Dynamic Properties Research Articles

Shear Wave Velocity Prediction with Hyperparameter Optimization

Shear wave velocity (Vs) is an important soil parameter to be known for earthquake-resistant structural design and an important parameter for determining the dynamic properties of soils such as modulus of elasticity and shear modulus. Different Vs measurement methods are available. However, these methods, which are costly and labor intensive, have led to the search for new methods for determining the Vs. This study aims to predict shear wave velocity (Vs (m/s)) using depth (m), cone resistance (qc) (MPa), sleeve friction (fs) (kPa), pore water pressure (u2) (kPa), N, and unit weight (kN/m3). Since shear wave velocity varies with depth, regression studies were performed at depths up to 30 m in this study. The dataset used in this study is an open-source dataset, and the soil data are from the Taipei Basin. This dataset was extracted, and a 494-line dataset was created. In this study, using HyperNetExplorer 2024V1, Vs prediction based on depth (m), cone resistance (qc) (MPa), shell friction (fs), pore water pressure (u2) (kPa), N, and unit weight (kN/m3) values could be performed with satisfactory results (R2 = 0.78, MSE = 596.43). Satisfactory results were obtained in this study, in which Explainable Artificial Intelligence (XAI) models were also used.

The Microstructures, Mechanical Properties, and Energetic Characteristics of a Novel Dual-Phase Ti40Zr40W10Mo10 High-Entropy Alloy

High-energy structural materials (ESMs) integrate a high energy density with rapid energy release, offering promising applications in advanced technologies. In this study, a novel dual-phase Ti40Zr40W10Mo10 high-entropy alloy (HEA) was synthesized and evaluated as a potential ESM. The alloy exhibited a body-centered cubic (BCC) matrix with Mo-W-rich BCC precipitates of varying sizes, which increased proportionally with the W content. The compressive mechanical properties were assessed across a range of strain rates, revealing that the W10 HEA sustained a compressive strength of 2300 MPa at a strain rate of 3000 s−1. This exceptional performance is attributed to the uniform distribution of circular Mo-W-rich BCC precipitates. Conversely, in the W13 HEA, the aggregated and large Mo-W-rich precipitates deteriorated its dynamic properties. Furthermore, deflagration behavior was observed during dynamic deformation of W10, highlighting its potential as a high-performance ESM.

Tuning the electronic structure and SMSI by integrating trimetallic sites with defective ceria for the CO2 reduction reaction

Heterogeneous catalysts have emerged as a potential key for closing the carbon cycle by converting carbon dioxide (CO 2 ) into value-added chemicals. In this work, we report a highly active and stable ceria (CeO 2 )-based electronically tuned trimetallic catalyst for CO 2 to CO conversion. A unique distribution of electron density between the defective ceria support and the trimetallic nanoparticles (of Ni, Cu, Zn) was established by creating the strong metal support interaction (SMSI) between them. The catalyst showed CO productivity of 49,279 mmol g −1 h −1 at 650 °C. CO selectivity up to 99% and excellent stability (rate remained unchanged even after 100 h) stemmed from the synergistic interactions among Ni-Cu-Zn sites and their SMSI with the defective ceria support. High-energy-resolution fluorescence-detection X-ray absorption spectroscopy (HERFD-XAS) confirmed this SMSI, further corroborated by in situ electron energy loss spectroscopy (EELS) and density functional theory (DFT) simulations. The in situ studies (HERFD-XAS & EELS) indicated the key role of oxygen vacancies of defective CeO 2 during catalysis. The in situ transmission electron microscopy (TEM) imaging under catalytic conditions visualized the movement and growth of active trimetallic sites, which completely stopped once SMSI was established. In situ FTIR (supported by DFT) provided a molecular-level understanding of the formation of various reaction intermediates and their conversion into products, which followed a complex coupling of direct dissociation and redox pathway assisted by hydrogen, simultaneously on different active sites. Thus, sophisticated manipulation of electronic properties of trimetallic sites and defect dynamics significantly enhanced catalytic performance during CO 2 to CO conversion.

Evaluation and Future Prospects of Data‐Driven Intelligence‐Based Framework for Predicting Cyclic Behavior of Reconstituted Sand

ABSTRACTMost of the robust artificial intelligence (AI)‐based constitutive models are developed with synthetic datasets generated from traditional constitutive models. Therefore, they fundamentally rely on the traditional constitutive models rather than laboratory test results. Also, their potential use within geotechnical engineering communities is limited due to the unavailability of datasets along with the model code files. In this study, the data‐driven constitutive models are developed using only laboratory test databases and deep learning (DL) techniques. The laboratory database was prepared by conducting cyclic direct simple shear (CDSS) tests on reconstituted sand, that is, PDX sand. The stacked long short‐term memory (LSTM) network and its variants are considered for developing the predictive models of the shear strain (γ [%]) and excess pore pressure ratio (ru) time histories. The suitable input parameters (IPs) are selected based on the physics behind the generation of ru and γ (%) of the liquefiable sands. The predicted responses of γ (%) and ru agree well in most cases and are used to predict the dynamic soil properties of the PDX sand. The same modeling framework is extended for other sand and compared with existing AI‐based constitutive models to verify its practical applicability. In summary, it is observed that though the trained models predicted the time histories of ru and γ reasonably well; however, they struggled to predict the hysteresis loops at higher cycles. Therefore, more research is needed to verify and enhance the predictability of existing AI‐based models in the future before using them in practice for simulating cyclic response.

Investigating nonlinear dynamic properties of an inertial sensor with rotational velocity-dependent rigidity

This study investigates the nonlinear dynamics of a system with frequency-dependent stiffness using a MEMS-based capacitive inertial sensor as a case study. The sensor is positioned directly on a rotating component of a machine and consists of a microbeam clamped at both ends by fixed supports with a fixed central proof mass. The nonlinear behavior is determined by electrostatic forces, axial and bending motion coupling, and frequency-dependent stiffness. The numerical Galerkin approach is employed for discretization of the coupled differential equations in spatial coordinates. To obtain the sensor response as a function of frequency, a continuation arc-length method based on weak formulation energy balance method is used. This approach uses a physical gradient descent learning based method to obtain unknown coefficients of the considered response. The presented method computes the periodic steady-state solution of the design by considering different frequency contents within the response, including different harmonics of the response expansion. However, the primary and secondary resonances at various harmonic frequencies within the response can be predicted. In this article, the dynamic behavior of the design is investigated by testing different levels of shaft acceleration and different bias voltages. The purpose of the evaluation is to determine the frequency-dependent stiffness of the accelerometer for different levels of shaft speed, taking into account various sensor geometric parameters. The analysis demonstrates the behavior of the sensors under different conditions, particularly highlighting the nonlinearity of the sensor, which causes primary and secondary resonances in the first, second, and third harmonic responses of the accelerometer, especially at higher bias voltages. Fast Fourier analyses indicated that the transversal vibration amplitude of the structure in the second and third harmonies are so considerable that cannot be neglected. The mathematical method used in this study can be applied by researchers and engineers in the design of such sensors to effectively create these devices.

Programing Green Light-Responsive Hydrogels: from Photo-Weakening to Photo-unresponsiveness and Photo-Strengthening.

Light-responsive hydrogels are highly valued for their dynamic mechanical properties and biocompatibility. In this study, we present a hydrogel system that can either soften or strengthen on green light exposure, or remain unresponsive to light, depending on the addition of adenosyl cobalamin (AdoCbl) and Co2+. These protein-based hydrogels were formed using genetically encoded SpyTag-SpyCatcher chemistry and included green light-sensitive CarHc protein domains. As previously reported, these hydrogels formed in the dark with the addition of AdoCbl, due to the tetramerization of the CarHc domains. Under green light exposure, the CarHc tetramers disassembled, leading to a rapid transition from gel to sol. Interestingly, we discovered that an excess of AdoCbl leads to photo-strengthening rather than photo-weakening. This occurred because light exposure induces interchain crosslinks between AdoCbl and poly-histidine tags (His6-tags) of the proteins. Furthermore, incorporating Co2+ ions enhanced hydrogel stiffness by coordinating to His6-tags. This not only suppressed photo-weakening but also promoted photo-strengthening behaviour. These findings highlight the role of His6-tags in photochemical crosslinking with excess AdoCbl and in coordination to Co2+ ions, providing a novel strategy for designing tuneable, light responsiveness in materials.

Chaos, Fractals and Bifurcation in Real Dynamics of Two-Parameter Family Associated to Logarithmic Functions

The purpose of this article is to provide bifurcation diagrams and observe chaotic behaviour in the real dynamics of two-parameter family of function \(\Phi(x)=x+(1-\lambda x)\ln(ax): x>0, \lambda>0, a>0\). We consider here parameter a is a positive and continuous real parameter while λ is positive but a discrete real parameter. The dynamical properties of this nonlinear system family analyze numerically as well as graphically by using fixed point iterative method. Bifurcation diagrams for the real dynamics of the function are plotted by varying the values of the parameter which are fractals in nature. Also, we show that chaos exists in the dynamics of the function by looking at period-doubling in the bifurcation diagram. Further, chaotic behaviour studies by simulation of the positive Lyapunov exponents which observe by varying the parameters similar to the bifurcation diagrams.

Accurate Force Field for Carbon Dioxide-Silica Interactions Based on Density Functional Theory.

Fluid-silica interfaces are ubiquitous in chemistry, occurring in both natural geochemical environments and practical applications ranging from separations to catalysis. Simulations of these interfaces have been, and continue to be, a significant avenue for understanding their behavior. A constraining factor, however, is the availability of accurate force fields. Most simulations use traditional "mixing rules" to determine nonbonded dispersion interactions, an approach that has not been critically examined. Here, we present Lennard-Jones parameters for the interaction of carbon dioxide with silica interfaces that are optimized to reproduce density functional theory (DFT)-based binding energies. The modeling is based on the recently developed silica-DDEC force field, whose atomic charges are consistent with DFT calculations. Standard mixing rules are found to predict weaker CO2 binding to silica than that obtained from DFT, an effect corrected by the optimized parameters given here. This behavior extends to other silica force fields (Clayff and Gulmen-Thompson), and the present Lennard-Jones parameters improve their performance as well. The effects of improved Lennard-Jones parameters on the structural and dynamical properties of condensed CO2 in silica slit pores are also examined.

Structural and Dynamic Heterogeneity of Deep Eutectic Solvents Composed of Choline Chloride and Ortho-Phenol Derivatives.

Structural, thermal, and dynamic properties of four deep eutectic solvents comprising choline chloride paired with ortho-phenolic derivative hydrogen-bond donors were probed using experiments and molecular simulations. The hydrogen-bond donors include phenol, catechol, o-chlorophenol, and o-cresol, in a 3:1 mixture with the hydrogen-bond acceptor choline chloride. Density, viscosity, and pulsed-field gradient NMR diffusivity measurements were conducted over a range of temperatures. Classical and ab initio molecular dynamics simulation results match experimental data reasonably well. The simulation results were then used to perform a more detailed analysis of the local structure and dynamics of these systems.

Investigation of magnetic properties in Y3Fe5O12/Cr2O3/Pt tri-layers

Abstract Artificial multiferroics combining ferroelectric and magnetic materials exhibits a sizable magnetoelectric coupling and further shows a great potential for realizing low power consumption information processing. In this work, hybrid structures consisting of the ferrimagnetic yttrium iron garnet (Y3Fe5O12, (YIG)) layer and anti-ferromagnetic chromium oxide (Cr2O3) layer are deposited directly onto (110)-oriented single-crystal Pb(Mg1/3Nb2/3)0.7Ti0.3O3 (PMN-PT) ferroelectric substrates by means of radio frequency magnetron sputtering and post-annealing procedures. The dynamical properties of YIG films are investigated via ferromagnetic resonance measurements and a low damping constant of 0.00258 is obtained. Combining the element-specific soft x-ray absorption spectroscopy, x-ray magnetic circular dichroism and x-ray magnetic linear dichroism, the magnetic order in YIG film and the Néel order in Cr2O3 layers are further characterized. By capping a platinum (Pt) layer on PMN-PT/YIG/Cr2O3, a visible spin-charge converted signal has been obtained via both spin pumping and spin Seebeck measurements. These results indicate that the artificial multiferroic structures PMN-PT/YIG/Cr2O3/Pt serve as a promising material platform for realizing an efficient manipulation of magnetism via electrical means.

Quantum-critical and dynamical properties of the XXZ bilayer with long-range interactions

Quantum-critical and dynamical properties of the XXZ bilayer with long-range interactions

Evaluating the Temporal Effect of Image Features on Dynamic Aesthetic Experience of Generative art

The emergence of dynamic digital arts, such as dynamic generative art, has reshaped how aesthetic experiences can be studied, emphasizing their inherently dynamic and evolving nature. Within the framework of computational aesthetics, which seeks to model and quantify human perceptions of beauty, this study extends the focus from static to dynamic stimuli. We investigate the temporal relationship between evolving image features and aesthetic judgments, exploring how dynamic visual properties influence the progression of aesthetic judgments over time. By introducing temporal offsets in the analysis and employing linear and nonlinear statistical models, we examine continuous aesthetic ratings alongside an extensive set of image features. Our findings offer a novel perspective on the enduring impact of image features on aesthetic experience.

Construction of Supramolecular Polymer Network Elastomers Based on Pillar[5]arene/Alkyl Chain Host-Guest Interactions.

As a special kind of supramolecular compound with many favorable properties, pillar[n]arene-based supramolecular polymer networks (SPNs) show potential application in many fields. Although we have come a long way using pillar[n]arene to prepare SPNs and construct a series of smart materials, it remains a challenge to enhance the mechanical strength of pillar[n]arene-based SPNs. To address this issue, a new supramolecular regulation strategy was developed, which could precisely control the preparation of pillar[n]arene-based SPN materials with excellent mechanical properties by adjusting the polymer network structures. Specifically, we utilized the host-guest interaction between pillar[5]arene and the alkyl chain of butyl acrylate monomer to form a supramolecular polymer network and achieved the transformation of different states by regulating the cross-linking density of the polymer networks. Additionally, the polymer networks exhibited good stimuli responsiveness as well as excellent dynamic properties and reconfigurable characteristics through a temperature change and the addition of competitive hosts or guests. The research provided new possibilities for the development of polymer materials.

Structural Origin of Dynamic Heterogeneity in Supercooled Liquids.

As a liquid is supercooled toward the glass transition point, its dynamics slow significantly, provided that crystallization is avoided. With increased supercooling, the particle dynamics become more spatially heterogeneous, a phenomenon known as dynamic heterogeneity. Since its discovery, this characteristic of metastable supercooled liquids has garnered considerable attention in glass science. However, the precise physical origins of dynamic heterogeneity remain elusive and widely debated. In this perspective, we examine the relationship between dynamic heterogeneity and structural order, based on numerical simulations of fragile liquids with isotropic potentials and strong liquids with directional interactions. We demonstrate that angular ordering, arising from many-body steric interactions, plays a crucial role in the slow dynamics and dynamic cooperativity of fragile liquids. Additionally, we explore how the growth of static order correlates with slower dynamics. In fragile liquids exhibiting super-Arrhenius behavior, the spatial extent of regions with high angular order grows upon cooling, and the sequential propagation of particle rearrangements within these ordered regions increases the activation energy for particle motion. In contrast, strong liquids with spatially constrained local ordering display a distinct "two-state" dynamic characteristic, marked by a transition between two Arrhenius-type behaviors. We argue that dynamic heterogeneity, irrespective of a liquid's fragility, arises from underlying structural order, with its spatial extent determined by static ordering. This perspective aims to deepen our understanding of the interplay between structural and dynamic properties in metastable supercooled liquids.

Dynamic Phosphorescence Behavior of Carbene-Metal-Amide Complexes from the Perspective of Excited State Modulation.

Carbene-metal-amide (CMA) complexes have diverse applications in luminescence, imaging and sensing. In this study, we designed and synthesized a series of CMA complexes, which were subsequently doped into a PMMA host. These materials demonstrate light-induced dynamic phosphorescence, attributed to their long intrinsic triplet state lifetime (τP,int, in the μs-ms scale), high intersystem crossing (ISC) rate constant (kISC, up to 107 s-1), and bright phosphorescence. The extended τP,int, and elevated kISC facilitate efficient sensitization of singlet oxygen (1O2) under light irradiation, which is rapidly consumed by the host material, creating a localized anaerobic environment conducive to bright phosphorescence emission. The Sn-T1 process exhibits a large spin-orbital coupling matrix element (SOCME) value, while the SOCME value between T1 and S0 is comparatively smaller, resulting in a large kISC and long τP,int, Computational results indicate that the hole-electron configuration in the lowest triplet state exhibits low contributions from gold. Based on the dynamic phosphorescence properties, an encryption material capable of achieving a "burn after reading" effect was developed. This work illustrates that those phosphorescent emitters with minimal heavy atom contribution can produce dynamic phosphorescent phenomena, providing a novel strategy for designing stimuli-responsive phosphorescent materials.

Effect of Divalent Cations on Polarity and Hydration at the Lipid/Water Interface Probed by 4-Aminophthalimide-Based Dyes.

The ion binding to the lipid/water interface can substantially influence the structural, functional, and dynamic properties of the cell membrane. Despite extensive research on ion-lipid interactions, the specific effects of ion binding on the polarity and hydration at the lipid/water interface remain poorly understood. This study explores the influence of three biologically relevant divalent cations─Mg2+, Ca2+, and Zn2+─on the depth-dependent interfacial polarity and hydration of zwitterionic DPPC lipid in its gel phase at room temperature. To measure these depth-dependent properties, we use a series of solvatochromic fluorescent probes synthesized based on 4-aminophthalimide with varying alkyl chain lengths (4AP-Cn; n = 5, 7, and 9). Employing steady-state fluorescence experiments and all-atom molecular dynamics (MD) simulations, we quantify changes in interfacial polarity and hydration induced by the cations binding to the lipid/water interface. Our results reveal that Zn2+ induces a significant blue shift in the fluorescence spectra of all 4AP-Cn dyes, indicating a marked decrease in local polarity (ETN ≤ 0.05) at the lipid/water interface compared to Mg2+ and Ca2+, which results in a higher polarity (ETN ≥ 0.2). The depth-dependent fluorescence spectra of dyes at the interface in the presence of Mg2+ and Ca2+ remain similar to those in the absence of cations, with only a minor red shift observed for Mg2+, implying a slight hydration effect. MD simulations show that cations primarily bind to the headgroup and glycerol regions of lipid. Simulations also reveal that Zn2+ causes substantial dehydration at the lipid/water interface, as detected by the 4AP-Cn dyes, while Mg2+ and Ca2+ have less pronounced effects, with only slight hydration induced by Mg2+. This study highlights the distinct positional effects of cations probed by 4AP-Cn probes at the lipid-water interface, underscoring the potential of 4AP-Cn dyes for monitoring depth-dependent changes in membrane properties induced by external agents or environmental conditions.

Applications of Random Matrix Theory in Neuroscience and Neural Network Analysis: Unraveling High-Dimensional Connectivity

Random Matrix Theory (RMT) has emerged as a powerful mathematical framework to study large-scale, high-dimensional data across various scientific fields. In neuroscience, it provides unparalleled tools to elucidate the functional connectivity and dynamic properties underlying neural activity. By modeling connectivity matrices, synaptic weight distributions, and neural correlation structures through random matrices, researchers can dissect large-scale neural systems’ stability, detect phase transitions in neural firing patterns, and explain emergent phenomena such as criticality in brain networks. Similarly, in the realm of artificial neural networks, RMT guides the understanding of weight initialization, spectral properties of the network’s Jacobian matrices, generalization, and optimization landscapes. This article provides a comprehensive introduction to Random Matrix Theory’s fundamental principles, their application in contemporary neuroscience, and their intersection with modern neural network paradigms. We introduce the theoretical underpinnings and methodological steps for applying RMT to neuroscience data, including sequentially numbered didactic equations. We further present an illustrative code snippet to generate and visualize RMT-based graphs, discussing the results and connecting them to cutting-edge research. In a detailed discussion, we explore the benefits, limitations, and future directions of RMT methodologies in neuroscience, including ongoing challenges and emerging frontiers. This work aims to equip researchers with a robust theoretical and methodological framework, encouraging the integration of RMT into the neuroscience toolbox and catalyzing further breakthroughs in understanding complex neural systems.

Dynamic Mechanical Analysis of Aegle Marmelos / Epoxy matrix Reinforced with Snake Grass Fibre Composite

Nowadays, Manufacturing and automotive industries have begun to employ renewable resources as a result of public awareness and rigorous legal requirements relating to the usage of polymers. This study has focused on employing Snake Grass (SG) fiber as reinforcement in Aegle Marmelos with Epoxy blended hybrid matrix (AME), since natural fiber reinforced composites provide a significant role in the development of lightweight structural components. The primary study investigated the effects of fibre length and fibre volume percentage on the mechanical properties of snake grass fibre. Based on the test results, the composite with a 15 mm fibre length and a volume percentage of 20% SG fiber has better mechanical qualities. Finally, mechanical and dynamic mechanical properties are used to assess the impact of Aegle marmelos with Epoxy blended hybrid matrix addition into the snake grass fibre composite. The inclusion of natural fiber with epoxy matrix and natural (Aegle marmelos) filler increased composite characteristics due to their synergistic effect. This enhances the adhesion and stress transmission uniformity between the reinforcements. The morphology of the fiber surface is assessed using micrographs acquired with a scanning electron microscope.

A First-Principles Investigation of the Structural, Mechanical, Dynamic, Electronic, Magnetic and Optical Properties of Ti2AC (A = Cd, S) MAX Phase Compounds

A First-Principles Investigation of the Structural, Mechanical, Dynamic, Electronic, Magnetic and Optical Properties of Ti2AC (A = Cd, S) MAX Phase Compounds

STIPS algorithm enables tracking labyrinthine patterns and reveals distinct rhythmic dynamics of actin microridges.

Tracking and motion analyses of semi-flexible biopolymer networks from time-lapse microscopy images are important tools that enable quantitative measurements to unravel the dynamic and mechanical properties of biopolymers in living tissues, crucial for understanding their organization and function. Biopolymer networks are challenging to track due to continuous stochastic transitions, such as merges and splits, which cause local neighbourhood rearrangements over short time and length scales. To address this, we propose the STIPS algorithm (Spatio Temporal Information on Pixel Subsets) to track these events by creating pixel subsets that link trajectories across frames. Using this method, we analysed actin-enriched protrusions, or 'microridges,' which form dynamic labyrinthine patterns on squamous cell epithelial surfaces, mimicking 'active Turing-patterns.' Our results reveal two distinct actomyosin-based rhythmic dynamics in neighbouring cells: a common pulsatile mechanism between 2 and 6.25 minutes period governing both fusion and fission events contributing to pattern maintenance, and cell area pulses predominantly exhibiting 10-minutes period.