When 1:00 PM - 3:00 PM Jul 14, 2022
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PhD defense: “Application of Data-Driven Analysis to Chiral Nanomaterials and Surfaces”


Minjeong Cha

The complex geometry of materials on different scales serves a critical role in determining their chemical, biological and optical properties. Therefore, understanding, measuring, and analyzing materials’ multidimensional structures are significant when designing functional materials for a specific application. However, these dependencies can be exceptionally complex, and their identification in the form of traditional physical laws can be daunting and unproductive. In this dissertation, we explore the applications of data driven analysis for such cases (1); 1) prediction of complex interactions between inorganic nanoparticles and proteins, 2) quantification of the degree of chirality for multidimensional nanomaterials using graph theory, 3) identification and prediction of chiral materials with a polarimetric data analysis (2, 3).

Biomimetic nanoparticles (NPs) are known to serve as nanoscale adjuvants, enzyme mimics, and amyloid fibrillation inhibitors. Their further development requires a better understanding of their interactions with proteins. However, the lack of comprehensive knowledge about nanoparticle-protein interactions impedes the prediction and design of NPs for biomedical applications. In Chapter 2 of this dissertation, the extensive protein-protein interactions (PPIs) database is utilized as a guide for designing protein-NP assemblies. To build unified descriptors that can be used for both biological and inorganic nanostructures, we developed and adopted geometrical (GE) and graph-theoretical (GT) descriptors. We found that GE and GT descriptors can predict interaction sites in protein pairs with accuracy >80% and classification probability ~90%. We extended the PPI-trained machine learning algorithms to inorganic biomimetic NPs and found a nearly exact match between experimental and predicted interaction sites with proteins(1).

Chirality is one of the essential geometrical properties not only in organic molecules, such as amino acids in peptides but in inorganic materials. Many physical, chemical, biological, and other properties are related to chirality as well as a multitude of applications. However, the direct relationships between chirality and properties, e.g., optical, or biological activity, are known to be challenging to establish. The problems include property-adequate quantification of chirality in different scales of molecular structures, limitations for sign-variable chirality continuum, and prohibitively demanding computations for molecular structures including thousands of atoms. In Chapter 3 of this dissertation, utilizing the concept of torsion in differential geometry to the graph representation of molecules, we present a new method to quantify chirality that can address these limitations and apply to different chemical compounds. We validate this method with chiral gold clusters and hierarchical multiscale chiral structures. Comparing the degree of chirality with signs, we confirmed that the chirality is caused by torsion from symmetry breaking. Also, from the 3D helixes model studies, we prove the relation between graph-theoretical chirality (GTC) and optical activities. 

Finally, Chapter 4 of this dissertation presents the multidimensional structural measurement of pattern printed chiral NPs and their signature optical activities using the polarization modulated light detection and ranging (LIDAR) devices. The printed left- and right-handed bowtie particles are utilized in printing photonically active metasurfaces and identified by their high contrast positive/negative polarization signature(3).

 

1. M. Cha, E. S. T. Emre, X. Xiao, J. Y. Kim, P. Bogdan, J. S. VanEpps, A. Violi, N. A. Kotov, Unifying structural descriptors for biological and bioinspired nanoscale complexes. Nature Computational Science 2022 2:4. 2, 243–252 (2022).

2. Nicholas. A. Kotov, S. Glotzer, B. Shahbazian, R. Branch, L. Xu, W. Choi, M. Cha, M. Spelling, MATERIAL - SENSING LIGHT IMAGING , DETECTION , AND RANGING ( LIDAR ) SYSTEMS (US 2021/0231852 A1 ) (2021), pp. 1–11.

3. N. Kotov, P. Kumar, T. Vo, M. Cha, A. Visheratina, J.-Y. Kim, W. Xu, J. Schwartz, A. Simon, D. Katz, E. Marino, J. Choi, S. Chen, C. Murray, R. Hovden, S. Glotzer, Photonically Active Bowtie Nanoassemblies with Chirality Continuum (2022), doi:10.21203/RS.3.RS-1614619/V1.

Zoom link: https://umich.zoom.us/j//96825408986