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Research Spending & Results

Award Detail

Awardee:THE UNIVERSITY OF AKRON
Doing Business As Name:University of Akron
PD/PI:
  • Mesfin Tsige
  • (330) 972-5631
  • mtsige@uakron.edu
Award Date:01/21/2020
Estimated Total Award Amount: $ 50,000
Funds Obligated to Date: $ 50,000
  • FY 2020=$50,000
Start Date:02/01/2020
End Date:07/31/2020
Transaction Type:Grant
Agency:NSF
Awarding Agency Code:4900
Funding Agency Code:4900
CFDA Number:47.041
Primary Program Source:040100 NSF RESEARCH & RELATED ACTIVIT
Award Title or Description:I-Corps: Virtual Lab for Coatings Design and Development
Federal Award ID Number:1952030
DUNS ID:045207552
Parent DUNS ID:045207552
Program:I-Corps
Program Officer:
  • Andre Marshall
  • (703) 292-2257
  • awmarsha@nsf.gov

Awardee Location

Street:302 Buchtel Common
City:Akron
State:OH
ZIP:44325-0001
County:Akron
Country:US
Awardee Cong. District:11

Primary Place of Performance

Organization Name:University of Akron
Street:302 Buchtel Common
City:Akron
State:OH
ZIP:44325-0001
County:Akron
Country:US
Cong. District:11

Abstract at Time of Award

The broader impact/commercial potential of this I-Corps project is to provide a software products that can catalyze high throughput design in the chemicals, materials, and energy space. The initial focus is to drastically cut the time to market, and development costs of functional organic coatings, which are under both regulatory and consumer pressure to develop custom solutions without loss of performance. An adjacent market is composites development, heavily dependent on fillers for its needs for lightweight materials. The computer-aided engineering market will be approximately $13 billion by 2025, while the industrial coatings market by itself is currently worth $115 billion globally. The benefits of these high-throughput solutions for development of advanced polymeric materials include reduced toxins and energy consumption. This I-Corps project leverages the use of molecular markers to develop a novel engineering design software for functional coatings. Using machine learning to correlate molecular markers to percolation threshold, and thereby failure through barrier ingress, the software would provide a unique method to down-select formulations to the most promising subset. The “virtual lab” approach is akin to high-throughput drug discovery. Current methods in coatings development fail to provide a physico-chemical or molecular underpinning to field performance and eventual failure. The design of experiments for coating assessment and formulation development commonly utilizes statistical software. This separation from mechanistic understanding often results in trial and error approaches, based largely on chemical intuition; furthermore, this leads to multiple accelerated aging runs that add significant lead time for field deployment of a given formulation as sub-optimal solutions. Developed software would provide a significant leap in design that translates to tailored polymeric advanced materials beyond functional coatings. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

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