CAREER: Investigating Nanosheets of Thermosensitive Polymer Grafted Nanoparticles: Integrated Research and Education

NONTECHNICAL SUMMARY

This award supports computational and theoretical research, and education aimed to advance understanding of nanomaterials and how they self-assemble. Different nanomaterials with various structures, properties, and functions have been developed by employing self-assembly approaches that spontaneously organize atoms or molecules into ordered structures.

A new class of emerging nanomaterials are sheets made of highly ordered metallic nanoparticle arrays separated by attached organic molecular chains. Spacing between these nanoparticles largely determines their functional properties such as optical, catalytic, and electronic. Mechanical properties such as strength and flexibility often determine whether a nanosheet can be used in an application, such as an optoelectronic or biomedical device.

Currently, the ability to design 2D nanosheets with desired spacing between nanoparticles and mechanical properties remains an open challenge. The PI aims to use simulation to develop design rules for controlling the spacing between the nanoparticles, and the mechanical properties of 2D nanosheets. The insights obtained from this research are aimed to help guide experimental efforts to engineer 2D nanosheets that possess desired functional and mechanical properties.

This project includes an integrated education and outreach plan that leverages results and software tools generated in the course of the research. The plan is intended to improve student training and enhance diversity. The educational activities will focus on integrating machine learning methods and data science concepts into the undergraduate and graduate curriculum.

In addition, an online three-week course will be developed; it will focus on applications of artificial intelligence in science and engineering for working professionals from industry as the target audience. The research team will develop virtual reality software, which will be used to stimulate the interest of middle- and high-school students in pursuing careers in science, technology, engineering, and mathematics.

TECHNICAL SUMMARY

This award supports computational and theoretical research, and education on two-dimensional (2D) monolayered nanosheets of functionalized nanoparticles, which are composed of highly ordered, hexagonally packed inorganic nanoparticle arrays separated by grafted organic ligands. These nanosheets have emerged as a fundamentally new class of nanomaterials.

The nanoparticle packing and mechanical properties of these nanosheets are important in determining their suitability for applications ranging from optoelectronics to biomedical fields. Packing controls functionality, for example optical, and catalytic properties of the nanosheets. However, due to a lack of a direct molecular-level characterization technique to study the structure of these nanosheets and lack of availability of accurate computational models, our ability to design 2D nanosheets with required nanoparticle packing and mechanical properties is limited.

The PI aims to establish a fundamental molecular-level understanding of the nanoparticle packing and mechanical properties of 2D nanosheets of thermosensitive polymer functionalized nanoparticles using multi-scale simulation models. A machine learning accelerated optimization framework will be used to develop transferable and compatible coarse-grained models of thermosensitive polymers, metals, and solvents.

These models will be employed to perform hypothesis-driven calculations aimed to address fundamental questions about the effect of polymer characteristics, nanoparticle design parameters, and metal type on the nanoparticle packing and mechanical properties of the nanosheets obtained through self-assembly of polymer grafted nanoparticles. The successful completion of this research would establish design rules for multifunctional nanosheets with desired nanoparticle packing and mechanical properties.

The integrated education plan is directly tied to these research activities. The PI intends to incorporate artificial intelligence into the undergraduate and graduate curriculum by introducing machine learning methods to engineering students. The PI will develop an online three-week course with focus on applications of artificial intelligence in science and engineering for working professionals from industry as the target audience.

As a part of integrated outreach activities, the research team will develop a "4-D Nanosimulator", which creates an interactive virtual reality environment that can be used to enter the world of atoms through simulation trajectories. This software will be used to engage middle- and high-school students and stimulate their interest in pursuing careers in science, technology, engineering, and mathematics.

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.