Hamid; Anwar, SohelVolatile 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. 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. The 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.Item Optimization and Characterization of Metal Oxide Nanosensors for the Analysis of Volatile Organic Compound Profiles in Breath Samples(2023-08) Maciel Gutierrez, Mariana; Agarwal, Mangilal; Dalir, Hamid; Nalim, Mohamed RaziVolatile organic compounds (VOCs) are byproducts of metabolic processes that can be uniquely dysregulated by various medical conditions and are expressed in biological samples. Therefore, VOCs expressed in breath, urine and other sample types may be utilized for noninvasive, rapid, and accurate diagnostics in a point-of-care setting. Currently, the most common methods for VOC detection include gas chromatography-mass spectrometry (GC-MS) and electronic noses (E-noses) that integrate nanosensors. Both methods present important advantages and challenges that allow their implementation for different applications. While GC-MS can be used to directly identify VOCs in complex matrices, it is a non-portable and high-cost instrument. On the other hand, E-noses are portable and user-friendly VOC detectors, but they do not allow for direct VOC identification or quantification. Among different VOC rich sample types, breath offers the advantage of being a virtually limitless source of endogenous biomarkers that can be implemented for noninvasive VOC detection. The presented thesis focuses on the optimization of the operating parameters (heater and sensor voltages) of a metal oxide (MOX) sensor and breath sampling techniques (sensor casing, breath fractionation, and exhalation volume) for their implementation in exhaled VOC analysis. In parallel, an in-house feature extraction algorithm was developed and implemented for the optimization of a MOX sensor composed of a tin oxide (SnO2) sensing layer. The optimized sensor parameters (heater voltage equal to 2 V and sensor voltage equal to 0.8 V) and breath sampling protocol (24 L of whole breath analyzed using the in-house sensor casing design) were tested with exhaled breath samples from distinct volunteers which could be successfully separated with 100% accuracy. The sensor response also showed a high degree of intrasubject reproducibility (RSD < 6%). Additionally, the sensor performance was further validated under ambient conditions, and sensor degradation was studied over the course of 3 months. Finally, sensor response to synthetic VOC profiles and individual VOC standards was explored. Optimized SnO2 sensors distinguished between VOC mixtures regardless of variations in relative humidity (RH) levels. Furthermore, the characteristic sensor response to VOC standards indicates that the sensors are most sensitive toward isopropanol by a factor of 1.15 in 45% RH and a factor of 3.58 in 85% RH relative to isoprene. To translate the potential of MOX sensors to point-of-care biomedical applications, there first exists the need to establish a reference of sensor baseline signals corresponding to exhaled breath samples from healthy individuals. SnO2 sensors and breath sampling methods were implemented for the collection of individual samples from 109 relatively healthy volunteers. 10 of these volunteers provided 9 additional samples over the course of six months. In parallel, exhaled breath samples were also analyzed by GC-MS to comprehensively profile VOCs present in the samples. The results from these experiments not only aid in the identification of the healthy breath signal baseline but also allow the exploration of VOC reproducibility over time. High variation between samples from distinct volunteers was observed, but samples longitudinally collected across volunteers could not be distinguished, alluding to the existence of a universal range of sensor signals that could describe the composition of exhaled breath from healthy subjects. Finally, results were compared with relevant confounding variables to better understand how VOCs are impacted by an array of factors that are not directly correlated to disease diagnosis. Sensor signals were significantly elevated in breath samples from male volunteers compared to samples from female subjects (p-value = 0.044). Interestingly, isoprene signals resulting from the GC-MS analysis were also higher in male subjects relative to females. No other relationships were identified between sensor signals and the confounding variables of interest. Future work would require a deeper understanding of sensor degradation and life cycle, along with sensor testing using a broader range of individual VOC standards and more complex VOC profiles. Additionally, further comparison between sensor signal and GC-MS signal of relevant VOC biomarkers present in breath would be beneficial. Nonetheless, the presented be leveraged in future investigations aiming to identify biomarkers for different medical conditions. Finally, the findings disclosed in the deposited thesis suggest the ability of a SnO2 nanosensor array to be implemented for breath analysis, providing a noninvasive, easy to use, and reliable diagnostic device in a point-of-care setting.Item Urinary Volatile Organic Compounds for Detection of Breast Cancer and Monitoring Chemical and Mechanical Cancer Treatments in Mice(2019-05) Teli, Meghana; Yokota, Hiroki; Agarwal, Mangilal; Ji, JulieThe aim of this study is to identify metabolic transformations in breast cancer through urinary volatile organic compounds in mammary pad or bone tumor mice models. Subsequently, it focuses on investigating the efficacy of therapeutic intervention through identified potential biomarkers. Methods for monitoring tumor development and treatment responses have technologically advanced over the years leading to significant increase in percent survival rates. Although these modalities are reliable, it would be beneficial to observe disease progression from a new perspective to gain greater understanding of cancer pathogenesis. Analysis of cellular energetics affected by cancer using bio-fluids can non-invasively help in prognosis and selection of treatment regimens. The hypothesis is altered profiles of urinary volatile metabolites is directly related to disrupted metabolic pathways. Additionally, effectiveness of treatments can be indicated through changes in concentration of metabolites. In this ancillary experiment, mouse urine specimens were analyzed using gas chromatography-mass spectrometry, an analytical chemistry tool in identifying volatile organic compounds. Female BALB/c mice were injected with 4T1.2 murine breast tumor cells in the mammary fat pad. Consecutively, 4T1.2 cells were injected in the right iliac artery of BALB/c mice and E0771 tumor cells injected in the tibia of C57BL/6 mice to model bone tumor. The effect of two different modes of treatment: chemical drug and mechanical stimulation was investigated through changes in compound profiles. Chemical drug therapy was conducted with dopamine agents, Triuoperazine, Fluphenazine and a statin, Pitavastatin. Mechanical stimulation included tibia and knee loading at the site of tumor cell injection were given to mice. A biological treatment mode included administration of A5 osteocyte cell line. A set of potential volatile organic compounds biomarkers differentiating mammary pad or bone confined tumors from healthy controls was identified using forward feature selection. Effect of treatments was demonstrated through hierarchical heat maps and multivariate data analysis. Compounds identified in series of experiments belonged to the class of terpenoids, precursors of cholesterol molecules. Terpene synthesis is a descending step of mevalonate pathway suggesting its potential role in cancer pathogenesis. This thesis demonstrates the ability of urine volatilomics to indicate signaling pathways inflicted in tumors. It proposes a concept of using urine to detect tumor developments at two distinct locations as well as to monitor treatment efficacy.