Microscopic Descriptors Research Articles

Influence of external electric field on molecular microscopic descriptors of non-polar insulating gases

Molecular microscopic descriptors without external electric field are commonly used for the prediction of insulating strength in gaseous medium. However, insulating gases are subjected to external electric field in electrical equipment, and the molecular microscopic descriptors are affected. Therefore, it is necessary to investigate the influence of external electric field on molecular microscopic descriptors. The Gaussian 16 software is employed to calculate the global parameter with the M06-2X functional and the 6–311++G (d, p) basis set. The wavefunction analysis software Multiwfn is used to obtain electrostatic potential parameter. In this paper, three directions of external electric field with magnitude ranging from 0 to 0.03 a.u. have been set to 10 non-polar insulating gas molecules. According to the type of molecular point group, the effect of direction and magnitude of external electric field on the molecular microscopic descriptors has been analyzed. The results indicate that the molecular microscopic descriptors are affected by the direction and magnitude of external electric field. The change rate of microscopic descriptors increases with the increase of external electric field magnitude. And the influence of the direction of external electric field is associated with the molecular symmetry. In conclusion, the effect of direction and magnitude of external electric field is needed to be considered when analyzing the effect of external electric field on molecular microscopic descriptors.

(Digital Presentation) New Advances and 24/7 Accessibility in Battery Research- Bringing Synchrotron X-Ray Techniques to Your Lab

Rechargeable battery research often involves improving electrodes to electrolyte materials with new chemistry with the end goals to lower cost, extend cycle life, higher energy densities and better safety. Advancements in battery and fuel cell research require the understanding of the complex interplay of several components and factors in a battery ecosystem. It calls for an integrated and multimodality approach involving several new analytical techniques which have to be capable of probing the batteries electrochemistry, structures and composition at different length and time scales, several of which have to be performed non destructively (ex-situ, in situ and in operando). Because of the need to study these in operando or at higher resolution or sensitivity, conventional lab based x-ray techniques are often inadequate. Most of this research is currently performed through synchrotron X-ray techniques. These include: X-ray Absorption Spectroscopy (XAS) to probe changes in oxidation states, bond lengths and coordination numbers of electrochemistry during charge-discharge cycles. XAS comprise XANES (X-ray absorption near edge spectroscopy) & EXAFS (Extended X-ray Absorption Fine Structure). They provide information on element-specific changes in oxidation state and local atomic structure. Such microscopic descriptors are crucial for elucidating charge transfer and structural changes associated with bonding or site mixing, two key factors in evaluating state of charge and modes of cell failure or catalytic efficiency. Another major technique is synchrotron X-ray Imaging at multiple lengthscales, from micrometers to 10s of nanometers through 3D X-ray Microscopy (XRM) to determine structural changes and degradation over time of the complex system, from the electrodes, separators, current collectors to binders. Trace level elemental composition at the ppm or sub-ppm level can be studied through high sensitivity synchrotron X-ray fluorescence spectroscopy (s-XRF)- to track the migration of metallic ions from cathode to anode during charge-discharge cycle or to investigate cross contamination during manufacturing.Unfortunately, many of these X-ray techniques such as XAS has to be performed almost exclusively at synchrotron X-ray light sources, where beamtime is infrequent and experiment time-frames are limited. As a consequence, high level battery, fuel cell or catalyst research in many research institutions have been largely curtailed. In this talk, we will discuss the advancements made in high flux, tunable lab x-ray sources and high efficiency optics for enabling novel synchrotron-equivalent XAS, XRM and XRF techniques in the laboratory. Breakthrough correlative applications through these suite of tools in the field of battery research and catalysts are now feasible in your own laboratory 24/7, without the constraint of limited access nor the research continuity challenges at synchrotron beamlines. Measurements results (including in operando) will be illustrated for conventional NMC batteries to novel solid-state lithium air batteries and next generation battery materials.

Decompression dynamics of high density amorphous ice above and below the liquid-liquid critical point

We investigate via numerical simulations of the TIP4P/Ice model the isothermal decompression of high density amorphous ice (HDA) mirroring the experimental protocol followed by recent experiments [H. K. Kim, et al., Science 370 , 978, 2020]. Taking advantage of the recent determination of the TIP4P/Ice liquid-liquid critical point, we decompress our samples at temperatures both below and above the critical temperature. We follow the crossover between high and low density, complementing the time evolution of the structure factor with the time evolution of microscopic descriptors. Our results support the interpretation of the experimental pathway as a structural change from high to low density states through a first-order transition line. In the simulation study we do not observe nucleation events, but rather a spinodal-like non-equilibrium transformation, which offers an explanation on why the decompression process even at sub-critical temperatures may result in a continuous time evolution of the density and of the microscopic descriptors.

Microscopic and Macroscopic Characterization of Grain Boundary Energy and Strength in Silicon Carbide via Machine-Learning Techniques.

Predicting the properties of grain boundaries poses a challenge because of the complex relationships between structural and chemical attributes both at the atomic and continuum scales. Grain boundary systems are typically characterized by parameters used to classify local atomic arrangements in order to extract features such as grain boundary energy or grain boundary strength. The present work utilizes a combination of high-throughput atomistic simulations, macroscopic and microscopic descriptors, and machine-learning techniques to characterize the energy and strength of silicon carbide grain boundaries. A diverse data set of symmetric tilt and twist grain boundaries are described using macroscopic metrics such as misorientation, the alignment of critical low-index planes, and the Schmid factor, but also in terms of microscopic metrics, by quantifying the local atomic structure and chemistry at the interface. These descriptors are used to create random-forest regression models, allowing for their relative importance to the grain boundary energy and decohesion stress to be better understood. Results show that while the energetics of the grain boundary were best described using the microscopic descriptors, the ability of the macroscopic descriptors to reasonably predict grain boundaries with low energy suggests a link between the crystallographic orientation and the resultant atomic structure that forms at the grain boundary within this regime. For grain boundary strength, neither microscopic nor macroscopic descriptors were able to fully capture the response individually. However, when both descriptor sets were utilized, the decohesion stress of the grain boundary could be accurately predicted. These results highlight the importance of considering both macroscopic and microscopic factors when constructing constitutive models for grain boundary systems, which has significant implications for both understanding the fundamental mechanisms at work and the ability to bridge length scales.

Reconstruction and uncertainty quantification of lattice Hamiltonian model parameters from observations of microscopic degrees of freedom

The emergence of scanning probe and electron beam imaging techniques has allowed quantitative studies of atomic structure and minute details of electronic and vibrational structure on the level of individual atomic units. These microscopic descriptors, in turn, can be associated with local symmetry breaking phenomena, representing the stochastic manifestation of the underpinning generative physical model. Here, we explore the reconstruction of exchange integrals in the Hamiltonian for a lattice model with two competing interactions from observations of microscopic degrees of freedom and establish the uncertainties and reliability of such analysis in a broad parameter-temperature space. In contrast to other approaches, we specifically specify a loss function inherent to thermodynamic systems and utilize it to estimate uncertainty in simulated realizations of different models. As an ancillary task, we develop a machine learning approach based on histogram clustering to predict phase diagrams efficiently using a reduced descriptor space. We further demonstrate that reconstruction is possible well above the phase transition and in the regions of parameter space when the macroscopic ground state of the system is poorly defined due to frustrated interactions. This suggests that this approach can be applied to the traditionally complex problems of condensed matter physics such as ferroelectric relaxors and morphotropic phase boundary systems, spin and cluster glasses, and quantum systems once the local descriptors linked to the relevant physical behaviors are known.

Characterizing the Tensile Strength of Metastable Grain Boundaries in Silicon Carbide Using Machine Learning

The local atomic structure, local chemistry, and stoichiometry of grain boundaries control in part the strength and fracture toughness of silicon carbide components. The predictions of the structure and properties of these grain boundaries are generally limited to their ground-state configurations. We investigated the tensile strength behavior of metastable grain boundaries in silicon carbide using high-throughput atomistic simulations combined with machine learning techniques. We analyzed and compared the ∑5 ⟨100⟩{120} and ∑9 ⟨110⟩{122} tilt grain boundary metastable configurations to identify structural and chemical attributes that dominate their tensile strength. We characterized these metastable grain boundaries using a set of microscopic descriptors representing the local grain boundary atomic structure and the local grain boundary stoichiometry and chemical-bound types. We used a boosted regression tree surrogate model for the successful prediction of metastable grain boundary strength as a function of these descriptors. Our results show that the tensile strength of generic (i.e., any random grain boundary from the entire grain boundary population), metastable grain boundaries is primarily dominated by the grain boundary excess free volume, closely followed by the type of structure composing the boundary and the amount of C–C bonds. The 5% strongest metastable grain boundaries have particular characteristics with a low amount of free volume and the highest density of C–C bonds. Our results reveal that the 5% strongest and weakest metastable grain boundaries are most sensitive to the local stoichiometry, regardless of the local atomic structure composing the grain boundary as compared to any other generic metastable grain boundaries. We show that the strongest and weakest metastable grain boundary configurations can be identified as specific regions in a low-dimensional-representation space of their microscopic descriptors. Taken together, these findings showcase the effectiveness and validity of using a low-dimensional representation of the grain boundary structure and machine-learned surrogate models to rapidly assess metastable grain boundary strength without the need to perform actual tensile simulations.

Laboratory-Based X-ray Absorption Spectroscopy on a Working Pouch Cell Battery at Industrially-Relevant Charging Rates

Li-ion battery (LIB) research has continuing importance for the entire range of applications from consumer products to vehicle electrification and grid stabilization. In many cases, standard electrochemical methods only provide an overall voltage or specific capacity, giving an inadequate description of parallel redox processes or chemical gradients at the particle and pack level. X-ray absorption fine structure (XAFS) is frequently used to augment bulk electrochemical data, as it provides element-specific changes in oxidation state and local atomic structure. Such microscopic descriptors are crucial for elucidating charge transfer and structural changes associated with bonding or site mixing, two key factors in evaluating state of charge and modes of cell failure. However, the impact of XAFS on LIB research has been significantly constrained by a logistical barrier: contemporary XAFS work is performed almost exclusively at synchrotron X-ray light sources, where beamtime is infrequent and experiment time-frames are limited. Here we show that modern laboratory-based XAFS cannot only be applied to, e.g., characterization of ex situ LIB electrode materials, but can also be used for operando studies at industrially-relevant charging rates in a standard pouch cell preparation. Such capability enables accelerated discovery of new materials and improved operation modes for LIBs.

Comparative Analysis of TM and Cytoplasmic \u03b2-barrel Conformations Using Joint Descriptor

Macroscopic descriptors have become valuable as coarse-grained features of complex proteins and are complementary to microscopic descriptors. Proteins macroscopic geometric features provide effective clues in the quantification of distant similarity and close dissimilarity searches for structural comparisons. In this study, we performed a systematic comparison of β-barrels, one of the important classes of protein folds in various transmembrane (TM) proteins against cytoplasmic barrels to estimate the conformational features using a joint-based descriptor. The approach uses joint coordinates and dihedral angles (β and γ) based on the β-strand joints and loops to determine the arrangements and propensities at the local and global levels. We then confirmed that there is a clear preference in the overall β and γ distribution, arrangements of β-strands and loops, signature patterns, and the number of strand effects between TM and cytoplasmic β-barrel geometries. As a robust and simple approach, we determine that the joint-based descriptor could provide a reliable static structural comparison aimed at macroscopic level between complex protein conformations.

A Hybrid Multiscale Model of Miscible Reactive Fronts

AbstractSubsurface processes can be simulated at multiple scales with variable degrees of fidelity. Some microscopic (pore‐scale) features of reactive transport cannot be properly resolved in macroscopic (Darcy‐scale) models. While microscopic descriptors might be closer to reality, they are computationally unfeasible when deployed on a macroscale. Hybrid algorithms combine the physical fidelity of a microscopic model with the computational efficiency of its macroscopic counterpart. We develop a hybrid model of dynamic reactive fronts in an open fracture, with a chemical reaction occurring in the zone of contact between two dissolved species. Away from the front, both fluid flow and solute transport are described by one‐dimensional models. In the front's proximity, two‐dimensional Stokes equations are used to model fluid flow, and solute transport is described with advection‐diffusion‐reaction equations. These two descriptors are coupled via an iterative procedure, which enforces the continuity of concentrations and mass fluxes across the interface between the two models. Our numerical experiments demonstrate that the hybrid model outperforms its microscopic and macroscopic counterparts in terms of computational time and representational accuracy, respectively.

Preovulatory follicle growth dynamics indicates daily spawning synchronicity in wild-caught Atlantic horse mackerel off Portugal

The Atlantic horse mackerel, Trachurus trachurus, is a commercially important species whose daily spawning dynamics are not yet fully understood. Existence of spawning synchronicity would facilitate application of the daily egg production method for estimating the spawning biomass of the southern stock of the species in the Northeast Atlantic. The intent of this work was to study preovulatory follicle (PREF) growth dynamics in wild Atlantic horse mackerel to determine the existence of daily spawning synchronicity in the population. The size and translucency of hydrated oocytes increased significantly between early morning and late afternoon; this pattern was the same for two study years. This morpho-developmental shift led to the appearance of previously indistinguishable oil drops in ovarian whole mounts which is an easily observed microscopic descriptor of PREF growth. The results clearly suggested that PREF growth was well synchronized between Atlantic horse mackerel that were capable of spawning and provided evidence for the existence of daily spawning synchronicity in the population. Apart from sampling time of day, the size of hydrated oocytes was shown to be affected by body size: a 10 cm difference in maternal length corresponded to a 0.030 mm difference in the diameter of hydrated oocytes. In contrast, the size of early germinal vesicle migration oocytes was correlated neither to sampling time nor to maternal size which suggests that the abrupt increase in PREF size only commences after the onset of the actively spawning sub-phase.

3-D DEM simulations of drained triaxial tests on inherently anisotropic granulates

A series of triaxial tests were simulated by the discrete element method (DEM) to study the effect of inherent anisotropy on the mechanical behaviours of granulates. In this study, clumps consisting of a collection of rigid spherical balls were used to simulate irregular-shaped particles, and the multi-layer under-compaction method was employed to prepare the anisotropic specimens with different initial density and fabric. The macroscopic responses including the strength characteristic, the volumetric response, the critical state and the stress–dilatancy relationship were carefully examined. In addition, two microscopic descriptors, the particle orientation (PO) fabric and contact normal (CN) fabric, were mainly monitored and investigated to shed light on the underlying micro-mechanics. The results show that comparing to the experimental results on natural sands, the typical macro-mechanical behaviours of anisotropic granulates can be successfully captured in the DEM simulations. Specimens with smaller inclination angle are more dilative. Nevertheless, the influence of the inherent anisotropy on the shear–dilatancy relation seems to be limited. The joint invariants NP representing the relative orientation between the particle fabric direction and stress direction can be taken as an indicator of critical state. The rearrangement of the CN is more sensitive to the applied force than that of PO.

Monitoring treatment response in psoriasis: current perspectives on the clinical utility of reflectance confocal microscopy.

Reflectance confocal microscopy (RCM) evaluation of inflammatory skin diseases represents a relatively new technique that, during the past 5 years, has attracted increasing interest, with consequent progressive increment of publications in literature. The success of RCM is directly related to the high need for noninvasive techniques able to both reduce the number of skin biopsies and support clinical diagnosis and patient management. RCM helps to visualize microscopic descriptors of plaque psoriasis (PP) with good reproducibility between observers and a high grade of correspondence with histopathology. Several clinical tests are used for the therapeutic management of PP, but they are limited by subjective interpretation. Skin biopsy presents objective interpretation, but the procedure is invasive and not repeatable. RCM has been used not only for the evaluation of skin cancer or inflammatory skin diseases, but also for monitoring the efficacy of different treatments in PP. In this review, we present some examples of RCM applications in therapeutic psoriasis follow-up.

Vapor-liquid equilibrium and polarization behavior of the GCP water model: Gaussian charge-on-spring versus dipole self-consistent field approaches to induced polarization.

We developed the Gaussian charge-on-spring (GCOS) version of the original self-consistent field implementation of the Gaussian Charge Polarizable water model and test its accuracy to represent the polarization behavior of the original model involving smeared charges and induced dipole moments. For that purpose we adapted the recently proposed multiple-particle-move (MPM) within the Gibbs and isochoric-isothermal ensembles Monte Carlo methods for the efficient simulation of polarizable fluids. We assessed the accuracy of the GCOS representation by a direct comparison of the resulting vapor-liquid phase envelope, microstructure, and relevant microscopic descriptors of water polarization along the orthobaric curve against the corresponding quantities from the actual GCP water model.

Lanthanide Loading of Luminescent Multi-Tridentate Polymers under Thermodynamic Control

This work illustrates the use of basic statistical mechanics for rationalizing the loading of linear multitridentate polymers with trivalent lanthanides, Ln(III), and identifies the specific ionic sizes of europium and yttrium as promising candidates for the further design of organized heterometallic f–f′ materials. Using [Ln(hfac)3] (hfac = hexafluoroacetylacetonate) as lanthanide carriers, the thermodynamically controlled formation of Wolf type-II lanthanidopolymers [{Ln(hfac)3}m(L4)] is modeled with the help of two simple microscopic descriptors: (i) the intrinsic affinity of Ln(III) for the tridentate binding sites fN3(Ln) and (ii) the intermetallic interactions ΔE1–2(Ln,Ln) operating between two occupied adjacent sites. Selective complexation (fN3La << fN3Eu > fN3(Y)) modulated by anticooperative interactions (ΔE1–2(La,La) ≃ ΔE1–2(Eu,Eu) > ΔE1–2(Y,Y) ≈ 0) favors the fixation of Eu(III) in semiorganized lanthanidopolymers [{Eu(hfac)3}m(L4)] displaying exploitable light-downshifting.

Shear strength and micro-descriptors of bidisperse ellipsoids under different loading paths

The paper examines the macroscopic and microscopic behaviors of a granular material, systems of bidisperse ellipsoids. The larger ellipsoids of these systems have an axial length ratio (major axial length to minor axial length) of 1.5 and the shorter ellipsoids have an axial length ratio of 1.2. The weights of these two kinds are identical in each sample. Samples are subjected to various confining pressures ranging from 30 to 3000 kPa. Then they are loaded following three stress paths including axial extension condition, triaxial extension condition, and axial compression condition. These drained tests are stopped till the critical state is reached. A linear critical state line in the void ratio and mean stress space was observed for each stress path. The slopes of these critical state lines are different. The critical state lines for the axial extension and triaxial extension tests are above that of the axial compression tests. We also examined different microscopic descriptors at peak and critical states. They are based on the micro-structural tensor of contact normals or normal contact forces. Excellent relationship was found between these descriptors and the peak shear strength in the compression tests. Good relationship was observed for the extension tests. The relationships between these descriptors and the critical shear strength are fair for the compression tests. However, these descriptors do not capture the trends of axial extension tests in critical state.

Microscopic Description of Le Châtelier's Principle

The analysis based on microscopic descriptors (energy levels and their populations) is given that provides visualization of free energies and conceptual rationalization of Le Châtelier's principle. The misconception nature favors equilibrium is highlighted.

Macro- and micro-behaviors of granular materials under different sample preparation methods and stress paths

A series of numerical drained tests have been performed to study the macroscopic and microscopic behaviors of granular materials prepared by three different techniques (isotropic compression, tamping, and dry pluviation). The samples consist of two kinds of ellipsoids with 1170 particles. The two kinds are equal by weight. The aspect ratios (major axis length/minor axis length) of these two kinds are 1.2 and 1.5, respectively. All samples were subjected to an isotropic confining pressure of 100 kPa before loaded along 11 different stress paths. The shear strengths of these drained tests were compared with two published yield models. Good agreement was found in general. One model showed an error of less than 5% for 90% of the time. Two microscopic parameters, the unit contact normal and the contact normal force, were examined for all specimens during axial compression loading. The principal stress ratio was found to correlate better with the contact normal force than the unit contact normal. The evolution of this microscopic descriptor was further investigated for all drained tests. The result showed a unique relationship between the strength descriptor of the contact normal force and the principal stress ratio. Excellent correlations were found for the drained tests with various intermediate stresses.