Matching Items (6)
Filtering by
- All Subjects: Biosensors
- All Subjects: Sensors
- Creators: Tao, Nongjian
- Resource Type: Text
Description
Human breath is a concoction of thousands of compounds having in it a breath-print of physiological processes in the body. Though breath provides a non-invasive and easy to handle biological fluid, its analysis for clinical diagnosis is not very common. Partly the reason for this absence is unavailability of cost effective and convenient tools for such analysis. Scientific literature is full of novel sensor ideas but it is challenging to develop a working device, which are few. These challenges include trace level detection, presence of hundreds of interfering compounds, excessive humidity, different sampling regulations and personal variability. To meet these challenges as well as deliver a low cost solution, optical sensors based on specific colorimetric chemical reactions on mesoporous membranes have been developed. Sensor hardware utilizing cost effective and ubiquitously available light source (LED) and detector (webcam/photo diodes) has been developed and optimized for sensitive detection. Sample conditioning mouthpiece suitable for portable sensors is developed and integrated. The sensors are capable of communication with mobile phones realizing the idea of m-health for easy personal health monitoring in free living conditions. Nitric oxide and Acetone are chosen as analytes of interest. Nitric oxide levels in the breath correlate with lung inflammation which makes it useful for asthma management. Acetone levels increase during ketosis resulting from fat metabolism in the body. Monitoring breath acetone thus provides useful information to people with type1 diabetes, epileptic children on ketogenic diets and people following fitness plans for weight loss.
ContributorsPrabhakar, Amlendu (Author) / Tao, Nongjian (Thesis advisor) / Forzani, Erica (Committee member) / Lindsay, Stuart (Committee member) / Arizona State University (Publisher)
Created2013
Description
Demand for biosensor research applications is growing steadily. According to a new report by Frost & Sullivan, the biosensor market is expected to reach $14.42 billion by 2016. Clinical diagnostic applications continue to be the largest market for biosensors, and this demand is likely to continue through 2016 and beyond. Biosensor technology for use in clinical diagnostics, however, requires translational research that moves bench science and theoretical knowledge toward marketable products. Despite the high volume of academic research to date, only a handful of biomedical devices have become viable commercial applications. Academic research must increase its focus on practical uses for biosensors. This dissertation is an example of this increased focus, and discusses work to advance microfluidic-based protein biosensor technologies for practical use in clinical diagnostics. Four areas of work are discussed: The first involved work to develop reusable/reconfigurable biosensors that are useful in applications like biochemical science and analytical chemistry that require detailed sensor calibration. This work resulted in a prototype sensor and an in-situ electrochemical surface regeneration technique that can be used to produce microfluidic-based reusable biosensors. The second area of work looked at non-specific adsorption (NSA) of biomolecules, which is a persistent challenge in conventional microfluidic biosensors. The results of this work produced design methods that reduce the NSA. The third area of work involved a novel microfluidic sensing platform that was designed to detect target biomarkers using competitive protein adsorption. This technique uses physical adsorption of proteins to a surface rather than complex and time-consuming immobilization procedures. This method enabled us to selectively detect a thyroid cancer biomarker, thyroglobulin, in a controlled-proteins cocktail and a cardiovascular biomarker, fibrinogen, in undiluted human serum. The fourth area of work involved expanding the technique to produce a unique protein identification method; Pattern-recognition. A sample mixture of proteins generates a distinctive composite pattern upon interaction with a sensing platform consisting of multiple surfaces whereby each surface consists of a distinct type of protein pre-adsorbed on the surface. The utility of the "pattern-recognition" sensing mechanism was then verified via recognition of a particular biomarker, C-reactive protein, in the cocktail sample mixture.
ContributorsChoi, Seokheun (Author) / Chae, Junseok (Thesis advisor) / Tao, Nongjian (Committee member) / Yu, Hongyu (Committee member) / Forzani, Erica (Committee member) / Arizona State University (Publisher)
Created2011
Description
In today's world there is a great need for sensing methods as tools to provide critical information to solve today's problems in security applications. Real time detection of trace chemicals, such as explosives, in a complex environment containing various interferents using a portable device that can be reliably deployed in a field has been a difficult challenge. A hybrid nanosensor based on the electrochemical reduction of trinitrotoluene (TNT) and the interaction of the reduction products with conducting polymer nanojunctions in an ionic liquid was fabricated. The sensor simultaneously measures the electrochemical current from the reduction of TNT and the conductance change of the polymer nanojunction caused from the reduction product. The hybrid detection mechanism, together with the unique selective preconcentration capability of the ionic liquid, provides a selective, fast, and sensitive detection of TNT. The sensor, in its current form, is capable of detecting parts per trillion level TNT in the presence of various interferents within a few minutes. A novel hybrid electrochemical-colorimetric (EC-C) sensing platform was also designed and fabricated to meet these challenges. The hybrid sensor is based on electrochemical reactions of trace explosives, colorimetric detection of the reaction products, and unique properties of the explosives in an ionic liquid (IL). This approach affords not only increased sensitivity but also selectivity as evident from the demonstrated null rate of false positives and low detection limits. Using an inexpensive webcam a detection limit of part per billion in volume (ppbV) has been achieved and demonstrated selective detection of explosives in the presence of common interferences (perfumes, mouth wash, cleaners, petroleum products, etc.). The works presented in this dissertation, were published in the Journal of the American Chemical Society (JACS, 2009) and Nano Letters (2010), won first place in the National Defense Research contest in (2009) and has been granted a patent (WO 2010/030874 A1). In addition, other work related to conductive polymer junctions and their sensing capabilities has been published in Applied Physics Letters (2005) and IEEE sensors journal (2008).
ContributorsDiaz Aguilar, Alvaro (Author) / Tao, Nongjian (Thesis advisor) / Tsui, Raymond (Committee member) / Barnaby, Hugh (Committee member) / Yu, Hongbin (Committee member) / Arizona State University (Publisher)
Created2012
Description
Advances in miniaturized sensors and wireless technologies have enabled mobile health systems for efficient healthcare. A mobile health system assists the physician to monitor the patient's progress remotely and provide quick feedbacks and suggestions in case of emergencies, which reduces the cost of healthcare without the expense of hospitalization. This work involves development of an innovative mobile health system with adaptive biofeedback mechanism and demonstrates the importance of biofeedback in accurate measurements of physiological parameters to facilitate the diagnosis in mobile health systems. Resting Metabolic Rate (RMR) assessment, a key aspect in the treatment of diet related health problems is considered as a model to demonstrate the importance of adaptive biofeedback in mobile health. A breathing biofeedback mechanism has been implemented with digital signal processing techniques for real-time visual and musical guidance to accurately measure the RMR. The effects of adaptive biofeedback with musical and visual guidance were assessed on 22 healthy subjects (12 men, 10 women). Eight RMR measurements were taken for each subject on different days under same conditions. It was observed the subjects unconsciously followed breathing biofeedback, yielding consistent and accurate measurements for the diagnosis. The coefficient of variation of the measured metabolic parameters decreased significantly (p < 0.05) for 20 subjects out of 22 subjects.
ContributorsKrishnan, Ranganath (Author) / Tao, Nongjian (Thesis advisor) / Forzani, Erica (Committee member) / Yu, Hongyu (Committee member) / Arizona State University (Publisher)
Created2012
Description
This paper summarizes the [1] ideas behind, [2] needs, [3] development, and [4] testing of 3D-printed sensor-stents known as Stentzors. This sensor was successfully developed entirely from scratch, tested, and was found to have an output of 3.2*10-6 volts per RMS pressure in pascals. This paper also recommends further work to render the Stentzor deployable in live subjects, including [1] further design optimization, [2] electrical isolation, [3] wireless data transmission, and [4] testing for aneurysm prevention.
ContributorsMeidinger, Aaron Michael (Author) / LaBelle, Jeffrey (Thesis director) / Frakes, David (Committee member) / Barrett, The Honors College (Contributor) / Mechanical and Aerospace Engineering Program (Contributor)
Created2014-05
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 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.
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