The Vice President for Research and Innovation, Dr. Magesh T. Rajan, initiated the VPRI Graduate Fellows Program to support excellence in graduate and to promote interdisciplinarity at the university. The initiative will engage the best and brightest students from graduate programs across the institution. The VPRI Graduate Fellows Program will ensure graduate students have opportunities for success through having them work with faculty researchers in the Faculty Research and Innovation for Scholarly Excellence (Faculty-RISE) program. It strategically invests in high-priority areas of research and innovation clusters identified for the university. The Faculty-RISE program will support in disciplines that can lead to the establishment of renowned scholarly and research centers and institutes. It will push the boundaries of knowledge and be a critical point of convergence for the university and the cultural experience for students and faculty, encouraging multi- and inter-disciplinary collaboration.
The VPRI Graduate Fellows may receive support such as scholarships, stipends, and travel support as well as opportunities to engage in professional development, workshops, networking opportunities with thought leaders in their industries. Listed are selected Graduate Fellows supported by the VPRI Graduate Fellows Program in support of the Faculty-RISE program. To see more details about the corresponding projects, please visit the faculty mentor’s laboratory, department, or college websites.
Thiamin and Thiamin Analogues as Carriers for Drug Delivery to Cancer Cells
Graduate Fellow: Julien Dubois, Department of Chemistry, College of Arts and Sciences
Faculty Mentor: Dr. Sameh Abdelwahed, Assistant Professor, Department of Chemistry, College of Arts and Sciences
Thiamin is an essential cofactor in all cell types. It is critical for the activity of many key enzymes in cellular metabolism, pyruvate dehydrogenase, alpha-ketoglutarate dehydrogenase, transketolase. Cancer cells require metabolism to tolerate their survival, and a corresponding 2-fold increase in thiamin transport was observed in the tumor cell, suggesting a significantly increased requirement for thiamin during hypoxic stress. Most anticancer drugs in clinical are limited by their toxicity, and many of these compounds still lack tumor selectivity and have not been therapeutically useful. For example, although, dichloroacetic acid (DCA) has been studied as a potential anticancer drug because it inhibits the enzyme pyruvate dehydrogenase kinase, an enzyme which plays an important role in carbohydrate metabolism, DCA is limited by its toxicity to normal rapidly growing cells. My approach, to enhance the specificity of DCA may be to exploit the upregulation of thiamin transport via the conjugation of DCA with thiamin molecules affording enhanced cancer cell-specific uptake. Our studies will not be limited to DCA, but it will include different anticancer small molecules, and use thiamin as a drug delivery vehicle.
Implementing Machine Learning (ML) in Sensor-based Systems
Graduate Fellow: Sheikh Tareq Ahmed, Department of Computer Science, College of Engineering
Faculty Mentor: Dr. Ahmed Ahmed, Assistant Professor, Department of Computer Science, College of Engineering
The goal of this project is to develop a prototype implementation for a sensor-based system, leveraging embedded Machine Learning (ML) capabilities to better collect, transfer, aggregate, and analyze the tremendous amount of sensor data generated every day. Adding ML to the sensing process of smart sensor-based systems provides the opportunity to significantly increase the ratio of relevant information content to raw sensor data. We have used the developed system to build intelligent applications in different domains such as transportation, health, cybersecurity, etc. Imagine a smart application for object detection that can be used for robotic navigation in dynamic environments. This application can avoid various obstacles and guide a robot in an unknown environment. One big challenge is to overcome the shortcomings of the processing capabilities in sensing devices. Therefore, we will transfer the immense amount of sensor data produced from the sensing devices to centralized servers on the cloud.
System Modelling and Performance Evaluation in the Internet of Things
Graduate Fellow: Kelechi G. Eze, Department of Electrical & Computer Engineering, College of Engineering
Faculty Mentor: Dr. Cajetan Akujuobi, Professor, Department of Electrical & Computer Engineering, College of Engineering
Recently, the Internet of Things (IoT) has gained widespread application across all sectors of the economy. Current issues facing the IoT are security, risk management, and operational efficiency. Existing solutions pose concerning security risk to critical assets and are inefficient. Hence, the primary objectives of this research are to develop models on which secure and efficient IoT solutions can be built to improve performance and reduce security risks. Mathematical tools, algorithms, and technologies (specifically the actor-model and blockchain) are leveraged to achieve the stated objectives. Also, based on the models and prototypes developed, performance evaluation are carried out. The results show how various IoT system configurations can lead to varying risk levels and how an optimal configuration would lead to low-risk levels. The result also gives insight into how the various issues posed by integrating IoT with the blockchain technology could be solved by the actor model.
Zirconium Oxide Catalyzed Conversion of Veratraldehyde to 1- (Dimethylamino)methyl-3,4-methoxybenzene
Graduate Fellow: Sara Delfan, Department of Chemistry, College of Arts & Sciences
Faculty Mentor: Dr. Ananda Amarasekara, Professor, Department of Chemistry, College of Arts & Sciences
Recently, the Internet of Things (IoT) has gained widespread application across all sectors of the economy. Current issues facing the IoT are security, risk management, and operational efficiency. Existing solutions pose concerning security risk to critical assets and are inefficient. Hence, the primary objectives of this research are to develop models on which secure and efficient IoT solutions can be built to improve performance and reduce security risks. Mathematical tools, algorithms, and technologies (specifically the actor-model and blockchain) are leveraged to achieve the stated objectives. Also, based on the models and prototypes developed, performance evaluation are carried out. The results show how various IoT system configurations can lead to varying risk levels and how an optimal configuration would lead to low-risk levels. The result also gives an insight into how the various issues posed by integrating IoT with the blockchain technology could be solved by the actor model.
Effects of Carrier Gas Flow Rate on Bio-oil yield from Pyrolysis of Lignocellulosic Biomass Using a Fixed Bed Reactor
Graduate Fellow: Abdelhadi Hussein, Department of Mechanical Engineering, College of Engineering
Faculty Mentor: Dr. Paul O. Biney, Professor, Department of Mechanical Engineering, College of Engineering
Increased Bio-oil production: Switchgrass pyrolysis experiments were performed in a medium-scale experimental fixed bed reactor with 48.2 liters volume. The effects of varying carrier Nitrogen gas flow rate on bio-oil yield were studied when the reaction’s final temperature was fixed at 520 oC. Carrier gas flow rate was varied from 75 L min -1 to 100 L min-1. At 75 L/min carrier gas flow, 303 g of oil was generated from 1128 gm biomass for a yield (bio-oil to feedstock weight ratio) of 26.8 %, At 100 L/min flow rate, 501 g of oil was generated from 1466 gm biomass for a yield of 34.1% which is 7.2% increase from the 75 L/min flow rate. Increased bio-oil production ( carbon-neutral sources) facilitates the transition from fossil-based fuels towards clean fuels where the transportation sector is responsible for 29% of CO2 emissions in the U.S, according to EPA.
Smart Sensing and Controlling System for Power Management of Multi-Sourced Renewable Energy
Graduate Fellow: Eze Bede, Department of Electrical & Computer Engineering, College of Engineering
Faculty Mentor: Dr. Shuza Binzaid, Research Associate Professor, Department of Electrical & Computer Engineering, College of Engineering
Research Overview: This project is aimed at the power management of multi-source renewable energy. The mode of control and management is centered on smart sensing using a network of sensors and microcontrollers. The project uses the Swarm Optimization Process to analyze data collected from programmable microcontrollers placed in remote locations for measuring generated energy from multiple sources. The expected results include learning about the various energy sources and understand their challenges to combine them in the smart grid system. A model of a microcontroller-based system will be developed that dynamically controls these renewable energy sources to serve the power needs in the grid infrastructure. This project can address and bring solutions to some of the issues in real-time and help develop an ideal power management scheme.
Multifunctional Sensor and Custom Electronic Module for Detection of Ionic, Electromagnetic and Radiation Environments
Graduate Fellow: Adeyemi Taylor, Department of Electrical & Computer Engineering, College of Engineering
Faculty Mentor: Dr. Shuza Binzaid, Research Associate Professor, Department of Electrical & Computer Engineering, College of Engineering
Solutions to detect dangerous radiation environments at early stages can save many lives through the application of the multifunctional sensors, and the electronic modules. A number of sensors are already developed and available today, but still severely short in multifunctional sensors, that can perform more than one type of detection tasks. Eight Multifunctional sensors were designed by using five types of polymer composite materials and tested. These polymers composites were applied as single and dual layers in the design. These sensors were first tested very successfully under X-ray at the Electrical and Computer Engineering department’s Microelectronic Test lab at PVAMU. They were then verified for ionic air test in the special test setup in the lab. To further proof the concept, the same sensors were tested in October 2019 at Los Alamos National Laboratory (LANL) under the highest flux of neutron energy radiation. These sensors had the same diameter of the radiation window, so collected data was easily analyzed. Four of them resulted in an outstanding performance in Los Alamos Neutron Science Centre (LANSCE) lab. These can be used in space technologies and many applications on the earth’s surface.
Synthesis, Characterization and Modeling Study of Environmentally Friendly Schiff-Based Catalyst with Improved Reducing Power
Graduate Fellow: Latrice Jerrells, Department of Chemistry, College of Arts & Sciences
Faculty Mentor: Dr. Gina Chiarella, Assistant Professor, Department of Chemistry, College of Arts & Sciences
Computational calculations were completed on salicylaldehyde-L-histidine imine ligand, and its derivative metal complexes with copper(II), nickel(II), and zinc(II); all those compounds were previously synthesized in this research group. The computational modeling was run on Linux, using Gaussian View, and density functional theory (DFT), the functional set 6-31g* and the basis set of B3LYP. The structures of those four compounds were optimized considering the formation of metal complexes in the ratio 1:1, and tetrahedral geometry following the reported shapes in the database SciFinder Scholar, the Infrared, UV-visible spectra were calculated and compared with the experimental data obtained in previous studies in our research group; the 1H-NMR spectra were also calculated, though we don’t have experimental data, the computational results would be used to predict our future experimental research. All the computational results were contrasted with the experimental ones, with enough degree of agreement.
Conversion Processes of CO2 into Useful Chemicals
Graduate Fellow: Elizabeth Osadare, Department of Chemical Engineering, College of Engineering
Faculty Mentor: Dr. Emmanuel Dada, Assistant Professor, Department of Chemical Engineering, College of Engineering
Carbon dioxide (CO2) is the key contributor to the greenhouse gas effect and one of the leading detrimental gaseous compounds that our planet faces. In 2017, CO2 gas was approximately 82% of all the greenhouse gases released through human activities in the US. Fossil fuel combustion accounts for the major human activities that cause CO2 emission. A high concentration of CO2 gas in the atmosphere alters the earth’s carbon cycle then result in climate change and global warming. This project focuses on the conversion processes of captured CO2 into useful chemicals like urea, dimethyl ether, methanol, and long-chain hydrocarbon fuels. This finding shows feasible ways of solving global concerns for the undesirable impact of CO2 gas emission. A catalyst for high selectivity and conversion for optimum product yield is ultimately needed. Development of necessary super-catalyst for high selectivity and optimum product-yields, particularly in the pilot-scale at a reduced cost, is recommended.
Qualitative Comparison of Milk Lipids using NMR Spectroscopy
Graduate Fellow: Brianna Williams, Department of Chemistry, College of Arts & Sciences
Faculty Mentor: Dr. Harshica Fernando, Assistant Professor, Department of Chemistry, College of Arts & Sciences
Lipidomics investigated the cellular pathways of lipids in biological systems and practiced through the basis of analytical chemistry principles such as NMR, GC-MS and/or LC-MS. Milk and milk products are essential to the human diet in nearly everyone ranging from infants to adults. Therefore, identifying the lipids in milk is imperative. The lipid fraction of milk is composed of a complex matrix and varies depending on the type of milk. Lipids in milk consist of sterols, triglycerides, free/esterified fatty acids, and phospholipids (PLs), which are essential for the maintenance of cellular membranes along with other biological roles. Hence, accurate quantification of lipids and comparison of them are crucial. The objectives of this project are the qualitative and quantitative identification of lipid metabolites in milk using spectroscopic methods. A modified Folch method was used in the extraction process, and the organic extracts were used in the analysis of lipids. The presence of these lipids with different fatty acyl chains has a significant effect on human health and plays a vital role in the self-absorption process.
Statewide Adolescent Needs Assessment
Graduate Fellow: Roberto deFreitas, Department of Agriculture, Nutrition, & Human Ecology, College of Agriculture & Human Sciences
Faculty Mentor: Dr. Angela Broadus, Training Specialist, Texas Juvenile Crime Prevention Center, College of Juvenile Justice & Psychology
Using virtual focus groups and online surveys, the Center will assess statewide training needs related to the reduction of juvenile crime. Our research builds upon the Council of Government’s (COG) valuable work with additional data from key community stakeholders and parents of adolescents within each of eight COGs. In this study, we will use a) virtual focus groups, b) paper surveys, and c) online surveys to collect community data from eight Regional COGS that encompass 100 Texas counties (39% of all Texas counties). Recruitment for the virtual focus groups and surveys will focus on parents of adolescents and other adults over age 18, key community stakeholders from middle schools and high schools, churches, law enforcement courts, juvenile justice, businesses, the mayor and/or council members, and other interested community members. Qualitative and quantitative outcome data will be analyzed to determine the top 10 training priorities per COG and across the selected regions.
Realizing an IoT-Based Home Area Network Model Using ZigBee in the Global Environment
Graduate Fellow: Ekele A. Asonye, Department of Electrical & Computer Engineering, College of Engineering
Faculty Mentor: Dr. Sarhan Musa, Professor, Department of Electrical & Computer Engineering, College of Engineering
As devices become increasingly connected, the Internet of Things (IoT) concept becomes ever so prominent. For Device-to-Device (D2D) communications in the home and industry like for power, oil and gas, utility transmission, and transport, the most profound challenge of the IoT has been organizing this large number of devices into a network of things. The Majority of these devices constituting the IoT setup are lightweight, low power wireless sensor nodes. The objective of the Home Area Network (HAN) is to continuously monitor connected devices, collect data, process data, and give feedback where and when needed. Within this chain of communication are numerous nontrivial and computational issues. In this project, we demonstrate the proof of concept using the ZigBee protocol as a smart data collection element in IoT based HAN in the global environment. A simulation study with different topologies and configurations using the ZigBee network platform in the global environment is presented. Based on traffic-demand, it is imperative to implement a network configuration that enables ideal network performance. To transfer the large volume of collected data from the HAN, we designate the Fifth Generation (5G) wireless device enabled with Mobile Edge Computing (MEC) connected to the cloud to process data.
Comparison Study of Structural Health Monitoring of Wind Blade Using Numerical Simulation between ElastoDyn and BeamDyn
Graduate Fellow: Pranay Krishna Katari Haribabu, Department of Mechanical Engineering, College of Engineering
Faculty Mentor: Dr. Ziaul Huque, Professor, Department of Mechanical Engineering, College of Engineering
Despite various advantages of the floating offshore wind turbine (FOWT), it is still a challenge to commercialize it. One of the disadvantages of FOWTs is uncertainty related to operation and maintenance costs (OPEX). To reduce the OPEX, an efficient vibration-based health monitoring method of FOWT wind blade is applied, and the feasibility of it through time-domain simulation is observed. Two Finite Element methods, modal based and exact beam theory-based, for the dynamics of tower and blade elasticity are introduced, and the advantages and disadvantages are discussed based on the comparison of the health monitoring results. The aerodynamic analysis tool, AeroDyn, was coupled with hydrodynamic and mooring analysis tool, OrcaFlex, so that it can calculate the fully coupled dynamics among wind turbines, floating platforms, and mooring lines. The validation is done by comparing the modal properties obtained in the simulation to those obtained in the FEM modal analysis.
Design and Synthesis of Graphene and Transition Metal Chalcogenides for Photothermal Therapy of Cancer
Graduate Fellow: Sanjuana Aguilar, Department of Chemistry, College of Arts & Sciences
Faculty Mentor: Dr. Gururaj M Neelgund, Assistant Professor, Department of Chemistry, College of Arts & Sciences
Cancer is one of the major current threats to humans, so it is crucially needed to develop novel therapeutic strategies having high efficiency, specificity, and precision to combat cancer. The conventional therapies presently in use to treat cancer such as surgery, chemotherapy, and radiotherapy have plenty of critical barriers, including lower efficacy and higher toxicity. Therefore, photothermal therapy (PTT), a novel and promising therapy, operated by near infrared (NIR) light has attained the recent importance on account of its critical benefits viz., noninvasiveness, safety, and efficiency. PTT is operated by conversion of photon energy into thermal energy in the presence of a photo-absorbing agent known as a photothermal agent under exposure to NIR radiations. By the introduction of a photothermal agent into cancer cells leads to the transformation of NIR radiations into thermal energy, elevate the local temperature of cancer cell, and ablate. Owing to the importance of photothermal agents in PTT, our aim is to design and synthesize robust photothermal agents comprised of graphene nanosheets and transition metal chalcogenides (TMCs) like CuS, ZnS, NiS, and CoS for the advancement of PTT.
Aircraft Location Prediction using Deep Learning
Graduate Fellow: Olutobi Adagunodo, Department of Electrical & Computer Engineering, College of Engineering
Faculty Mentor: Dr. Lijun Qian, AT&T Professor, Department of Electrical & Computer Engineering, College of Engineering
The localization of aircraft is necessary to control air traffic safely and effectively. In less than perfect conditions such as during bad weather, when the Global Positioning System (GPS) is unavailable, or in an adversarial situation such as jamming or spoofing attacks, it is imperative to have complementary or redundant methods of localization that are independent of the aircraft. The goal of this project is to study the feasibility to localize aircraft (estimate the longitude, latitude, and altitude of an aircraft) based on crowd-sourced air traffic control communication data, specifically the time of arrival and signal strength measurements reported by many different sensors. We design and test a deep neural network model for aircraft location prediction using real-world data from OpenSky Network, a crowd-sourced receiver network that obtains volumes of air traffic data from thousands of sensors. It is demonstrated that the proposed deep neural network outperforms the time difference of arrival (TDOA) and support vector regressor (SVR) in terms of the mean absolute percentage error (MAPE), and the proposed deep learning-based method using crowd-sourced air traffic control communication data is an effective solution for accurate aircraft location prediction that is independent of the aircraft.
Thermal Analysis of Open and Closed Parabolic Solar Trough Collector
Graduate Fellow: Michael Adeniran, Department of Mechanical Engineering, College of Engineering
Faculty Mentor: Dr. Rambod Rayegan, Associate Professor, Department of Mechanical Engineering, College of Engineering
Due to the environmental and political issues that have arisen over the last decades, society has made great efforts to provide cleaner forms of energy. With recent technological advancements, solar energy has become a significant alternative for clean energy. The parabolic trough collector (PTC) has been intensely studied and researched over the last decade. The focus of this study is to analyze a new design for the PTC in which a transparent glass is placed over the aperture and closed the ends of the PTC. This design aims to reduce thermal losses occurring in the PTC system by eliminating the effects of natural convection and trapping the heat in the trough. An open and closed PTC system was simulated in CFD ANSYS Fluent. The closed PTC system resulted in decreased levels of convective coefficient along the absorber tube and reduced levels of convective heat flux loss compared to the open case.
Comparison of Watershed Delineation Outcomes Using LiDAR, ASTER and UAS-Generated Geospatial Datasets
Graduate Fellow: Uchenna S. Igwe, Department of Civil & Environmental Engineering, College of Engineering
Faculty Mentor: Dr. Emmanuel U. Nzewi, Professor, Department of Civil & Environmental Engineering, College of Engineering
A watershed is defined as the land surface that concentrates runoff to a common location – called the “outlet.” Topographic (contour) maps provide elevation data needed for determining runoff flow patterns and delineating watershed boundaries. A Digital Elevation Model (DEM), a digital representation of a contour map, of a watershed area is needed. The DEM provides the three-dimensional (3-D) ground elevation dataset for delineating the watershed boundary. ArcGIS Pro, a Geographic Information System software, can delineate a watershed using appropriate analysis tools integrated into ArcGIS Pro using a DEM as input. Geospatial datasets derived from three sources – LiDAR (Light Detection and Ranging), Unmanned Aerial Systems (UAS), or drones and 30-m Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM), will be used to generate comparable DEMs. The competing DEMs will be used in ArcGIS Pro as input to delineate watersheds, and the outcomes will be statistically compared.
Morphology and Mechanical Properties of Polystyrene and Graphene oxide Nanocomposites
Graduate Fellow: Veronica Williams, Department of Chemical Engineering, College of Engineering
Faculty Mentor: Dr. Nabila Shamim, Assistant Professor, Department of Chemical Engineering, College of Engineering
Graphene oxide (GO) is an amazing nanostructured material with a wide range of possible technological applications, including its use as filler for thermoplastic polymers or thermosetting resins. A combination of graphene-related substances with other systems often leads to promising nanocomposite materials with unique mechanical, chemical, and physical properties. In this work, we study the morphology and elastic properties of a composite consisting of polystyrene (PS) and graphene oxide (GO). We prepared several composite films of this nature with a varying polystyrene concentration on a fixed amount of graphene oxide. The morphology of the composites was observed by Scanning Electron Microscopy (SEM). The SEM image shows the higher the concentration in polystyrene, the more compact structure of graphene oxide and polystyrene are observed. The mechanical properties of the composite films were analyzed using Atomic Force Microscopy (AFM). It is found that Young’s elastic modulus of the composite film changes drastically from the value of its pure constituent solutions though it shows a rather weak dependence on the polystyrene concentration for the values considered.