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 2023 RFP (Word file)
Download the 2023 ERISF Budget Template (Excel File)

CY2021 ERISF Projects

Principal Investigator: Han Hu, Mechanical Engineering

Collaborators: Justin Zhan, Computer Science and Computer Engineering


Boiling is central to the high-performance cooling of nuclear reactors and high-power electronics, whereas the boiling crisis with a sudden and rapid reduction in heat transfer coefficient has raised significant safety concerns. The proposed project aims to enable early detection and prevention of the boiling crisis by leveraging the convolutional long short-term memory-based sequential classification. We hypothesize that sequential classification will lead to much higher accuracy and faster response than traditional image/thermal data analysis by capturing the dynamics in the image sequences. Furthermore, by extracting the key features of the boiling image sequences and correlating them with the thermal performance data, we aspire to obtain new physical insights into the mechanism triggering the boiling crisis. This collaborative effort will integrate the two PIs’ expertise in thermal engineering and data science to solve long-standing thermal challenges and promote the applications of advanced data analytics in engineering applications.

CY2020 ERISF Projects

Principal Investigator: Xiangbo Meng, Mechanical Engineering

Collaborators: David Huitink, Mechanical Engineering


Emerging applications including electric vehicles, wind generators, solar converters, aerospace power conditioning, etc. are driving an ever-increasing need for high-temperature dielectric materials. In these systems, power systems and electronic devices have to operate at elevated temperatures (>140 oC), where current dielectric materials cannot survive. In this project, we propose to rationally develop novel organic-inorganic hybrid dielectric materials with desirable dielectric properties. To this end, atomic and molecular layer deposition (A/MLD) will be combined for developing new high-temperature dielectric materials. This high-risk investigation holds great potential to revolutionize power electronics through improved capacitor design. To develop and evaluate the proposed hybrid materials, Dr. Xiangbo Meng and Dr. David Huitink from Department of Mechanical Engineering will form a highly complementary team. This new collaboration is intended to deliver a new research capability and it is also expected that the synergic activities between the two groups will make University of Arkansas a leader in Energy, Materials, and Electronics.

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.

CY2019 ERISF Projects

Principal Investigator: Alexander Nelson, Computer Science and Computer Engineering

Collaborator: Yue Chen, Mechanical Engineering


This pilot work is an exploratory project to combine fabric capacitor sensor arrays into the construction of soft robotic actuators to improve functionality. The inclusion of these sensors with additional kinematic modeling enable mechanical analogs to biological somatosensation, proprioception, and hand-eye coordination – all key features in hand motor coordination. The goal of the project is to produce a working prototype to produce initial data that will enable a competitive collaborative proposal when seeking external funding. The long-term goal is to produce a soft hand exoskeleton that aids in hand motor rehabilitation – enabling repeated proper motions that aid in neurological recovery during plasticity post trauma. Supporting proper motor function prevents maladaptive compensatory behaviors that significantly alter motor function.

Principal Investigator: Xiao Liu, Industrial Engineering

Collaborator: Miaoqing Huang, Computer Science and Computer Engineering


This project addresses a rapidly emerging area known as the Data Intensive Physical Analytics (DIPA), which is a direct response to COE’s Big Data/Analytics research area and others. The objective of this project is to create a physical-statistical modeling framework and GPU-accelerated computing tools for the real-time monitoring and prediction of large-scale spatio-temporal data sets arising from physical convection-diffusion processes. The applications/impacts of the research range from the modeling, monitoring and prediction of the regional outbreaks and epidemics of Influenza A (H3N2), California wildfires, major storms of the Atlantic season such as Hurricane Florence, fine Aerosol pollution over central California, and so on. Due to the significance and wide applicability of the proposed research, the PIs have successfully identified a number of external funding opportunities, especially NSF, DOE and Walmart. In addition, leveraging PI Liu’s industry connection, the team will engage with IBM Research on an NSF GOALI project.

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.