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Wang, Xijie

Publications and source records attributed to Wang, Xijie.

UV-Induced Reaction Pathways in Bromoform Probed with Ultrafast Electron Diffraction

For many chemical reactions, it remains notoriously difficult to predict and experimentally determine the rates and branching ratios between different reaction channels. This is particularly the case for reactions involving short-lived intermediates, whose observation requires ultrafast methods. The UV photochemistry of bromoform (CHBr 3 ) is among the most intensely studied photoreactions. Yet, a detailed understanding of the chemical pathways leading to the production of atomic Br and molecular Br 2 fragments has proven challenging. In particular, the role of isomerization and/or roaming and their competition with direct C–Br bond scission has been a matter of continued debate. Here, in this work, gas-phase ultrafast megaelectronvolt electron diffraction (MeV-UED) is used to directly study structural dynamics in bromoform after single 267 nm photon excitation with femtosecond temporal resolution. The results show unambiguously that isomerization contributes significantly to the early stages of the UV photochemistry of bromoform. In addition to direct C–Br bond breaking within <200 fs, formation of iso-CHBr 3 (Br-CH-Br-Br) is observed on the same time scale and with an isomer lifetime of >1.1 ps. The branching ratio between direct dissociation and isomerization is determined to be 0.4 ± 0.2:0.6 ± 0.2, i.e., approximately 60% of molecules undergo isomerization within the first few hundred femtoseconds after UV excitation. The structure and time of formation of iso-CHBr 3 compare favorably with the results of an ab initio molecular dynamics simulation. The lifetime and interatomic distances of the isomer are consistent with the involvement of a roaming reaction mechanism.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH

Tracking dissociation pathways of nitrobenzene via mega-electron-volt ultrafast electron diffraction

As the simplest nitroaromatic compound, nitrobenzene is an interesting model system to explore the rich photochemistry of nitroaromatic compounds. Previous investigations of nitrobenzene's photochemical dynamics have probed structural and electronic properties. These investigations paint, at times, a convoluted and sometimes contradictory description of the photochemical landscape. Here, we investigate the ultrafast dynamics of nitrobenzene triggered by photoexcitation at 267 nm for the first time using a structural probe with femtosecond time resolution. Our probe complements previous measurements of nitrobenzene's electronic structure evolution and aids in determining the photochemical dynamics with less ambiguity. We employ megaelectronvolt ultrafast electron diffraction to follow nitrobenzene's structural evolution within the first 5 ps after photoexcitation. We observe ground state recovery within 160 ± 60 fs through nonadiabatic dynamics. Based on comparisons of the experimental signal with molecular dynamics simulations, we exclude a significant population of the triplet manifold. Furthermore, we do not observe fragmentation of nitrobenzene within the investigated time window, which indicates that previously observed photofragmentation reactions take place in the vibrationally 'hot' ground state on timescales considerably beyond 5 ps.

74 ATOMIC AND MOLECULAR PHYSICS

Multi-objective Bayesian active learning for MeV-ultrafast electron diffraction

Ultrafast electron diffraction using MeV energy beams(MeV-UED) has enabled unprecedented scientific opportunities in the study of ultrafast structural dynamics in a variety of gas, liquid and solid state systems. Broad scientific applications usually pose different requirements for electron probe properties. Due to the complex, nonlinear and correlated nature of accelerator systems, electron beam property optimization is a time-taking process and often relies on extensive hand-tuning by experienced human operators. Algorithm based efficient online tuning strategies are highly desired. Here, we demonstrate multi-objective Bayesian active learning for speeding up online beam tuning at the SLAC MeV-UED facility. The multi-objective Bayesian optimization algorithm was used for efficiently searching the parameter space and mapping out the Pareto Fronts which give the trade-offs between key beam properties. Such scheme enables an unprecedented overview of the global behavior of the experimental system and takes a significantly smaller number of measurements compared with traditional methods such as a grid scan. This methodology can be applied in other experimental scenarios that require simultaneously optimizing multiple objectives by explorations in high dimensional, nonlinear and correlated systems.

43 PARTICLE ACCELERATORS