Joseph Buchanan-Vega

Mr. Joseph Buchanan-Vega

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Research Focus & Metrics

Academic Career Length

8Years (2019-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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24 December 2019 in Sensors
Verified
Journal Article
Open access
PDF file
MAMohammad Abdallah
Mohammad Abdallah
JBJoseph Buchanan-Vega
Joseph Buchanan-Vega
KLKyu J. Lee
Kyu J. Lee
BWBrett Wenner
Brett Wenner
JAJeffery Allen
Jeffery Allen
RMRobert Magnusson
Robert Magnusson
Assessing levels of neuropeptide Y (NPY) in the human body has many medical uses. Accordingly, we report the quantitative detection of NPY biomarkers applying guided-mode resonance (GMR) biosensor methodology. The label-free sensor operates in the near-infrared spectral region exhibiting distinctive resonance signatures. The interaction of NPY with bioselective molecules on the sensor surface causes spectral shifts that directly identify the binding event without additional processing. In the experiments described here, NPY antibodies are attached to the sensor surface to impart specificity during operation. For the low concentrations of NPY of interest, we apply a sandwich NPY assay in which the sensor-linked anti-NPY molecule binds with NPY that subsequently binds with anti-NPY to close the sandwich. The sandwich assay achieves a detection limit of ~0.1 pM NPY. The photonic sensor methodology applied here enables expeditious high-throughput data acquisition with high sensitivity and specificity. The entire bioreaction is recorded as a function of time, in contrast to label-based methods with single-point detection. The convenient methodology and results reported are significant, as the NPY detection range of 0.1–10 pM demonstrated is useful in important medical circumstances.

26 November 2020 in IEEE Sensors Journal
Verified
Journal Article
Closed access
MAMohammad Abdallah
Mohammad Abdallah
JBJoseph Buchanan-Vega
Joseph Buchanan-Vega
BWBrett Wenner
Brett Wenner
JAJeffery Allen
Jeffery Allen
MAMonica S. Allen
Monica S. Allen
RMRobert Magnusson
Robert Magnusson
This research addresses fuel biocontaminant sensing using resonant photonic gratings. The fundamental photonic resonance physics enables compact, economic sensors that provide a rapid, quantitative, and effective modality for detecting and monitoring biochemical bindings. Thus, we present resonant sensors and attendant surface chemistry targeting yeast cells as representative biofouling agents. We apply biorecognition elements (BREs) to functionalize the dielectric grating surface (TiO2) to produce a sensitive, quantitative wavelength shift-based assay for detecting Yarrowia yeast cells. Concanavalin A (Con A) is used as a BRE consisting of carbohydrate-binding proteins binding to ligands on the yeast cell surface. Corresponding resonance wavelength shifts are measured relative to concentration expressed in log10[C] where C represents the concentration of cells in solution. The assay is shown to be highly specific for detecting Yarrowia cells in various concentrations. The experimental limit of detection (LOD) of the assay is 1.92 for yeast cells in culture media and 1.22 for cells in phosphate buffer saline (PBS). The comparisons of the various functionalization methods show that (3-aminopropyl)triethoxysilane (APTES) with glutardialdehyde (GA) and Con A protocol provides optimal detection of cells in PBS.

17 December 2022 in Chemosensors
Verified
Journal Article
Open access
PDF file
JBJoseph Buchanan-Vega
Joseph Buchanan-Vega
RMRobert Magnusson
Robert Magnusson
A guided-mode resonance (GMR) sensor with multiple resonant modes is used to measure the collection of biomolecules on the sensor surface and the index of refraction of the sensor environment (bulk). The number of sensor variables that can be monitored (biolayer index of refraction, biolayer thickness, and bulk, or background, index of refraction) is determined by the number of supported resonant modes that are sensitive to changes in these variable values. The sensor we use has a grating and homogeneous layer, both of which are made of silicon nitride (Si3N4), on a quartz substrate. In this work, we simulate the sensor reflection response as a biolayer grows on the sensor surface at thicknesses from 0 to 20 nm and biolayer indices of refraction from 1.334 to 1.43 RIU; simultaneously, we vary the bulk index of refraction from 1.334 to 1.43 RIU. In the specified span of sensor variable values, the resonance wavelength shifts for 2023 permutations of the biolayer index of refraction, biolayer thickness, and bulk index of refraction are calculated and accurately inverted. Inversion is the process of taking resonant wavelength shifts, for resonant modes of a sensor, as input, and finding a quantitative variation of sensor variables as output. Analysis of the spectral data is performed programmatically with MATLAB. Using experimentally measured resonant wavelength shifts, changes in the values of biolayer index of refraction, biolayer thickness, and bulk index of refraction are determined. In a model experiment, we deposit Concanavalin A (Con A) on our sensor and subsequently deposit yeast, which preferentially bonds to Con A. A unique contribution of our work is that biolayer index and biolayer thickness are simultaneously determined.