(287j) Synergy of Nanomaterials and Deep Learning for Advancing Material Science

• Paper 262e: "Deep Learning-Based Segmentation of Complex Microparticles in Scanning Electron Microscopy Images", Monday November 6, 1:50-2:10 pm, Discovery 44 (https://aiche.confex.com/aiche/2023/meetingapp.cgi/Paper/666874)

Research Interests

As an experimental Materials Scientist, I have been focusing on the development of multifunctional nanomaterials, their comprehensive optical characterization, and studying nano-bio interactions. By training, I am an experimental researcher but COVID-19 lockdowns motivated me to get experience in computational simulations of optical properties of nanoparticles and machine learning. Currently, I am studying the application of deep learning for detection and classification of complex objects in electron microscopy images. By leveraging my diverse expertise, I thrive on tackling really hard research and engineering problems to develop impactful technologies for better healthcare, next-generation optoelectronics, and advanced materials.

Key Technical Skills

• Nanomaterials preparation: synthesis and post-synthetic treatment, surface modification, self-assembly, hybrid nanostructures formation
• Nanoparticles: quantum dots (CdSe, CdSe/ZnS, CdSe/CdS, ZnS:Mn, AgInS, AgInS/ZnS), quantum dots-in-rods (CdSe/CdS), magnetic nanoparticles (CoFe2O4), mesoporous silica nanoparticles, carbon dots
• Drug delivery platform: nanoparticle-based platforms for the delivery of photosensitizer molecules (photodynamic cancer therapy)
• Material characterization expertise: ultraviolet-visible spectroscopy, photoluminescence spectroscopy (steady-state and time-resolved), circular dichroism spectroscopy, Fourier-transform infrared spectroscopy, dynamic light scattering, zeta-potential
• Biomedical sciences: cytotoxicity studies on lung carcinoma epithelial cells line (A549), investigation of the neutrophil extracellular traps (NETs) formation using human data
• Computational simulations: modeling of optical properties and chiroptical activity of nanoparticles in COMSOL Multiphysics
• Data science and machine learning: Python, data collection/analysis, deep learning for image analysis

Selected publications (5 out of 21)

• Visheratina A, Visheratin A, Kumar P, Veksler M, Kotov NA. Chirality Analysis of Complex Microparticles using Deep Learning on Realistic Sets of Microscopy Images. ACS Nano. 2023, 14;17(8):7431-42.
• Kumar P, Vo T, Cha M, Visheratina A, Kim JY, Xu W, Schwartz J, Simon A, Katz D, Nicu VP, Marino E. Photonically active bowtie nanoassemblies with chirality continuum. Nature. 2023, 16;615(7952):418-24.
• Visheratina A, Hesami L, Wilson AK, Baalbaki N, Noginova N, Noginov MA, Kotov NA. Hydrothermal synthesis of chiral carbon dots. Chirality. 2022, 34(12):1503-14.
• Visheratina A, Kumar P, Kotov N. Engineering of inorganic nanostructures with hierarchy of chiral geometries at multiple scales. AIChE Journal. 2021, 68:e17438.
• Visheratina A, Kotov NA. Inorganic nanostructures with strong chiroptical activity. CCS Chemistry. 2020, 1;2(3):583-604.