PhD Research Projects
During my PhD, I performed classical molecular dynamics (MD) simulations and developed machine learning algorithms to predict the effect of parameter selection on system properties for two industrially relevant areas, guiding the selection of gold nanoparticle parameters for biomedical applications and solvent composition for new biomass conversion processes.
Application 1: Screening Gold Nanoparticle Properties for Biomedical Applications
Monolayer-protected gold nanoparticles (GNP) are promising synthetic materials applicable in many fields (e.g. drug delivery, pollutant detection, etc.) because of their ease of fabrication, detectability, and tunable parameters. GNPs are often coated by self-assembled monolayers (SAMs), a set of ligands that consist of a sulfur head group, carbon backbone, and terminal end groups. SAM-protected GNPs have a vast design space (e.g. gold core shape, size, and ligand selection), making it challenging to predict how these parameters may affect GNP interactions with other molecules, such as proteins. I hypothesize that MD simulations could help guide parameter selection by computing MD-descriptors that account for variations in GNP parameters could predict GNP interactions with biomolecules.
I developed a generalized system preparation workflow that could model a wide-range of SAM-protected GNPs. Then, I developed MD-descriptors that account for collective interactions (e.g. formation of bundles) and encode GNP surface properties (e.g. hydrophilicity). These MD-descriptors are advantageous because they account for ligand-ligand and ligand-water interactions that are missed in conventional descriptors, such as single-ligand octanol-water partition coefficients (logP). Using data-centric tools (e.g. principal component analysis), MD-descriptors could accurately predict GNP behavior in the body, such as immune response, which outperform prediction models using single-ligand descriptors (logP). I am also collaborating with experimentalists (Prof. Pedersen group, UW-Madison) to identify new mechanisms for GNP-lipid membrane interactions and developing MD-descriptors that could predict GNP behaviors more broadly. My computational models enable cost-efficient screening of GNP properties that could be tuned for selective biomolecular interactions, useful in a wide-range of applications.
A. K. Chew and R. C. Van Lehn. “Effect of Core Morphology on the Structural Asymmetry of Alkanethiol Monolayer-Protected Gold Nanoparticles.” J. Phys. Chem. C 2018, 122 (45), 26288-26297. [Link] [Data]
A. K. Chew, B. C. Dallin, and R. C. Van Lehn. “Interplay of Ligand Properties and Core Size Dictate the Hydrophobicity of Monolayer-Protected Gold Nanoparticles.” ACS Nano. 2021,15, 3, 4534–4545. [Link]
C. A. Lochbaum*, A. K. Chew*, X. Zhang, V. Rotello, R. C. Van Lehn, and J. A. Pedersen. “The lipophilicity of cationic ligands promotes irreversible adsorption of nanoparticles to lipid bilayers.” ACS Nano. 2021, 15, 4, 6562–6572. [Link]
Application 2: Solvent Screening for Biomass Conversion Reactions
Lignocellulosic biomass (organic material from plants) is a promising renewable, sustainable resource that can be converted into transportation fuels or commodity chemicals. Biomass conversion is performed through acid-catalyzed reactions in aqueous solution, but the process is hindered by low reactivity and poor selectivity. One way to improve reactivity is to modify the solvent composition by mixing water with organic cosolvents, which have been shown to improve the reactivity of biomass conversion reactions by 100-fold compared to the same reactions in pure water. However, solvent selection is challenging, so I hypothesized that computational tools can help guide solvent selection by gaining physical insight into the effects of solvent composition on reaction rates and selectivities.
I developed classical MD simulations that modeled biomass-derived reactants in mixtures of water with polar aprotic cosolvent in collaboration with experimentalists (Profs. Huber and Dumesic groups, UW-Madison). I found that inclusion of polar aprotic cosolvents leads to the formation of water-enriched local domains around hydrophilic reactants, which draws the acid catalyst to these regions due to preferred catalyst-water interactions and results in improved reaction rates. By quantifying the extent of water-enrichment around the reactant, I found that MD-descriptors can accurately predict experimental reaction rates for dioxane-water mixtures, showing that classical simulation techniques can inform reaction rates without modeling the reaction mechanism or the catalyst. I also used MD to gain physical insight into how solvents influence selectivities in biomass conversion reactions and combined these tools into a generalized workflow for screening solvents in biomass-related reactions.
A. K. Chew*, T. W. Walker*, B. Demir, Z. Shen, L. Witteman, J. Euclide, G. W. Huber, J. A. Dumesic, and R. C. Van Lehn. “Effect of mixed-solvent environments on the selectivity of acid-catalyzed dehydration reactions.” ACS Catalysis. 2020, 10, 1679-1691. [Link]
T. W. Walker*, A. K. Chew*, R. C. Van Lehn, J. A. Dumesic, and G. W. Huber. “Rational Design of Mixed Solvent Systems for Acid-Catalyzed Biomass Conversion Processes Using a Combined Experimental, Molecular Dynamics and Machine Learning Approach.” Topics in Catalysis 2020. [Link]
A. K. Chew, S. Jiang, W. Zhang, V. M. Zavala, and R. C. Van Lehn. “Fast Predictions of Liquid Acid-Catalyzed Reaction Rates Using Molecular Dynamics Simulations and Convolutional Neural Networks.” Chemical Science. 2020. [Link]