Driven by physical questions pertaining to quantifying particle dynamics, microscopy can now resolve complex systems at the single-particle level, from cellular organisms to individual ions. Yet, available analysis techniques face challenges reconstructing trajectories in dense and heterogeneous systems where accurately labeling particles is difficult. Furthermore, the inescapable finite field of view of experiments hinders the measurement of collective effects. Inspired by Smoluchowski, we introduce a broadly applicable analysis technique that probes dynamics of interacting particle suspensions based on a remarkably simple principle: counting particles in finite observation boxes. Using colloidal experiments, advanced simulations, and theory, we first demonstrate that statistical properties of fluctuating counts can be used to determine self-diffusion coefficients, so alleviating the hurdles associated with trajectory reconstruction. We also provide a recipe for practically extracting the diffusion coefficient from experimental data at variable particle densities, which is sensitive to steric and hydrodynamic interactions. Remarkably, by increasing the observation box size, counting naturally enables the study of collective dynamics in dense suspensions. Using our novel analysis of particle counts, we uncover a surprising enhancement of collective behavior due to hydrodynamics as well as a new length scale which can be connected with hyperuniform structure. Our counting framework, the “countoscope,” thus enables efficient measurements of self and collective dynamics in dense suspensions and opens the way to quantifying dynamics and identifying novel physical mechanisms in diverse complex systems where single particles can be resolved.

Recent work has shown that axions can be efficiently produced via nonstationary pair plasma discharges in the polar cap region of pulsars. Here, we point out that for axion masses ${10}^{-9}\lesssim m_a\lesssim {10}^{-4}\mathrm{eV}$ a sizable fraction of the sourced axion population will be gravitationally confined to the neutron star. These axions accumulate over astrophysical timescales, thereby forming a dense “axion cloud” around the star. We argue that the existence of such a cloud, with densities reaching and potentially exceeding $\mathcal{O}({10}^{22})\mathrm{GeV}{\mathrm{cm}}^{-3}$, is a generic expectation across a wide range of parameter space. For axion masses $m_a\gtrsim {10}^{-7}\mathrm{eV}$, energy is primarily radiated from the axion cloud via resonant axion-photon mixing, generating a number of distinctive signatures that include a sharp line in the radio spectrum of each pulsar (located at the axion mass, and with an order percent-level width) and transient events arising from the reconfiguration of charge densities in the magnetosphere. While a deeper understanding of the systematic uncertainties in these systems is required, our current estimates suggest that existing radio telescopes could improve sensitivity to the axion-photon coupling by more than an order of magnitude.

In organic light-emitting diodes (OLEDs), understanding the efficiency loss mechanism known as exciton-polaron quenching (EPQ) has been a longstanding challenge. Traditionally, EPQ was believed to occur mainly within the bulk of the OLED emission layer (EML) due to the coexistence of polarons and excitons, limiting our understanding of its full impact. Here, we report a previously overlooked phenomenon termed “interfacial EPQ (Inf. EPQ),” which occurs at the heterointerface between the EML and the adjacent charge transport layer (CTL). We observe the direct transfer of EML excitons to CTL polarons across this interface, spanning distances of up to 4 nm. Notably, Inf. EPQ exceeds EPQ within the EML bulk, even in devices with a slight interfacial energy barrier ($>0.2\mathrm{eV}$). We propose a methodology that enables independent probing of Inf. EPQ, systematic validation of its essential parameters, and the identification of Dexter energy transfer as its governing mechanism. Importantly, our findings highlight Inf. EPQ as a ubiquitous phenomenon across devices with various luminescent mechanisms, wavelengths, and injection levels. By effectively addressing Inf. EPQ, we achieve efficiency enhancements in red, green, and blue phosphorescent OLEDs, by $70\%\pm 8\%$, $47\%\pm 5\%$, and $66\%\pm 6\%$, respectively. Our work advances the physical understanding of exciton and polaron dynamics at organic heterointerfaces, providing universally applicable solutions to Inf. EPQ-related issues in practical devices.

We report measurements characterizing the performance of a kinetic inductance detector array designed for a wavelength of 25 microns and very low optical background level suitable for applications such as a far-infrared instrument on a cryogenically cooled space telescope. In a pulse-counting mode of operation at low optical flux, the detectors can resolve individual 25-micron photons. In an integrating mode, the detectors remain photon noise limited over more than 6 orders of magnitude in absorbed power from 70 zW to 200 fW, with a limiting noise equivalent power of $4.6\times {10}^{-20}\mathrm{W}{\mathrm{Hz}}^{-1}$ at 1 Hz. In addition, the detectors are highly stable with flat power spectra under optical load down to 1 mHz. Operational parameters of the detector are determined including the efficiency of conversion of the incident optical power into quasiparticles in the aluminum absorbing element and the quasiparticle self-recombination constant.

Quantifying how difficult it is to control an emerging infectious disease is crucial to public health decision-making, providing valuable evidence on if targeted interventions, e.g., quarantine and isolation, can contain spread or when population wide controls, e.g., lockdowns, are warranted. The disease reproduction number $R$ or growth rate $r$ are universally assumed to measure controllability because $R=1$ and $r=0$ define when infections stop growing and hence the state of critical stability. Outbreaks with larger $R$ or $r$ are therefore interpreted as less controllable and requiring more stringent interventions. We prove this common interpretation is impractical and incomplete. We identify a positive feedback loop among infections intrinsically underlying disease transmission and evaluate controllability from how interventions disrupt this loop. The epidemic gain and delay margins, which, respectively, define how much we can scale infections (this scaling is known as gain) or delay interventions on this loop before stability is lost, provide rigorous measures of controllability. Outbreaks with smaller margins necessitate more control effort. Using these margins, we quantify how presymptomatic spread, surveillance limitations, variant dynamics, and superspreading shape controllability and demonstrate that $R$ and $r$ measure controllability only when interventions do not alter timings between the infections and are implemented without delay. Our margins are easily computed, interpreted, and reflect complex relationships among interventions, their implementation, and epidemiological dynamics.

By applying a microwave drive to a specially designed Josephson circuit, we have realized a double-well model system: a Kerr oscillator submitted to a squeezing force. We have observed, for the first time, the spectroscopic fingerprint of a quantum double-well Hamiltonian when its barrier height is increased: a pairwise level kissing (coalescence) corresponding to the exponential reduction of tunnel splitting in the excited states as they sink under the barrier. The discrete levels in the wells also manifest themselves in the activation time across the barrier which, instead of increasing smoothly as a function of the barrier height, presents steps each time a pair of excited states is captured by the wells. This experiment illustrates the quantum regime of Arrhenius’s law, whose observation is made possible here by the unprecedented combination of low dissipation, time-resolved state control, 98.5% quantum nondemolition single shot measurement fidelity, and complete microwave control over all Hamiltonian parameters in the quantum regime. Direct applications to quantum computation and simulation are discussed.

Imaging of nanoscale magnetic textures within extended material systems is of critical importance to both fundamental research and technological applications. While high-resolution magnetic imaging of thin nanoscale samples is well established with electron and soft x-ray microscopy, the extension to micrometer-thick systems currently requires hard x rays, which limits high-resolution imaging to rare-earth magnets. Here, we overcome this limitation by establishing soft x-ray magnetic imaging of micrometer-thick systems using the pre-edge phase x-ray magnetic circular dichroism signal, thus making possible the study of a wide range of magnetic materials. By performing dichroic spectroptychography, we demonstrate high spatial resolution imaging of magnetic samples up to $1.7\mathrm{\mu }\mathrm{m}$ thick, an order of magnitude higher than conventionally possible with soft x-ray absorption-based techniques. We demonstrate the applicability of the technique by harnessing the pre-edge phase to image thick chiral helimagnets, and naturally occurring magnetite particles, gaining insight into their three-dimensional magnetic configuration. This new regime of magnetic imaging makes possible the study of extended non-rare-earth systems that have until now been inaccessible, including magnetic textures for future spintronic applications, non-rare-earth permanent magnets for energy harvesting, and the magnetic configuration of giant magnetofossils.

Cells contain multiple condensates which spontaneously form due to the heterotypic interactions between their components. Although the proteins and disordered region sequences that are responsible for condensate formation have been extensively studied, the rule of interactions between the components that allow demixing, i.e., the coexistence of multiple condensates, is yet to be elucidated. Here, we construct an effective theory of the interaction between heteropolymers by fitting it to the molecular dynamics simulation results obtained for more than 200 sequences sampled from the disordered regions of human proteins. We find that the sum of amino acid pair interactions across two heteropolymers predicts the Boyle temperature qualitatively well, which can be quantitatively improved by the dimer pair approximation, where we incorporate the effect of neighboring amino acids in the sequences. The improved theory, combined with the finding of a metric that captures the effective interaction strength between distinct sequences, allowed the selection of up to three disordered region sequences that demix with each other in multicomponent simulations, as well as the generation of artificial sequences that demix with a given sequence. The theory points to a generic sequence design strategy to demix or hypermix thanks to the low-dimensional nature of the space of the interactions that we identify. As a consequence of the geometric arguments in the space of interactions, we find that the number of distinct sequences that can demix with each other is strongly constrained, irrespective of the choice of the coarse-grained model. Altogether, we construct a theoretical basis for methods to estimate the effective interaction between heteropolymers, which can be utilized in predicting phase separation properties as well as rules of assignment in the localization and functions of disordered proteins.

Numerous natural and human-made systems exhibit critical transitions whereby slow changes in environmental conditions spark abrupt shifts to a qualitatively distinct state. These shifts very often entail severe consequences; therefore, it is imperative to devise robust and informative approaches for anticipating the onset of critical transitions. Real-world complex systems can comprise hundreds or thousands of interacting entities, and implementing prevention or management strategies for critical transitions requires knowledge of the exact condition in which they will manifest. However, most research so far has focused on low-dimensional systems and small networks containing fewer than ten nodes or has not provided an estimate of the location where the transition will occur. We address these weaknesses by developing a deep-learning framework which can predict the specific location where critical transitions happen in networked systems with size up to hundreds of nodes. These predictions do not rely on the network topology, the edge weights, or the knowledge of system dynamics. We validate the effectiveness of our machine-learning-based framework by considering a diverse selection of systems representing both smooth (second-order) and explosive (first-order) transitions: the synchronization transition in coupled Kuramoto oscillators; the sharp decline in the resource biomass present in an ecosystem; and the abrupt collapse of a Wilson-Cowan neuronal system. We show that our method provides accurate predictions for the onset of critical transitions well in advance of their occurrences, is robust to noise and transient data, and relies only on observations of a small fraction of nodes. Finally, we demonstrate the applicability of our approach to real-world systems by considering empirical vegetated ecosystems in Africa.

Two-dimensional (2D) transition metal dichalcogenides (TMDs) is a versatile class of quantum materials of interest to various fields including, e.g., nanoelectronics, optical devices, and topological and correlated quantum matter. Tailoring the electronic properties of TMDs is essential to their applications in many directions. Here, we report that a highly controllable and uniform on-chip 2D metallization process converts a class of atomically thin TMDs into robust superconductors, a property belonging to none of the starting materials. As examples, we demonstrate the introduction of superconductivity into a class of 2D air-sensitive topological TMDs, including monolayers of ${\mathrm{T}}_{\mathrm{d}}\text{−}{\mathrm{WTe}}_2$, $1{\mathrm{T}}^{\prime }\text{−}{\mathrm{MoTe}}_2$, and $2\mathrm{H}\text{−}{\mathrm{MoTe}}_2$, as well as their natural and twisted bilayers, metallized with an ultrathin layer of palladium. This class of TMDs is known to exhibit intriguing topological phases ranging from topological insulator, Weyl semimetal to fractional Chern insulator. The unique, high-quality two-dimensional metallization process is based on our recent findings of the long-distance, non-Fickian in-plane mass transport and chemistry in 2D that occur at relatively low temperatures and in devices fully encapsulated with inert insulating layers. Highly compatible with existing nanofabrication techniques for van der Waals stacks, our results offer a route to designing and engineering superconductivity and topological phases in a class of correlated 2D materials.

Transportation and distribution networks are a class of spatial networks that have been of interest in recent years. These networks are often characterized by the presence of complex structures such as central loops paired with peripheral branches, which can appear both in natural and manmade systems, such as subway and railway networks. In this study, we investigate the conditions for the emergence of these nontrivial topological structures in the context of human transportation in cities. We propose a simple model for spatial networks generation, where a network lattice acts as a planar substrate and edge speeds define an effective temporal distance which we aim to optimize and quantifies the efficiency in exploring the urban space. Complex network topologies can be recovered from the optimization of edges’ speeds and we study how the interplay between a flow probability between two nodes in space and the associated travel cost influences the resulting optimal network. In the perspective of urban transportation we simulate these flows by means of human mobility models to obtain origin-destination matrices. We find that when using simple lattices, the obtained optimal topologies transition from treelike structures to more regular networks, depending on the spatial range of flows. Remarkably, we find that branches paired to large loops structures appear as optimal structures when the network is optimized for an interplay between heterogeneous mobility patterns of small range travels and longer-range ones typical of commuting. Moreover, when congestion dynamics in traffic routing is considered, we study the emergence of additional edges from the tree structure to mitigate temporal delays. Finally, we show that our framework is able to recover the statistical spatial properties of the Greater London area subway network.

Paramagnetic transition metals play a crucial role as cofactors in various cellular catalytic processes. However, their high concentrations in reactive oxidation states can induce oxidative stress, resulting in cell dysfunction or death. Hence, it is vital to have methods to monitor metal concentrations and paramagnetic properties in cells for medicine and cell biology. Here we present a novel multimodal method for in-cell magnetometry enabling direct measurement of metal magnetic properties within individual cells in tissue, without prior isolation and at room temperature. Individual cell magnetic moments are measured using superresolution magnetic resonance imaging (MRI) microscopy at 9.4 T by detecting microscopic magnetic-field perturbations around the cells. The cellular metal content is quantified using ion-beam microscopy or synchrotron micro-x-ray fluorescence for the same cells. The metal magnetic susceptibility at 9.4 T is then obtained from the slope of the cell magnetic moments’ dependence on cell metal content. To estimate the susceptibility at lower fields, multifield MR relaxometry and biophysical modeling are employed, extrapolating the 9.4-T susceptibility values to fields as low as 3 T. We apply the new method to determine the susceptibility of iron accumulated in human dopaminergic neurons inside neuromelanin, the by-product of dopamine synthesis. The susceptibility of iron in neuromelanin is measured to be ${\chi }_{\rho }=(2.98\pm 0.19)\times {10}^{-6}{\mathrm{m}}^3/\mathrm{kg}$ providing unique insights into the biochemistry of iron inside dopaminergic neurons. The obtained value reveals a predominant monoatomic low-affinity iron-binding site within neuromelanin, indicating a higher neurotoxicity of iron than previously suggested. Furthermore, the measured susceptibility value establishes a quantitative relationship between cellular iron concentration and iron-sensitive MRI parameters, which can be noninvasively measured in vivo. This breakthrough paves the way for the in vivo detection of dopaminergic neuron density and iron load, requiring a standard clinical MRI scanner only. It promises to facilitate early diagnosis of Parkinson’s disease. In conclusion, our presented novel method enables the direct measurements of magnetic properties of paramagnetic metals within single cells with high sensitivity and across large cell groups within a macroscopic volume, providing invaluable information about the cellular biology of metals.

Detection of critical slowing down (CSD) is the dominant avenue for anticipating critical transitions from noisy time-series data. Most commonly, changes in variance and lag-1 autocorrelation [AC(1)] are used as CSD indicators. However, these indicators will only produce reliable results if the noise driving the system is white and stationary. In the more realistic case of time-correlated red noise, increasing (decreasing) the correlation of the noise will lead to spurious (masked) alarms for both variance and AC(1). Here, we propose two new methods that can discriminate true CSD from possible changes in the driving noise characteristics. We focus on estimating changes in the linear restoring rate based on Langevin-type dynamics driven by either white or red noise. We assess the capacity of our new estimators to anticipate critical transitions and show that they perform significantly better than other existing methods both for continuous-time and discrete-time models. In addition to conceptual models, we apply our methods to climate model simulations of the termination of the African Humid Period. The estimations rule out spurious signals stemming from nonstationary noise characteristics and reveal a destabilization of the African climate system as the dynamical mechanism underlying this archetype of abrupt climate change in the past.

We use symmetry analysis and density-functional theory to determine and characterize surface terminations that have a finite equilibrium magnetization density in antiferromagnetic materials. A nonzero magnetic dipole moment per unit area or “surface magnetization” can arise on particular surfaces of many antiferromagnets due to the bulk magnetic symmetries. Such surface magnetization underlies intriguing physical phenomena like interfacial magnetic coupling and can be used as a readout method of antiferromagnetic domains. However, a universal description of antiferromagnetic surface magnetization is lacking. We first introduce a classification system based on whether the surface magnetization is either sensitive or robust to roughness and on whether the magnetic dipoles at surface of interest are compensated or uncompensated when the bulk magnetic order is retained at the surface. We show that roughness-sensitive categories can be identified by a simple extension of a previously established group-theory formalism for identifying roughness-robust surface magnetization. We then map the group-theory method of identifying surface magnetization to a novel description in terms of bulk magnetic multipoles, which are already established as symmetry indicators for bulk magnetoelectric responses at both linear and higher orders. We use density-functional calculations to illustrate that nominally compensated surfaces in magnetoelectric ${\mathrm{Cr}}_2{\mathrm{O}}_3$ and centrosymmetric altermagnetic ${\mathrm{FeF}}_2$ develop a finite magnetization density at the surface, in agreement with our predictions based on both group theory and the ordering of the bulk multipoles. Our analysis provides a comprehensive basis for understanding the surface magnetic properties and their intimate correspondence to bulk magnetoelectric effects in antiferromagnets and has important implications for technologically relevant phenomena such as exchange-bias coupling.

Optimizing the energy efficiency of driving processes provides valuable insights into the underlying physics and is of crucial importance for numerous applications, from biological processes to the design of machines and robots. Knowledge of optimal driving protocols is particularly valuable at the microscale, where energy supply is often limited. Here, we experimentally and theoretically investigate the paradigmatic optimization problem of moving a potential carrying a load through a fluid, in a finite time and over a given distance, in such a way that the required work is minimized. An important step towards more realistic systems is the consideration of memory effects in the surrounding fluid, which are ubiquitous in real-world applications. Therefore, our experiments were performed in viscous and viscoelastic media, which are typical environments for synthetic and biological processes on the microscale. Despite marked differences between the protocols in both fluids, we find that the optimal control protocol and the corresponding average particle trajectory always obey a time-reversal symmetry. We show that this symmetry, which surprisingly applies here to a class of processes far from thermal equilibrium, holds universally for various systems, including active, granular, and long-range correlated media in their linear regimes. The uncovered symmetry provides a rigorous and versatile criterion for optimal control that greatly facilitates the search for energy-efficient transport strategies in a wide range of systems. Using a machine learning algorithm, we demonstrate that the algorithmic exploitation of time-reversal symmetry can significantly enhance the performance of numerical optimization algorithms.

The flux quantization is a key indication of electron pairing in superconductors. For example, the well-known $h/2e$ flux quantization is considered strong evidence for the existence of charge-$2e$, two-electron Cooper pairs. Here we report evidence for multicharge flux quantization in mesoscopic ring devices fabricated using the transition-metal kagome superconductor ${\mathrm{CsV}}_3{\mathrm{Sb}}_5$. We perform systematic magnetotransport measurements and observe unprecedented quantization of magnetic flux in units of $h/4e$ and $h/6e$ in magnetoresistance oscillations. Specifically, at low temperatures, magnetoresistance oscillations with period $h/2e$ are detected, as expected from the flux quantization for charge-$2e$ superconductivity. We find that the $h/2e$ oscillations are suppressed and replaced by resistance oscillations with $h/4e$ periodicity when the temperature is increased. Increasing the temperature further suppresses the $h/4e$ oscillations, and robust resistance oscillations with $h/6e$ periodicity emerge as evidence for charge-$6e$ flux quantization. Our observations provide the first experimental evidence for the existence of multicharge flux quanta and emergent quantum matter exhibiting higher-charge superconductivity in the strongly fluctuating region above the charge-$2e$ Cooper pair condensate, revealing new insights into the intertwined and vestigial electronic order in kagome superconductors.

Coherence time and gate fidelities in Rydberg atom quantum simulators and computers are fundamentally limited by the Rydberg state lifetime. Circular Rydberg states are highly promising candidates to overcome this limitation by orders of magnitude, as they can be effectively protected from decay due to their maximum angular momentum. We report the first realization of alkaline-earth circular Rydberg atoms trapped in optical tweezers, which provide unique and novel control possibilities due to the optically active ionic core. Specifically, we demonstrate creation of very high-$n$ ($n=79$) circular states of ${}^{88}\mathrm{Sr}$. We measure lifetimes as long as 2.55 ms at room temperature, which are achieved via cavity-assisted suppression of black-body radiation. We show coherent control of a microwave qubit encoded in circular states of nearby manifolds, and characterize the qubit coherence time via Ramsey and spin-echo spectroscopy. Finally, circular-state tweezer trapping exploiting the ${\mathrm{Sr}}^{+}$ core polarizability is quantified via measurements of the trap-induced light shift on the qubit. Our work opens routes for quantum simulations with circular Rydberg states of divalent atoms, exploiting the emergent toolbox associated with the optically active core ion.

Given a unitary evolution $U$ on a multipartite quantum system and an ensemble of initial states, how well can $U$ be simulated by local operations and classical communication (LOCC) on that ensemble? We answer this question by establishing a general, efficiently computable upper bound on the maximal LOCC simulation fidelity—what we call an “LOCC inequality.” We then apply our findings to the fundamental setting where $U$ implements a quantum Newtonian Hamiltonian over a gravitationally interacting system. Violation of our LOCC inequality can rule out the LOCCness of the underlying evolution, thereby establishing the nonclassicality of the gravitational dynamics, which can no longer be explained by a local classical field. As a prominent application of this scheme we study systems of quantum harmonic oscillators initialized in coherent states following a normal distribution and interacting via Newtonian gravity, and discuss a possible physical implementation with torsion pendula. One of our main technical contributions is the analytical calculation of the above LOCC inequality for this family of systems. As opposed to existing tests based on the detection of gravitationally mediated entanglement, our proposal works with coherent states alone, and thus it does not require the generation of largely delocalized states of motion nor the detection of entanglement, which is never created at any point in the process.

Quantum electrodynamics in $2+1$ dimensions (${\mathrm{QED}}_3$) has been proposed as a critical field theory describing the low-energy effective theory of a putative algebraic Dirac spin liquid or of quantum phase transitions in two-dimensional frustrated magnets. We provide compelling evidence that the intricate spectrum of excitations of the elementary but strongly frustrated $J_1\text{−}{J}_2$ Heisenberg model on the triangular lattice is in one-to-one correspondence to a zoo of excitations from ${\mathrm{QED}}_3$, in the quantum spin liquid regime. This evidence includes a large manifold of explicitly constructed monopole and bilinear excitations of ${\mathrm{QED}}_3$, which is thus shown to serve as an organizing principle of phases of matter in triangular lattice antiferromagnets and their low-lying excitations. Moreover, we observe signatures of emergent valence-bond solid (VBS) correlations, which can be interpreted either as evidence of critical VBS fluctuations of an emergent Dirac spin liquid or as a transition from the 120° Néel order to a VBS whose quantum critical point is described by ${\mathrm{QED}}_3$. Our results are obtained by comparing ansatz wave functions from a parton construction to exact eigenstates obtained using large-scale exact diagonalization up to $N=48$ sites.

The continued operation of the Advanced LIGO and Advanced Virgo gravitational-wave detectors is enabling the first detailed measurements of the mass, spin, and redshift distributions of the merging binary black hole population. Our present knowledge of these distributions, however, is based largely on strongly parametric models. Such models typically assume the distributions of binary parameters to be superpositions of “building block” features like power laws, peaks, dips, and breaks. Although this approach has yielded great progress in the initial characterization of the compact binary population, the strong assumptions entailed often leave it unclear which physical conclusions are driven by observation and which by the specific choice of model. In this paper, we instead model the merger rate of binary black holes as an unknown autoregressive process over the space of binary parameters, allowing us to measure the distributions of binary black hole masses, redshifts, component spins, and effective spins with near-complete agnosticism. We find the primary mass spectrum of binary black holes to be doubly peaked, with a fairly flat continuum that steepens at high masses. We identify signs of unexpected structure in the redshift distribution of binary black holes: a uniform-in-comoving volume merger rate at low redshift followed by an increase in the merger rate beyond redshift $z\approx 0.5$. Finally, we find that the distribution of black hole spin magnitudes is unimodal and concentrated at small but nonzero values, and that spin orientations span a wide range of spin-orbit misalignment angles but are also moderately unlikely to be truly isotropic.