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 2019 RFP (Word file)
Download the 2019 ERISF Budget Template (Excel File)

FY2019 ERISF Projects

Principal Investigator: David Huitink, Mechanical Engineering

Collaborator: Lauren Greenlee, Chemical Engineering


Induction heating of nanoparticles offers novel avenues toward localized heating in unique environments such as cancer tumor ablation and microfluidic chemical processes where traditional heating methods are obtrusive, yet there is still much to be understood in this frontier technology. Chemical interactions between heated NPs and their surroundings can affect their performance and stability; and in this effort, a brand new technique of using silica capped iron oxide NPs is investigated for enhanced stability during induction heating. The Huitink group will study the heat output of various iron oxide core NPs with silica shells that will be produced by the Greenlee group, for enhancing the stability and thereby enabling new technological capabilities using NP induction heating. Moreover, the chemical stability of these silica-capped magnetic NPs will also be investigated using TEM and spectroscopic analysis, for determining evolution of the NPs and their shell structures caused by the induction process.

Principal Investigator: Miaoqing Huang, Mechanical Engineering

Collaborator: Zhenghui Sha, Mechanical Engineering


The primary research objective of this interdisciplinary research is to significantly increase the scale of network modeling and analysis using GPU platform in supporting complex systems design. To achieve this objective, we will design new parallel and scalable methodologies for manipulating large-scale graphs (of millions of nodes) on GPU computer clusters in order to accomplish the network analysis in minutes. The central hypothesis is that the network models developed with large-scale network data set have a better performance than those obtained from studies at small scales. To validate the hypothesis, we propose to conduct a comparative study on modeling the U.S domestic transportation system using small-scale and large-scale network analyses, respectively. The proposed research is potentially transformative and, if successful, could fundamentally change the landscape of complex network. This project will be carried out by a joint force between Huang (PI), with expertise in high-performance computing and algorithm design, and Sha (Co-PI), with expertise in complex networks and engineering design.

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.