Local Packing Research Articles

Local volume concentration, packing domains, and scaling properties of chromatin

Local volume concentration, packing domains, and scaling properties of chromatin

Local volume concentration, packing domains, and scaling properties of chromatin.

We propose the Self Returning Excluded Volume (SR-EV) model for the structure of chromatin based on stochastic rules and physical interactions. The SR-EV rules of return generate conformationally defined domains observed by single-cell imaging techniques. From nucleosome to chromosome scales, the model captures the overall chromatin organization as a corrugated system, with dense and dilute regions alternating in a manner that resembles the mixing of two disordered bi-continuous phases. This particular organizational topology is a consequence of the multiplicity of interactions and processes occurring in the nuclei, and mimicked by the proposed return rules. Single configuration properties and ensemble averages show a robust agreement between theoretical and experimental results including chromatin volume concentration, contact probability, packing domain identification and size characterization, and packing scaling behavior. Model and experimental results suggest that there is an inherent chromatin organization regardless of the cell character and resistant to an external forcing such as RAD21 degradation.

A method for the determination of local packing factor distribution of a packed pebble bed by the improved line-based averaging method

The packing factor is an important parameter for describing the internal structural features in pebble beds, which have a significant influence on the heat and mass transfer behavior and the thermo-mechanical properties of the pebble bed. A comprehensive understanding of the packing structure is essential for the design and optimization of the pebble bed and can promote the application of the pebble bed. In this work, an improved line-based averaging method was proposed to calculate the local packing factor or local porosity distribution and validated by comparing the results with those obtained from experimental and numerical studies of cylindrical packed pebble bed. Furthermore, the local packing factor distributions in the angular-radial plane of the cylindrical pebble bed were revealed for the first time. In addition, the line-based averaging method has been applied to reveal the local packing factor distributions in the annular pebble beds, U-shaped pebble beds and hexagonal pebble beds. The main feature of this method is the ability to calculate and plot contour maps of local packing factor or porosity distributions for columnar pebble beds of arbitrary shapes, especially the local packing factor distributions in the cross-sectional plane and the angular-radial plane.

A comprehensive numerical approach to coil placement in cerebral aneurysms: mathematical modeling and in silico occlusion classification.

Endovascular coil embolization is one of the primary treatment techniques for cerebral aneurysms. Although it is a well-established and minimally invasive method, it bears the risk of suboptimal coil placement which can lead to incomplete occlusion of the aneurysm possibly causing recurrence. One of the key features of coils is that they have an imprinted natural shape supporting the fixation within the aneurysm. For the spatial discretization, our mathematical coil model is based on the discrete elastic rod model which results in a dimension-reduced 1D system of differential equations. We include bending and twisting responses to account for the coils natural curvature and allow for the placement of several coils having different material parameters. Collisions between coil segments and the aneurysm wall are handled by an efficient contact algorithm that relies on an octree based collision detection. In time, we use a standard symplectic semi-implicit Euler time stepping method. Our model can be easily incorporated into blood flow simulations of embolized aneurysms. In order to differentiate optimal from suboptimal placements, we employ a suitable in silico Raymond-Roy-type occlusion classification and measure the local packing density in the aneurysm at its neck, wall region and core. We investigate the impact of uncertainties in the coil parameters and embolization procedure. To this end, we vary the position and the angle of insertion of the micro-catheter, and approximate the local packing density distributions by evaluating sample statistics.

Hole statistics of equilibrium 2D and 3D hard-sphere crystals.

The probability of finding a spherical "hole" of a given radius r contains crucial structural information about many-body systems. Such hole statistics, including the void conditional nearest-neighbor probability functions GV(r), have been well studied for hard-sphere fluids in d-dimensional Euclidean space Rd. However, little is known about these functions for hard-sphere crystals for values of r beyond the hard-sphere diameter, as large holes are extremely rare in crystal phases. To overcome these computational challenges, we introduce a biased-sampling scheme that accurately determines hole statistics for equilibrium hard spheres on ranges of r that far extend those that could be previously explored. We discover that GV(r) in crystal and hexatic states exhibits oscillations whose amplitudes increase rapidly with the packing fraction, which stands in contrast to GV(r) that monotonically increases with r for fluid states. The oscillations in GV(r) for 2D crystals are strongly correlated with the local orientational order metric in the vicinity of the holes, and variations in GV(r) for 3D states indicate a transition between tetrahedral and octahedral holes, demonstrating the power of GV(r) as a probe of local coordination geometry. To further study the statistics of interparticle spacing in hard-sphere systems, we compute the local packing fraction distribution f(ϕl) of Delaunay cells and find that, for d ≤ 3, the excess kurtosis of f(ϕl) switches signat a certain transitional global packing fraction. Our accurate methods to access hole statistics in hard-sphere crystals at the challenging intermediate length scales reported here can be applied to understand the important problem of solvation and hydrophobicity in water at such length scales.

Molecular dynamics simulation of salt diffusion in constituting phosphazene-based polymer electrolyte

A growing demand to visualize polymer models in liquid poses a computational challenge in molecular dynamics (MD) simulation, as this requires emerging models under suitable force fields (FFs) to capture the underlying molecular behaviour accurately. In our present study, we have employed TIP3P potential on water and all atomistic optimized potentials for liquid simulations FFs to study the liquid electrolyte behavior of phosphazene-based polymer by considering its potential use in lithium-ion polymer batteries. We have explored the polymer's local structure, chain packing, wettability, and hydrophobic tendencies against the silicon surface using a combination of a pseudocontinuum model in MD simulation, and surface-sensitive sum frequency generation (SFG) vibrational spectroscopy. The finding yields invaluable insights into the molecular architecture of phosphazene. This approach identifies the importance of hydrophobic interactions with air and hydrophilic units with water molecules in understanding the behavior and properties of phosphazene-based polymers at interfaces, contributing to its advancements in materials science. The MD study uniquely captures traces of the polymer-ion linkage, which is observed to become more pronounced with the increase in polymer weight fraction. The theoretical observation of this linkage's influence on lithium-ion diffusion motion offers valuable insights into the fundamental physics governing the behavior of atoms and molecules within phosphazene-based polymer electrolytes in aqueous environments. Further these predictions are corroborated in the molecular-level depiction at the air-aqueous interface, as evidenced from the OH-oscillator strength variation measured by the SFG spectroscopy.The fundamental findings from this study open new avenues for utilizing MD simulation as a versatile methodology to gain profound insights into intermolecular interactions of polymer. It could be useful in the application of biomedical and energy-related research, such as polymer lithium-ion batteries, fuel cells, and organic solar cells.

Plasmid DNA Complexes in Powder Form Studied by Spectroscopic and Diffraction Methods.

Currently, new functional materials are being created with a strong emphasis on their ecological aspect. Materials and devices based on DNA biopolymers, being environmentally friendly, are therefore very interesting from the point of view of applications. In this paper, we present the results of research on complexes in the powder form based on plasmid DNA (pDNA) and three surfactants with aliphatic chains containing 16 carbon atoms (cetyltrimethylammonium chloride, benzyldimethylhexadecylammonium chloride and hexadecylpyridinium chloride). The X-ray diffraction results indicate a local hexagonal packing of DNA helices in plasmid DNA complexes, resembling the packing for corresponding complexes based on linear DNA. Based on the Fourier-transform infrared spectroscopy results, the DNA conformation in all three complexes was determined as predominantly of A-type. The two relaxation processes revealed by dielectric spectroscopy for all the studied complexes are connected with two different contributions to total conductivity (crystallite part and grain boundaries). The crystallite part (grain interior) was interpreted as an oscillation of the polar surfactant head groups and is dependent on the conformation of the surfactant chain. The influence of the DNA type on the properties of the complexes is discussed, taking into account our previous results for complexes based on linear DNA. We showed that the type of DNA has an impact on the properties of the complexes, which has not been demonstrated so far. It was also found that the layer of pDNA-surfactant complexes can be used as a layer with variable specific electric conductivity by selecting the frequency, which is interesting from an application point of view.

Machine Learning Classification of Local Environments in Molecular Crystals.

Identifying local structural motifs and packing patterns of molecular solids is a challenging task for both simulation and experiment. We demonstrate two novel approaches to characterize local environments in different polymorphs of molecular crystals using learning models that employ either flexibly learned or handcrafted molecular representations. In the first case, we follow our earlier work on graph learning in molecular crystals, deploying an atomistic graph convolutional network combined with molecule-wise aggregation to enable per-molecule environmental classification. For the second model, we develop a new set of descriptors based on symmetry functions combined with a point-vector representation of the molecules, encoding information about the positions and relative orientations of the molecule. We demonstrate very high classification accuracy for both approaches on urea and nicotinamide crystal polymorphs and practical applications to the analysis of dynamical trajectory data for nanocrystals and solid-solid interfaces. Both architectures are applicable to a wide range of molecules and diverse topologies, providing an essential step in the exploration of complex condensed matter phenomena.

Research on charging monitoring method for lithium-ion batteries based on magnetic field sensing

With the rapid and continuous development of new energy vehicles, safety issues during charging have become increasingly prominent. Thus, there is an urgent need to strengthen charging safety management. However, current charging detection methods have limitations, such as difficulty in monitoring local battery pack currents, and indirect monitoring through temperature and gas sensors lead to lag. To address these limitations, this article proposes a safety monitoring method based on an improved lithium-ion battery magnetic field sensing model that covers the entire charging process. Specifically, thermal field analysis was introduced to the existing electric magnetic coupling model, resulting in an electric thermal magnetic multi-physical field coupling model. By conducting experiments, the study verified that the accuracy of the improved model has increased by 15.5 %. Based on this model, the study proposed a method for predicting and warning of abnormal currents using BP neural network and mathematical statistical laws. The results showed that the current prediction accuracy of this method reached 96.4 %. Such high accuracy ensures real-time comprehensive monitoring of the current information of lithium-ion battery packs, thereby eliminating the hidden dangers of charging safety accidents caused by the lack of local current detection.

Windstone Medical Packaging Local Lower Extremity Pack, In House Ocular Pack, and Closure Kit

Windstone Medical Packaging Local Lower Extremity Pack, In House Ocular Pack, and Closure Kit

Measuring and Simulating the Transient Packing Density During Ultrasound Directed Self‐Assembly and Vat Polymerization Manufacturing of Engineered Materials

AbstractUltrasound‐directed self‐assembly (DSA) uses ultrasound waves to organize and orient particles dispersed in a fluid medium into specific patterns. Combining ultrasound DSA with vat photopolymerization (VP) enables manufacturing materials layer‐by‐layer, wherein each layer the organization and orientation of particles in the photopolymer is controlled, which enables tailoring the properties of the resulting composite materials. However, the particle packing density changes with time and location as particles organize into specific patterns. Hence, relating the ultrasound DSA process parameters to the transient local particle packing density is important to tailor the properties of the composite material, and to determine the maximum speed of the layer‐by‐layer VP process. This paper theoretically derives and experimentally validates a 3D ultrasound DSA model and evaluates the local particle packing density at locations where particles assemble as a function of time and ultrasound DSA process parameters. The particle packing density increases with increasing particle volume fraction, decreasing particle size, and decreasing fluid medium viscosity is determined. Increasing the particle size and decreasing the fluid medium viscosity decreases the time to reach steady‐state. This work contributes to using ultrasound DSA and VP as a materials manufacturing process.

Numerical Analysis of Non-Sphericity of Particles of Powder Material and their Effect on Packing Structure for Concentrated Solar Power Applications

Concentrated solar power technology represents a novel approach to generating solar power, characterized by high solar radiation density and uninterrupted operation. To store thermal energy and extend system operating hours, concentrated solar power systems rely on thermal energy storage. Selecting the appropriate heat storage media is crucial for designing a cost-efficient and straightforward system capable of withstanding high processing conditions. Among many powder materials, bauxite powder stands out as alternatives to conventional molten salt for thermal storage media due to its widespread availability and suitability for operating conditions. Particle shape, particularly its sphericity, plays a vital role in efficiently packing particles which directly affects to heat transfer properties. To investigate this effect, three samples of particles with different shape were formed: spheres, ellipses, and cylinders using Discrete Element Method with equal volume and material properties, and only variation of their sphericity factors. The study focused on the analysis of the porosity of the packed bed of particles with different shapes and its influence on the packing structure properties. The analysis of local packing fraction revealed that elliptical and cylindrical particles samples exhibited reduced heap formation and a more uniform distribution along z direction during vertical packing. The coordination number and radial distribution function analyses for these non-spherical particles showed a greater number of contacts between particles and disordered distribution of particles due to mechanical interlocking of non-spherical particles. The results indicated that the deviation of sphericity has a positive impact on the denser packing of particles generated more contacts and higher local packing fraction.

Structural fluctuations in thin cohesive particle layers in powder-based additive manufacturing

Producing dense and homogeneous powder layers with smooth free surface is challenging in additive manufacturing, as interparticle cohesion can strongly affect the powder packing structure and therefore influence the quality of the end product. We use the Discrete Element Method to simulate the spreading process of spherical powders and examine how cohesion influences the characteristics of the packing structure with a focus on the fluctuation of the local morphology. As cohesion increases, the overall packing density decreases, and the free surface roughness increases, which is calculated from digitized surface height distributions. Local structural fluctuations for both quantities are examined through the local packing anisotropy on the particle scale, obtained from Voronoï tessellation. The distributions of these particle-level metrics quantify the increasingly heterogeneous packing structure with clustering and changing surface morphology.

Physics-Informed Deep Learning Approach for Reintroducing Atomic Detail in Coarse-Grained Configurations of Multiple Poly(lactic acid) Stereoisomers.

Multiscale modeling of complex molecular systems, such as macromolecules, encompasses methods that combine information from fine and coarse representations of molecules to capture material properties over a wide range of spatiotemporal scales. Being able to exchange information between different levels of resolution is essential for the effective transfer of this information. The inverse problem of reintroducing atomistic degrees of freedom in coarse-grained (CG) molecular configurations is particularly challenging as, from a mathematical point of view, it is an ill-posed problem; the forward mapping from the atomistic to the CG description is typically defined via a deterministic operator ("one-to-one" problem), whereas the reversed mapping from the CG to the atomistic model refers to creating one representative configuration out of many possible ones ("one-to-many" problem). Most of the backmapping methods proposed so far balance accuracy, efficiency, and general applicability. This is particularly important for macromolecular systems with different types of isomers, i.e., molecules that have the same molecular formula and sequence of bonded atoms (constitution) but differ in the three-dimensional configurations of their atoms in space. Here, we introduce a versatile deep learning approach for backmapping multicomponent CG macromolecules with chiral centers, trained to learn structural correlations between polymer configurations at the atomistic level and their corresponding CG descriptions. This method is intended to be simple and flexible while presenting a generic solution for resolution transformation. In addition, the method is aimed to respect the structural features of the molecule, such as local packing, capturing therefore the physical properties of the material. As an illustrative example, we apply the model on linear poly(lactic acid) (PLA) in melt, which is one of the most popular biodegradable polymers. The framework is tested on a number of model systems starting from homopolymer stereoisomers of PLA to copolymers with randomly placed chiral centers. The results demonstrate the efficiency and efficacy of the new approach.

Efficacy of balneotherapy on pain, function, and sleep quality in patients with chronic low-back pain: a prospective observational study.

The aim of this prospective observational study was to evaluate the efficacy of a cycle of sulfate-arsenical-ferruginous mud bath therapy on pain, function, and sleep quality in patients with chronic low-back pain (CLBP). One hundred twenty-three patients treated at Levico Spa Center (Italy) with 12 daily local mud packs and generalized thermal baths were included in this experience. General medical assessments were performed before starting the therapy, at the end of the treatment, and 3months later. Pain intensity and stiffness were measured by a 0 to 10cm visual analogue scale (VAS); the range of mobility of the lumbar spine was evaluated using the Schober test and functional disability by the Roland-Morris Disability Questionnaire (RMDI). The Pittsburgh Sleep Quality Index (PSQI) was used to assess the sleep quality. The VAS score for pain and stiffness showed a significant reduction (p < 0.0001) at the end of the treatment persisting for 3months of follow-up. Similarly, a significant improvement was found for the Schober test and RMDQ. Finally, we showed a significant decrease of PSQI score at the end of the cycle of mud bath and at 3months of follow-up. The Spearman analysis showed a significant positive correlation between the score of PSQI and VAS pain, VAS stiffness, and RMDQ. In conclusion, this preliminary study confirms the beneficial and long-term efficacy of balneotherapy on pain and function and, for the first one, shows the positive effect on quality of sleep in patients with CLBP treated with a cycle of mud bath therapy.

Chain trajectories, domain shapes, and terminal boundaries in block copolymers

AbstractThe packing geometry of macromolecules in complex mesophases is of key importance to self‐organization in synthetic and biological soft materials. While approximate or heuristic models rely on often‐untested assumptions about how flexible molecules “fit in” to distinct locations of complex assemblies, physical assemblies derive from ensembles of fluctuating conformations, obscuring the connection between mesophase geometry and the underlying arrangements. Here, we present an approach to extract and analyze features of molecular packing in diblock block copolymer (BCP) melts, a prototypical soft matter system, based on the statistical description of chain conformations in self‐consistent field (SCF) theory. We show how average BCP chain trajectories in ordered morphologies can be analyzed from the SCF‐derived orientational order parameter of chain segments. We use these extracted trajectories to analyze the features of local packing geometry, including chain bending and tilt, as well as the terminal boundaries that delineate distinct domains in ordered BCP morphologies. We illustrate this analysis by focusing on measurable features of packing frustration in 2D (columnar) and 3D (spherical and bicontinuous) morphologies, notably establishing an explicit link between chain conformations in complex morphologies and their medial geometry.

Experimental and CFD-DEM study on local packing distribution and thermofluidic analysis of binary packed bed

In recent years, binary-packed beds have drawn attention due to their higher packing density and greater heat extraction capacity than mono-sized packed beds. In the past, the research on binary-packed beds mainly focused on finding empirical correlations for hydraulic permeability and equivalent thermal conductivity. Studies on fluid flow and heat transfer characterisation of the packed bed filled with binary particles are relatively scarce. In this work, the fluid flow and heat transfer analysis of purge gas flowing through the binary packed bed (1 ≤ size ratio ≤ 7 and at a volume of large particles = 40 %) is investigated using non-destructive experimental and Computational fluid dynamics-Discrete element modelling (CFD-DEM) approaches. An empirical correlation is developed for the local packing distribution inside binary packed beds. Based on the CFD-DEM results, the Ergun equation is modified to present the analytical equation for calculating the pressure drop for binary-packed beds at lower and higher Reynolds numbers. Further, the effect of the different size ratios on the fluid flow and heat transfer behaviour of the purge gas for binary-packed beds is investigated. The local packing factor increases with increasing the size ratio since small particles occupy the void among the large particles. This effect also increases the region with zero or negative flow at a higher size ratio. It is found that the purge gas penetrates deeper with an increasing size ratio; therefore, the heat extraction behaviour and the Nusselt number increase with increasing size ratios.

Integrating ultrasound transducers in polymer additive manufacturing technologies to fabricate engineered composite materials with tailored properties

Engineered composite materials that comprise filler material embedded in a polymer matrix may exhibit exotic physical properties that derive from the specific type, geometry, organization, and orientation of the particles in the matrix. However, existing techniques to manufacture such engineered materials are limited to laboratory scale, specific materials, and/or 2-D implementations, because it is challenging to organize and orient large quantities of particles into specific patterns. We utilize the acoustic radiation force associated with a standing ultrasound wave field to organize and orient particles of any material type dispersed in a fluid medium, into a user-specified pattern. We integrate the ultrasound wave field with several polymer additive manufacturing technologies to manufacture engineered materials in a layer-by-layer fashion, where in each layer we organize a user-specified pattern of filler using the acoustic radiation force. We demonstrate and discuss the integration of ultrasound transducers with several AM technologies and highlight their operating envelope and limitations. This research finds application in 3-D printing engineered materials with tailored properties by tuning the geometry, surface area, local packing density and, thus, the properties of the material.

Concentration dependence of microstructural evolution of Zr-Pt liquid alloys: A molecular dynamics study

The concentration dependence of the atomic structure and glass formation process of Zr100-xPtx (10 ≤ x ≤ 60) alloys were investigated by molecular dynamics simulations in this study. The agreement between the structural functions and the experimental data showed that the local order is reliably predicted. It was observed that the Zr40Pt60 alloy has the highest glass transition temperature and the fraction of crystal-like clusters. Common neighbor analysis revealed that the icosahedra and icosahedra-like clusters are more frequent for x < 50 concentrations, while bcc-like clusters are effective for x ≥ 50. It was seen that the local environment around Pt atoms would be more flexible, while Zr atoms would more easily adopt a preferential local packing according to their preferences. Present findings suggest that the strong chemical affinity between Zr and Pt plays an influential role in the nucleation of local structure in the system, and the addition of Pt weakens the icosahedra short-range order.

Bond Orientation-Determined Enthalpic Stress in Polymer Glasses upon Deformation.

The mechanical properties of polymer glass are determined by both intermolecular local packing structures and aligned intrachain configurations. These configurations involve multiple space scales, and the underlying mechanism is not well understood yet. By applying mechanical stimulation to cold-drawn polymer glasses, the present simulation work shows a one-to-one correspondence between arising retractive stress and the segment orientation parameter on the length scale of the intrachain connecting bond. Such retractive stress is a newly produced enthalpic stress when segment orientation on the length scale of bonds and particle mobility coexist. This reveals a potential mechanism of how the intrachain orientation on the length scale of bonds influences the mechanical behaviors of polymer glasses.