Hi there! I am

Siri P.

Chemical Engineering PhD Candidate @ UIUC.

I am a chemical engineer with a strong passion for computational simulation, machine learning, and quantum computing. My expertise involve computational polymer physics, numerical simulations, and modeling physical systems, along with a solid background in experimental inorganic chemistry. Beyond research, I’m deeply committed to teaching and mentoring students in STEM fields and always eager to connect, collaborate, and contribute to new advancements in science and technology. I’d love to hear from you if you're interested in connecting!

Resume
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About

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    Sirikarn (Siri) Phuangthong

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    sp55 at illinois dot edu

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    PhD candidate in Sing Lab. Computational polymer physicist specialized in modeling and simulation.

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    PhD and M.S in Chemical and Biomolecular Engineering at University of Illinois, Urbana-Champaign

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    August 2021 - present

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    Undergraduate researcher in Lanorio Lab. Experimental inorganic chemist specialized in organometallic compounds and ionic liquids.

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    B.S. in Chemistry and Physics, Minor in Mathematics at Illinois College

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    January 2018 - May 2021

Projects

  • Role of Surface Electrostatics in Particle-Polyelectrolyte (PE) Coacervation

    My PhD work investigates how changes in properties such as charge density of non-flexible charged species(particles), sequences of polyelectrolytes(PE), and salt concentration in the system impact phase behaviors of particle-PE coacervation. We implement both simulation methods, such as Monte Carlo simulations and Brownian dynamics simulations, and polymer field theory for many-body systems. By understanding phase behaviors of particle-PE coacervation in molecular scale, we can design more suitable materials to serve specific needs for industrial, environmental, and biological settings.

    • Modeling & Simulation
    • Polymer Physics
    • C
    • Python
    • June 2022 - Present

  • Comparison of Classical and Hybrid Quantum Neural Network in Tornado Damage Prediction

    Womanium Quantum+AI Bootcamp group project for climate change. Here, we created a classical neural network using TensorFlow and Keras and implemented a quantum layer using Pennylane to investigate the benefit of quantum properties in tornado damage prediction. This project was one of the finalists out of 128 projects.

    • Machine Learning
    • Quantum Computing
    • TensorFlow
    • Python
    • June 2024 - August 2024

  • Synthesis and Applications of Ionic Liquid

    The goal of this project was to investigate if green ionic liquids can serve as a replacement to volatile organic solvents commonly used in laboratories, as well as investigating possible catalytic properties. Imidazolium ionic liquids were synthesized and applied in Suzuki-Miyaura cross-coupling reactions to observe both its ability to serve as solvents and its catalytic properties. Molecular structures were obtained using Nuclear Magnetic Resonance, and cross-coupling products were purified through chromatograpy techniques.

    • Experimental Research
    • Inorganic Chemistry
    • Synthesis
    • Applications
    • May - July 2019

  • Synthesis of Air-Stable Ruthenium and Nickel Catalysts

    Air-stable Ruthenium and Nickel complexes were synthesized, characterized, and investigated for its catalytic properties. Schlenk techniques were executed for the synthesis of air-sensitive compounds, and characterization tools such as NMR, UV-Vis, and magnetic susceptibility balance were used.

    • Experimental Research
    • Inorganic Chemistry
    • Synthesis
    • Characterization
    • May - July 2018