Investigation Of Phase Transitions Research Articles

Micro-electromechanical system-based cryogenic and heating in situ transmission electron microscopy for investigating phase transitions and domain evolution in single-crystal BaTiO3

Investigating phase transitions between ferroelectric states is crucial for understanding the nucleation, dynamics, and kinetics of domains, both before and after transformation. Here, we assess all phase transitions and domain evolutions in single-crystal BaTiO3 by implementing microelectromechanical systems (MEMS)-based in situ cryogenic (cryo-) and heating transmission electron microscopy (TEM) by continuously varying sample temperatures from -175 °C to 200 °C. Every possible phase-cubic, tetragonal, orthorhombic, and rhombohedral - was identified. An ultra-stable imaging condition was achieved with a mean drift speed of 1.52 nm/min, providing unique opportunities for atomic resolution in situ scanning TEM with a wide temperature range. Furthermore, domain nucleation and evolution across phase transitions were investigated using complementary dielectric measurements, optical microscopy, and a phenomenological model. This study underscores the effectiveness and utility of MEMS-based in situ cryo-/heating TEM in revealing phase transitions and domain structures in ferroelectric materials.

The precursor of the critical transitions in majority vote model with the noise feedback from the vote layer

In this paper, we investigate phase transitions in the majority-vote model coupled with noise layers of different structures. We examine the square lattice and random-regular networks, as well as their combinations, for both vote layers and noise layers. Our findings reveal the presence of independent third-order transitions in all cases and dependent third-order transitions when critical transitions occur. This suggests that dependent third-order transitions may serve as precursors to critical transitions in non-equilibrium systems. Furthermore, we observe that when the structure of vote layers is decentralized, the coupling between the vote layer and the noise layer leads to the absence of critical phenomena.

Quantum Coding Transitions in the Presence of Boundary Dissipation

We investigate phase transitions in the encoding of quantum information in a quantum many-body system due to the competing effects of unitary scrambling and boundary dissipation. Specifically, we study the fate of quantum information in a one-dimensional qudit chain, subject to local unitary quantum circuit evolution in the presence of depolarizing noise at the boundary. If the qudit chain initially contains a finite amount of locally accessible quantum information, unitary evolution in the presence of boundary dissipation allows this information to remain partially protected when the dissipation is sufficiently weak, and up to timescales growing linearly in the system size L. In contrast, for strong enough dissipation, this information is completely lost to the dissipative environment. We analytically investigate this “quantum coding transition” by considering dynamics involving Haar-random, local unitary gates, and confirm our predictions in numerical simulations of Clifford quantum circuits. Scrambling the quantum information in the qudit chain with a unitary circuit of depth O(logL) before the onset of dissipation can perfectly protect the information until late times. The nature of the coding transition changes when the dynamics extend for times much longer than L. We further show that at weak dissipation, it is possible to code at a finite rate, i.e., a fraction of the many-body Hilbert space of the qudit chain can be used to encode quantum information. Published by the American Physical Society 2024

In-depth investigation of phase transition phenomena in network models derived from lattice models

Lattice models exhibit significant potential in investigating phase transitions, yet they encounter numerous computational challenges. To address these issues, this study introduces a Monte Carlo-based approach that transforms lattice models into a network model with intricate inter-node correlations. This framework enables a profound analysis of Ising, JQ, and XY models. By decomposing the network into a maximum entropy component and a conservative component, under the constraint of detailed balance, this work derives an estimation formula for the temperature-dependent magnetic induction in Ising models. Notably, the critical exponent β in the Ising model aligns well with the established results, and the predicted phase transition point in the three-dimensional Ising model exhibits a mere 0.7% deviation from numerical simulations.

First-principles calculation to investigate phase transition and associated changes in electronic, excitonic, and optical behavior of RbMgH3 perovskite polymorphs

Motivated by recent experimental and theoretical research on the ability to store and release hydrogen from a hydride perovskite with a large ionic character called RbMgH3, we conducted a study using density functional theory (DFT) to investigate the optoelectronic behavior of RbMgH3 in both Cubic and Hexagonal structures, as well as its phase transition.Our findings reveal that RbMgH3 exhibits an indirect band gap of 2.41 eV in the Hexagonal phase and a direct band gap of 2.15 eV in the Cubic phase. Additionally, we calculated the Gibbs energy to predict the occurrence of the phase transition. It was determined that RbMgH3 undergoes a structural transformation from Cubic to Hexagonal at a pressure of 1.78 GPa. This transition has the potential to enhance the storage kinetics of hydrogen, making RbMgH3 suitable for various applications.

Density functional theory of freezing transition in a binary mixture of spheres and soft-ellipsoids

ABSTRACT This study uses the classical density functional theory (DFT) of freezing to investigate phase transitions in ellipsoidal-spherical binary mixtures interacting via a generalised Gay-Berne (GB) intermolecular potential, with a focus on I-N, I-SmA and N-SmA transitions. The mixtures, consisting mainly of ellipsoids, contain spherical additives at concentrations of 5 % , 10 % and 15 % . The pair correlation functions, which are crucial for the DFT, are derived from Percus-Yevick integral equation theory. Our results show a significant influence of spherical additives on liquid crystalline phases formed by ellipsoids. At an ellipsoid length-to-width ratio of 3.0, only the I-N transition is observed, with the SmA phase remaining unstable under all conditions. Increasing the ellipsoidal length-to-width ratio to κ = 4.0 introduces the SmA phase, whose stability depends on the concentration and size of the spherical additives. The stability of the SmA phase decreases with increasing concentration and size of the spheres. Phase diagrams, which are shown in temperature-density and pressure-temperature planes, emphasise the influence of the size of the spherical dopants on the phase stability.

Exploring Colloidal Phase Transitions of Imogolite Nanotubes by Evaporation Induced Self‐Assembly in Levitation

AbstractEvaporation‐induced self‐assembly (EISA) is a versatile method for generating organized superstructures from colloidal particles, offering diverse design possibilities through the manipulation of colloid size, shape, substrate nature, and environmental conditions. While some work highlighted the potential of EISA to investigate phase transitions of inorganic liquid crystals, the influence of sample environment to determine their phase diagrams is often overlooked. In this work, the self‐assembly of lyotropic liquid crystals is compared by EISA on substrates, and by acoustic levitation (absence of substrate). The focus is on imogolite nanotubes, a model colloidal system of 1D charged objects, due to their tunable morphology and rich liquid‐crystalline phase behavior. It demonstrates the feasibility to obtain phase transitions in levitating droplets and on soft hydrophobic substrates, whereas self‐assembly is limited on rigid hydrophilic supports. Moreover, the aspect ratio of the nanotubes proves to be a pivotal factor, influencing both transitions and the resulting materials shape and surface. Besides material shaping, acoustic levitation emerges as a promising method for studying phase transitions by EISA, toward the rapid establishment of phase diagrams from diluted to highly concentrated states using a limited volume of sample.

Thermodynamic analysis on the fate of ash elements in chemical looping combustion of solid fuels – Manganese-Based oxygen carriers

Chemical looping combustion (CLC) is an innovative technology suitable for converting waste-derived fuels into heat and power. The process inherently produces pure CO2, which is highly favorable for carbon capture and storage and could be instrumental for achieving negative emissions. CLC operates by utilizing solid oxygen carriers (OCs) to transfer heat and oxygen between two reactors. The OC play a crucial role in achieving an efficient combustion. Manganese-based OCs are particularly interesting, due to their ability to release gaseous oxygen. However, ash components from solid fuels could alter their oxygen transfer capacity, and cause problems related to corrosion and agglomeration. The objective of this work is to obtain in-depth insights about Mn-based OCs for CLC of waste-derived fuels. This is achieved by investigating phase transitions during CLC of solid fuels when utilizing two manganese-based OCs: manganese oxide and a representative manganese ore. For this purpose, thermodynamic modeling is employed, and a specific focus is given to K, Na, Cu, Zn, and Pb, due to their important role in corrosion and/or agglomeration. Thermodynamic databases are expanded by calculating properties from first-principles. It is shown that Mn-based OCs are suitable for effectively converting waste-derived fuels while limiting corrosion. Furthermore, the iron in manganese ores is found to have positive implications for oxygen-transfer reactions. In terms of alkali release to the gas phase, manganese ore seems to be a more promising material compared to manganese oxide. The pathways for the heavy metals Zn, Cu, and Pb were, meanwhile, independent of the OC type.

Microfluidics-Based Drying-Wetting Cycles to Investigate Phase Transitions of Small Molecules Solutions.

Drying-wetting cycles play a crucial role in the investigation of the origin of life as processes that both concentrate and induce the supramolecular assembly and polymerization of biomolecular building blocks, such as nucleotides and amino acids. Here, we test different microfluidic devices to study the dehydration-hydration cycles of the aqueous solutions of small molecules, and to observe, by optical microscopy, the insurgence of phase transitions driven by self-assembly, exploiting water pervaporation through polydimethylsiloxane (PDMS). As a testbed, we investigate solutions of the chromonic dye Sunset Yellow (SSY), which self-assembles into face-to-face columnar aggregates and produces nematic and columnar liquid crystal (LC) phases as a function of concentration. We show that the LC temperature-concentration phase diagram of SSY can be obtained with a fair agreement with previous reports, that droplet hydration-dehydration can be reversibly controlled and automated, and that the simultaneous incubation of samples with different final water contents, corresponding to different phases, can be implemented. These methods can be further extended to study the assembly of diverse prebiotically relevant small molecules and to characterize their phase transitions.

Unsymmetrical and C3-Symmetrical Partially Fluorinated Hexaarylbenzenes: Effect of Terminal Alkoxy Chain Length on Photophysical and Thermophysical Behavior

Solution-state photoluminescence (PL) is affected by the electronic state; however, solid-state PL varies widely depending on the aggregated state. Although the synthesis and photophysical properties of unsymmetrical and C3-symmetrical hexaarylbenzenes (HABs) have been reported, the influence of their terminal alkoxy chains on their physical properties remains unclear. Therefore, we synthesized a series of unsymmetrical and C3-symmetrical partially fluorinated HABs with different alkoxy chains and investigated the effects of alkoxy chain length on the thermophysical and photophysical properties. While investigating phase transition behavior, the ethoxy-substituted unsymmetrical derivative revealed a columnar liquid-crystalline phase, whereas the other derivatives only exhibited a phase transition between crystalline and isotropic phases. While evaluating PL behavior, both the unsymmetrical and C3-symmetrical analogs exhibited relatively strong blue PL, independent of the alkoxy chain length. Through-space π-conjugation caused the PL spectra of C3-symmetrical derivatives to redshift compared to those of unsymmetrical derivatives. Partially fluorinated HABs exhibited relatively strong fluorescence, even in the crystalline state, depending on the alkoxy chain length, owing to the formation of various aggregated structures. Crystalline fluorinated HABs exhibited photochromism, resulting in the appearance of long-wavelength PL bands when exposed to ultraviolet (UV) irradiation, making them promising candidates for PL sensing materials for UV detection.

Exploring the phase change and structure of carbon using a deep learning interatomic potential.

Small-scale systems based on periodic boundary conditions often cannot accurately describe real-world situations, especially when conducting molecular dynamics simulations to study phase transitions, where it is very necessary to use large-scale systems. However, studying phase transitions in large-scale systems is an important and difficult task. Though ab initio molecular dynamics (AIMD), based on density functional theory (DFT), provides advantages in terms of accuracy, it is very difficult to study phase transitions in large-scale systems due to the considerable computational time required. In addition, although traditional empirical potentials are faster, their lower calculation accuracy makes it difficult to use them for phase transition studies. It is crucial to devise a method that has enabled a promising fusion of computational efficiency and precision to effectively investigate phase transitions in large-scale systems. In this work, the obtained machine learning potential function of carbon through deep neural networks not only demonstrates strong scalability but also effectively enables the study of the formation mechanisms of amorphous diamond and polycrystalline diamond using C60 crystals and graphene as precursors under high-pressure high-temperature conditions (HPHT). Furthermore, the structure search software (AIRSS) was used to generate numerous initial structures which were optimized using the machine learning potential, a process which led to finding new structural clusters of carbon. Interestingly, the predictive capabilities of the machine learning potential for symmetric and asymmetric carbon clusters aligned well with the Gaussian approximation potential (GAP), yet the former demonstrated higher computational efficiency, making it more suitable for carbon material research. The results of this work signify significant progress in the field of carbon transition study, opening up new possibilities for exploring and understanding carbon materials with improved computational efficacy.

A Siamese neural network framework for glass transition recognition.

A Siamese neural network, which is a deep learning technique, was applied to investigate phase transitions based on polarising microscopic textures of liquid crystals like: antiferroelectric smectic CA* phase and its glass, smectic I phase and its glass, and smectic G and its glass. It is an example of a subtle transition without significant structural changes, where textures above and below the glass transition temperature are similar. The Siamese neural network could distinguish textures of the chosen liquid crystal phases from a glass of that phase. This publication provides details of the Siamese neural network and its implementation based on three different convolutional neural networks has been tested.

Phase Transition Study Meets Machine Learning

In recent years, machine learning (ML) techniques have emerged as powerful tools for studying many-body complex systems, and encompassing phase transitions in various domains of physics. This mini review provides a concise yet comprehensive examination of the advancements achieved in applying ML to investigate phase transitions, with a primary focus on those involved in nuclear matter studies.

Theory of phase transitions in the assembly of biaxial molecules interacting via dispersion forces II: on phase transitions of flexible molecules

Previously, I investigated phase transitions in an assembly of biaxial molecules interacting via dispersion forces. Consequently, a uniaxial and two biaxial nematic phases, as well as a disco-nematic phase, were reported to appear in such an assembly of biaxial molecules. However, the investigation was performed at limited points in the space of anisotropy parameters ɛx and ɛy. In the present study, phase transitions at all points in the space of anisotropy parameters are investigated because understanding phase transitions in a system of flexible molecules, whose shape and/or anisotropy of molecular interaction change with temperature or density, is valuable and important.

The development of EFL students’ speech fluency

Abstract This study investigated phase transitions in EFL students’ speech fluency development using a complex dynamic systems perspective. Two students with different proficiency levels were selected from an intact speaking class. These students learned and practiced specific strategies to improve their speech fluency. Phase transitions were analyzed based on three criteria: sudden jumps, anomalous variance, and qualitative change in the attractor. Number of syllables was used as the speech fluency measure. The results suggest that only the higher-proficiency student underwent one phase transition. These findings imply that a short explicit fluency strategy training intervention could improve the speech fluency development of high-proficiency students in EFL classrooms. The results have implications for theory and pedagogical practice relating to EFL students’ speech fluency development.

Real-time latent heat emission during dynamic-compression freezing of water

Dynamic compression studies have been used to study the nucleation kinetics of water to ice VII for decades. Diagnostics such as photon Doppler velocimetry, transmission loss, and imaging have been used to measure pressure/density, and phase fraction, while temperature has remained the difficult thermodynamic property to quantify. In this work, we measured pressure/density and implemented a diagnostic to measure the temperature. In doing so the temperature shows quasi-isentropically compressed liquid water forms ice at pressures below the previously defined metastable limit, and the liquid phase is not hypercoooled as previously thought above that limit. Instead, the latent heat raises the temperature to the liquid-ice-VII melt line, where it remains with increasing pressure. We propose a hypothesis to corroborate these results with previous work on dynamic compression freezing. These results provide constraints for nucleation models, and suggest this technique be used to investigate phase transitions in other materials.

Defective graphene decorated with TiO2 nanoparticles as negative electrode in Li-ion batteries

In this work, the performance of novel negative electrodes for Li-ion batteries based on defective graphene synthesized via a scalable thermal exfoliation of graphite oxide and decorated with TiO2 nanoparticles is investigated. Titania polymorphs are interesting as battery electrode materials, owing to their high cycle stability, safety, abundance and negligible solid electrolyte interphase formation and volume changes upon cycling. Defective graphene, on the other hand, can embed TiO2 nanoparticles, forming a conductive hybrid nanocomposite anode material for Li-ion batteries, with improved Li-ion and electron transport, optimising power density. Here we propose two different synthetic approaches for the decoration of graphene with TiO2 nanoparticles: I) a novel chemical route where TiO2 nanoparticles, mainly anatase, were grown on graphene in hydrothermal mild conditions and II) a physical solid-state approach where hydrothermal TiO2 nanoparticles and graphene were mixed together via high energy ball-milling. The synthesized materials were analysed via powder X-ray diffraction, micro-Raman spectroscopy and HR-TEM, while the electrodes were electrochemically tested. Operando synchrotron diffraction was conducted on half-cell to investigate phase transitions in the electrode materials. Even in presence of small amount of graphene, significant improvement in capacity, reversibility, and high-rate capability were observed. In particular, the sample obtained through the addition of 1 wt% of graphene displayed a reversible capacity of more than 180 mA h/g after prolonged and wearing cycling. This result outperforms by 327 % the reversible capacity of pure TiO2 electrode.

Insights into the structure–performance relationship in La–Y–Ni-based hydrogen storage alloys

La–Y–Ni-based alloys with different phase structures possess various physicochemical properties and thereby different hydrogen storage performances. In this work, the hydrogen storage and electrochemical properties of LaY1·9Ni10Mn0·5Al0.2 alloys with different phase structures (2H-A2B7, 3R-A2B7 and 2H-A5B19) are investigated. All the investigated phases present two plateaus in pressure-composition-temperature (PCT) curves, which is induced by the different hydrogen location (A2B4 or AB5) during the hydrogen absorption process. All of the LaY1·9Ni10Mn0·5Al0.2 series alloys possess good hydrogen storage capacities and electrochemical properties. The cyclic stability of the alloys is determined by the anti-corrosive properties of the alloys to electrolyte, neither the phase transition nor the previously believed pulverization. This work, by systematically investigating phase transitions during the annealed process and elucidating the key factors of influencing the cyclic stability, is useful for the design of La–Y–Ni-based alloy for the application of hydrogen storage and beyond.

Laser‐Induced Phase Transition and Patterning of hBN‐Encapsulated MoTe2 (Small 17/2023)

hBN-Encapsulated MoTe2 In article number 2205224, Gwan-Hyoung Lee and co-workers investigate phase transition in a laser-irradiated MoTe2 with various stacked geometries. Depending on the passivation and temperature of the laser-irradiated sample, 2H-MoTe2 transforms into various phases, such as Td, 1T′, Mo3Te4, and Te. In the hBN-encapsulated MoTe2, 2H-1T′ heterophase can be patterned by local laser-irradiation, leading to a low contact resistivity of 1.13 kΩ μm.

Neural network evidence of a weakly first-order phase transition for the two-dimensional 5-state Potts model

A universal (supervised) neural network (NN), which is trained only once on a one-dimensional lattice of 200 sites, is employed to study the phase transition of the two-dimensional (2D) 5-state ferromagnetic Potts model on the square lattice. In particular, the NN is obtained by using two artificially made configurations as the training set. Due to the unique features of the employed NN, results associated with systems consisting of over 4,000,000 spins can be obtained with ease, and convincing NN evidence showing that the investigated phase transition is weakly first order is reached.