Length Scales Research Articles

Spherulite formation in green nonaqueous media: The impact of urea on gelation in glycerol

HypothesisLarge macroscopic assemblies formed by a surfactant, sodium dodecylsulfate (SDS), and glycerol, can be directed to assemble in a hierarchical manner by the addition of a strong hydrogen-bond donor/acceptor, such as urea. ContextSelf-assembly in complex media is important to a range of applications, for instance in biological media, which are multi-component, to industrial formulations, where additives are present for flavour, texture, and preservation. Here, the gelation and self-assembly of sodium dodecylsulfate (SDS) in glycerol is explored in the presence of an additive, urea. Urea was chosen due to its importance both fundamentally and industrially, but also because of its ability to form strong H-bonds and interact with both glycerol and SDS. ExperimentalTo cover the variety of length scales present in the gel-like phase, a combination of optical microscopy and small-angle X-ray scattering techniques were used to probe the micro- to nanoscale. FindingsOn the microscale, the formation of a spectacular spherulite phase, even at low urea contents - 0.1 wt%, upon cooling was observed, a stark difference to the microfibrillar phase observed in the absence of urea. Interestingly, the nanostructure of the two crystalline phases were similar and showed negligible differences. This suggests that urea is not involved in the SDS/glycerol microfibril formation but instead directs the assembly of spherulites by bundling the microfibrils. These ternary systems are also probed as a function of urea content, SDS concentration, and temperature. The observations in this work highlight the importance of small molecules in the self-assembly process, which is relevant both fundamentally but also industrially, where small molecules are often added to formulations.

Linear operator theory of phase mixing

Abstract We study solutions of the collisionless Boltzmann equation (CBE) in a functional Koopman representation. This facilitates the use of linear spectral techniques characteristic of the analysis of Schrödinger-type equations. For illustrative purposes, we consider the classical phase mixing of a non-interacting distribution function in a quartic potential. Solutions are determined perturbatively relative to a harmonic oscillator. We impose a form of coarse-graining by choosing a finite dimensional basis to represent the distribution function and time evolution operators, which sets a minimum length scale on phase space structure. We observe a relationship between the dimension of the representation and the multiplicity of the harmonic oscillator eigenvalues. System dynamics are understood in terms of degenerate subspaces of the linear operator spectra. Each subspace is associated with a mode of the harmonic oscillator, the first two being bending and breathing structures. The quartic potential splits the degenerate eigenvalues within each subspace. This facilitates the formation of spiral structure as deformations from the harmonic oscillator modes. We ultimately argue that this construction provides a promising avenue for study of self-interacting systems experiencing phase mixing, which is an outstanding problem in the context of the Gaia DR2 vertical phase space spirals.

Our Growing Volume Electron Microscopy Community

Abstract Volume electron microscopy (vEM) represents a powerful group of electron microscopy workflows that enable collection of large three-dimensional volumes at ultrastructural resolution to interrogate cells and tissues across length scales. Due to the breadth of approaches, users are challenged with numerous specialized and nuanced workflows and the requirements for handling, segmenting, visualizing, and quantifying large and complex datasets. As such, a vEM community of core facilities, early adopters, and commercial vendors of vEM solutions have come together to reduce the barriers for successful adoption of vEM. Supported by the Chan Zuckerberg Initiative, the rapidly expanding community has built a broad coalition sharing knowledge and working together to accelerate improvements in results, speed, and automation in vEM workflows. Here we provide an overview of the vEM concept, our growing resources, and an open invitation to join our community and upcoming vEM Technology Forum with technical talks, discussions, hands-on demonstrations, and networking.

An exploration of potentially reversible controls on millennial-scale variations in the slip rate of seismogenic faults: Linking structural observations with variable earthquake recurrence patterns

Paleoseismic studies show that faults within a fault system may trade off slip over time, with mechanically complementary faults displaying alternating fast- and slow periods. Each of these periods spans multiple seismic cycles, and typically involves ~20-25m of slip. This suggests that the relative strength (or tendency to slip) of individual faults varies, over time and displacement scales larger than those of individual seismic cycles. The mechanisms responsible for these strength variations must: affect rocks in the strongest portion of the fault (the brittle-ductile transition) as this likely controls the overall slip rate of the fault; be reversible (or able to be counteracted) on a cyclical basis; provide a negative feedback that operates to change the fault from its current state; and have a measurable effect on fault strength over a time or length scale that corresponds to the observed fast and slow periods of fault slip. In this paper, we systematically explore 19 potentially weakening and 11 potential strengthening mechanisms and evaluate them in light of these criteria. This analysis reveals a relatively small subset of mechanisms that could account for the observed behavior, leading us to suggest a possible model for fault strength evolution.

Crystal Structure Prediction and Performance Assessment of Hydrogen Storage Materials: Insights from Computational Materials Science

Hydrogen storage materials play a pivotal role in the development of a sustainable hydrogen economy. However, the discovery and optimization of high-performance storage materials remain a significant challenge due to the complex interplay of structural, thermodynamic and kinetic factors. Computational materials science has emerged as a powerful tool to accelerate the design and development of novel hydrogen storage materials by providing atomic-level insights into the storage mechanisms and guiding experimental efforts. In this comprehensive review, we discuss the recent advances in crystal structure prediction and performance assessment of hydrogen storage materials from a computational perspective. We highlight the applications of state-of-the-art computational methods, including density functional theory (DFT), molecular dynamics (MD) simulations, and machine learning (ML) techniques, in screening, evaluating, and optimizing storage materials. Special emphasis is placed on the prediction of stable crystal structures, assessment of thermodynamic and kinetic properties, and high-throughput screening of material space. Furthermore, we discuss the importance of multiscale modeling approaches that bridge different length and time scales, providing a holistic understanding of the storage processes. The synergistic integration of computational and experimental studies is also highlighted, with a focus on experimental validation and collaborative material discovery. Finally, we present an outlook on the future directions of computationally driven materials design for hydrogen storage applications, discussing the challenges, opportunities, and strategies for accelerating the development of high-performance storage materials. This review aims to provide a comprehensive and up-to-date account of the field, stimulating further research efforts to leverage computational methods to unlock the full potential of hydrogen storage materials.

Identification, characterization and classification of prokaryotic nucleoid-associated proteins.

Common throughout life is the need to compact and organize the genome. Possible mechanisms involved in this process include supercoiling, phase separation, charge neutralization, macromolecular crowding, and nucleoid-associated proteins (NAPs). NAPs are special in that they can organize the genome at multiple length scales, and thus are often considered as the architects of the genome. NAPs shape the genome by either bending DNA, wrapping DNA, bridging DNA, or forming nucleoprotein filaments on the DNA. In this mini-review, we discuss recent advancements of unique NAPs with differing architectural properties across the tree of life, including NAPs from bacteria, archaea, and viruses. To help the characterization of NAPs from the ever-increasing number of metagenomes, we recommend a set of cheap and simple invitro biochemical assays that give unambiguous insights into the architectural properties of NAPs. Finally, we highlight and showcase the usefulness of AlphaFold in the characterization of novel NAPs.

Grey‐zone simulations of shallow‐to‐deep convection transition using dynamic subgrid‐scale turbulence models

AbstractWe examine the ability of two dynamic turbulence closure models to simulate the diurnal development of convection and the transition from dry to shallow cumuli and then to deep convection. The dynamic models are compared with the conventional Smagorinsky scheme at a range of cloud‐resolving and grey‐zone resolutions. The dynamic schemes include the Lagrangian‐averaged, scale‐dependent dynamic Smagorinsky model and a Lagrangian‐averaged, dynamic mixed model. The conventional Smagorinsky model fails to reproduce the shallow convection stage beyond the large‐eddy simulation regime, continuously building up the convective available potential energy that eventually leads to an unrealistic deep convection phase. The dynamic Smagorinsky model significantly improves the representation of shallow and deep convection; however, it exhibits issues similar to the conventional scheme at coarser resolutions. In contrast, the dynamic mixed model closely follows the large‐eddy simulation results across the range of sub‐kilometre simulations. This is achieved by the combined effect of an adaptive length scale and the inclusion of the Leonard terms, which can produce counter‐gradient fluxes through the backscatter of energy from the subgrid to the resolved scales and enable appropriate non‐local contributions. A further sensitivity test on the inclusion of the Leonard terms on all hydrometeor fluxes reveals the strong interaction between turbulent transport and microphysics and the possible need for further optimisation of the dynamic mixed model coefficients together with the microphysical representation.

Entanglement structures from modified IR geometry

We investigate a new proposal connecting the geometry at various radial scales in asymptotic AdS spacetime with entanglement structure at corresponding real-space length scales of the boundary theory. With this proposal, the bulk IR geometry encodes the long-scale entanglement structure of the dual quantum system. We consider two distinct types of IR geometries, namely the spherical case and the hyperbolic case, which are intimately related to the physics of differential entropy and brane-world holography separately. We explore the corresponding change in the dual long-scale entanglement structures, utilizing the tools of the Ryu-Takayanagi formula, conditional mutual information, and partial entanglement entropy. The results indicate that modifying the IR geometry leads to a redistribution of entanglement at scales longer than a critical length determined by the location of the IR region, with the two modified IR geometries corresponding to two opposite ways of redistribution. Furthermore, we establish the maximum amount of entanglement that can be modified, which is proportional to the area of the IR region.

Addressing the Hubble tension in Yukawa cosmology?

In Yukawa cosmology, a recent discovery revealed a relationship between baryonic matter and the dark sector. The relation is described by the parameter α and the long-range interaction parameter λ - an intrinsic property of the graviton. Applying the uncertainty relation to the graviton raises a compelling question: Is there a quantum mechanical limit to the measurement precision of the Hubble constant (H0)? We argue that the uncertainty relation for the graviton wavelength λ can be used to explain a running of H0 with redshift. We show that the uncertainty in time has an inverse correlation with the value of the Hubble constant. That means that the measurement of the Hubble constant is intrinsically linked to length scales (redshift) and is connected to the uncertainty in time. On cosmological scales, we found that the uncertainty in time is related to the look-back time quantity. For measurements with a high redshift value, there is more uncertainty in time, which leads to a smaller value for the Hubble constant. Conversely, there is less uncertainty in time for local measurements with a smaller redshift value, resulting in a higher value for the Hubble constant. Therefore, due to the uncertainty relation, the Hubble tension is believed to arise from fundamental limitations inherent in cosmological measurements. Finally, our findings indicate that the mass of the graviton fluctuates with specific scales, suggesting a possible mass-varying mechanism for the graviton.

Source/drain extension asymmetric counter-doping for suppressing channel leakage in stacked nanosheet transistors

In the relentless pursuit of semiconductor device scaling, stacked silicon nanosheet gate-all-around field-effect transistors (NSFETs) are emerging as key candidates for sub-3nm technology nodes. However, the challenge of channel leakage in these devices is critical and necessitates innovative solutions. A novel SDE asymmetric counter-doping technique is proposed in this study. It investigates the impact of source/drain extension on device performance using different process schemes through three-dimensional technical computer-aided design (3D TCAD) simulations. The simulations demonstrate a comprehensive technically advantages for 25.2 %/16.65 % reduction in the off-state leakage, 27.36 %/15.03 % improvement in the on-off current ratio of N/P NSFETs, respectively. Furthermore, it shows more performance gain as the gate length scaling beyond 3 nm technology nodes. The compatibility of the asymmetric counter-doping method with mainstream NSFET integration flows and its scalability to 10 nm gate lengths indicate that it is a promising approach to optimize NSFETs performance with little extra process cost.

Uncertainty relations in thermodynamics of irreversible processes on a mesoscopic scale

Studies of mesoscopic structures have become a leading and rapidly evolving research field ranging from physics, chemistry, and mineralogy to life sciences. The increasing miniaturization of devices with length scales of a few nanometers is leading to radical changes in the realization of new materials and in shedding light on our understanding of the fundamental laws of nature that govern the dynamics of systems at the mesoscopic scale. We investigate thermodynamic processes in small systems in Onsager’s region based on recent experimental results and previous theoretical research. We show that fundamental quantities such as the total entropy production, the thermodynamic variables conjugate to the thermodynamic forces, and the Glansdorff–Prigogine’s dissipative variable may be discretized at the mesoscopic scale. We establish the canonical com- mutation rules (CCRs) valid at the mesoscopic scale. The numerical value of the discretization constant is estimated experimentally. The ultraviolet divergence problem is solved by applying the correspondence principle with Einstein–Prigogine’s fluctuations theory in the limit of macroscopic systems. Examples of quantization of thermodynamic systems out of the Onsager region are currently being finalized.

Scale Characteristics of Six Fish Species of the Genus Cyprinion (Teleostei: Cypriniformes): A Microscopic Analysis.

This study investigated the morphological characteristics of scales in six Cyprinion species, using light and scanning electron microscopy focusing on key features such as scale type, key scales, lateral line scales, radius/radii, rostral margin, focus, circuli, lepidonts, tubercles, and scale indices. The research analyzed the scales using ultramicroscopy and light microscopy imaging, categorizing them based on size classes and body regions. The morphological variations in scale characteristics were examined across different species, regions, and size classes. Notable findings included the tetra-sectioned form of scales, representing a unique characteristic of the Cyprinion genus. Morphological changes in scale features were observed with fish growth, particularly in the overall shape, focus shape, and size. Quantitative analysis revealed variations in average relative scale length and width among different species, regions, and size classes. The study utilized canonical discriminant analysis for multivariate assessment, classifying the species into distinct groups based on morphometric indices. The findings contribute to the understanding of scale morphology in Cyprinion species and exploring morphological variation between the examined species.

A Coupled CFD-DEM Approach to Model Reactive Granular Beds

Packed and moving bed reactors are widely used in process industry to process granular material. Shaft furnaces are one example of this. Due to their counterflow layout, a high thermal efficiency can be achieved. Shaft furnaces have a wide range of length and time scales that makes them challenging to model. Geometric details, like injection nozzles, are smaller than the particle size, which need a computationally highly expensive resolved Discrete Element Method (DEM) approach. To overcome this problem, a computing technique called Volume Fraction Smoother (VFS) was developed. Since the time scales in those systems reach from milliseconds for the particle collisions to hours of process time, the process time scale must be separated to model a quasi-steady state with reasonable computing time. For this, the Time Scale Splitting Method (TSSM) was introduced. This method was also adapted to model the self-heating behaviour of direct reduced iron (DRI).

Computer Simulation for Determination of Critical Buckling Temperatures of Sandwich Microplates with Cellular Core and CNT-RC Piezoelectric Patches via MSGT

This study introduces a computational simulation which leads to obtaining the critical buckling temperatures of a sandwich microplate which is modeled based on the modified strain gradient theory. This theory includes three material length scale parameters. The core of the microstructure is made from cellular materials which is treated with piezoelectric carbon nanotubes-reinforced composite patches. An advanced trigonometric hyperbolic shear deformation theory, eliminating the need for shear correction factors, is employed to analyze the microstructure. Thickness-dependent variations in structural characteristics are observed based on predefined functions. The equilibrium equations are derived using the virtual displacement principle. Fourier series functions provide an analytical way of solving these equations. The findings are verified by comparison with earlier research that was published and used fewer complex combinations. The study looks at how several important factors affect the structure’s reaction to thermal buckling.

Structure and scaling of Kitaev chain across a quantum critical point in real space.

The spatial Kibble-Zurek mechanism (KZM) is applied to the Kitaev chain with inhomogeneous pairing interactions that vanish in half of the lattice and result in a quantum critical point separating the superfluid and normal-gas phases in real space. The weakly-interacting BCS theory predicts scaling behavior of the penetration of the pair wavefunction into the normal-gas region different from conventional power-law results due to the non-analytic dependence of the BCS order parameter on the interaction. The Bogoliubov-de Gennes (BdG) equation produces numerical results confirming the scaling behavior and hints complications in the strong-interaction regime. The limiting case of the step-function quench reveals the dominance of the BCS coherence length in absence of additional length scale. Furthermore, the energy spectrum and wavefunctions from the BdG equation show abundant in-gap states from the normal-gas region in addition to the topological edge states.

Rapid detection of rare events from in situX-ray diffraction data using machine learning.

High-energy X-ray diffraction methods can non-destructively map the 3D microstructure and associated attributes of metallic polycrystalline engineering materials in their bulk form. These methods are often combined with external stimuli such as thermo-mechanical loading to take snapshots of the evolving microstructure and attributes over time. However, the extreme data volumes and the high costs of traditional data acquisition and reduction approaches pose a barrier to quickly extracting actionable insights and improving the temporal resolution of these snapshots. This article presents a fully automated technique capable of rapidly detecting the onset of plasticity in high-energy X-ray microscopy data. The technique is computationally faster by at least 50 times than the traditional approaches and works for data sets that are up to nine times sparser than a full data set. This new technique leverages self-supervised image representation learning and clustering to transform massive data sets into compact, semantic-rich representations of visually salient characteristics (e.g. peak shapes). These characteristics can rapidly indicate anomalous events, such as changes in diffraction peak shapes. It is anticipated that this technique will provide just-in-time actionable information to drive smarter experiments that effectively deploy multi-modal X-ray diffraction methods spanning many decades of length scales.

The transition between the collision-dominated and ballistic electron transport regimes as the device length is reduced: A continuum analysis

Noting that the conventional collision-dominated electron transport perspective is only relevant when the length scale over which the transit occurs is greater than the electron’s mean free path, one can conceptually partition the electron transport “space” into collision-dominated and ballistic electron transport regimes. As the boundaries between these regimes are quite porous, in this analysis, we devise a means of quantitatively examining the transition between electron transport regimes as the length scale is reduced on a continuum basis. Our approach introduces a collision-dominated fractional scattering parameter, this parameter quantifying the fraction of the total scattering rate that arises purely from bulk scattering processes, contact scattering also contributing to the total scattering rate. We pursue this analysis for two conventional semiconductors of interest, silicon and gallium arsenide. A determination of the dependence of the results on both the length scale and the crystal temperature is pursued. Finally, for the specific case of room temperature, a comparison with the results of experiment is performed.

Dispersive interaction between two atoms in Proca quantum electrodynamics

We analyze the influence of a massive photon in the dispersive interaction between two atoms in their fundamental states. We work in the context of Proca quantum electrodynamics. The photon mass not only introduces a new length scale but also gives rise to a longitudinal polarization for the electromagnetic field. We obtain explicitly the interaction energy between the atoms for any distance regime and consider several particular cases. We show that, for a given interatomic distance, the greater the photon mass the better it is the nonretarded approximation. Published by the American Physical Society 2024

GCMe: Efficient Implementation of the Gaussian Core Model with Smeared Electrostatic Interactions for Molecular Dynamics Simulations of Soft Matter Systems.

In recent years, molecular dynamics (MD) simulations have emerged as an essential tool for understanding the structure, dynamics, and phase behavior of charged soft matter systems. To explore phenomena across greater length and time scales in MD simulations, molecules are often coarse-grained for better computational performance. However, commonly used force fields represent particles as hard-core interaction centers with point charges, which often overemphasizes the packing effect and short-range electrostatics, especially in systems with bulky deformable organic molecules and systems with strong coarse-graining. This underscores the need for an efficient soft-core model to physically capture the effective interactions between coarse-grained particles. To this end, we implement a soft-core model uniting the Gaussian core model with smeared electrostatic interactions that is phenomenologically equivalent to recent theoretical models. We first parametrize it generically using water as the model solvent. Then, we benchmark its performance in the OpenMM toolkit for different boundary conditions to highlight a computational speedup of up to 34 × compared to commonly used force fields and existing implementations. Finally, we demonstrate its utility by investigating how boundary polarizability affects the adsorption behavior of a polyelectrolyte solution on perfectly conducting and nonmetal boundaries.

Ocean Currents Resolved on Regional Length Scales

Ocean Currents Resolved on Regional Length Scales