Browsing by Subject "Biosensors"

Serological testing—detection of antibodies—plays a key role in the diagnosis, management, and surveillance of infectious diseases. Serological assays can detect both active and past infections which is essential in understanding epidemiological variables such as incidence and fatality rates. Another important metric, antibody avidity, provides additional insight into recency of infection, can be used to discriminate between closely related infectious species, assess vaccine efficacy and provides estimates of who is and who is not immune to certain infections. However, conventional methods of measuring antibody avidity are costly, time consuming, and utilize harsh denaturing reagents that negatively impact automated immuno-ELISA equipment. These challenges have deterred the development of point of care tests for antibody avidity. In this thesis, we investigated the performance of four assay formats for antibody detection developed by inkjet-printing assay reagents on glass surfaces coated with a non-fouling polymer brush. We then adopted the antibody detection formats to determine antibody avidity by measuring resistance of the antibody-antigen bonds to chaotropic agents. We further developed a new technique of measuring antibody avidity by reducing the concentration of capture antigen (cAg) on the immunoassay platform. In this new technique, avidity index was determined as the ratio of fluorescence intensity measured at a lower cAg concentration to intensity measured at a higher cAg concentration. This technique showed strong correlation (R > 0.8) with the conventional method of antibody avidity measurement (resistance to chaotropic agent) in three antibody-antigen systems. Additionally, we showed that the proposed platform can detect key biomarkers for identifying recent HIV1 infections. The targeted biomarkers were based on measuring titers and avidity of antibodies secreted against specific clades of HIV envelope proteins. They included clade C GP140 IgG3, transmitted/founder clade C GP140 IgG4 avidity, clade B GP140 IgG4 avidity, and GP41 immunodominant region (GP41-ID) IgG avidity. The proposed assay detected all four biomarkers with wide dynamic ranges (>103.6) and high sensitivity in diluted pooled human serum. The proposed platform for antibody avidity testing is rapid, easy to use and has high correlation with chaotropic resistance. It therefore has potential to enable measurement of antibody avidity at the point of care for clinical applications.

Biosensing is a critical area of research that involves detecting and measuring biological molecules. Among the various types of biosensors, acoustic biosensors are attractive for their simplicity, robustness, and low cost, particularly in point-of-care (POC) applications. However, the quality factor (Q-factor) of acoustic biosensors is often low, limiting their sensitivity and accuracy in terms of in-liquid gravimetric detection for biosensing applications. In this dissertation, we present a novel approach that eliminates nearly all dissipation and damping from sample liquids, rendering a significant improvement in Q-factor for in-liquid gravimetric detection. We constructed rigid microfluidic channels to confine liquids and the associated acoustic energy, thereby eliminating acoustic radiation damping. We also used the channels' side walls to create pressure waves, confining the liquids within and suppressing acoustic damping due to the viscous layer. The quartz crystal microbalance (QCM) was selected as the model system for implementing the new paradigm due to its widespread usage in various applications, simplicity, cost-effectiveness, and relevance of its principles to other types of acoustic biosensors. We hypothesized that the ratio of the wavelength of the pressure wave to the width of the channels is a crucial determining factor for optimal performance. We then tested the hypothesis by building the microfluidic QCM (the µ-QCM) to improve the Q-factor of conventional QCM. The combination of experiments, simulations, and theoretical studies demonstrated a 10-fold improvement in the Q-factor. The new system offers many other advantages, including direct data interpretation, minimized sample volume requirement, and easier temperature control for in-liquid gravimetric detection. Additionally, the same principles can be applied to other acoustic biosensors, benefiting the entire field.

With the advent of the internet-of-things (IoT) and a more connected digital ecosystem, new electronic sensors and systems are needed. Printing has been identified as a means of fabricating low-cost electronics on non-rigid, large-area substrates. Printed electronics have been demonstrated to have the required electrical and mechanical properties to facilitate new and unique flexible electronic sensors for the IoT. One printable material that has demonstrated significant promise, specifically when compared to more traditional printed semiconductors, is solution-processed carbon nanotubes (CNTs). While some work has been done to facilitate the fabrication of CNT thin-film transistors (TFTs), little work has been done to assess the viability and potential of CNT-TFTs and other CNT thin films for real-world sensing applications.

The work contained in this dissertation describes the use of aerosol jet printing to fabricate CNT-TFTs, and the resulting study of their capability for various sensing applications. Aerosol jet printing allows for printing all the materials necessary for a fully-functional CNT-TFT, including the semiconducting thin film, conducting contacts and gate, and insulating gate dielectric. Using this system, flexible and fully printed CNT-TFTs were developed and characterized. Fully printed transistors were fabricated with field-effect mobilities as a high as 16 cm2/(Vs). The transistors were also resilient to substantial bending/strain, showing no measurable performance degradation after 1000 bending cycles at a radius of curvature of 1 mm.

The printed CNT-TFTs were evaluated for several sensing applications, including environmental pressure sensing and point-of-care biological sensing. The biological sensors, which were electronically transduced immunoassays, consisted of an antifouling polymer brush layer to enhance the CNT-TFT sensitivity and printed antibodies for detection of target analytes. Unparalleled sensitivity in unfiltered biological milieus was realized with these printed biosensors, detecting protein concentrations as low as 10 pg/ml in whole blood. In addition to demonstrating an electronically transduced TFT-based biosensor, work was done to develop a stable platform with high yield that will provide the means for a deeper understanding of the biosensing mechanisms of transistor-based sensors. As part of this biosensor platform development, novel solution-gated CNT-TFTs were demonstrated, with stable operation in ionic solutions for periods as long as 5 hours.

Another important electronic sensing technique is capacitive-based sensing. Using aerosol jet printed carbon nanotubes, a capacitive sensor has been developed and demonstrated for measuring insulating material thickness. The sensors rely on the fringing field between two adjacent electrodes interacting with the material out-of-plane, and that interaction being perturbed differently based on the thickness of the overlaid material. This sensor was also demonstrated in a one-dimensional array, which can be used to map tire tread thickness from the outside of the tire.

Overall, this dissertation explores the use of printed carbon nanotubes for diverse sensing applications. While this work provides real-world demonstrations that have potential impact for the IoT, there are also substantial scientific advancements made. Namely, insight into biosensing mechanisms, operation of solution-gated nanomaterial-based transistors, and demonstration of porosity and thickness effects on printed capacitive sensor electrodes.

Biosensors incorporating proteins as molecular recognition elements for analytes are used in clinical diagnostics, as biological research tools, and to detect chemical threats and pollutants. This work describes the application of protein engineering techniques to address three aspects in the design of protein-based biosensors; the transduction of binding into an observable, the manipulation of affinities, and the diversification of specificities. The periplasmic glucose-binding protein from the hyperthermophile Thermotoga maritima (tmGBP) was fused with green fluorescent protein variants to construct a fluorescent ratiometric sensor that is sufficiently robust to detect glucose up to 67°C. Ligand-binding affinities of tmGBP were changed by altering a C-terminal helical domain that tunes ligand binding affinity through conformational coupling effects. This method was extended to the Escherichia coli arabinose-binding protein. Computational design techniques were used to diversify the specificity of the E. coli maltose-binding protein (ecMBP) to bind ibuprofen, a non-steroidal antiinflammatory drug. These designs ranged in affinity from 0.24 to 0.8 mM and function as reagentless fluorescent sensors. The ligand affinities of ecMBP are tuned by complex interactions that control conformational coupling. These experiments demonstrate that long-range conformational effects as well as molecular recognition interactions need to be considered in the design of high-affinity receptors.

As an increasingly prevalent chronic disease, diabetes represents one of the fastest growing health burdens to both the developed and developing world. In an effort to improve the management and treatment of diabetes, implantable sensors that continuously monitor glucose levels have become popular alternatives to patient-administered finger prick measurements of blood glucose. However, following implantation, the performance of these implants suffers from inaccurate and erratic readings that compromise their useful lives. As a result, implantable glucose sensors remain limited as a platform for the reliable management of diabetes. While the interaction between the sensor and its surrounding tissue has been posited as a culprit for erroneous in vivo sensor performance, there remains little evidence to support that theory.

This dissertation describes the effects that implant-associated tissue reactions have on implantable sensor function. Since tissue response to an implant changes over time, the overall effect of these tissue reactions is broken into two temporal phases: (1) the phase of weeks to months following implantation when a mature foreign body capsule is present around the sensor and (2) the phase of days to weeks immediately following sensor implantation when a provisional matrix of proteins and inflammatory cells envelops the sensor.

Late stage sensor responses to implantation are marked by both an attenuated sensor signal and a significant time lag relative to blood glucose readings. For this later stage of sensor response, a computational model of glucose transport through the interstitial space and foreign body capsule was derived and implemented. Utilizing physiologically relevant parameters, the model was used to mechanistically study how each constituent part of the capsular tissue could affect sensor response with respect to signal attenuation and lag. Each parameter was then analyzed using logarithmic sensitivity analysis to study the effects of different transport variables on both lag and attenuation. Results identified capsule thickness as the strongest determinant of sensor time lag, while subcutaneous vessel density and capsule porosity had the largest effects on attenuation of the sensor signal.

For the phase of early stage tissue response, human whole blood was used as a simple ex vivo experimental system. The impacts of protein accumulation at the sensor surface (biofouling effects) and cellular consumption of glucose in both the biofouling layer and in the bulk (metabolic effects) on sensor response were assessed. Medtronic Minimed SofSensor glucose sensors were incubated in whole blood, plasma diluted whole blood, and cell-free platelet poor plasma (PPP) to analyze the effects of different blood constituents on sensor function. Experimental conditions were then simulated using MATLAB to predict the relative impacts of biofouling and metabolic effects on the observed sensor responses. It was found that the physical barrier to glucose transport presented by protein biofouling did not hinder glucose movement to the sensor surface. Instead, glucose consumption by inflammatory cells was identified as the major culprit for generating poor sensor performance immediately following implantation.

Lastly, a novel, biomimetic construct was designed to mimic the in vivo 3D cellular setting around the sensor for the focused in vitro investigation of early stage effects of implantation on glucose sensor performance. Results with this construct demonstrate similar trends in sensor signal decline to the ex vivo cases described above, suggesting this construct could be used as an in vitro platform for assessing implantable glucose sensor performance.

In total, it may be concluded from this dissertation that instead of sensors "failing" in vivo, as is often reported, that different physiological factors mediate long term sensor function by altering the environment around the implant. For times immediately following implantation, sensor signals are mediated by the presence of inflammatory macrophages adhered on the surface. However, at longer times post-implantation, sensor signals are mediated not by the consumptive capacity of macrophages, but instead by the subcutaneous vessel density surrounding the sensor as well as the porosity and thickness of the foreign body capsule itself. Taken in concert, the results of this dissertation provide a temporal framework for outlining the effects of tissue response on sensor performance, hopefully informing more biocompatible glucose sensor designs in the future.