We correlate the terminal relaxation of supramolecular polymer networks, based on unentangled telechelic poly(isobutylene) linear chains forming micellar end-group clusters, with the microscopic chain dynamics as probed by proton NMR. For a series of samples with increasing molecular weight, we find a quantitative agreement between the terminal relaxation times and their activation energies provided by rheology and NMR. This finding corroborates the validity of the transient-network model and the special case of the sticky Rouse model, and dismisses more dedicated approaches treating the terminal relaxation in terms of micellar rearrangements. Also, we confirm previous results showing reduction of the activation energy of supramolecular dissociation with increasing molecular weight and explain this trend with an increasing elastic penalty, as corroborated by small angle x-ray scattering data.

Using in situ nanodielectric spectroscopy we demonstrate that the imbibition kinetics of cis-1,4-polyisoprene in native alumina nanopores proceeds in two time regimes both with higher effective viscosity than bulk. This finding is discussed by a microscopic picture that considers the competition from an increasing number of chains entering the pores and a decreasing number of fluctuating chain ends. The latter is a direct manifestation of increasing adsorption sites during flow. At the same time, the longest normal mode is somewhat longer than in bulk. This could reflect an increasing density of topological constraints of chains entering the pores with the longer loops formed by other chains.

Neurodegenerative diseases, such as Alzheimer’s or Parkinson’s disease, show characteristic degradation of structural brain networks. This degradation eventually leads to changes in the network dynamics and degradation of cognitive functions. Here, we model the progression in terms of coupled physical processes: The accumulation of toxic proteins, given by a nonlinear reaction-diffusion transport process, yields an evolving brain connectome characterized by weighted edges on which a neuronal-mass model evolves. The progression of the brain functions can be tested by simulating the resting-state activity on the evolving brain network. We show that while the evolution of edge weights plays a minor role in the overall progression of the disease, dynamic biomarkers predict a transition over a period of 10 years associated with strong cognitive decline.

We show that the entropy production rate bounds the rate at which physical processes can be performed in stochastic systems far from equilibrium. In particular, we prove the fundamental tradeoff $⟨{\dot{S}}_e⟩\mathcal{T}\ge k_B$ between the entropy flow $⟨{\dot{S}}_e⟩$ into the reservoirs and the mean time $\mathcal{T}$ to complete any process whose time-reversed is exponentially rarer. This dissipation-time uncertainty relation is a novel form of speed limit: the smaller the dissipation, the larger the time to perform a process.

In a binary black hole merger, it is known that the inspiral portion of the waveform corresponds to two distinct horizons orbiting each other and that the merger and ringdown signals correspond to the final horizon being formed and settling down to equilibrium. However, we still lack a detailed understanding of the relation between the horizon geometry in these three regimes and the observed waveform. Here we show that the well-known inspiral chirp waveform has a clear counterpart on black hole horizons, namely, the shear of the outgoing null rays at the horizon. We demonstrate that the shear behaves very much like a compact binary coalescence waveform with increasing frequency and amplitude. Furthermore, the parameters of the system estimated from the horizon agree with those estimated from the waveform. This implies that even though black hole horizons are causally disconnected from us, assuming general relativity to be true, we can potentially infer some of their detailed properties from gravitational wave observations.

Any $2+1$ dimensional Einsteinian spacetime with positive cosmological constant and compact hyperbolic spatial topology evolves in the expanding direction to a de Sitter model with homogeneous hyperbolic spatial geometry. In particular, the latter constitutes the uniform future attractor for this class of spacetimes for arbitrary large initial data. This is an example of large-data homogenization and demonstrates the ability of gravitation to cause late time homogenization in the evolution of cosmological models independently of their initial state.

We reexamine evidence that the Galactic Center Excess (GCE) originates primarily from point sources (PSs). We show that in our region of interest, non-Poissonian template fitting evidence for GCE PSs is an artifact of unmodeled north-south asymmetry of the GCE. This asymmetry is strongly favored by the fit (although it is unclear if this is physical), and when it is allowed, the preference for PSs becomes insignificant. We reproduce this behavior in simulations, including detailed properties of the spurious PS population. We conclude that the non-Poissonian template fitting evidence for GCE PSs is highly susceptible to certain systematic errors and should not at present be taken to robustly disfavor a dominantly smooth GCE.

Transverse momentum broadening and energy loss of a propagating parton are dictated by the space-time profile of the jet transport coefficient $\widehat{q}$ in a dense QCD medium. The spatial gradient of $\widehat{q}$ perpendicular to the propagation direction can lead to a drift and asymmetry in parton transverse momentum distribution. Such an asymmetry depends on both the spatial position along the transverse gradient and path length of a propagating parton as shown by numerical solutions of the Boltzmann transport in the simplified form of a drift-diffusion equation. In high-energy heavy-ion collisions, this asymmetry with respect to a plane defined by the beam and trigger particle (photon, hadron, or jet) with a given orientation relative to the event plane is shown to be closely related to the transverse position of the initial jet production in full event-by-event simulations within the linear Boltzmann transport model. Such a gradient tomography can be used to localize the initial jet production position for more detailed study of jet quenching and properties of the quark-gluon plasma along a given propagation path in heavy-ion collisions.

Potential advantages of chiral molecules for a sensitive search for parity violating cosmic fields are highlighted. Such fields are invoked in different models for cold dark matter or in the Lorentz-invariance violating standard model extensions and thus are signatures of physics beyond the standard model. The sensitivity of a 20-year-old experiment with the molecule CHBrClF to pseudovector cosmic fields as characterized by the parameter $|b_0^e|$ is estimated to be $\mathcal{O}({10}^{-12}\mathrm{GeV})$ employing ab initio calculations. This allows us to project the sensitivity of future experiments with favorable choices of chiral heavy-elemental molecular probes to be $\mathcal{O}({10}^{-17}\mathrm{GeV})$, which will be an improvement of the present best limits by at least two orders of magnitude.

We predict the existence of a universal class of ultralong-range Rydberg molecular states whose vibrational spectra form trimmed Rydberg series. A dressed ion-pair model captures the physical origin of these exotic molecules, accurately predicts their properties, and reveals features of ultralong-range Rydberg molecules and heavy Rydberg states with a surprisingly small Rydberg constant. The latter is determined by the small effective charge of the dressed anion, which outweighs the contribution of the molecule’s large reduced mass. This renders these molecules the only known few-body systems to have a Rydberg constant smaller than $R_{\infty }/2$.

Quantum state tomography (QST) has been the traditional method for characterization of an unknown state. Recently, many direct measurement methods have been implemented to reconstruct the state in a resource efficient way. In this Letter, we present an interferometric method, in which any qubit state, whether mixed or pure, can be inferred from the visibility, phase shift, and average intensity of an interference pattern using a single-shot measurement—hence, we call it quantum state interferography. This provides us with a “black box” approach to quantum state estimation, wherein, between the incidence of the photon and extraction of state information, we are not changing any conditions within the setup, thus giving us a true single shot estimation of the quantum state. In contrast, standard QST requires at least two measurements for pure state qubit and at least three measurements for mixed state qubit reconstruction. We then go on to show that QSI is more resource efficient than QST for quantification of entanglement in pure bipartite qubits. We experimentally implement our method with high fidelity using the polarization degree of freedom of light. An extension of the scheme to pure states involving $d-1$ interferograms for $d$-dimensional systems is also presented. Thus, the scaling gain is even more dramatic in the qudit scenario for our method, where, in contrast, standard QST, without any assumptions, scales roughly as $d^2$.

Since its inception in the 1940s, Monin-Obukhov similarity theory (MOST), which relates turbulent fluxes to mean vertical gradients in the lower atmosphere, has become ubiquitous for predicting surface fluxes of quantities transported by the flow in numerical weather, climate, and hydrological forecasting models. Despite its widespread use, MOST does not account for the effects of large coherent structures in the flow, which modulate the amplitude of turbulent fluctuations, and are responsible for a large fraction of the total transport. Herein, we demonstrate that the incorporation of the large-scale streamwise velocity $u_l(\mathit{x},t)=G_{\delta }\star u(\mathit{x},t)$, where $G_{\delta }$ is a low-pass filtering kernel, into dimensional analysis leads to an additional dimensionless parameter $\alpha (\mathit{x},t)$, which captures the modulating influence of these structures on flux-gradient relationships. Atmospheric observations and large-eddy simulations are used to demonstrate that observed deviations from MOST can indeed be explained by this new parameter; coherent structures induce an alternating loading and unloading of the mean velocity gradient near the surface.

We present a unifying description for the martensitic transformation of steel that accounts for important experimentally observable features of the transformation, namely, the Neumann bands, the interfacial (habit) plane between the transformed and untransformed phases and their orientation relationship. It is obtained through a simple geometric minimization of the total distance traveled by all the atoms from the austenite (fcc or $\gamma$) phase to the martensite (bcc or $\alpha$) phase, without the need for any explicit energy minimization. Our description unites previously proposed mechanisms but it does not rely on assumptions and experimental knowledge regarding the shear planes and directions, or external adjustable parameters. We show how the Kurdjumov-Sach orientation relationship between the two phases and the $\{225{\}}_{\gamma }$ habit plane, which have both been extensively reported in experiments, naturally emerge from the distance minimization. We also propose an explanation for the occurrence of a different orientation relationship (Pitsch) in thin films.

In this work, we address the question of how a closed quantum system thermalizes in the presence of a random external potential. By investigating the quench dynamics of the isolated quantum spherical $p$-spin model, a paradigmatic model of a mean-field glass, we aim to shed new light on this complex problem. Employing a closed-time Schwinger-Keldysh path integral formalism, we first initialize the system in a random, infinite-temperature configuration and allow it to equilibrate in contact with a thermal bath before switching off the bath and performing a quench. We find evidence that increasing the strength of either the interactions or the quantum fluctuations can act to lower the effective temperature of the isolated system and stabilize glassy behavior.

We define a class of machine-learned flow-based sampling algorithms for lattice gauge theories that are gauge invariant by construction. We demonstrate the application of this framework to U(1) gauge theory in two spacetime dimensions, and find that, at small bare coupling, the approach is orders of magnitude more efficient at sampling topological quantities than more traditional sampling procedures such as hybrid Monte Carlo and heat bath.