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At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Excitons: Energetics and spatiotemporal dynamics

The concept of an exciton as a quasiparticle that represents collective excited states was originally adapted from solid-state physics and has been successfully applied to molecular aggregates by relying on the well-established limits of the Wannier exciton and the Frenkel exciton. However, the study of excitons in more complex chemical systems and solid materials over the past two decades has made it clear that simple concepts based on Wannier or Frenkel excitons are not sufficient to describe detailed excitonic behavior, especially in nano-structured solid materials, multichromophoric macromolecules, and complex molecular aggregates. In addition, important effects such as vibronic coupling, the influence of charge-transfer (CT) components, spin-state interconversion, and electronic correlation, which had long been studied but not fully understood, have turned out to play a central role in many systems. This has motivated new experimental approaches and theoretical studies of increasing sophistication. This article provides an overview of works addressing these issues that were published for A Special Topic of the Journal of Chemical Physics on ``Excitons: Energetics and spatio-temporal dynamics" and discusses their implications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Machine learning-based ethylene concentration estimation, real-time optimization and feedback control of an experimental electrochemical reactor

With the increase in electricity supply from clean energy sources, electrochemical reduction of carbon dioxide (CO 2 ) has received increasing attention as an alternative source of carbon-based fuels. As CO 2 reduction is becoming a stronger alternative for the clean production of chemicals, the need to model, optimize and control the electrochemical reduction of the CO 2 process becomes inevitable. However, on one hand, a first-principles model to represent the electrochemical CO 2 reduction has not been fully developed yet because of the complexity of its reaction mechanism, which makes it challenging to define a precise state-space model for the control system. On the other hand, the unavailability of efficient concentration measurement sensors continues to challenge our ability to develop feedback control systems. Gas chromatography (GC) is the most common equipment for monitoring the gas product composition, but it requires a period of time to analyze the sample, which means that GC can provide only delayed measurements during the operation. Moreover, the electrochemical CO 2 reduction process is catalyzed by a fast-deactivating copper catalyst and undergoes a selectivity shift from the product-of-interest at the later stages of experiments, which can pose a challenge for conventional control methods. To this end, machine learning (ML) techniques provide a potential approach to overcome those difficulties due to their demonstrated ability to capture the dynamic behavior of a chemical process from data. Motivated by the above considerations, we propose a machine learning-based modeling methodology that integrates support vector regression and first-principles modeling to capture the dynamic behavior of an experimental electrochemical reactor; this model, together with limited gas chromatography measurements, is employed to predict the evolution of gas-phase ethylene concentration. The model prediction is directly used in a proportional-integral (PI) controller that manipulates the applied potential to regulate the gas-phase ethylene concentration at energy-optimal set-point values computed by a real-time process optimizer (RTO). Specifically, the RTO calculates the operation set-point by solving an optimization problem to maximize the economic benefit of the reactor. Finally, suitable compensation methods are introduced to further account for the experimental uncertainties and handle catalyst deactivation. The proposed modeling, optimization, and control approaches are the first demonstration of active control for a CO 2 electrolyzer and contribute to the automation and scale-up efforts for electrified manufacturing of fuels and chemicals starting from CO 2 .

42 ENGINEERING↗
DOE OSTI2021

Ab Initio Direct Dynamics

The reactivity and dynamics of molecular systems can be explored computationally by classical trajectory calculations. The traditional approach involves fitting a functional form of a potential energy surface (PES) to the energies from a large number of electronic structure calculations and then integrating numerous trajectories on this fitted PES to model the molecular dynamics. The ever-decreasing cost of computing and continuing advances in computational chemistry software have made it possible to use electronic structure calculations directly in molecular dynamics simulations without first having to construct a fitted PES. In this “on-the-fly” approach, every time the energy and its derivatives are needed for the integration of the equations of motion, they are obtained directly from quantum chemical calculations. This approach started to become practical in the mid-1990s as a result of increased availability of inexpensive computer resources and improved computational chemistry software. The application of direct dynamics calculations has grown rapidly over the last 25 years and would require a lengthy review article. The present Account is limited to some of our contributions to methods development and various applications. To improve the efficiency of direct dynamics calculations, we developed a Hessian-based predictor-corrector algorithm for integrating classical trajectories. Hessian updating made this even more efficient. Furthermore, this approach was also used to improve algorithms for following the steepest descent reaction paths. For larger molecular systems, we developed an extended Lagrangian approach in which the electronic structure is propagated along with the molecular structure. Strong field chemistry is a rapidly growing area, and to improve the accuracy of molecular dynamics in intense laser fields, we included the time-varying electric field in a novel predictor-corrector trajectory integration algorithm. Since intense laser fields can excite and ionize molecules, we extended our studies to include electron dynamics. Specifically, we developed code for time-dependent configuration interaction electron dynamics to simulate strong field ionization by intense laser pulses. Our initial application of ab initio direct dynamics in 1994 was to CH 2 O → H 2 + CO; the calculated vibrational distributions in the products were in very good agreement with experiment. In the intervening years, we have used direct dynamics to explore energy partitioning in various dissociation reactions, unimolecular dissociations yielding three fragments, reactions with branching after the transition state, nonstatistical dynamics of chemically activated molecules, dynamics of molecular fragmentation by intense infrared laser pulses, selective activation of specific dissociation channels by aligned intense infrared laser fields, angular dependence of strong field ionization, and simulation of sequential double ionization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Spontaneous dynamical disordering of borophenes in MgB2 and related metal borides

Abstract Layered boron compounds have attracted significant interest in applications from energy storage to electronic materials to device applications, owing in part to a diversity of surface properties tied to specific arrangements of boron atoms. Here we report the energy landscape for surface atomic configurations of MgB 2 by combining first-principles calculations, global optimization, material synthesis and characterization. We demonstrate that contrary to previous assumptions, multiple disordered reconstructions are thermodynamically preferred and kinetically accessible within exposed B surfaces in MgB 2 and other layered metal diborides at low boron chemical potentials. Such a dynamic environment and intrinsic disordering of the B surface atoms present new opportunities to realize a diverse set of 2D boron structures. We validated the predicted surface disorder by characterizing exfoliated boron-terminated MgB 2 nanosheets. We further discuss application-relevant implications, with a particular view towards understanding the impact of boron surface heterogeneity on hydrogen storage performance.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Adsorption, Orientation, and Speciation of Amino Acids at Air–Aqueous Interfaces for the Direct Air Capture of CO 2

Amino acids make up a promising family of molecules capable of direct air capture (DAC) of CO 2 from the atmosphere. Under alkaline conditions, CO 2 reacts with the anionic form of an amino acid to produce carbamates and deactivated zwitterionic amino acids. The presence of the various species of amino acids and reactive intermediates can have a significant effect on DAC chemistry, the role of which is poorly understood. In this study, all-atom molecular dynamics (MD) based computational simulations and vibrational sum frequency generation (vSFG) spectroscopy studies were conducted to understand the role of competitive interactions at the air–aqueous interface in the context of DAC. We find that the presence of potassium bicarbonate ions, in combination with the anionic and zwitterionic forms of amino acids, induces concentration and charge gradients at the interface, generating a layered molecular arrangement that changes under pre- and post-DAC conditions. In parallel, an enhancement in the surface activity of both anionic and zwitterionic forms of amino acids is observed, which is attributed to enhanced interfacial stability and favorable intermolecular interactions between the adsorbed amino acids in their anionic and zwitterionic forms. The collective influence of these competitive interactions, along with the resulting interfacial heterogeneity, may in turn affect subsequent capture reactions and associated rates. Finally, these effects underscore the need to consider dynamic changes in interfacial chemical makeup to enhance DAC efficiency and to develop successful negative emission and storage technologies.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

CFD modeling of pre-spark heat release in a boosted direct-injection spark-ignition engine

Accurate predictions of low-temperature heat release (LTHR) are critical for modeling auto-ignition processes in internal combustion engines. While LTHR is typically obscured by deflagration, extremely late ignition phasing can lead to LTHR prior to the spark, a behavior known as pre-spark heat release (PSHR). In this research, PSHR in a boosted direct-injection spark-ignition engine was studied using 3-D computational fluid dynamics (CFD) and detailed chemical kinetics. The turbulent combustion was modeled via a hybrid approach that incorporates the G-equation model for tracking the turbulent flame front, and the well-stirred reactor model with detailed chemistry for assessing the low-temperature reactions in unburnt gas. Simulations were conducted using Co-Optima alkylate and E30 fuels at operating conditions characterized by different PSHR intensities. The predicted in-cylinder pressure and heat release rate were found to agree well with experiments. It was found the estimate of previous-cycle trapped residuals is of utmost importance for capturing PSHR correctly. A simulation best practice was developed which keeps the detailed chemistry solver active throughout the entire simulation, allowing to track the evolution of intermediate species from one cycle to the next. Following the validation, the dynamics of PSHR were analyzed in detail employing the pressure-temperature (P-T) trajectory framework. It was shown that PSHR correlated with the first-stage ignition delay of the fuel, hence showing close relation to the in-cylinder P-T trajectory and the chemical kinetics. Besides, it was indicated that LTHR is a self-limiting process that has the effect of attenuating the thermal stratification in the combustion chamber. Furthermore, it was observed the occurrence of PSHR caused the P-T trajectory of end-gas to overlap with the negative temperature coefficient region of the fuel’s ignition-delay maps. This effect was more significant in the fuel-rich regions where engine knock tendency would be generally higher, with potential implications on knock control and mitigation.

42 ENGINEERING↗
DOE OSTI2021

Stochastic Kinetics of Nanocatalytic Systems

Catalytic reaction events occurring on the surface of a nanoparticle constitute a complex stochastic process. Although advances in modern single-molecule experiments enable direct measurements of individual catalytic turnover events occurring on a segment of a single nanoparticle, we do not yet know how to measure the number of catalytic sites in each segment or how the catalytic turnover counting statistics and the catalytic turnover time distribution are related to the microscopic dynamics of catalytic reactions. Here, we address these issues by presenting a stochastic kinetics for nanoparticle catalytic systems. We propose a new experimental measure of the number of catalytic sites in terms of the mean and variance of the catalytic event count. By considering three types of nanocatalytic systems, we investigate how the mean, the variance, and the distribution of the catalytic turnover time depend on the catalytic reaction dynamics, the heterogeneity of catalytic activity, and communication among catalytic sites. This work enables accurate quantitative analyses of single-molecule experiments for nanocatalytic systems and enzymes with multiple catalytic sites.

Statistical Physics↗
NASA NTRSDate not supplied

Building and Breaking Carbon Composites with REACTER

Carbon composites have become indispensable for aerospace and other high-performance applications, and a detailed picture of their morphology and failure mechanisms remains difficult to obtain through experiment. REACTER is a versatile computational modeling tool for atomistic molecular dynamics designed to model chemical reactions at the speed and length scales of classical force fields.1 In this work, several recent features of REACTER were applied to the creation and subsequent mechanical testing of two classes of carbon composites, carbon-fiber reinforced polymers (CFRP) and carbon nanotube (CNT) composites. Carbon fiber core morphologies were created by the method of Desai et al.,2 but using the advanced reaction constraints framework of REACTER, their proposed multistep procedure was reduced to a single uninterrupted molecular dynamics simulation. The carbon fiber filler was embedded into a polymer matrix by simulated in situ polymerization of several thermosetting resins, including bismaleimide and polyarylacetylene, to obtain the final CFRP model. To generate the second class of carbon composite, CNT networks were grown dynamically using the new ‘create atoms’ feature of REACTER, and similarly infiltrated with resin to obtain CNT composites. The resulting models were compared directly to experiment using simulated high resolution transmission electron microscopy and x-ray diffraction. Failure mechanisms were elucidated by simulating mechanically induced bond breaking, as characterized by third-order DFT-based tight-binding (DFTB3) simulations, via a reaction constraint on the total potential energy of the involved atoms.

Molecular Dynamics↗
DOE OSTI2021

Real-time interfacial electron dynamics revealed through temporal correlations in x-ray photoelectron spectroscopy

We present a novel technique to monitor dynamics in interfacial systems through temporal correlations in x-ray photoelectron spectroscopy (XPS) signals. To date, the vast majority of time-resolved x-ray spectroscopy techniques rely on pump–probe schemes, in which the sample is excited out of equilibrium by a pump pulse, and the subsequent dynamics are monitored by probe pulses arriving at a series of well-defined delays relative to the excitation. By definition, this approach is restricted to processes that can either directly or indirectly be initiated by light. It cannot access spontaneous dynamics or the microscopic fluctuations of ensembles in chemical or thermal equilibrium. Enabling this capability requires measurements to be performed in real (laboratory) time with high temporal resolution and, ultimately, without the need for a well-defined trigger event. The time-correlation XPS technique presented here is a first step toward this goal. The correlation-based technique is implemented by extending an existing optical-laser pump/multiple x-ray probe setup by the capability to record the kinetic energy and absolute time of arrival of every detected photoelectron. The method is benchmarked by monitoring energy-dependent, periodic signal modulations in a prototypical time-resolved XPS experiment on photoinduced surface-photovoltage dynamics in silicon, using both conventional pump–probe data acquisition, and the new technique based on laboratory time. The two measurements lead to the same result. The findings provide a critical milestone toward the overarching goal of studying equilibrium dynamics at surfaces and interfaces through time correlation-based XPS measurements.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Quantification of Hydrogen Isotopes Utilizing Raman Spectroscopy Paired with Chemometric Analysis for Application across Multiple Systems

On-line and real-time analysis of a chemical process is a major analytical challenge that can drastically change the way the chemical industry or chemical research operates. With in situ analyses, new and powerful understanding of chemistry can be gained; however, building robust tools for long-term monitoring faces many challenges that include compensating for instrument drift, instrument replacement, and sensor or probe replacement. Accounting for these changes by recollecting calibration data and rebuilding quantification models can be costly and time consuming. Here, in this study, methods to overcome these challenges are demonstrated with an application of Raman spectroscopy to monitoring hydrogen isotopes with varied speciation within dynamic gas streams. Specifically, chemical data science tools such as chemometric modeling are leveraged along with several examples of calibration transfer approaches. Furthermore, the optimization of instrument and sensors cell parameters for targeted gas phase analyses is discussed. While the particular focus on hydrogen is highly beneficial within the nuclear energy sector, mechanisms built and demonstrated here are widely applicable to optical spectroscopy monitoring in numerous other chemical systems that can be leveraged in other hazardous processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure --- this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis.

Wu, Yulun↗
DOE OSTI2022

SPATIAL GRAPH ATTENTION AND CURIOSITY-DRIVEN POLICY FOR ANTIVIRAL DRUG DISCOVERY

We developed Distilled Graph Attention Policy Network (DGAPN), a reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention (sGAT) mechanism that leverages self-attention over both node and edge attributes as well as encoding the spatial structure - this capability is of considerable interest in synthetic biology and drug discovery. An attentional policy network is introduced to learn the decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with stability. Exploration is driven by the stochasticity of the action space design and the innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while reducing the complexity of paths to chemical synthesis.

Wu, Y↗
DOE OSTI2022

Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

We developed Distilled Graph Attention Policy Networks (DGAPNs), a curiosity-driven reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. The framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention Network (sGAT) that leverages self-attention over both node and edge attributes as well as encoding spatial structure -- this capability is of considerable interest in areas such as molecular and synthetic biology and drug discovery. An attentional policy network is then introduced to learn decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with enhanced stability. Exploration is efficiently encouraged by incorporating innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while increasing the diversity of proposed molecules and reducing the complexity of paths to chemical synthesis.

Wu, Yulun↗
DOE OSTI2021

Spatial Graph Attention and Curiosity-driven Policy for Antiviral Drug Discovery

We developed Distilled Graph Attention Policy Networks (DGAPNs), a curiosity-driven reinforcement learning model to generate novel graph-structured chemical representations that optimize user-defined objectives by efficiently navigating a physically constrained domain. Here, the framework is examined on the task of generating molecules that are designed to bind, noncovalently, to functional sites of SARS-CoV-2 proteins. We present a spatial Graph Attention Network (sGAT) that leverages self-attention over both node and edge attributes as well as encoding spatial structure -- this capability is of considerable interest in areas such as molecular and synthetic biology and drug discovery. An attentional policy network is then introduced to learn decision rules for a dynamic, fragment-based chemical environment, and state-of-the-art policy gradient techniques are employed to train the network with enhanced stability. Exploration is efficiently encouraged by incorporating innovation reward bonuses learned and proposed by random network distillation. In experiments, our framework achieved outstanding results compared to state-of-the-art algorithms, while increasing the diversity of proposed molecules and reducing the complexity of paths to chemical synthesis.

Wu, Yulun↗
DOE OSTI2020

High Explosive Ignition through Chemically Activated Nanoscale Shear Bands

Shock initiation and detonation of high explosives is considered to be controlled through hot spots, which are local regions of elevated temperature that accelerate chemical reactions. Using classical molecular dynamics, we predict the formation of nanoscale shear bands through plastic failure in shocked TATB high explosive crystal. By scale-bridging with quantum-based molecular dynamics, we show that shear bands exhibit lower reaction barriers. While shear bands quickly cool,they remain chemically activated and support increased reaction rates without the local heating typically evoked by the hot spot paradigm. We describe this phenomenon as chemical activation through shear banding

Chemistry - Inorganic↗
DOE OSTI2020

Neutron Spin–Echo Studies of the Structural Relaxation of Network Liquid ZnCl 2 at the Structure Factor Primary Peak and Prepeak

Using neutron spin-echo spectroscopy, we studied the microscopic structural relaxation of a prototypical network ionic liquid ZnCl 2 at the structure factor primary peak and pre-peak. The results show that the relaxation at the primary peak is faster than the pre-peak and the activation energy is ≈33% higher. Stretched exponential relaxation is observed even at temperatures well above the melting point T m . Surprisingly, the stretching exponent shows a rapid increase upon cooling, especially at the primary peak, where it changes from stretched exponential to simple exponential on approaching T m . Furthermore, these results suggest that the appearance of glassy dynamics typical of the supercooled state even in the equilibrium liquid state of ZnCl 2 as well as the difference of activation energy at the two investigated length scales are related to the formation of network structure on cooling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

A nonlinear optical perspective on the chemistry at liquid/liquid interfaces

In this review, we discuss some of the subtle (and not-so-subtle) rules that govern chemistry and transport at liquid/liquid (L/L) interfaces. An overview of the importance of L/L interfaces is provided with context about why these interfaces are so challenging to study. Emphasis is placed on the use of vibrational sum-frequency generation (SFG), a surface-specific spectroscopy, to probe these interfaces at and away from equilibrium. Highlights from our laboratory’s work probing these interfaces in the context of chemical separations and soft-matter electronics are discussed. In conclusion, revealing the rules that govern self-assembly, chemical recognition, and transport at these dynamic, highly heterogeneous interfaces can allow formulation of avenues to control chemical and physical transformations, impacting a range of basic and applied fields.

Doughty, Benjamin L. [Oak Ridge National Laborator↗
DOE OSTI2020

Ultrafast formation of a transient two-dimensional diamondlike structure in twisted bilayer graphene

Due to the absence of matching carbon atoms at honeycomb centers with carbon atoms in adjacent graphene sheets, theorists predicted that a sliding process is needed to form AA, AB', or ABC stacking when directly converting graphite into sp 3 bonded diamond. Here, using twisted bilayer graphene, which naturally provides AA and AB' stacking configurations, we report the ultrafast formation of a transient two-dimensional diamondlike structure (which is not observed in aligned graphene) under femtosecond laser irradiation. This photoinduced phase transition is evidenced by the appearance of bond lengths of 1.94 and 3.14 Å in the time-dependent differential pair distribution function using MeV ultrafast electron diffraction. Molecular dynamics and first-principles calculation indicate that sp 3 bonds nucleate at AA and AB' stacked areas in a moiré pattern. This work sheds light on the direct graphite-to-diamond transformation mechanism, which has not been fully understood for more than 60 years.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗