RII Track-4: NSF: Data-driven Computational and Machine Learning Assessment of Structure-Toxicity Relationship of Micro/NanoPlastics

In this project, funded by the NSF EPSCoR Program, Professor B. Rasulev of the Department of Coatings and Polymeric Materials at North Dakota State University is investigating properties and toxicity of environmental contaminants, such as microplastics and nanoplastics, by combination of experimental and computational methods. The developed new methodology within this project will offer an efficient and cost-effective prediction of microplastics and nanoplastics properties to assess a possible environmental impact of these contaminants.

An interdisciplinary collaboration between the PI and host institution at Rutgers University will put together state-of-the-art methods to reveal the main factors responsible for the biodegradation and toxicity of microplastics and nanoplastics in environment. The project aims to close the gap in understanding mechanisms of the micro-nanoplastics degradation and toxicity mechanism.

Based on results of this research the scientific community will develop an understanding in potential environmental impact of microplastics and various cost-effective ways for their toxicity assessment. The project lies at the interface of organic, materials chemistry, theoretical quantum chemistry, cheminformatics and data mining approaches, and is therefore well suited to the education of scientists at all levels.

The involved PIs in this project are also well-positioned to provide the highest level of education and training for STEM students underrepresented in science. Outreach activities involving tribal college students and K-12 students will also be part of the funded project.

This Research Infrastructure Improvement Track-4 EPSCoR Research Fellows (RII Track-4) project would provide a fellowship to an Assistant Professor and training for a graduate student at North Dakota State University (NDSU). The project is intended to investigate properties and toxicity of microplastics and nanoplastics by combination of experimental and computational methods that cover a complete integrated mechanistic study and development cycle of these environmental contaminants.

The research project will result in (1) experimental characterization of a set of microplastic/nanoplastic materials; (2) characterization of micro/nanoplastics by experimental and computational data; (3) structure-property/toxicity analysis; and (4) predictions and validation of structure-property models as a potential method for property and toxicity assessment of various microplastic/nanoplastic materials. A strong interdisciplinary collaboration between PI and host institution at Rutgers University will put together state-of-the-art methods to reveal the main factors responsible for the biodegradation and toxicity of microplastics and nanoplastics in environment.

All results from this multi-disciplinary investigation which focuses on the fundamental molecular mechanistic studies will be used as a foundation for the long-term collaboration with host institution and the PI. The project aims to close the gap in understanding mechanisms of the micro-nanoplastics degradation and toxicity mechanism, reveal the factors facilitating microplastics degradation and develop predictive models to assess the properties of various microplastic/nanoplastic materials.

The innovations of the project are two-fold: (1) Development of machine learning structure-property/toxicity relationship models to understand the physico-chemical properties, degradation and toxicity of investigated polymeric systems; (2) The developed methodology will allow a preliminary assessment of degradation and toxicity properties of new polymeric materials before their mass production. The methodology will be transformative to be applicable for structure-property relationship modeling of other important properties of polymeric materials, for further rational design.

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.