Probing the structure and dynamics of gas-phase molecules with coincidence momentum imaging

dc.contributor.authorVenkatachalam, Anbu Selvam
dc.date.accessioned2026-01-26T17:43:03Z
dc.date.available2026-01-26T17:43:03Z
dc.date.graduationmonthMay
dc.date.issued2026
dc.description.abstractUnderstanding how molecular structure evolves following interaction with light is essential for explaining chemical reactivity and for developing strategies to control molecular transformations. Coincidence momentum imaging of charged fragments (ions and electrons), in particular Coulomb explosion imaging (CEI), provides a direct route to this information for gas-phase molecules by mapping nuclear geometry and dynamics onto fragment-ion momenta. This thesis covers four closely related research projects that span time-resolved and static CEI, high-dimensional multi-ion coincidence combined with machine learning, and ion–electron coincidence spectroscopy. First, ultrafast structural dynamics in diiodomethane (CH2I2) are explored using an ultraviolet (UV) pump–infrared (IR) probe CEI scheme. Momentum-resolved ion coincidence data reveal several competing dissociation pathways, including direct C–I bond fission, three-body breakup channels, and molecular iodine formation. Delay-dependent kinetic energy and angular observables provide evidence for a short-lived iso-CH2I2-like configuration that forms and decays on a sub-100 fs timescale. These measurements demonstrate how time-resolved CEI can isolate weak, transient configurations in the presence of other dominant reaction channels. Second, static CEI is used to establish three-dimensional momentum fingerprints of molecules relevant to ring-opening photochemistry. Strong-field Coulomb explosion of molecules such as isoxazole, 3-chloro-1-propanol, and epichlorohydrin yields multi-ion Newton maps that encode out-of-plane motion and angular correlations. Distinct momentum patterns associated with planar ring, open-chain, and ring–chain structures are identified and qualitatively reproduced by classical Coulomb explosion simulations. These results show that robust structural discrimination for medium-sized molecules is achievable with tabletop laser systems and motivate efforts toward complete coincidence detection for these molecules. Third, high-dimensional CEI with complete six-ion coincidence is combined with machine learning to extract structural information from complex momentum data. Dimensionality-reduction methods and density-based clustering separate cis- and trans-1,2-dichloroethylene (DCE) isomers directly from experimental mixtures, while supervised models trained on simulated data sets extend the analysis to additional geometries such as 1,1-DCE and transient twisted configurations. Eight-fold coincidence data for isoxazole illustrate how complete channels sharpen angular momentum and energy distributions. Together, these studies demonstrate that multi-coincidence CEI combined with machine learning can automatically identify subtle structural differences and minor channels from high-dimensional data. Finally, ion–electron coincidence measurements on core-ionized methyl iodide (CH3I) at a synchrotron beamline are used to connect inner-shell electronic decay to specific ionic fragmentation outcomes. Channel-resolved Auger–Meitner electron spectra, obtained by correlating electrons with well-defined ion channels, reveal systematic changes in band positions and widths as a function of final charge state and hydrogen loss. Comparison of two-body and three-body kinetic energy releases isolates contributions from channels involving neutral iodine fragments and highlights the sensitivity of the low energy electron spectrum to details of the fragmentation pathway. These measurements provide benchmark data for state resolved Auger decay and complement the laser-based CEI studies by adding an explicit electronic-structure perspective. Overall, this thesis advances coincidence momentum imaging as a structural and dynamical probe for polyatomic molecules. Time-resolved and static CEI establish momentum space fingerprints of transient and equilibrium geometries, high dimensional multi-coincidence measurements combined with machine learning enable event-level molecular structure differentiation, and ion–electron coincidence spectroscopy links core-level decay to fragmentation patterns. Together, these approaches lay the groundwork for future efforts to image and control molecular dynamics on their natural femtosecond timescales.
dc.description.advisorDaniel Rolles
dc.description.degreeDoctor of Philosophy
dc.description.departmentDepartment of Physics
dc.description.levelDoctoral
dc.description.sponsorshipThe operation of the J.R. Macdonald Laboratory are supported by the Chemical Sciences, Geosciences, and Biosciences Division, Office of Basic Energy Sciences, Office of Science, U.S. Department of Energy, Grant grant no. DE-FG02-86ER13491. Supported by the National Science Foundation (NSF) under Grant Nos. PHYS-1753324 and PHYS-2409365. The machine learning aspect of this work was supported by a GRIPex award from Kansas State University.
dc.identifier.urihttps://hdl.handle.net/2097/47065
dc.language.isoen_US
dc.subjectCoulomb explosion imaging
dc.subjectCoincidence momentum imaging
dc.subjectPump–probe spectroscopy
dc.subjectMachine learning
dc.titleProbing the structure and dynamics of gas-phase molecules with coincidence momentum imaging
dc.typeDissertation

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