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

在这个由NSF EPSCoR项目资助的项目中，B教授。北达科他州州立大学涂料和聚合物材料系的Rasulev正在通过实验和计算方法相结合的方式，研究微塑料和纳米塑料等环境污染物的性质和毒性。该项目中开发的新方法将提供一种有效且具有成本效益的微塑料和纳米塑料特性预测，以评估这些污染物可能对环境造成的影响。PI和罗格斯大学主办机构之间的跨学科合作将汇集最先进的方法，以揭示环境中微塑料和纳米塑料生物降解和毒性的主要因素。该项目旨在缩小在理解微纳米塑料降解机制和毒性机制方面的差距。根据这项研究的结果，科学界将了解微塑料的潜在环境影响以及各种具有成本效益的毒性评估方法。该项目位于有机，材料化学，理论量子化学，化学信息学和数据挖掘方法的接口，因此非常适合各级科学家的教育。参与该项目的PI也有能力为科学领域代表性不足的STEM学生提供最高水平的教育和培训。涉及部落大学生和K-12学生的外联活动也将是资助项目的一部分。 这个研究基础设施改善轨道-4 EPSCoR研究员（RII轨道-4）项目将提供奖学金，以助理教授和培训研究生在北达科他州州立大学（NDSU）。该项目旨在通过实验和计算方法相结合来研究微塑料和纳米塑料的特性和毒性，这些方法涵盖了这些环境污染物的完整综合机制研究和开发周期。该研究项目将导致（1）一组微塑料/纳米塑料材料的实验表征;（2）通过实验和计算数据表征微/纳米塑料;（3）结构-性能/毒性分析;和（4）预测和验证结构-性能模型作为各种微塑料/纳米塑料材料的性能和毒性评估的潜在方法。PI与罗格斯大学主办机构之间的跨学科合作将汇集最先进的方法，以揭示环境中微塑料和纳米塑料生物降解和毒性的主要因素。这项多学科研究的所有结果都集中在基础分子机制研究上，将作为与主办机构和PI长期合作的基础。该项目旨在缩小在理解微纳米塑料降解机制和毒性机制方面的差距，揭示促进微塑料降解的因素，并开发预测模型以评估各种微塑料/纳米塑料材料的性能。该项目的创新有两个方面：（1）开发机器学习结构-性能/毒性关系模型，以了解所研究的聚合物系统的物理化学性质，降解和毒性;（2）开发的方法将允许在大规模生产之前对新聚合物材料的降解和毒性进行初步评估。该方法将是变革性的，适用于聚合物材料的其他重要性能的结构-性能关系建模，以进一步合理的design.This奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

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.