Date of Award

2026-05-01

Degree Name

Doctor of Philosophy

Department

Chemistry

Advisor(s)

Lela Vukovic

Abstract

Single-walled carbon nanotubes (SWCNTs) exhibit chirality-dependent optoelectronic properties and near-infrared fluorescence that make them promising platforms for biosensing. When functionalized with single-stranded DNA (ssDNA), SWCNTs form conjugates capable of selective molecular recognition. However, the molecular mechanisms governing chirality recognition and analyte-induced optical modulation remain incompletely understood. This dissertation investigates these mechanisms through an integrated computational approach combining molecular dynamics simulations, enhanced sampling techniques, and machine learning. First, atomistic molecular dynamics (MD) and replica exchange MD simulations were used to investigate how short ssDNA sequences interact with enantiomers of (7,5) SWCNTs. Analyses of base stacking, nucleotide orientation, sugar-phosphate positioning, and contact areas revealed that differences in stacking geometry and sugar conformations contribute to enantioselective recognition of nanotubes, providing mechanistic insight into how chiral DNA molecules can bind differently to nanotube enantiomers. Second, microsecond-scale MD simulations of (GT)6-(9,4) SWCNT nanosensors in the presence of catecholamine-like analytes were performed to elucidate the structural origins of fluorescence modulation. Analyte binding modes, residence times, hydrogen bonding patterns, and electrostatic interactions were correlated with experimentally measured changes in optical responses of nanosensors to the analytes (labeled as ΔF/F). The results demonstrate that analyte charge, functional group identity, and spatial arrangement critically influence interactions of analytes within the DNA corona and with the nanotube surface, thereby modulating optical output. Finally, support vector machine models were developed to predict analyte-induced fluorescence changes using molecular fingerprints and quantum chemical descriptors. The regression and classification models, trained on experimentally acquired datasets, capture structure-response relationships and enable predictive identification of high-response chemotypes. My results also indicate that it is important to obtain and use larger experimental datasets if more predictive models are required. Overall, this work establishes a multiscale framework linking atomic-level interactions to experimentally observable optical behavior in DNA-SWCNT nanosensors, advancing mechanistic understanding and rational sensor design.

Language

en

Provenance

Received from ProQuest

File Size

144 p.

File Format

application/pdf

Rights Holder

Sayantani Chakraborty

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Chemistry Commons

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