Session P58: Journal of Chemical Physics Editor's Choice Awards

Electron transfer in confined electromagnetic fields

The interaction between molecular (atomic) electron(s) and the vacuum field of an optical cavity has drawn significant attention thanks to the rapid developments in nano-optics. Such interaction which is a subject of cavity quantum electrodynamic can substantially affect transport properties of molecular systems. In this work we consider non-adiabatic electron transfer process in the presence of a cavity mode. We present a generalized framework for the interaction between a charged molecular system and a quantized electromagnetic field of a cavity and apply it to the problem of electron transfer between a donor and an acceptor placed in a confined vacuum electromagnetic field. The effective system Hamiltonian corresponds to a unified Rabi and spin-boson model which includes a self-dipole energy term. Two limiting cases are considered: one where the electron is assumed to be much faster than the cavity mode and another in which the electron tunneling time is significantly larger than the mode period. In both cases a significant rate enhancement can be produced by coupling to the cavity mode in the Marcus inverted region. The results of this work offer new possibilities for controlling electron transfer processes using visible and infrared plasmonics
  

Laser-based imaging of gas phase molecules

Watching a molecule at work, the so-called making a molecular movie, is a long standing
goal in the physical chemistry community. With the recent advent of X-ray free electron
lasers and relativistic electron guns, femtosecond diffractive imaging studies with intense
short-wavelength pulses and with ultrashort electron bunchs became possible. In this talk,
I will discuss alternative routes based on strong field ionization by intense femtosecond
laser pulses to image molecular structures and dynamics. Two techniques, namely laser-
induced Coulomb explosion imaging and laser-induced electron diffraction, will be
presented and recent applications of these techniques to the retrieval of molecular
structure and ultrafast molecular dynamics will be provided.   

Diffusion, plasticity, and excess entropy in complex colloidal fluids

Excess entropy is the difference between the “true” system entropy and that of an equivalent ideal gas. Rosenfeld first observed that the diffusivity and viscosity of simple liquids scale exponentially with excess entropy. This intriguing discovery had motivated numerous studies of excess entropy scaling in a wide variety of materials spanning different particle type, size, density, interaction, temperature, and even shear rate, most of which are computer simulation work. In this talk, I will discuss our recent experiments utilizing excess entropy concept to understand the dynamics of complex fluids. In the first experiment, we investigate the structure and dynamics in dense colloidal fluids with tunable short-range attractions. From particle trajectory data we measure the two-body excess entropy and the long-time diffusion coefficients from samples with different packing fractions and attraction strengths. These results are found to follow Rosenfeld’s excess entropy scaling that is independent of sample packing fraction and attraction strength. In the second experiment, we study plastically deformed colloidal solids. We directly imaged the dynamics and micro-structure induced by plastic shear flow in a series of amorphous solids under oscillatory shear. Data reveal novel scaling relationships between plastic flow, viscous response, and excess entropy. These findings extend the application of excess entropy concept from equilibrium to nonequilibrium systems.   

Markov state models of peptide aggregation

Markov state models have become popular in the computational biophysics community as
a technique for identifying stationary and kinetic information of protein dynamics from
molecular dynamics (MD) simulation data. We extended the applicability of automated
Markov state modeling to simulation data of molecular self-assembly by constructing
collective coordinates from molecular descriptors that are invariant to permutations of
molecular indexing. Understanding molecular self-assembly is of critical importance if we
want to deepen our understanding of neurodegenerative diseases where the aggregation
of disordered peptides or misfolded proteins is thought to be the main culprit. I will present
the Markov state models that we obtained for the self-assembly of different peptides and
demonstrate that the Markov state models clearly map out the different aggregation
pathways, something which has not been possible with standard MD analysis tools.