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Nuclear-Electronic Orbital Ehrenfest Dynamics
The recently developed real-time nuclear-electronic orbital (RT-NEO) approach provides an elegant framework for treating electrons and selected nuclei, typically pro- tons, quantum mechanically in nonequilibrium dynamical processes. However, the RT-NEO approach neglects the motion of the other nuclei, preventing a complete de- scription of the coupled nuclear-electronic dynamics and spectroscopy. In this work, we describe the dynamical interaction between the other nuclei and the electron-proton subsystem with the mixed quantum-classical Ehrenfest dynamics method. The NEO- Ehrenfest approach propagates the electrons and quantum protons in a time-dependent variational framework, while the remaining nuclei move classically on the correspond- ing average electron-proton vibronic surface. This approach includes the non-Born- Oppenheimer eects between the electrons and quantum protons with RT-NEO and between the classical nuclei and the electron-proton subsystem with Ehrenfest dynam- ics. Spectral features for vibrational modes involving both quantum and classical nuclei are resolved from the time-dependent dipole moments. Moreover, nuclear quantum ef- fects are included directly in nonadiabatic dynamics simulations. The excited state intramolecular proton transfer in the o-hydroxybenzaldehyde molecule is shown to be faster and to exhibit a larger kinetic isotope eect with NEO-Ehrenfest compared to classical Ehrenfest dynamics. This work shows that the NEO-Ehrenfest method is a powerful tool to study dynamical processes with coupled electronic and nuclear degrees of freedom.
Dynamic fracture processes in hydrogen embrittled iron
Mechanistic understanding of hydrogen embrittlement is critical to the transport and storage of hydrogen as a sustainable fuel. Experimental observation of dynamic fracture processes in hydrogen environments has been challenging. Here, in-situ transmission X-ray microscopy is used to image polycrystalline iron foils that are concurrently strained and electrochemically charged with hydrogen. Using this technique, voids and microcracks down to ∼30 nm in size are identified at or near the crack tip and tracked as strain is increased. We observe both hydrogen-induced transgranular failure and hydrogen-induced intergranular failure with distinct fracture processes. We find that transgranular fracture occurs through the formation of sharp branched structures at the main crack tip that are reminiscent of slip band formation. This is followed by void growth and coalescence, and interaction with secondary cracks that lead to propagation of the main crack. Intergranular fracture is found to occur through void coalescence along grain boundaries, along with significant secondary cracking near the crack tip. The crack growth rate is greater for void-mediated intergranular failure than for slip-mediated transgranular failure. Additionally, the removal of hydrogen results in recovered ductility for a crack initially propagated under hydrogen charging.
The effects of carbonaceous inclusions and their distributions on dynamic failure processes in boron carbide ceramics
Commercially available boron carbide ceramics typically have heterogeneous microstructures that contain distributions of processing-induced inclusions. The inclusions that are rich in carbon (i.e., carbonaceous) govern the underlying mechanisms of brittle fracture through wing crack formation, and thus dictate the mechanical response of the ceramic. In this study, we investigate the dynamic failure of five boron carbide ceramic materials with different inclusion populations. All of the materials were prepared by hot-pressing; four of these boron carbides contained different sizes and concentrations of carbonaceous inclusions, while one contained no carbonaceous inclusions. The heterogeneity distributions were characterized in some detail for statistical analysis using scanning electron microscopy and quantitative image analysis. A modified compression Kolsky bar setup with in situ ultra-high-speed microscopic imaging (10 million frames per second) was then used to study the influence of the inclusion distributions on the dynamic failure processes in these materials, at nominal high strain rates of 10 2 -10 3 s -1 . The in situ ultra-high-speed microscopy highlighted the link between micro and macroscale failure processes and demonstrated that the carbonaceous inclusions are indeed the preferential sites for nucleation of wing cracks, as previously hypothesized based on post-mortem observations. The relative orientation of an inclusion with respect to the compression axis was shown to affect the likelihood that it would participate in crack nucleation. In conclusion, all of the ceramics were also found to have orientation-dependent peak compressive stress, regardless of the presence of carbonaceous inclusions, suggesting that grain orientation distributions are also important.
Probing Supercritical Phase Transition using Ultrafast X-ray Diagnostics (Final Technical Report)
Over recent years, several hypotheses have been put forward to elucidate supercritical transition states. Despite significant progress on the fundamental understanding of fluids at these supercritical conditions, important questions concerning the microstructure and dynamical processes remain. A particular research issue is hereby the fundamental understanding of the morphology of the molecular microstructure and its effect on the macroscopic behaviour and thermodynamic response functions. It is widely believed that the supercritical state is homogeneous without structural and dynamic observables to distinguish between a liquid and a vapor. However, recent investigations have identified regions of distinct liquid-like or vapor-like properties even under supercritical conditions. Specifically, it was shown that the transition between liquid-like and vapor-like states occurs across an extension to the coexistence line, marked by almost discontinuous changes in fluid properties. This transition was first identified experimentally by Nishikawa and Tanaka and stands in contrast to the classical presentation of the supercritical state space as a featureless, homogeneous domain. Structurally, the most important properties of supercritical fluids are the dynamic heterogeneities and local density fluctuations that are present within the fluid state at the microscopic level. These inhomogeneities are associated with the formation of molecular clusters of various sizes with liquid-like properties separated by voids of unbound gas-phase molecules which continuously restructure itself at picosecond timescales. By utilizing ultrafast X-ray Photon Correlation Spectroscopy (XPCS) at the Linac Coherent Light Source (LCLS) and X-ray synchrotron measurements as an experimental probe of these structural changes at the molecular level we seek to elucidate the higher-order phase transition from a liquid-like to a vapor-like state in the region extending the critical point. Open questions we seek to address particularly with XPCS are: (i) the underlying mechanisms responsible for the density fluctuations at supercritical conditions and how these dynamical processes at the molecular level affect the thermodynamic response functions and (ii) on what timescales do dynamical processes evolve that are associated with thermalization, molecular diffusion, and intermolecular cluster transfer.
Assessing mechanisms for microbial taxa and community dynamics using process models
Disentangling the assembly mechanisms controlling community composition, structure, distribution, functions, and dynamics is a central issue in ecology. Although various approaches have been proposed to examine community assembly mechanisms, quantitative characterization is challenging, particularly in microbial ecology. Here, we present a novel approach for quantitatively delineating community assembly mechanisms by combining the consumer–resource model with a neutral model in stochastic differential equations. Using time-series data from anaerobic bioreactors that target microbial 16S rRNA genes, we tested the applicability of three ecological models: the consumer–resource model, the neutral model, and the combined model. Our results revealed that model performances varied substantially as a function of population abundance and/or process conditions. The combined model performed best for abundant taxa in the treatment bioreactors where process conditions were manipulated. In contrast, the neutral model showed the best performance for rare taxa. Our analysis further indicated that immigration rates decreased with taxa abundance and competitions between taxa were strongly correlated with phylogeny, but within a certain phylogenetic distance only. The determinism underlying taxa and community dynamics were quantitatively assessed, showing greater determinism in the treatment bioreactors that aligned with the subsequent abnormal system functioning. Given its mechanistic basis, the framework developed here is expected to be potentially applicable beyond microbial ecology.
Dynamics of polymer segments, polymer chains, and nanoparticles in polymer nanocomposite melts: A review
The addition of nanoparticles (NPs) to a polymer matrix, forming a polymer nanocomposite (PNC), is known to alter the microscopic dynamic processes of both species which leads to unique macroscopic material properties of the PNC. Because the NPs and polymers have overlapping characteristic length, time, and energy scales, the interactions within these materials are complex and the dynamics are interrelated. In this review, we present an overview of experimental, simulation, and theoretical results that probe multi-scale polymer and nanoparticle dynamics in polymer nanocomposites and navigate the dense parameter space presented by these multicomponent systems. Although a variety of PNC systems are mentioned, we focus this discussion on linear thermoplastics filled with hard spherical or cylindrical NPs in the melt state. We begin by introducing PNCs, the dynamic processes within them, and the importance of dynamics for properties and processing. At the smallest length and time scales, we discuss segmental dynamics in PNCs, including the role of polymer attributes, NP attributes, and NP-polymer interactions. Then, we present measurements of collective motions and intermediate (Rouse) dynamics in various PNC materials. At longer length and time scales, we discuss polymer center-of-mass diffusion in PNCs with either spherical or anisotropic NPs. We then explore the dynamics of the NPs in PNCs and polymer melts, including theoretical predictions, simulation results, and experimental observations. Lastly, we note some of the remaining challenges in probing dynamics in PNC materials and fundamentally studying PNCs more generally.
Electron-beam-driven chemical processes during liquid phase transmission electron microscopy
Liquid phase (or liquid cell) transmission electron microscopy (LP-TEM) has been established as a powerful tool for observing dynamic processes in liquids at nanometer to atomic length scales. However, the simple act of observation using electrons irreversibly alters the nature of the sample. A clear understanding of electron beam-driven processes during LP-TEM is required to interpret in situ observations and utilize the electron beam as a stimulus to drive nanoscale dynamic processes. In this review, we discuss recent advances toward understanding, quantifying, mitigating, and harnessing electron beam-driven chemical processes occurring during LP-TEM. We will highlight progress in several research areas, including modeling electron beam-induced radiolysis near interfaces, electron beam-induced nanocrystal formation, and radiation damage of soft materials and biomolecules.
Ultrafast photoinduced charge carrier dynamics of L-cysteine and oleylamine stabilized CsPbBr3 perovskite quantum dots coupled with gold nanoparticles
We have synthesized L-cysteine and oleylamine stabilized CsPbBr3 perovskite quantum dots (PQDs) and coupled them with gold nanoparticles (AuNPs). The PQDs and AuNPs, as well as their hybrid nanostructures (HNS), were characterized using UV–visible (UV–vis) and photoluminescence (PL) spectroscopy. The UV–vis spectra show absorption bands of the HNS at 503 and 520 nm, attributed mainly to PQDs and AuNPs, respectively. The PQDs show a strong excitonic PL band peaked at 513 nm from PQDs. The HR-TEM results show the formation of hybrid structures between PQDs and AuNPs, which is also supported by the PL quenching of the PQDs by the coupled AuNPs. Ultrafast dynamics of the exciton and charge carriers in the HNS and pristine PQD were studied using femtosecond transient absorption. Multiexponential fitting of the dynamic data revealed the existence of shallow and deep trap states in pristine PQDs and ultrafast electron transfer from PQDs to AuNPs in the HNS. A kinetic model was proposed to account for the key dynamic processes involved and to extract the time for electron transfer from PQDs to AuNPs in the HNS, found to be ∼2 ps. Dynamic processes in pristine PQDs are largely unchanged by HNS formation with AuNPs.
Novel transition dynamics of topological solitons
Continuous phase transitions can be classified into ones characterized by local-order parameters and others that need additional topological constraints. The critical dynamics near the former transitions have been extensively studied, but the latter is less understood. We fill this gap in knowledge by studying the transition dynamics to a parity-breaking topological ground state called the chiral soliton lattice in quantum chromodynamics at finite temperature, baryon chemical potential, and an external magnetic field. We find a slowing down of the soliton’s translational motion as the critical magnetic field approaches, while the local relaxation rate to the solitonic state remains finite. The time required to converge to the stationary state strongly depends on the symmetry of the initial configuration, determining whether translational motion occurs during the dynamic process. Published by the American Physical Society 2024
Understanding the eco-geomorphologic feedback of coastal marsh under sea level rise: vegetation dynamic representations, processes interaction, and parametric sensitivity
A growing number of coastal eco-geomorphologic modeling studies have been conducted to understand coastal marsh evolution under sea-level rise (SLR). Although these models quantify marsh topographic change as a function of sedimentation and erosion, their representations of vegetation dynamics that control organic sedimentation differ. How vegetation dynamic schemes contribute to simulation outcomes is not well quantified. Additionally, the sensitivity of modeling outcomes to parameter selection in the available formulations has not been rigorously tested to date, especially under the influence of an accelerating SLR. In this paper, we used a coastal eco-geomorphologic model with different vegetation dynamic schemes to investigate the eco-geomorphologic feedbacks of coastal marshes and parametric sensitivity under SLR scenarios. We found that marsh platform relief increased with sea level rise rate. The simulations with different vegetation schemes exhibited different spatial-temporal variations in elevation and biomass. The nonlinear Spartina scheme presented the most resilient prediction with generally the highest marsh accretion and vegetation biomass, and the least elevation relief under SLR. But the linear Spartina scheme predicts the lowest unvegetated-vegetated ratio. We also found that vegetation-related parameters and sediment diffusivity, which were not well measured or discussed in previous studies, were identified as some of the most critical parameters. Additionally, the model sensitivity to vegetation-related parameters increased with SLR rates. The identified most sensitive parameters may inform how to appropriately choose modeling representations of key processes and parameters for different coastal marsh landscapes under SLR, and demonstrate the importance of future field measurements of these key parameters.
Direct Imaging of Antiferromagnet-Ferromagnet Phase Transition in van der Waals Antiferromagnet CrSBr
The advent of van der Waals (vdW) ferromagnetic (FM) and antiferromagnetic (AFM) materials offers unprecedented opportunities for spintronics and magneto-optic devices. Combining magnetic Kerr microscopy and density functional theory calculations, the AFM-FM transition is investigated and a surprising abnormal magneto-optic anisotropy in vdW CrSBr associated with different magnetic phases (FM, AFM, or paramagnetic state) is discovered. This unique magneto-optic property leads to different anisotropic optical reflectivity from different magnetic states, permitting direct imaging of the AFM Néel vector orientation and the dynamic process of the AFM-FM transition within a magnetic field. Using Kerr microscopy, not only the domain nucleation and propagation process is imaged but also the intermediate spin-flop state in the AFM-FM transition is identified. In conclusion, the unique magneto-optic property and clear identification of the dynamics process of the AFM-FM phase transition in CrSBr demonstrate the promise of vdW magnetic materials for future spintronic technology.
Connecting Femtosecond Transient Absorption Microscopy with Spatially Coregistered Time Averaged Optical Imaging Modalities
Multimodal all-optical imaging involving coregistered femtosecond transient absorption microscopy (TAM), time-integrated photoluminescence (PL), and steady-state modalities such as confocal reflectance and transmission offers an appealing approach to gain a comprehensive understanding of complex electronic excited-state phenomena in spatially heterogeneous systems. A unique combination of these modalities allows us to unravel not only the competing electronic excited-state dynamical processes but also the underlying morphological information with simultaneous high temporal and spatial resolution. However, correlating the various images obtained from time-resolved and time-independent modalities is generally nontrivial and particularly challenging when the electronic dynamics under study evolve in both time and space. Here, we demonstrate a new approach for rationally correlating time-resolved microscopy with coregistered time-integrated or steady-state modalities. Specifically, our approach involves an extended global lifetime analysis of the time-resolved microscopic data set to separate distinct dynamical processes taking place on commensurate time scales, and the resulting decay-associated amplitude maps (DAAMs) were applied to explore correlations with the images acquired using time-independent modalities. The feasibility of our approach was validated through analyzing a multimodal data set acquired from a thin film of chloride-containing mixed lead halide perovskites (CH 3 NH 3 PbI 3–x Cl x ) using femtosecond transient absorption, time-integrated PL, and confocal reflectance microscopies. Analysis of the results obtained enable us to gain new insight into the complex ultrafast relaxation dynamics in this highly heterogeneous system.
Visualizing lattice dynamic behavior by acquiring a single time-resolved MeV diffraction image
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Deconvolution of dynamic heterogeneity in protein structure
Heterogeneity is intrinsic to the dynamic process of a chemical reaction. As reactants are converted to products via intermediates, the nature and extent of heterogeneity vary temporally throughout the duration of the reaction and spatially across the molecular ensemble. The goal of many biophysical techniques, including crystallography and spectroscopy, is to establish a reaction trajectory that follows an experimentally provoked dynamic process. It is essential to properly analyze and resolve heterogeneity inevitably embedded in experimental datasets. We have developed a deconvolution technique based on singular value decomposition (SVD), which we have rigorously practiced in diverse research projects. In this review, we recapitulate the motivation and challenges in addressing the heterogeneity problem and lay out the mathematical foundation of our methodology that enables isolation of chemically sensible structural signals. We also present a few case studies to demonstrate the concept and outcome of the SVD-based deconvolution. Finally, we highlight a few recent studies with mechanistic insights made possible by heterogeneity deconvolution.
Coupling solidification kinetics with phase-behavior computations in hydrodynamic simulations of high-pressure, dynamic-compression processes
In this study, we report a numerical scheme to integrate models for the kinetics of solidification processes together with phase-behavior computations in the context of continuum-scale hydrodynamic simulations. The objective of the phase-behavior computations is to determine the pressure and temperature, given the following three sets of inputs: (1) an appropriate equation of state to describe our system, (2) the phase fraction(s) produced by the kinetic models, (3) and the volume and internal energy obtained by solving the conservation equations that govern the hydrodynamic behavior. The kinetics are assumed to be governed by the Kolmogorov–Johnson–Mehl–Avrami equation, and the nucleation and growth rates that enter into that equation are functions of the pressure and temperature produced by the phase-behavior computations. Our formulation allows for the fluid and solid phases to be at different temperatures (thermal nonequilibrium) and pressures (arising from surface-tension-induced Laplace contributions). The formulation is presented in a fairly general setting that is independent of any particular material, although we demonstrate it in some examples related to high-energy-density science applications where materials are rapidly compressed to pressures exceeding several gigapascals in less than a microsecond. We conclude with a critical evaluation of our approach and provide suggestions for future work to improve the predictive capabilities and generality of the models.
Operational resilience of additively manufactured parts to stealthy cyberphysical attacks using geometric and process digital twins
Cyberphysical attacks on the digital backbone of Additive Manufacturing (AM) can compromise the printed part’s functionality. They can alter features in the digital geometry to introduce geometric defects (e.g., missing fillets) or alter process parameters to create local defects (e.g., voids). Addressing the downtime, waste, and quality deterioration associated with existing solutions requires operational resilience, i.e., rapid elimination or disruption of defect formation (to retain part function) without production stoppage or part disposal (to retain yield). This need is unmet due to the inherently unpredictable nature of attack-induced alterations, lack of access to the original geometric model for identification of altered geometric features, and in-process imposition of unknown process dynamics via attack-driven alteration of real-time-uncontrolled (or exogenous) parameters. This work establishes the above-mentioned operational resilience for the first time by creating two Digital Twins (DT). The Geometric DT (Geo-DT) is based on a unique physical-field-driven soft sensor and topology optimization method. The Process Digital Twin (Pro-DT) combines local defect quantification with a novel Reinforcement Learning formulation and training method. The importance of these methodological advances and the scalability of our approach are examined on a real AM testbed. It is shown that Geo-DT can correct geometric defects without access to the original digital geometry or explicit knowledge of attack-altered geometric features. Further, Pro-DT can accelerate real-time disruption of local defects despite attack-driven imposition of unknown process dynamics. We discuss how our framework goes beyond the contemporary focus on pre-attack security and in-attack detection towards resilience for AM and beyond.
Demographic Microsimulator for Integrated Urban Systems: Adapting Panel Survey of Income Dynamics to Capture the Continuum of Life
Agent-based models (ABMs) in transportation modeling simulate activity and travel decisions at the disaggregate level of households and individuals. To do this, ABMs require detailed and realistic information on agents’ socioeconomic and demographic characteristics. Various synthetic population generators have been proposed to address this need. However, most of those currently in practice are cross-sectional in nature and do not account for the dynamics within households and individuals as they progress through life events over time. This is a major shortcoming, as literature has shown that transportation decisions are affected by the transition between and co-occurrence of life cycle events. While some demographic evolution simulators have been proposed to address this issue, they are developed using cross-sectional data and capture only a small set of life cycle events and their interdependence. Addressing these drawbacks, we propose a demographic microsimulator (DEMOS) that captures the “continuum of life” by considering a range of household- and individual-level life cycle events. DEMOS is developed using the Panel Survey of Income Dynamics, one of the world’s longest-running longitudinal surveys. The DEMOS submodels consider key life cycle events that are influenced by agents’ demographic variables. DEMOS is applied to evolve the population of the San Francisco Bay Area over a 9-year horizon. Results demonstrate how DEMOS generates life trajectories and how DEMOS outputs match the observed demographic trends. DEMOS is expected to enable longitudinal analysis in the context of ABMs and expand ABMs analyses relating to dynamic processes such as household-level vehicle transactions.