High Entropy States Research Articles

Thermodynamics of the Einstein-Maxwell system

At first glance, thermodynamic properties of gravity with asymptotically AdS conditions and those with box boundary conditions, where the spatial section of the boundary is a sphere of finite radius, appear similar. Both exhibit a similar phase structure and Hawking-Page phase transition. However, when we introduce a U(1) gauge field to the system, discrepancies in thermodynamic properties between the two cases has been reported in [7] (JHEP 11 (2016) 041). In this paper, by accepting the assumption that all Euclidean saddles contribute to the partition function, I found that these discrepancies are resolved due to the contribution from the “bag of gold (BG),” which is the class of Euclidean geometries whose area of bolt is bigger than that of the boundary. As a result, the Hawking-Page phase structure is restored, with the unexpected properties that the upper bound of thermodynamic entropy is always larger than the boundary area divided by 4G when the chemical potential is non-zero, and that such high entropy states are realized at sufficiently high temperature.

Role of the structure order in the transport and magnetic properties of high-entropy alloy films

The fabrication and development of high-entropy alloys (HEAs) with exceptional functionalities is a rapidly expanding field with numerous applications. When the role of entropy in HEAs is considered, the extrinsic factors, such as the existence of grains and different phases, need to be separated from the intrinsic configurations of the atomic lattice. Here, we fabricated the CoCrFeNi2Al0.5 HEA/muscovite heterostructures, and some were prepared as epitaxial bilayers and others were prepared as an amorphous system. These two systems are classified into atomic-site disordered (ASD) and structurally disordered (SD) states, respectively, without the extrinsic effects for the determination of the crystal lattice role in high-entropy states. In this study, we determined the role of the structure order in correlation with the structural, electronic, and magnetic properties of HEAs using a combination of energy-dispersive X-ray spectrometry, X-ray diffraction, transmission electron microscopy, magneto-transport, ac magnetometry, and X-ray absorption spectroscopy with magnetic circular dichroism. The ASD state showed fully metallic behavior. In contrast, the SD state showed a metallic behavior with intense magnetic saturation, which was called Kondo-like behavior, under 50 K with a low-temperature coefficient of resistivity of ~64 ppm/°C. The difference between the saturation magnetic moment and the electron relaxation behavior in the ASD and SD states resulted from the existence of the structural order affecting the atomic distance and periodicity to modify the exchange interaction and tune the electron-phonon interaction for scattering. The ferromagnetic behavior contributed by Co, Fe, and Ni atoms was probed by X-ray absorption and magnetic circular dichroism to understand the magnetic interactions in the ASD and SD states.

Recent advances in the synthesis and fabrication methods of high-entropy alloy nanoparticles

High-entropy alloy nanoparticles (HEA NPs) containing four or more elements possess several advantages over traditional alloy nanoparticles (NPs), such as higher strength, greater thermal stability, enhanced oxidation resistance, stronger catalytic activity, and greater flexibility in adjusting element composition and composition ratio. However, the development of HEA NPs has been limited by preparation difficulties caused by the challenge of achieving complete miscibility between multiple-component elements and the unique high-entropy states. In this review, we provide a comprehensive summary of recent breakthroughs in synthesizing and fabricating HEA NPs. We describe the experimental procedures and principles of various synthesis methods, including furnace pyrolysis, carbothermal shock (CTS) method, pulse laser, solvothermal method, microwave heating, hydrogen spillover-driven, sputtering deposition, annealing on mesoporous materials, arc discharge methods and using liquid metal. Additionally, we delve into recent improvements made to some of these methods or novel NPs synthesized using them. Finally, we review the current applications of HEA NPs and provide insights into potential applications of this rapidly emerging research field.

Neurophysiology State Dynamics Underlying Acute Neurologic Recovery After Cardiac Arrest.

Epileptiform activity and burst suppression are neurophysiology signatures reflective of severe brain injury after cardiac arrest. We aimed to delineate the evolution of coma neurophysiology feature ensembles associated with recovery from coma after cardiac arrest. Adults in acute coma after cardiac arrest were included in a retrospective database involving 7 hospitals. The combination of 3 quantitative EEG features (burst suppression ratio [BSup], spike frequency [SpF], and Shannon entropy [En]) was used to define 5 distinct neurophysiology states: epileptiform high entropy (EHE: SpF ≥4 per minute and En ≥5); epileptiform low entropy (ELE: SpF ≥4 per minute and <5 En); nonepileptiform high entropy (NEHE: SpF <4 per minute and ≥5 En); nonepileptiform low entropy (NELE: SpF <4 per minute and <5 En), and burst suppression (BSup ≥50% and SpF <4 per minute). State transitions were measured at consecutive 6-hour blocks between 6 and 84 hours after return of spontaneous circulation. Good neurologic outcome was defined as best cerebral performance category 1-2 at 3-6 months. One thousand thirty-eight individuals were included (50,224 hours of EEG), and 373 (36%) had good outcome. Individuals with EHE state had a 29% rate of good outcome, while those with ELE had 11%. Transitions out of an EHE or BSup state to an NEHE state were associated with good outcome (45% and 20%, respectively). No individuals with ELE state lasting >15 hours had good recovery. Transition to high entropy states is associated with an increased likelihood of good outcome despite preceding epileptiform or burst suppression states. High entropy may reflect mechanisms of resilience to hypoxic-ischemic brain injury.

Stability of 20S Proteasome Configurations: Preopening the Axial Gate.

Mass spectrometry studies of the stability of the S. cerevisiae 20S proteasome from 11 to 55 °C reveal a series of related configurations and coupled transitions that appear to be associated with opening of the proteolytic core. We find no evidence for dissociation, and all transitions are reversible. A thermodynamic analysis indicates that configurations fall into three general types of structures: enthalpically stabilized, tightly closed (observed as the +54 to +58 charge states) configurations; high-entropy (+60 to +66) states that are proposed as precursors to pore opening; and larger (+70 to +79) partially and fully open pore structures. In the absence of the 19S regulatory unit, the mechanism for opening the 20S pore appears to involve a charge-priming process that loosens the closed-pore configuration. Only a small fraction (≤2%) of these 20S precursor configurations appear to open and thus expose the catalytic cavity.

Improved Hilbert space exploration algorithms for finite temperature calculations

Computing correlation functions in strongly-interacting quantum systems is one of the most important challenges of modern condensed matter theory, due to their importance in the description of many physical observables. Simultaneously, this challenge is one of the most difficult to address, due to the inapplicability of traditional perturbative methods or the few-body limitations of numerical approaches. For special cases, where the model is integrable, methods based on the Bethe Ansatz have succeeded in computing the spectrum and given us analytical expressions for the matrix elements of physically important operators. However, leveraging these results to compute correlation functions generally requires the numerical evaluation of summations over eigenstates. To perform these summations efficiently, Hilbert space exploration algorithms have been developed which has resulted most notably in the ABACUS library. While this performs quite well for correlations on ground states or low-entropy states, the case of high entropy states (most importantly at finite temperatures or after a quantum quench) is more difficult, and leaves room for improvement. In this work, we develop a new Hilbert space exploration algorithm for the Lieb-Liniger model, specially tailored to optimize the computational order on finite-entropy states for correlations of density-related operators.

Witnessing non-Markovianity by quantum quasi-probability distributions

We employ frames consisting of rank-one projectors (i.e. pure quantum states) and their induced informationally complete quantum measurements to represent generally mixed quantum states by quasi-probability distributions. In the case of discrete frames on finite dimensional systems this results in a vector like representation by quasi-probability vectors, while for the continuous frame of coherent states in continuous variable (CV) systems the approach directly leads to the celebrated representation by Glauber–Sudarshan P-functions and Husimi Q-functions. We explain that the Kolmogorov distances between these quasi-probability distributions lead to upper and lower bounds of the trace distance which measures the distinguishability of quantum states. We apply these results to the dynamics of open quantum systems and construct a non-Markovianity witness based on the Kolmogorov distance of the P- and Q-functions. By means of several examples we discuss the performance of this witness and demonstrate that it is useful in the regime of high entropy states for which a direct evaluation of the trace distance is typically very demanding. For Gaussian dynamics in CV systems we even find a suitable non-Markovianity measure based on the Kolmogorov distance between the P-functions which can alternatively be used as a witness for non-Gaussianity.

Psychoanalytic psychotherapies and the free energy principle.

In this paper I propose a model of the fundamental components of psychoanalytic psychotherapies that I try to explicate with contemporary theories of the Bayesian brain and the Free Energy Principle (FEP). I first show that psychoanalytic therapies require a setting (made up of several envelopes), a particular psychic state and specific processes (transference, free association, dreaming, play, reflexivity and narrativity) in order to induce psychic transformations. I then analyze how these processes of transformations operate and how they can be enlightened by the FEP. I first underline the fact that psychoanalytic therapies imply non-linear processes taking time to unfold and require a setting containing high entropy processes. More precisely, these processes are characterized by an interplay between extension and reduction of free energy. This interplay also favors the emergence of new orders of subjective experience, which occur following states of disorder, according to a certain energetic threshold allowing the modification and improvement of mental functioning. These high entropy states are also characterized by random functioning and psychic malleability which favors the exploration of subjective experience in an original manner. Overall, the approach proposed in this paper support the dialogue between psychoanalysis and other fields of research while underlining how psychoanalytical theoretical and conceptual constructs can also be useful to other disciplines, in particular the neurosciences of subjectivity.

Synthesis and Characterization of High-Entropy-Alloy-Type Layered Telluride MBi2Te4 (M = Ag, In, Sn, Pb, Bi).

Recently, high-entropy alloys (HEAs) and HEA-type compounds have been extensively studied in the fields of material science and engineering. In this article, we report on the synthesis of a layered system MBi2Te4 where the M site possesses low-, middle-, and high-entropy states. The samples with M = Pb, Ag1/3Pb1/3Bi1/3, and Ag1/5In1/5Sn1/5Pb1/5Bi1/5 were newly synthesized and the crystal structure was examined by synchrotron X-ray diffraction and Rietveld refinement. We found that the M-Te2 distance was systematically compressed with decreasing lattice constants, where the configurational entropy of mixing at the M site is also systematically increased. The details of structural refinements and the electrical transport property are presented.

Native state of natural proteins optimizes local entropy.

The differing ability of polypeptide conformations to act as the native state of proteins has long been rationalized in terms of differing kinetic accessibility or thermodynamic stability. Building on the successful applications of physical concepts and sampling algorithms recently introduced in the study of disordered systems, in particular artificial neural networks, we quantitatively explore how well a quantity known as the local entropy describes the native state of model proteins. In lattice models and all-atom representations of proteins, we are able to efficiently sample high local entropy states and to provide a proof of concept of enhanced stability and folding rate. Our methods are based on simple and general statistical-mechanics arguments, and thus we expect that they are of very general use.

Squeezing Out the Catalysts: A Sustainable Approach to Disulfide Bond Exchange in Aryl Disulfides

High-pressure conditions can be considered as an alternative, environmentally responsible way for the equilibrium reactions, such as disulfide exchange. This approach is capable of eliminating the catalysts or stimuli, such as reducing agents, strong bases, ultraviolet light, or ultrasound. The exchange of aryl disulfides in the absence of catalysts or other stimuli has been systematically studied. The effects of pressure in the range between 100 and 400 MPa, accessible in large-scale technological installations, have been tested for promoting exchange reactions between various homodimeric aryl disulfides. For the optimized conditions, 100% yields of the heterodimer in pure single-crystal form were obtained. The reactions were performed in a diamond anvil cell, as well as in a hydraulic piston-and-cylinder press, and the products were characterized by X-ray diffraction, mass spectrometry, and nuclear magnetic resonance (NMR) spectroscopy. The prominent role of high-entropy states, not attainable under ambient conditions, as well as the high-entropy nucleation, kinetic crystallization, and other effects associated with the high-pressure environment, has been demonstrated. These entropy-driven reactions represent an efficient, environmentally friendly, one-pot method for obtaining pure crystalline heterodimeric disulfides.

Accurate by Being Noisy: A Formal Network Model of Implicit Measures of Attitudes

In this article, we model implicit attitude measures using our network theory of attitudes. The model rests on the assumption that implicit measures limit attitudinal entropy reduction, because implicit measures represent a measurement outcome that is the result of evaluating the attitude object in a quick and effortless manner. Implicit measures therefore assess attitudes in high entropy states (i.e., inconsistent and unstable states). In a simulation, we illustrate the implications of our network theory for implicit measures. The results of this simulation show a paradoxical result: Implicit measures can provide a more accurate assessment of conflicting evaluative reactions to an attitude object (e.g., evaluative reactions not in line with the dominant evaluative reactions) than explicit measures, because they assess these properties in a noisier and less reliable manner. We conclude that our network theory of attitudes increases the connection between substantive theorizing on attitudes and psychometric properties of implicit measures.

Fractal dimension of cortical functional connectivity networks & severity of disorders of consciousness.

Recent evidence suggests that the quantity and quality of conscious experience may be a function of the complexity of activity in the brain and that consciousness emerges in a critical zone between low and high-entropy states. We propose fractal shapes as a measure of proximity to this critical point, as fractal dimension encodes information about complexity beyond simple entropy or randomness, and fractal structures are known to emerge in systems nearing a critical point. To validate this, we tested several measures of fractal dimension on the brain activity from healthy volunteers and patients with disorders of consciousness of varying severity. We used a Compact Box Burning algorithm to compute the fractal dimension of cortical functional connectivity networks as well as computing the fractal dimension of the associated adjacency matrices using a 2D box-counting algorithm. To test whether brain activity is fractal in time as well as space, we used the Higuchi temporal fractal dimension on BOLD time-series. We found significant decreases in the fractal dimension between healthy volunteers (n = 15), patients in a minimally conscious state (n = 10), and patients in a vegetative state (n = 8), regardless of the mechanism of injury. We also found significant decreases in adjacency matrix fractal dimension and Higuchi temporal fractal dimension, which correlated with decreasing level of consciousness. These results suggest that cortical functional connectivity networks display fractal character and that this is associated with level of consciousness in a clinically relevant population, with higher fractal dimensions (i.e. more complex) networks being associated with higher levels of consciousness. This supports the hypothesis that level of consciousness and system complexity are positively associated, and is consistent with previous EEG, MEG, and fMRI studies.

Improvement of superconducting properties by high mixing entropy at blocking layers in BiS2-based superconductor REO0.5F0.5BiS2

To investigate the interlayer interaction in the recently synthesized high-entropy-alloy-type (HEA-type) REO0.5F0.5BiS2 superconductors (RE: rare earth), we have systematically synthesized two sets of samples with close lattice parameters (close to those of PrO0.5F0.5BiS2 or CeO0.5F0.5BiS2) but having different mixing entropy (ΔSmix) for the RE site. The crystal structure was investigated using synchrotron X-ray diffraction and Rietveld refinement. For the examined samples having different ΔSmix, the increase in ΔSmix does not largely affect the bond lengths and the bond angle of the BiS2 conducting layer but clearly suppresses the in-plane disorder at the in-plane S1 site, which is the parameter essential for the emergence of bulk superconductivity in the REO0.5F0.5BiS2 system. Bulk nature of superconductivity is improved by the increase in ΔSmix for the present samples. The results of this work clearly show that the increase in mixing entropy at the blocking layer can positively affect the emergence of bulk superconductivity by modifying the local structure of the conducting layer. This is the evidence of the interaction between the high entropy states of the blocking layers and the physical properties of the conducting layers.

Classical many-particle systems with unique disordered ground states.

Classical ground states (global energy-minimizing configurations) of many-particle systems are typically unique crystalline structures, implying zero enumeration entropy of distinct patterns (aside from trivial symmetry operations). By contrast, the few previously known disordered classical ground states of many-particle systems are all high-entropy (highly degenerate) states. Here we show computationally that our recently proposed "perfect-glass" many-particle model [Sci. Rep. 6, 36963 (2016)10.1038/srep36963] possesses disordered classical ground states with a zero entropy: a highly counterintuitive situation . For all of the system sizes, parameters, and space dimensions that we have numerically investigated, the disordered ground states are unique such that they can always be superposed onto each other or their mirror image. At low energies, the density of states obtained from simulations matches those calculated from the harmonic approximation near a single ground state, further confirming ground-state uniqueness. Our discovery provides singular examples in which entropy and disorder are at odds with one another. The zero-entropy ground states provide a unique perspective on the celebrated Kauzmann-entropy crisis in which the extrapolated entropy of a supercooled liquid drops below that of the crystal. We expect that our disordered unique patterns to be of value in fields beyond glass physics, including applications in cryptography as pseudorandom functions with tunable computational complexity.

TMS–EEG: Methodology

The combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) provides a possibility for perturb-and-measure (PAM) studies of the brain. In such studies, the ongoing electrical activity is modulated or new activity is elicited by TMS and the changes (perturbations) are observed using EEG. This is a powerful method, allowing one to measure and map cortical excitability, area-to-area effective connectivity, and other characteristics that reflect the instantaneous state of the brain. Furthermore, one can study the neurophysiological dynamics of the brain by exciting it from its default or «normal» state into a low-entropy state (low-probability state), from which it tends to return to high-entropy states (high-probability states). This behavior may provide us information about relevant time scales and plasticity of neuronal networks. However, the recording and analysis of microvolt-level EEG signals after the very strong electromagnetic TMS pulse that also activates cranial muscles is highly nontrivial. Technology, methodology, and signal-processing algorithms to perform PAM studies with TMS–EEG will be described.

Role of single-point mutations and deletions on transition temperatures in ideal proteinogenic heteropolymer chains in the gas phase.

A coarse-grained statistical mechanics-based model for ideal heteropolymer proteinogenic chains of non-interacting residues is presented in terms of the size K of the chain and the set of helical propensities [Formula: see text] associated with each residue j along the chain. For this model, we provide an algorithm to compute the degeneracy tensor [Formula: see text] associated with energy level [Formula: see text] where [Formula: see text] is the number of residues with a native contact in a given conformation. From these results, we calculate the equilibrium partition function [Formula: see text] and characteristic temperature [Formula: see text] at which a transition from a low to a high entropy states is observed. The formalism is applied to analyze the effect on characteristic temperatures [Formula: see text] of single-point mutations and deletions of specific amino acids [Formula: see text] along the chain. Two probe systems are considered. First, we address the case of a random heteropolymer of size K and given helical propensities [Formula: see text] on a conformational phase space. Second, we focus our attention to a particular set of neuropentapeptides, [Met-5] and [Leu-5] enkephalins whose thermodynamic stability is a key feature on their coupling to [Formula: see text] and [Formula: see text] receptors and the triggering of biochemical responses.

Accurate estimation of entropy in very short physiological time series: the problem of atrial fibrillation detection in implanted ventricular devices

Entropy estimation is useful but difficult in short time series. For example, automated detection of atrial fibrillation (AF) in very short heart beat interval time series would be useful in patients with cardiac implantable electronic devices that record only from the ventricle. Such devices require efficient algorithms, and the clinical situation demands accuracy. Toward these ends, we optimized the sample entropy measure, which reports the probability that short templates will match with others within the series. We developed general methods for the rational selection of the template length m and the tolerance matching r. The major innovation was to allow r to vary so that sufficient matches are found for confident entropy estimation, with conversion of the final probability to a density by dividing by the matching region volume, 2r(m). The optimized sample entropy estimate and the mean heart beat interval each contributed to accurate detection of AF in as few as 12 heartbeats. The final algorithm, called the coefficient of sample entropy (COSEn), was developed using the canonical MIT-BIH database and validated in a new and much larger set of consecutive Holter monitor recordings from the University of Virginia. In patients over the age of 40 yr old, COSEn has high degrees of accuracy in distinguishing AF from normal sinus rhythm in 12-beat calculations performed hourly. The most common errors are atrial or ventricular ectopy, which increase entropy despite sinus rhythm, and atrial flutter, which can have low or high entropy states depending on dynamics of atrioventricular conduction.

TURBULENCE IN GALAXY CLUSTER CORES: A KEY TO CLUSTER BIMODALITY?

We study the effects of externally imposed turbulence on the thermal properties of galaxy cluster cores, using three-dimensional numerical simulations including magnetic fields, anisotropic thermal conduction, and radiative cooling. The imposed "stirring" crudely approximates the effects of galactic wakes, waves generated by galaxies moving through the intracluster medium (ICM), and/or turbulence produced by a central active galactic nucleus. The simulated clusters exhibit a strong bimodality. Modest levels of turbulence, ~100 km/s (~10% of the sound speed), suppress the heat-flux-driven buoyancy instability (HBI), resulting in an isotropically tangled magnetic field and a quasi-stable, high entropy, thermal equilibrium with no cooling catastrophe. Thermal conduction dominates the heating of the cluster core, but turbulent mixing is critical because it suppresses the HBI and (to a lesser extent) the thermal instability. Lower levels of turbulent mixing (approximately less than 100 km/s) are insufficient to suppress the HBI, rapidly leading to a thermal runaway and a cool-core cluster. Remarkably, then, small fluctuations in the level of turbulence in galaxy cluster cores can initiate transitions between cool-core (low entropy) and non cool-core (high entropy) states.

Cause of Chirality Consensus

Biological macromolecules, proteins and nucleic acids are composed exclusively of chirally pure monomers. The chirality consensus appears vital for life and it has even been considered as a prerequisite of life. However the primary cause for the ubiquitous handedness has remained obscure. We propose that the chirality consensus is a kinetic consequence that follows from the principle of increasing entropy, i.e. the 2nd law of thermodynamics. Entropy increases when an open system evolves by decreasing gradients in free energy with more and more efficient mechanisms of energy transduction. The rate of entropy increase is the universal fitness criterion of natural selection that favors diverse functional molecules and drives the system to the chirality consensus to attain and maintain high-entropy non-equilibrium states.