Engineering Research and Innovation Seed Funding Program

To encourage new research collaborations, the College of Engineering associate dean for research has initiated the Engineering Research and Innovation Seed Funding program (ERISF). The overarching goal of the ERISF program is to assist engineering researchers in developing new innovative research programs that have strong potential for significant future support from government agencies, corporations, industry, consortia, or foundations. Proposals must develop research initiatives with high potential for significant extramural funding.

Read the 2017 RFP (PDF file)
Download the 2017 ERISF Budget Template (Excel File)

FY2018 ERISF Projects

Principal Investigator: Zhong Chen, Electrical Engineering

Collaborators: Yanbin Li and Ronghui Wang, Biological and Agricultural Engineering


Huge medical costs and tremendous productivity losses every year in the world are associated with major foodborne pathogens (such as E. coli O157:H7, Salmonella Typhimurium, Listeria monocytogenes). The goal of this research is to investigate and develop optofluidic biosensor based on integrated photonics and microfluidic devices for simultaneous detection of multiple foodborne pathogenic bacteria. Optofluidic biosensor provides unique integration capability of both liquid fluidic handling and optical spectrum analysis into one chip. It can substantially improve the sensitivity of the bacteria detection and reduce the total size and weight of bio-sensing system. By utilizing the immunomagnetic nanobead for bacteria separation and fluorescent semiconductor quantum dots for simultaneous detection of multiple bacteria, optofluidic biosensor can be developed for highly sensitive portable bio-sensing system for foodborne pathogenic bacteria detection.

Principal Investigator: Sarah Nurre, Industrial Engineering

Collaborators: Kelly Sullivan, Industrial Engineering and Benjamin Runkle, Biological Engineering


Food and agriculture (ag) is a critical infrastructure making up almost 20% of the economic Activity in the US. In spite of its importance, researchers have overlooked food and ag when Classifying infrastructure interdependencies. We propose to fill this need by identifying and Classifying the interdependencies of the food and ag sector within the state of Arkansas (AR) with the aim of strengthening its resilience. Ag is the top industry in AR wherein rice and poultry comprise two of the top commodities. Using rice and poultry as case studies, we will interview key personnel in the food and ag industry such as farmers, packaging managers, researchers, and members of the AR Division of Ag. Complementing this effort, we will interview managers of interdependent infrastructures (e.g., water). We will augment the interview data with data from GIS and literature to classify and create visualizations for the types and characteristics of interdependencies within food and ag in AR.

Principal Investigator: Zhenghui Sha, Mechanical Engineering

Collaborators: Michael Gashler, Computer Science and Computer Engineering


In the last decade, improvements to AI have enhanced many services and products. However, domains still exist where modern AI is not yet being utilized. One important example is engineering design. Integrating AI with engineering design has potential to make a large impact by simplifying and accelerating engineering efforts. The first step toward making this happen is to enable machines to learn design thinking from humans who already know how to do it well. The primary objective of this interdisciplinary research is to characterize, mine and model engineering design thinking, specifically designers' divergent-convergent reasoning, system thinking and sequential decision-making, in complex systems design. To achieve this objective, we apply big data analytics in design research with a smart computer-aided design (CAD) platform – Energy3D. The central hypothesis is that unobserved design thinking can be mined from the fine-grained data of CAD logs. To validate this hypothesis, we propose to conduct human-subject experiments, and integrate data mining techniques with Markov models. The accomplishment of this objective will establish the utility of big data methods in design research and lay the foundation for our future study towards realizing AI-assisted engineering design.

FY2017 ERISF Projects

Principal Investigator: Lauren Greenlee, Chemical Engineering

Collaborators: Ranil Wickramasinghe, Chemical Engineering and Xianghong Qian, Biomedical Engineering


Many municipal and agricultural wastewaters contain ammonia as a water contaminant. Ammonia, however, is an energy-dense high-value molecule considered to be a potential fuel source for power generating fuel cells. We propose to conduct a key set of initial experiments to demonstrate that a non-precious metal nanoparticle catalyst can electrochemically oxidize ammonia in simulated wastewater, establishing the basis for further development of this ammonia fuel cell technology. We will focus on wastewater from fish aquaculture as an example wastewater system and propose to perform the initial nanocatalyst synthesis and electrochemistry testing with a key set of water chemistry parameters including pH, ammonia concentration, chloride concentration, and the presence of organic matter.

Principal Investigator: Benjamin Runkle, Biological Engineering

Collaborator: Brian Haggard, Biological Engineering


We propose a new collaboration to significantly improve the measurement of evapotranspiration from agricultural fields by using the “surface renewal” micrometeorological technique. We will develop a set of strategies to provide error estimates for this method’s measurements. The evapotranspiration measurements will lead to better control over water use and may potentially reduce irrigation applications in the Mississippi River Valley, whose aquifers are already unsustainably overdrawn. Our team combines different expertise: (1) in micrometeorological measurements of water vapor transfer to the atmosphere (Runkle), (2) in hydrological time series analysis (Haggard), and (3) the surface renewal method (Suvočarev).

Principal Investigator: Wen Zhang, Civil Engineering

Collaborator: Lauren Greenlee, Chemical Engineering


The goal of this proposal is to investigate interactions between engineered nanoparticles (ENPs) and biofilms. The use of ENPs in commercial products has increased exponentially in recent years, such as silver (Ag-NPs) and titanium dioxide nanoparticles (TiO2-NPs). The ENPs applied and released from various settings inevitably interact with ubiquitous biofilms in multiple environments, such as wastewater treatment plants, water transmission pipes, food packaging, hospitals and medical implants. These interactions can directly cause adverse impact to public and ecosystem health, yet little is understood about the underlying mechanisms that cause nanoparticle retention, release, and transformation. This research will reveal the capability of biofilms to retain and release ENPs in bacteria species representative of various key environments. Additionally, changes in biofilm property will be studied to elucidate ENP-biofilm interactions. Ultimately, the knowledge gained will enable innovative and effective use of biofilm processes in multiple industrial settings.

FY2016 ERISF Projects

Principal Investigator: Magda El-Shenawee, Electrical Engineering

Collaborator: Fisher Yu, Electrical Engineering


This research represents cross-disciplinary approach of optoelectronics, nanomaterials, and high frequency devices. The goal is to produce a prototype semiconductor device capable of producing a high power, high frequency signal. The particular high frequency of interest is the terahertz band, which has demonstrated great potential in numerous applications. Some examples include the semiconductor industry, medical imaging, pharmaceuticals, national security, material characterization and non-destructive evaluation of electronic devices. The current terahertz technology suffers from low power sources that hinder the technology from advancing to more potential applications. The proposed research includes multi-scale modeling for the design, nanomaterial integration for the fabrication, and optoelectronics for the measurements and characterization. The proposed prototype will be used as preliminary device for submission in full proposals to the NSF and DOD, which have expressed high interest in advancing terahertz technology.

Principal Investigator: Michael Gashler, Computer Science and Computer Engineering

Collaborator: Harry Pierson, Industrial Engineering


Deep artificial neural network learning will be applied to the field of collaborative robotics for industrial applications. The investigators intend to show that this application will lead to human-friendly robotic task specification, significantly reduce the time and expense associated with implementing robotic solutions to industrial problems, and allow robots to complete tasks that require complex physics without the need for explicit modeling. This work will lead to six distinct future funding opportunities that are explicitly identified.

Principal Investigator: Gary S. Prinz, Civil Engineering

Collaborator: Julian Fairey, Civil Engineering


This proposal seeks preliminary data to support a novel fast rebuilding paradigm for urban environments in developing regions affected by natural disasters. Earthquakes and hurricanes in developing regions often destroy homes and contribute to unstable economic conditions, forcing people to live in temporary shelters. The current reconstruction paradigm is based on prefabricated housing methods (tents, T-shelters, etc.) that use materials manufactured abroad, imported to the country of need, and then assembled by teams of skilled foreign labor. These practices do little to stimulate local economies and the resultant structures are often not suitable for dense urban environments. The project team will build several prototype multi-story shelters, intended to leverage structural elements that can be fabricated in the country of need and assembled with local unskilled labor. The shelters will be assessed structurally for their ability to withstand future disasters and integrated with water collection systems that have the potential to supply potable water for months to years.

Principal Investigator: Narasimhan Rajaram, Biomedical Engineering

Collaborator: David Zaharoff, Biomedical Engineering


It is well known that 90% of breast cancer patients die due to metastatic spread and not due to the primary tumor. However, there are currently no prognostic markers that can accurately predict long-term tumor recurrence or risk of metastasis in breast cancer. Therefore, nearly 80% of breast cancer patients will receive adjuvant chemotherapy, in addition to surgery and radiation, with 40% of patients succumbing to metastatic disease. Thus, there is a significant population of breast cancer patients who are over treated, and suffer unnecessarily due to the side effects of chemotherapy. Indeed, it is estimated that there are one to three deaths due to overtreatment for each breast cancer patient saved. The development of prognostic biomarkers based on the primary tumor that accurately point to future risk of metastasis could lead to a reduction in the number of patients receiving unnecessary adjuvant therapy for benign breast tumors.