Session K01: Chemical Dynamics and Kinetics

Sub-ms translational and orientational dynamics of a freely moving single nanoprobe; theory and experiment

Observing 3D translational and orientational motion of single particles in solution at high time resolution is an outstanding problem in chemical physics. Orientation, encoded in a particle’s polar (θ) and azimuthal (ϕ) angles, impacts chemical reactivity and provides a sensitive report on the local environment, thus being relevant to chemical, biological, polymer and soft matter physics. One way to access the real-time orientation of a particle is to split the emitted/scattered light into multiple polarizations and to measure the light intensity at these.¹ What previous uses of this experiment lack is 3D translational motion. Most 3D localization methods cannot access a Z range far beyond the Rayleigh length of the illuminating light, limiting depth imaging to ~5 μm. Here, we show an experiment that measures 3D translation with a time and spatial resolution of 10 μs and ~10 nm in XYZ. It provides access to 15 μm of depth information, and measures 3D orientation with a time resolution of 250 μs and precisions of ~6° in ϕ and ~11° in θ.² These are compared with precision limits derived using information theory.³

1. J. T. Fourkas, Opt. Lett. 26, 211 (2001)

2. J. S. Beckwith and H. Yang, J. Phys. Chem B, In Peer Review

3. J. S. Beckwith and H. Yang, J. Chem. Phys. 155, 144110 (2021)   

Evaluating Homogeneous Catalysts for De-hydrogenation of Liquid Organic Hydrogen Carriers

In the times of an ever-increasing rate of global warming and rapidly depleting fossil fuels, renewable sources of energy are attracting vast attention in the scientific community. Numerous efforts have been directed towards switching over to a hydrogen-based economy with the aim of bringing down combustion-associated emissions. However, so far, the biggest technical challenge has been the development of materials and the required infrastructure for efficient storage and transportation of hydrogen. To this end, the use of Liquid Organic Hydrogen Carriers (LOHCs) has been the focus of a number of studies over the past couple of decades. Since the release of hydrogen from LOHCs requires the use of catalysts, a 'hydrogen economy' is only feasible when expensive, noble metal catalysts that are traditionally used in dehydrogenation reactions are substituted with inexpensive but equally efficient alternatives.

 

In this search for alternate catalysts, we employ our group’s cyberinfrastructure for data-driven discovery and design of new chemistry.  We have developed a computational protocol for the dehydrogenation reaction of LOHCs which encompasses identification of descriptors for the catalytic activities associated with these pathways. For the dehydrogenation of perhydro-N-ethyl carbazole as the model LOHC candidate, thermodynamic and kinetic parameters are calculated for various classes of pincer catalysts using Density Functional Theory. The results thus obtained are then benchmarked against experimental values. This protocol is then cast to virtual high-throughput screening of screening libraries generated from our group’s library generator package, ChemLG. The libraries created from ChemLG span several metal centers as well as side-chain substitutions on a base pincer catalyst. The massive data thus collected is then cast through our machine learning and informatics program package for the analysis of hidden structure-property relationships in these complex systems.   

Role of Physics in Environmental and Air Quality Research

Poor air quality resulting in “air pollution is slashing years from billions of people’s lives around the world and is a greater threat to life expectancy than smoking, HIV/AIDS or war” and disproportionately impacts low income and minority neighborhoods. Air quality research is highly interdisciplinary involves experimental laboratory and field (ground, aircraft, and satellite) research and theoretical research involving modeling. Physics plays a broad role, contributing directly to atmospheric and environmental projects and indirectly through basic research, providing technological spin-offs from research programs, and helping to educate a technically literate population capable of responding to environmental issues. Basic physics has played a central role and where it is crucial for further progress. This talk highlights work in our lab at NCAT devoted to the use of several spectroscopic techniques applied to the understanding the role of biomass burning (wildfire and domestic) emissions aerosol nanoparticles on climate, weather, air quality and health.  We investigate the role of photochemical aging, burning conditions, morphology, relative humidity on the optical and chemical properties  of these particles and emission factors of  particulate and gaseous emission and how these changes impact air quality and health.   

Kinetic and equilibrium competition of gases during mixture adsorption on planar surfaces

We present results of a Kinetic Monte Carlo investigation of the adsorption dynamics of a binary mixture of gases on a planar surface, with a focus on the role that molecular interactions play during equilibration. We show how increasing the strength of molecular interactions enhances the weaker species overshoot, a phenomenon driven by the faster adsorption rate of this species. Snapshots of the adsorbed mixture configurations as a function of time allow us to follow in detail the evolution of the system towards equilibrium, including the complete displacement of one species by the other. Clustering effects due to both molecular interactions and differences in the adsorption/desorption rates determine how the competition between the species for empty spaces on the surface takes place, leading to the final equilibrium composition. We compare our results to some available experimental measurements of the adsorption kinetics of argon/methane and nitrogen/methane mixtures of graphite.    

The role of partial charges in data-driven analytics of gas adsorption simulations

High-Throughput Computational Materials Screening has been used to evaluate the gas capture and storage potential of several thousands of crystalline nanoporous materials. These screening workflows often involve molecular-level computationally expensive techniques such as Grand Canonical Monte Carlo (GCMC). To avoid the computational cost of running Physics-based simulations for all candidate materials, one often tries to identify correlations between adsorption metrics and geometrical/topological descriptors obtained from inexpensive computational methods, and build a data-driven model to estimate the desired physical properties while avoiding the costly physics simulations. In the GCMC technique, the electrostatic energy plays an important role in the Boltzmann factor that determines the acceptance/rejection of a Monte Carlo move. Therefore, the assignment of partial atomic charges is a crucial step in any screening workflow. Many charge calculation methods exist, ranging from simpler charge-equilibration to DFT-derived DDEC methods, each one with associated computational cost (dis)advantages. In this work, we explore the cost-benefit balance over a range of charge calculation methods and the impact they cause in our ability to identify correlations and build data-driven predictive models for adsorption.   

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Vibrational dynamics of different ice phases and water in minerals have been widely studied by inelastic neutron scattering (INS). The observed intramolecular O-H stretching (390-460meV), and intermolecular translational (0-40meV) and librational (50-140meV) modes have been well described computationally. However, in ice-Ih the intramolecular bending mode peak (BMP, the “scissors mode”) H-O-H is very broad (from 175 to 235meV, FWHM=45meV), whereas that for water confined in minerals (e.g., beryl, cordierite, gypsum, bassanite, hemimorphite, mordenite) is much narrower (FWHM=4-11meV). In these minerals water either does not have hydrogen bonds (beryl, cordierite), or has ordered hydrogen bonds with the confinement. The BMP broadening in ice-Ih could be due to quantum effects but, if so, why do these quantum effects not affect the BMP for water in beryl, where water has been shown to exhibit quantum tunneling? Alternatively, this could be due to the proton-disordered structure of ice-Ih, but available calculations do not reproduce the BMP broadening. Recent INS spectra for D₂O ice-Ih, obtained to study the isotopic effect, and for proton-ordered H₂O ice-VIII, shed light on this question, and the results of these and parallel computer simulations will be discussed.   

Simulating quantum chemical dynamics on ion-trap quantum computers.

The accurate computational determination of chemical, materials, biological, and atmospheric properties has a critical impact on a wide range of health and environmental problems. However, such studies are deeply limited by the steep algebraic scaling of electron correlation methods, and the exponential scaling in studying quantum nuclear dynamics. We, here, present an algorithm for simulating quantum nuclear dynamics on a quantum device using the parameters of a generalized Ising Hamiltonian that can be realized on a spin-lattice quantum computer. Our method radically differs from the commonly used gate-based circuit implementation facilitated by spin-statistics based transformations for electronic structure problems in quantum chemistry. Here, the Born-Oppenheimer potential energy and the kinetic energy that constitute the quantum nuclear Hamiltonian are directly used to compute the on-site and inter-site parameters of the Ising Hamiltonian. This method, well in line with Feynman's vision of simulating a quantum system using another controllable quantum system, is demonstrated by mapping the proton-transfer dynamics in a short-strong hydrogen bonded system onto an ion-trap quantum computer.     

Brownian bridge method for stochastic chemical processes – An approximation method under diffusion limit

A Brownian bridge is a continuous random walk conditioned to end in a given region by adding an effective drift to guide paths towards the desired region of phase space. This idea has many applications in chemical physics where one wants to control the endpoint of a stochastic process – e.g., polymer physics, chemical reaction pathways, heat/mass transfer, and Brownian dynamics simulations. Despite its broad applicability, the biggest limitation of the Brownian bridge technique is that it is often difficult to determine the effective drift, as it comes from a solution of a Backwards Fokker Planck (BFP) equation that is infeasible to compute for complex or high-dimensional systems. This paper introduces a fast approximation method to generate a Brownian bridge process without solving the BFP equation explicitly. Specifically, the paper will use the asymptotic properties of the BFP equation to generate an approximate drift, and determine ways to correct (i.e., re-weight) any errors incurred from this approximation. Because such a procedure avoids the solution of BFP equation, we show that it drastically accelerates the generation of conditioned random walks and allows the generation of such processes to be scaled to higher dimensions and complex systems.   

New concept to improve salinity energy harvesting by capacitive layers.

To effectively combat global warming, it is necessary to increase the use of clean renewable energy. Blue energy is a less-known source with enormous potential since it can be generated directly by mixing the fresh and the sea water. However, current processes for extracting this energy remain inefficient. Indeed, the existing approaches such as the reverse electrodialysis or the pressure retarded osmosis are still not economically viable. Hopes for nonselective nanoporous charged membranes designed to reduce the internal resistance of the cell seem to be in vain. Here we present a novel solution that involves increasing the potential of the membrane by attaching tailored capacitive layers negatively charged on the surface to adsorb ions. Such a configuration allows us to double the potential of the cell without modifying the global ohmic resistance and thus to multiply by 4 the recoverable power.

After a thorough study carried out to characterize the process and optimise of the energy consumption caused by the hydraulic pressure drop, we introduce a centimeter-scale device with only one membrane. We manage to harvest a power density of 5.4 W/m² using brines solutions, which makes the system economically viable.   

Accurate detection of spherical objects in a complex background

The automated detection of particles in microscopy images has become a routinely used method for quantitative image analysis in biology, physics, and other research fields. While the majority of particle detection algorithms have been developed for bulk materials, the detection of particles in a heterogenous environment due to surfaces or other objects in the studied material is of great interest. However, particle detection is hindered by a complex background due to the diffraction of light resulting in a decreased contrast and image noise. We present a new heuristic method for the reliable detection of spherical particles that is based on the brightness and brightness gradient of the image and suppresses false detections due to a heterogenous background without additional background measurements. Further, we discuss methods to obtain particle coordinates with improved accuracy and compare with other methods, in particular with that of Crocker and Grier.   

Measurements and characterization of the dynamics of tracer particles in an actin network

The underlying physics governing the diffusion of a tracer particle in a viscoelastic material is a topic of some dispute. The long-term memory in the mechanical response of such materials should induce diffusive motion with a memory kernel, such as fractional Brownian motion (fBM). This is the reason that microrheology is able to provide the shear modulus of polymer networks. Surprisingly, the diffusion of a tracer particle in a network of a purified protein, actin, was found to conform to the continuous time random walk type (CTRW). We set out to resolve this discrepancy by studying the tracer particle diffusion using two different tracer particle sizes, in actin networks of different mesh sizes. We find that the ratio of tracer particle size to the characteristic length scale of a bio-polymer network plays a crucial role in determining the type of diffusion it performs. We find that the diffusion of the tracer particles has features of fBm when the particle is large compared to the mesh size, of normal diffusion when the particle is much smaller than the mesh size, and of the CTRW in between these two limits. Based on our findings, we propose and verify numerically a new model for the motion of the tracer in all regimes. Our model suggests that diffusion in actin networks consists of fBm of the tracer particle coupled with caging events with power-law distributed escape times.   

Insights from density functional theory into the formation and rotation of an enantiospecific assembly of molecular raffle wheels

We have performed a joint theoretical and experimental study of the assembly formed when BPP-COOH ( 2,6-bis(1H-pyrazol-1-yl)pyridine-4-carboxylic acid) is deposited on Ag(111). Three-fourths of the molecules form a rigid Kagome 'host' network. The cavities of this network are occupied by the remaining 'guest' molecules, which display a punctuated rotation between positions corresponding to global minima in the rotational energy landscape. Calculations show that the topography of this landscape can be explained by the making and breaking of hydrogen bonds between the guest molecules and the host network. The height of the rotational barrier computed theoretically is in excellent agreement with that extracted from temperature-dependent experiments. The host network also bestows enantiospecificity on the system, due to the twist between the host network and the underlying Ag(111) surface.   

Mechanical and ferroelectric properties of orthorhombic flourinated polyethylene

Fluorinated polyethylene (PE) is a class of polymers that has potential application for ferroelectric-based sensing that is mechanically pliable. By replacing either one or both hydrogens with fluorine on every other carbon on the PE chain one attains polyvinyl fluoride (PVF) or polyvinylidene fluoride (PVDF). The inter-strain cohesion in the polymer crystals is set primarily by the van der Waals (vdW) interaction, for which the vdW density functionals (vdW-DF) method provides a computationally efficient and robust description. In the present work we predict a candidate for the thermodynamically stable ground state PVF crystal, and we compare the structure, cohesion and elastic response for different orthorhombic PVDF and PVF crystal forms [1]. We also predict and compare the ferroelectric properties using the modern theory of polarization. Furthermore, we study the generalized stacking fault landscapes associated with slip to identify energy barriers for sliding dislocations [2]. Finally, we compute spatially resolved mappings of the exchange and correlation contributions to the crystal cohesion to discuss the nature of those barriers as well as of the shear resistance.