Microscopic Quantities Research Articles

Formation of a magnetized hybrid star with a purely toroidal field from phase-transition-induced collapse

ABSTRACT Strongly magnetized neutron stars are popular candidates for producing detectable electromagnetic and gravitational-wave signals. Gravitational collapses of neutron stars triggered by a phase transition from hadrons to deconfined quarks in the cores could also release a considerable amount of energy in the form of gravitational waves and neutrinos. Hence, the formation of a magnetized hybrid star from such a phase-transition-induced collapse is an interesting scenario for detecting all these signals. These detections may provide essential probes for the magnetic field and composition of such stars. Thus far, a dynamical study of the formation of a magnetized hybrid star from a phase-transition-induced collapse has yet to be realized. Here, we investigate the formation of a magnetized hybrid star with a purely toroidal field and its properties through dynamical simulations. We find that the maximum values of rest-mass density and magnetic field strength increase slightly and these two quantities are coupled in phase during the formation. We then demonstrate that all microscopic and macroscopic quantities of the resulting hybrid star vary drastically when the maximum magnetic field strength goes beyond a threshold of $\sim 5 \times 10^{17}$ G, but they are insensitive to the magnetic field below this threshold. Specifically, the magnetic deformation makes the rest-mass density drop significantly, suppressing the matter fraction in the mixed phase. These behaviours agree with those in the equilibrium models of previous studies. Therefore, this work provides a solid support for the magnetic effects on a hybrid star.

Understanding the Response of Poly(ethylene glycol) diacrylate (PEGDA) Hydrogel Networks: A Statistical Mechanics-Based Framework.

Thanks to many promising properties, including biocompatibility and the ability to experience large deformations, poly(ethylene glycol) diacrylate (PEGDA) hydrogels are excellent candidate materials for a wide range of applications. Interestingly, the polymerization of PEGDA leads to a network microstructure that is fundamentally different from that of the "classic" polymeric gels. Specifically, PEGDA hydrogels comprise PEG chains that are interconnected by multifunctional densely grafted rod-like polyacrylates (PAs), which serve as cross-linkers. In this work, we derive a microstructurally motivated model that captures the essential features which enable deformation in PEGDA hydrogels: (1) entropic elasticity of PEG chains, (2) deformation of PA rods, and (3) PA-PA interactions. Expressions for the energy-density functions and the stress associated with each of the three contributions are derived. The model demonstrates the microstructural evolution of the network during loading and reveals the role of key microscopic quantities. To validate the model, we fabricate and compress PEGDA hydrogel discs. The model is in excellent agreement with our experimental findings for a broad range of PEGDA compositions. Interestingly, we show that the response of PEGDA hydrogels with short PEG chains and long PA rods is governed by PA-PA interactions, whereas networks with longer PEG chains are dominated by entropy. To enable design, we employ the model to investigate the influence of key microstructural quantities, such as the length of the PEG and the PA chains, on the macroscopic properties and response. The findings from this work pave the way to the efficient design of PEGDA hydrogels with tunable properties and behaviors, which will enable the optimization of their performance in various applications.

Probabilistic analysis of homogenized elastic property for resin products fabricated by additive manufacturing based on three-dimensional random field modeling of microstructure

Additive manufacturing (AM) techniques have been used in several fields of science and industry, and fabrication techniques are being updated. For this fact, especially, for industrial use, mechanical property evaluation methodologies for AM products and standards for product quality assessment should also be well established. In this paper, a probabilistic evaluation of the homogenized elastic properties of a resin product fabricated by a material extrusion-based AM technique is attempted by considering the randomness of both material and microscopic geometrical quantities. This AM method fabricates a resin structure by piling up melted resin, and to decrease consumed material and influence of thermal deformation, the inner structure of the fabricated products will include many pores and its geometry is difficult to be well controlled. From this fact, the products will be regarded as a heterogeneous material with complex random microstructure. This will cause difficulty in the evaluation of its apparent material properties and therefore a probabilistic homogenization analysis is attempted for their quantitative estimation in this study. In particular, to investigate probabilistic properties of microscopic geometry, a random field modeling technique is employed for the evaluation of autocorrelation of the microscopic geometrical parameter, and the results of the autocorrelation identified by experimental observation are introduced to the probabilistic homogenization analysis. The two-dimensional or three-dimensional random field modeling is attempted, and the effectiveness of this approach is investigated by comparing it with the experimental result.

Fate of oscillating homogeneous ℤ2-symmetric scalar condensates in the early Universe

Dark matter, if represented by a ℤ2-symmetric scalar field, can manifest as both particles and condensates. In this paper, we study the evolution of an oscillating homogeneous condensate of a ℤ2-symmetric scalar field in a thermal plasma in an FLRW universe. We focus on the perturbative regime where the oscillation amplitude is sufficiently small so that parametric resonance is inefficient. This perturbative regime necessarily comprises the late stage of the condensate decay and determines its fate. The coupled coarse-grained equations of motion for the condensate, radiation, and spacetime are derived from first principles using nonequilibrium quantum field theory. We obtain analytical expressions for the relevant microscopic quantities that enter the equations of motion and solve the latter numerically. We find that there is always a nonvanishing relic abundance for a condensate with a ℤ2 symmetry that is not spontaneously broken. This is because its decay rate decreases faster than the Hubble parameter at late times due to either the amplitude dependence or the temperature dependence in the condensate decay rate. Consequently, accounting for the condensate contribution to the overall dark matter relic density is essential for ℤ2 scalar singlet dark matter.

Angle-resolved photoemission spectroscopy (ARPES): probing electronic structure and many-body interactions

Complex phenomenon in quantum materials is a major theme of physics today. As better controlled model systems, a sophisticated understanding of the universality and diversity of these solids may lead to revelations well beyond themselves. Angle-resolved photoemission spectroscopy (ARPES), formulated after Einstein’s photoelectric effect, has been a key tool to uncover the microscopic processes of the electrons that give rise to the rich physics in these solids. Over the last three decades, the improved resolution and carefully matched experiments have been the keys to turn this technique into a leading experimental probe of electronic structures and many-body effects. Drawing upon examples spanning from novel superconductors and topological materials to magnetic and one-dimensional materials, we illustrate ARPES's pivotal role in testing ideas, benchmarking theoretical frameworks, uncovering unexpected phenomena, and elucidating the fingerprints of many-body interactions. Moreover, we demonstrate how the integration of modern ultrafast UV lasers and spin polarimetry has empowered photoemission spectroscopy to capture essential microscopic quantities of electrons—energy, momentum, spin, and temporal dynamics—yielding invaluable insights from a wealth of rich and precise information.

Order-disorder on cluster dynamics in the Q2R-Potts cellular automaton

Cellular automata (CA) are mathematical models that allow the study of emergent behavior from a bottom-up point of view, while the Potts model is renowned for its rich dynamics capable of developing both first and second order phase transitions. Here, we study the Q2R-Potts cellular automaton, which is a model that merges cellular automata and the Potts model through a microcanonical ensemble characterized by a conservative energy-like function. Our study, conducted via numerical simulations, focuses on the one-dimensional Q2R-Potts CA with three (q = 3) states, examining its dynamics through both macroscopic and microscopic quantities. We discover that the model exhibits a first-order phase transition from order to disorder around of a critical energy density, characterized by a discontinuity in a phase diagram and self-organizing clusters that follow a power-law behavior.

A microstructure-based graph neural network for accelerating multiscale simulations

Simulating the mechanical response of advanced materials can be done more accurately using concurrent multiscale models than with single-scale simulations. However, the computational costs stand in the way of the practical application of this approach. The costs originate from microscale Finite Element (FE) models that must be solved at every macroscopic integration point. A plethora of surrogate modeling strategies attempt to alleviate this cost by learning to predict macroscopic stresses from macroscopic strains, completely replacing the microscale models. In this work, we introduce an alternative surrogate modeling strategy that allows for keeping the multiscale nature of the problem, allowing it to be used interchangeably with an FE solver for any time step. Our surrogate provides all microscopic quantities, which are then homogenized to obtain macroscopic quantities of interest. We achieve this for an elasto-plastic material by predicting full-field microscopic strains using a graph neural network (GNN) while retaining the microscopic constitutive material model to obtain the stresses. This hybrid data-physics graph-based approach avoids the high dimensionality originating from predicting full-field responses while allowing non-locality to arise. In addition, this approach introduces beneficial inductive bias to the model by encoding microscopic geometrical features. By training the GNN on a variety of meshes, it learns to generalize to unseen meshes, allowing a single model to be used for a range of microstructures. The embedded microscopic constitutive model in the GNN implicitly tracks history-dependent variables and leads to improved accuracy. While the microscopic stresses are fully dependent on the microscopic strains, we found it crucial to include both microscopic strains and stresses in the loss function. We demonstrate for several challenging scenarios that the surrogate can predict complex macroscopic stress–strain paths. As the computation time of our method scales favorably with the number of elements in the microstructure compared to the FE method, our method can significantly accelerate FE2 simulations.

Adsorption–desorption characteristics of coal-bearing shale gas under three-dimensional stress state studied by low field nuclear magnetic resonance spectrum experiments

The micro-scale gas adsorption–desorption characteristics determine the macro-scale gas transport and production behavior. To reveal the three-dimensional stress state-induced gas adsorption–desorption characteristics in coal-bearing shale reservoirs from a micro-scale perspective, the coal-bearing shale samples from the Dongbaowei Coal Mine in the Shuangyashan Basin were chosen as the research subject. Isothermal adsorption–desorption experiments under three-dimensional stress state were conducted using the low field nuclear magnetic resonance (L-NMR) T2 spectrum method to simulate the in-situ coal-bearing shale gas adsorption–desorption process. The average effective stress was used as the equivalent stress indicator for coal-bearing shale, and the integral of nuclear magnetic resonance T2 spectrum amplitude was employed as the gas characterization indicator for coal-bearing shale. A quantitative analysis was performed to examine the relationship between gas adsorption in coal-bearing shale and the average effective stress. And a quantitative analysis was performed to examine the relationship between the macroscopic and microscopic gas quantities of coal-bearing shale. Experimental findings: (1) The adsorption–desorption process of coal-bearing shale gas follows the L-F function model and the D-A-d function model respectively with respect to the amount of gas and the average effective stress. (2) There is a logarithmic relationship between the macroscopic and microscopic gas quantities of coal-bearing shale during the adsorption–desorption process. This quantitatively characterizes the differences in the curves, which may be related to the elastic–plastic deformation, damage and fracture of the micropores in coal-bearing shale, as well as the hysteresis of gas desorption and the stress field of the gas occurrence state.

IMPORTANCE OF CHEMICAL ELEMENTS IN THE HUMAN BODY

Human body substancially needs chemical elements for various biochemical processes and metabolic reactions. Microelements are also nesesaary for in the human body. Microelements are a group of chemical elements that are found in the human body in very small quantities, in the range of 10-3-10-12%. Microelements perform essential functions in the human body. Even in microscopic quantities they are extremely effective. Microelements are part of the structure of biologically active substances: enzymes, hormones and vitamins. Their lack leads to serious diseases of the body. Microelements are involved in the metabolism of proteins, fats, carbohydrates, protein synthesis in the body, heat exchange, hematopoiesis, bone formation, reproduction, and immune reactions. In our body, the composition of chemical elements can change, which certainly affects our health and well-being, and various factors can cause an imbalance of useful elements.

Microscopic interpretation of generalized entropy

Generalized entropy, that has been recently proposed, puts all the known and apparently different entropies like The Tsallis, the Rényi, the Barrow, the Kaniadakis, the Sharma-Mittal and the loop quantum gravity entropy within a single umbrella. However, the microscopic origin of such generalized entropy as well as its relation to thermodynamic system(s) is not clear. In the present work, we will provide a microscopic thermodynamic explanation of generalized entropy(ies) from canonical and grand-canonical ensembles. It turns out that in both the canonical and grand-canonical descriptions, the generalized entropies can be interpreted as the statistical ensemble average of a series of microscopic quantity(ies) given by various powers of (−kln⁡ρ)n (with n being a positive integer and ρ symbolizes the phase space density of the respective ensemble), along with a term representing the fluctuation of Hamiltonian and number of particles of the system under consideration (in case of canonical ensemble, the fluctuation on the particle number vanishes).

Efficient NMR measurement and data analysis supported by the Bayesian inference: The case of the heavy fermion compound YbCo[formula omitted]Zn20

We propose a data-driven technique to infer microscopic physical quantities from nuclear magnetic resonance (NMR) spectra, in which the data size and quality required for the Bayesian inference are investigated. The 59Co-NMR measurement of YbCo2Zn20 single crystal generates complex spectra with 28 peaks. By exploiting the site symmetry in the crystal structure, the isotropic Knight shift Kiso and nuclear quadrupole resonance (NQR) frequency νQ were respectively estimated to be Kiso=0.7822±0.0090% and νQ=2.008±0.016 MHz (T=20 K and H≃10.2 T) by analyzing only 30 data points from one spectrum. The estimated νQ is consistent with the precise value obtained in the NQR experiment. Our method can significantly reduce the measurement time and the computational cost of data analysis in NMR experiments.

Experimental teaching of the Avogadro constant

Although Avogadro’s number is a fundamental concept in chemistry, it is often overlooked in secondary education textbooks in Greece. Students are taught about mol, the number Avogadro, the molar volumes, and the equation of the state of gases. However, in no case is a detailed examination held about the importance of this number and the connection of macroscopic and microscopic quantities through the Avogardo constant. Also, the references related to the topic fail to raise students’ awareness of the atomic and molecular structure and the procedure with which the matter becomes quantic. Furthermore, the experimental approach is non-existent. To address these shortcomings, this project proposes a new and straightforward experimental method for students to determine the value of Avogadro’s number. The carefully designed experimental procedure can be completed within one teaching hour, encouraging active student participation. More importantly, the proposed experimental setup does not require sophisticated instruments and poses no risks, making it feasible for students to conduct it themselves.

Emergent quantum probability from full quantum dynamics and the role of energy conservation

We propose and study a toy model for the quantum measurements that yield the Born’s rule of quantum probability. In this model, the electrons interact with local photon modes and the photon modes are dissipatively coupled with local photon reservoirs. We treat the interactions of the electrons and photons with full quantum mechanical description, while the dissipative dynamics of the photon modes are treated via the Lindblad master equation. By assigning double quantum dots setup for the electrons coupling with local photons and photonic reservoirs, we show that the Born’s rule of quantum probability can emerge directly from microscopic quantum dynamics. We further discuss how the microscopic quantities such as the electron–photon coupling, detuning, and photon dissipation rate affect the quantum dynamics. Surprisingly, in the infinite long time measurement limit, the energy conservation already dictates the emergence of the Born’s rule of quantum probability. For finite-time measurement, the local photon dissipation rate determines the characteristic time-scale for the completion of the measurement, while other microscopic quantities affect the measurement dynamics. Therefore, in genuine measurements, the measured probability is determined by both the local devices and the quantum mechanical wavefunction.

Thermodynamically consistent determination of free energies and rates in kinetic cycle models

Kinetic and thermodynamic models of biological systems are commonly used to connect microscopic features to system function in a bottom-up multiscale approach. The parameters of such models—free energy differences for equilibrium properties and in general rates for equilibrium and out-of-equilibrium observables—have to be measured by different experiments or calculated from multiple computer simulations. All such parameters necessarily come with uncertainties so that when they are naively combined in a full model of the process of interest, they will generally violate fundamental statistical mechanical equalities, namely detailed balance and an equality of forward/backward rate products in cycles due to Hill. If left uncorrected, such models can produce arbitrary outputs that are physically inconsistent. Here, we develop a maximum likelihood approach (named multibind) based on the so-called potential graph to combine kinetic or thermodynamic measurements to yield state-resolved models that are thermodynamically consistent while being most consistent with the provided data and their uncertainties. We demonstrate the approach with two theoretical models, a generic two-proton binding site and a simplified model of a sodium/proton antiporter. We also describe an algorithm to use the multibind approach to solve the inverse problem of determining microscopic quantities from macroscopic measurements and, as an example, we predict the microscopic pKa values and protonation states of a small organic molecule from 1D NMR data. The multibind approach is applicable to any thermodynamic or kinetic model that describes a system as transitions between well-defined states with associated free energy differences or rates between these states. A Python package multibind, which implements the approach described here, is made publicly available under the MIT Open Source license.

Nano-Modified Epoxy Based Adhesive Testings: An Investigation into Apparent Shear Strength, Failure Modes, Chemical Functionals, and Microstructural Failure

Single lap joints are the most used bonded joints in engineering. Unfortunately, modeling correlations between apparent shear strength and failure modes is not an easy task. This paper tries to address relationships between apparent shear strength, failure modes, and micro-structural failures. To be able to establish a correlation between chemical changes and the failure morphology of an industrial epoxy-based adhesive nano-modified by carbon nanotubes, a series of single-lap joints were prepared and tested. For this adhesive, the ductile-like fracture is controlled by changes in the aromatic ring (C-O stretching at 1,176 cm-1, C-C stretching at 1,514 cm-1, and C = C stretching at 1,593 cm-1) and ethers group (C-O-C stretching at 1,247 cm-1). Changes in the ethers group, however, have less influence than the ones in aromatic rings. Variations in oxirane groups (C-O-C stretching at 836 cm-1, and rotations of CH2 at 695 and 756 cm-1) are more related to brittle-like microscopic failure. A small number of chemical bonds, specifically characterized by low chemical functional intensity, are associated with premature or brittle-like failure. However, concurrently, another mechanism that existed simultaneously was the ability of carbon nanotubes to alter the path of propagating cracks. This phenomenon was experimentally spotted. The macroscopic cohesive failure is controlled by the ductile-like failure at microscopic failure, while the adhesive failure is related to the brittle-like failure. Moreover, for the first time two different conditions, i.e., macroscopic quantities, apparent shear strength, failure modes, and microscopic quantities of brittle/ductile areas aspect ratios are combined in a mathematical model. This model can predict single-lap joint failure strength based on microscopic quantities.

Atomic stiffness for bulk modulus prediction and high-throughput screening of ultraincompressible crystals

Determining bulk moduli is central to high-throughput screening of ultraincompressible materials. However, existing approaches are either too inaccurate or too expensive for general applications, or they are limited to narrow chemistries. Here we define a microscopic quantity to measure the atomic stiffness for each element in the periodic table. Based on this quantity, we derive an analytic formula for bulk modulus prediction. By analyzing numerous crystals from first-principles calculations, this formula shows superior accuracy, efficiency, universality, and interpretability compared to previous empirical/semiempirical formulae and machine learning models. Directed by our formula predictions and verified by first-principles calculations, 47 ultraincompressible crystals rivaling diamond are identified from over one million material candidates, which extends the family of known ultraincompressible crystals. Finally, treasure maps of possible elemental combinations for ultraincompressible crystals are created from our theory. This theory and insights provide guidelines for designing and discovering ultraincompressible crystals of the future.

Spin-Derived Electric Polarization and Chirality Density Inherent in Localized Electron Orbitals.

In solid state physics, any phase transition is commonly observed as a change in the microscopic distribution of charge, spin, or current. However, there is an exotic order parameter inherent in the localized electron orbitals that cannot be primarily captured by these three fundamental quantities. This order parameter is described as the electric toroidal multipoles connecting different total angular momenta under the spin-orbit coupling. The corresponding microscopic physical quantity is the spin current tensor on an atomic scale, which induces spin-derived electric polarization aligned circularly and the chirality density of the Dirac equation. Here, elucidating the nature of this exotic order parameter, we obtain the following general consequences that are not restricted to localized electron systems; chirality density is indispensable to unambiguously describe electronic states and it is a species of electric toroidal multipoles, just as the charge density is a species of electric multipoles. Furthermore, we derive the equation of continuity for chirality and discuss its relation to chiral anomaly and optical chirality. These findings link microscopic spin currents and chirality in the Dirac theory to the concept of multipoles and provide a new perspective for quantum states of matter.

Small-Signal Modeling of Dissipative Carrier Transport in Nanodevices with Nonequilibrium Green’s Functions

Emerging applications in the field of finite-frequency mesoscopic physics require accurate modeling tools for the evaluation of carrier-transport dynamics, modulation bandwidth, and frequency conversion effects in nanodevices. With foundations in advanced concepts of many-body and quantum field theories, the nonequilibrium Green's function technique is widely adopted in the calculation of steady-state carrier-transport properties of nanostrucures, while the evaluation of the frequency response is so far largely unexplored by genuine quantum models. Guided by the connection with drift-diffusion solvers within a local description of carrier-phonon scattering, we propose an accurate, yet computationally efficient nonequilibrium Green's function model of dissipative carrier transport to study the small-signal properties of semiconductor nanostructures. From the numerical evaluation of steady-state Green's functions and their functional derivatives, we compute spectrally resolved observables expressed in terms of familiar microscopic quantities germane to the drift-diffusion framework, the most prevalent tool in semiclassical device simulation. Besides drastically improving the convergence properties, the exact Jacobian, complemented with the contribution of the displacement current, gives access to the small-signal admittance. Current-conserving boundary conditions suitable for small-signal analysis provide the correct physical behavior near the contacts. Numerical examples show the accuracy and flexibility of the proposed model.

EUS-guided fine-needle biopsy sampling of solid pancreatic tumors with 3 versus 12 to-and-fro movements: a multicenter prospective randomized controlled study.

A novel EUS-guided fine-needle biopsy sampling (EUS-FNB) needle enabled physicians to obtain sufficient pathologic samples with fewer to-and-fro movements (TAFs) within the lesion. We compared the diagnostic yields of EUS-FNB with 3 and 12 TAFs at each puncture pass. The primary endpoint of this multicenter, noninferiority, crossover, randomized controlled trial involving 6 centers was diagnostic sensitivity. Secondary endpoints were diagnostic accuracy and quantity and quality evaluation of EUS-FNB specimens. Length of the macroscopically visible core (MVC) and microscopic histologic quantity were used for quantitative evaluation. Macroscopic visual and microscopic histologic evaluations were performed for qualitative evaluation. Among 110 patients (220 punctures, 110 for 3 TAFs and 12 TAFs each), 105 (210 punctures) had malignant histology. Diagnostic sensitivity for malignancy of 3 TAFs (88.6%) was not inferior to that of 12 TAFs (89.5%; difference, -.9%; 95% confidence interval, -9.81 to 7.86). Diagnostic accuracy for malignancy was 92.7% for 3 TAFs and 94.6% for 12 TAFs. Overall median MVC length was 13.5mm in both groups. The 3-TAF group had a significantly higher rate of score≥3 on macroscopic visual quality evaluation than the 12-TAF group (71.8% vs 52.7%, P= .009). No significant intergroup differences existed in microscopic histologic quantity and quality evaluations (quantity evaluation, 88.2% for 3 TAFs vs 83.6% for 12 TAFs; quality evaluation, 90.0% for 3 TAFs vs 89.1% for 12 TAFs). Diagnostic sensitivity and accuracy of EUS-FNB with 3 TAFs were not inferior to those with 12 TAFs for solid pancreatic lesions. The 3-TAF group showed significantly less blood contamination in sampled tissues than the 12-TAF group. (Clinical trial registration number: UMIN000037309.).

Space–time upscaling of reactive transport in porous media

Reactive transport in saturated/unsaturated porous media is numerically upscaled to the space–time scale of a hypothetical measurement through coarse-grained space–time (CGST) averages. The reactive transport is modeled at the fine-grained Darcy scale by the actual number of molecules involved in reactions which undergo advective and diffusive movements described by global random walk (GRW) simulations. The CGST averages verify identities similar to a local balance equation which allow us to derive expressions for the flow velocity and the intrinsic diffusion coefficient in terms of averaged microscopic quantities. The latter are further used to verify the CGST-GRW numerical approach. The upscaling approach is applied to biodegradation processes in saturated aquifers and variably saturated soils and the CGST averages are compared to classical volume averages. One finds that if the process is characterized by slow variations in time, as in homogeneous reaction systems, the differences between the two averages are negligible. Instead, the differences are significant and can be extremely large in simulations of time-dependent biodegradation processes in both variably saturated soils and saturated aquifers.