Matching Items (35)
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Description
The purpose of this research was to determine and evaluate glutamate oxidase's ability to detect levels of glutamate as part of a working sensor capable of quantifying and detecting stress within the body in the case of adverse neurological events such as traumatic brain injury. Using electrochemical impedance spectroscopy (EIS),

The purpose of this research was to determine and evaluate glutamate oxidase's ability to detect levels of glutamate as part of a working sensor capable of quantifying and detecting stress within the body in the case of adverse neurological events such as traumatic brain injury. Using electrochemical impedance spectroscopy (EIS), a linear dynamic range of glutamate was detected with a slope of 36.604 z/ohm/[pg/mL], a lower detection limit at 12.417 pg/mL, correlation of 0.97, and an optimal binding frequency of 117.20 Hz. After running through a frequency sweep the binding frequency was determined based on the highest consistent reproducibility and slope. The sensor was found to be specific against literature researched non-targets glucose, albumin, and epinephrine and working in dilutions of whole blood up to a concentration of 25%. With the implementation of Nafion, the sensor had a 250% improvement in signal and 155% improvement in correlation in 90% whole blood, illustrating the promise of a working blood sensor. Future work includes longitudinal studies and utilizing mesoporous carbon as the immobilization platform and incorporating this as part of a continuous, multiplexed blood sensor with glucose oxidase.
ContributorsLam, Alexandria Nicole (Author) / LaBelle, Jeffrey (Thesis director) / Ankeny, Casey (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
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Description
The project is mainly aimed at detecting the gas flow rate in Biosensors and medical health applications by means of an acoustic method using whistle based device. Considering the challenges involved in maintaining particular flow rate and back pressure for detecting certain analytes in breath analysis the proposed system along

The project is mainly aimed at detecting the gas flow rate in Biosensors and medical health applications by means of an acoustic method using whistle based device. Considering the challenges involved in maintaining particular flow rate and back pressure for detecting certain analytes in breath analysis the proposed system along with a cell phone provides a suitable way to maintain the flow rate without any additional battery driven device. To achieve this, a system-level approach is implemented which involves development of a closed end whistle which is placed inside a tightly fitted constant back pressure tube. By means of experimentation pressure vs. flowrate curve is initially obtained and used for the development of the particular whistle. Finally, by means of an FFT code in a cell phone the flow rate vs. frequency characteristic curve is obtained. When a person respires through the device a whistle sound is generated which is captured by the cellphone microphone and a FFT analysis is performed to determine the frequency and hence the flow rate from the characteristic curve. This approach can be used to detect flow rate as low as low as 1L/min. The concept has been applied for the first time in this work to the development and optimization of a breath analyzer.
ContributorsRavichandran, Balaje Dhanram (Author) / Forzani, Erica (Thesis advisor) / Xian, Xiaojun (Committee member) / Huang, Huei-Ping (Committee member) / Arizona State University (Publisher)
Created2012
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Description
Air pollution has been linked to various health problems but how different air pollutants and exposure levels contribute to those diseases remain largely unknown. Researchers have mainly relied on data from government air monitoring stations to study the health effects of air pollution exposure. The limited information provided by sparse

Air pollution has been linked to various health problems but how different air pollutants and exposure levels contribute to those diseases remain largely unknown. Researchers have mainly relied on data from government air monitoring stations to study the health effects of air pollution exposure. The limited information provided by sparse stations has low spatial and temporal resolution, which is not able to represent the actual exposure of individuals. A tool that can accurately monitor personal exposure provides valuable data for epidemiologists to understand the relationship between air pollution and certain diseases. It also allows individuals to be aware of any ambient air quality issues and prevent air pollution exposure. To build such a tool, sensors with features of fast response, small size, long lifetime, high sensitivity, high selectivity, and multi-analyte sensing are of great importance.

In order to meet these requirements, three generations of novel colorimetric sensors have been developed. The first generation is mosaic colorimetric sensors based on tiny sensor blocks and by detecting absorbance change after each air sample injection, the target analyte concentration can be measured. The second generation is a gradient-based colorimetric sensor. Lateral transport of analytes across the colorimetric sensor surface creates a color gradient that shifts along the transport direction over time, and the sensor tracks the gradient shift and converts it into analyte concentration in real-time. The third generation is gradient-based colorimetric arrays fabricated by inkjet-printing method that integrates multiple sensors on a miniaturized sensor chip. Unlike traditional colorimetric sensors, such as detection tubes and optoelectronic nose, that are typically for one-time use, the presented three generations of colorimetric sensors aim to continuously monitor multiple air pollutants and the sensor lifetime and fabrication methods have been improved over each generation. Ozone, nitrogen dioxide, formaldehyde and carbon monoxide are chosen as analytes of interest. The performance of sensors has been validated in the lab and field tests, proving the capability of the sensors to be used for personal exposure monitoring.
ContributorsLin, Chenwen (Author) / Tao, Nongjian (Thesis advisor) / Borges, Chad R (Committee member) / Hayes, Mark A. (Committee member) / Arizona State University (Publisher)
Created2019
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Description
The advent of commercial inexpensive sensors and the advances in information and communication technology (ICT) have brought forth the era of pervasive Quantified-Self. Automatic diet monitoring is one of the most important aspects for Quantified-Self because it is vital for ensuring the well-being of patients suffering from chronic diseases as

The advent of commercial inexpensive sensors and the advances in information and communication technology (ICT) have brought forth the era of pervasive Quantified-Self. Automatic diet monitoring is one of the most important aspects for Quantified-Self because it is vital for ensuring the well-being of patients suffering from chronic diseases as well as for providing a low cost means for maintaining the health for everyone else. Automatic dietary monitoring consists of: a) Determining the type and amount of food intake, and b) Monitoring eating behavior, i.e., time, frequency, and speed of eating. Although there are some existing techniques towards these ends, they suffer from issues of low accuracy and low adherence. To overcome these issues, multiple sensors were utilized because the availability of affordable sensors that can capture the different aspect information has the potential for increasing the available knowledge for Quantified-Self. For a), I envision an intelligent dietary monitoring system that automatically identifies food items by using the knowledge obtained from visible spectrum camera and infrared spectrum camera. This system is able to outperform the state-of-the-art systems for cooked food recognition by 25% while also minimizing user intervention. For b), I propose a novel methodology, IDEA that performs accurate eating action identification within eating episodes with an average F1-score of 0.92. This is an improvement of 0.11 for precision and 0.15 for recall for the worst-case users as compared to the state-of-the-art. IDEA uses only a single wrist-band which includes four sensors and provides feedback on eating speed every 2 minutes without obtaining any manual input from the user.
ContributorsLee, Junghyo (Author) / Gupta, Sandeep K.S. (Thesis advisor) / Banerjee, Ayan (Committee member) / Li, Baoxin (Committee member) / Chiou, Erin (Committee member) / Kudva, Yogish C. (Committee member) / Arizona State University (Publisher)
Created2019
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Description
Proteins are, arguably, the most complicated molecular machines found in nature. From the receptor proteins that decorate the exterior of cell membranes to enzymes that catalyze the slowest of chemical reactions, proteins perform a wide variety of essential biological functions. A reductionist view of proteins as a macromolecular group, however,

Proteins are, arguably, the most complicated molecular machines found in nature. From the receptor proteins that decorate the exterior of cell membranes to enzymes that catalyze the slowest of chemical reactions, proteins perform a wide variety of essential biological functions. A reductionist view of proteins as a macromolecular group, however, may hold that they simply interact with other chemical species. Notably, proteins interact with other proteins, other biological macromolecules, small molecules, and ions. This in turn makes proteins uniquely qualified for use technological use as sensors of said chemical species (biosensors). Several methods have been developed to convert proteins into biosensors. Many of these techniques take advantage of fluorescence spectroscopy because it is a fast, non-invasive, non-destructive and highly sensitive method that also allows for spatiotemporal control. This, however, requires that first a fluorophore be added to a target protein. Several methods for achieving this have been developed from large, genetically encoded autofluorescent protein tags, to labeling with small molecule fluorophores using bioorthogonal chemical handles, to genetically encoded fluorescent non-canonical amino acids (fNCAA). In recent years, the fNCAA, L-(7-hydroxycoumarin-4yl)ethylglycine (7-HCAA) has been used in to develop several types of biosensors.
The dissertation I present here specifically addresses the use of the fNCAA L-(7-hydroxycoumarin-4-yl)ethylglycine (7-HCAA) in protein-based biosensors. I demonstrate 7-HCAA’s ability to act as a Förster resonance energy transfer (FRET) acceptor with tryptophan as the FRET donor in a single protein containing multiple tryptophans. I the describe efforts to elucidate—through both spectroscopic and structural characterization—interactions within a 7-HCAA containing protein that governs 7-HCAA fluorescence. Finally, I present a top-down computational design strategy for incorporating 7-HCAA into proteins that takes advantage of previously described interactions. These reports show the applicability of 7-HCAA and the wider class of fNCAAs as a whole for their use of rationally designed biosensors.
ContributorsGleason, Patrick Ray (Author) / Mills, Jeremy H (Thesis advisor) / Hecht, Sidney M. (Committee member) / Fromme, Petra (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2020