Photon counting is a cornerstone of quantum optics. Here, we demonstrate precisely counting from 0 to over 9000 photons, beating the Poisson noise limit by at least $4.1~\mathrm{dB}$ across this range. We achieve sub-single-photon precision up to 276 photons per pulse. To do so, we multiplex eight intrinsically photon-number-resolving superconducting nanowire single-photon detectors across 128 temporal modes. We use a model-informed characterization of each of the 1024 detection bins, for optimal precision. We perform quantum detector tomography to reconstruct the positive operator valued measures (POVMs) of the complete device, which consists of $1.38\cdot10^8$ matrix elements. At the repetition rate of our experiment of $80~\mathrm{kHz}$, we can precisely count photons corresponding to an optical power of approximately $71~\mathrm{pW}$, bridging the gap from single-photon measurements to high-sensitivity optical power meters. A photon-number-resolving detector of this size, and the tools used to analyze it, will become increasingly important to characterize large quantum states, as well as tasks in precision metrology and optical power standards.
Bosonic quantum error correction encodes a logical qubit in an oscillator, avoiding the hardware overhead of large qubit arrays. Among such encodings, Gottesman-Kitaev-Preskill (GKP) states are paticularly powerful because their phase-space grid structure protects against small displacement errors simultaneously in both conjugate quadratures. Here we provide the first protocol for preparing magnonic GKP states, which involves an ellipsoidal magnetic crystal effectively coupled to a superconducting qubit via a microwave cavity. The geometric anisotropy intrinsically squeezes the magnon mode, while the cavity-mediated qubit control realizes an effective conditional-displacement interaction. We show that two rounds of a conditional-displacement interaction and a qubit projective measurement yield three- and four-component magnonic GKP-like states. We also show how to realize single logical qubit gate operations, such as Pauli, Hadamard and phase gates, completing the logical Pauli basis of the approximate GKP code. Our results establish hybrid magnon-qubit systems as a promising platform for preparing bosonic code states, with applications in magnonic fault-tolerant quantum computation and quantum sensing.
The Quantum Lattice Boltzmann Method (QLBM) has emerged as one of the most promising quantum computing approaches for the numerical simulation of problems in computational fluid dynamics (CFD). The dynamics is formulated in terms of mesoscopic particle distribution functions governed by a discrete Boltzmann transport equation, comprising local streaming and collision operations. In this work, the resulting macroscopic behavior corresponds to the advection-diffusion equation, which we adopt as a canonical model problem for transport phenomena. Building upon recent progress in QLBM implementations, we advance towards more realistic problem settings that better reflect conventional CFD requirements. We address, for the first time, transport under the action of non uniform velocity fields on quantum hardware. We implement our demonstration using IonQ's trapped-ion systems including Forte generation systems and a 64-qubit Barium development system similar to the forthcoming IonQ Tempo line. We identify the density readout and subsequent reloading of the fluid density as a potential bottleneck of the current algorithm and discuss several approaches to mitigate this bottleneck. We identify the use of MPS shadow tomography as a promising method to efficiently scale the readout to large system with complex density distributions. Lastly, we introduce and simulate a novel method to implement wall boundaries for advection-diffusion in QLBM, and discuss the prospects of scaling to higher-complexity problems.
Marco Pizzocaro, Clara Zyskind, Anne Amy-Klein, Erik Benkler, Sebastien Bize, Davide Calonico, Etienne Cantin, Christian Chardonnet, Cecilia Clivati, Stefano Condio, E. Anne Curtis, Simone Donadello, Sören Dörscher, Chen-Hao Feng, Melina Filzinger, Jacques-Olivier Gaudron, Rachel M. Godun, Irene Goti, Ian R. Hill, Wei Huang, et al (24) Optical clocks have achieved remarkable estimated fractional frequency uncertainties reaching the $10^{-18}$ level and below, enabling applications in fundamental physics, general relativity, and geodesy. However, the challenge of verifying the international consistency of optical clocks remains critical as efforts intensify toward redefining the SI second based on an optical transition or transitions. We report on a two-month international clock comparison campaign involving seven optical clocks in four national metrology institutes (INRIM, LNE-OP, NPL, and PTB) connected via the optical fiber network established in Europe. The campaign resulted in optical frequency ratios with uncertainties ranging from $7.7\times10^{-18}$ to $6.1\times10^{-17}$. Among the results, the $^{171}$Yb$^+$(E3) clocks at NPL and PTB demonstrated agreement within an uncertainty of $7.7\times10^{-18}$, marking the first international verification of two independently developed optical clocks below one part in $10^{17}$. The operation of the $^{199}$Hg clock at LNE-OP (formerly LNE-SYRTE) resulted in frequency ratios with improved uncertainties with $^{171}$Yb$^+$(E3), $^{171}$Yb, and $^{87}$Sr optical clocks. These results provide input for the redefinition of the second and underscore how fiber-linked clock networks can advance metrology and scientific applications.
Living systems routinely consume energy to achieve motility, often using intricate biomolecular machinery. In this work, we show that active droplets can sustain indefinite self-propulsion of a spherical colloid in an otherwise homogeneous, isotropic, and autonomous environment. Our proposed minimal mechanism consists of phase-separating proteins, enzymes passivating them, and complementary enzymes anchored to the colloid surface that reactivate the proteins. This passivation-activation cycle gives rise to a symmetry breaking - nucleation and stabilization of a condensate near the colloid surface, which in turn exerts a repulsive force on the colloid. We numerically demonstrate that this mechanism can propel micron-sized colloids at speeds of up to a hundred microns per second. This propulsion mode is strongly resistant to Brownian fluctuations and external forces, suggesting that propulsion mechanisms based on biomolecular condensates may offer a complementary, motor-free route to biological transport.
We propose an OAM sorter based on a novel optical element that we refer to as a wavefront twister. It is a generalization of the conventional wavefront rotators such as the Dove prism. However, unlike a Dove prism, which simply rotates a wavefront, the rotation generated by a wavefront twister varies linearly with radial position, resulting in the twisting of the wavefront. We demonstrate that the wavefront twister, followed by a lens, maps each OAM mode to an annulus of distinct radius at the back focal plane of the lens with negligible inter-modal overlap and preserves the circular symmetry. Thus, the proposed wavefront twister offers a scalable scheme for high-dimensional OAM mode sorting, with important consequences for the practical realization of OAM-based applications.
Yu Gu, Yuhan Ma, Yiqi Song, Meixue Chen, Hui Chen, Huaibin Zheng, Yuchen He, Yu Zhou, Fuli Li, Zhuo Xu, Jianbin Liu Understanding the boundary between classical and nonclassical phenomena is important for both fundamental researches in quantum optics and applications in quantum information. One of the most interesting research directions in this field is exploring nonclassical effects with classical light. In this paper, we will show that it is possible to observe antibunching with thermal light in a Hanbury Brown-Twiss interferometer by treating single-photon detectors as photon-number-resolving detectors to perform photon-number projection measurements. Both temporal and spatial antibunching is observed via the correlation of two detectors detecting one and zero photon, respectively. By comparing the measured results of thermal and laser light, it is found that the observed antibunching arises from the combined effect of photon statistics of thermal light and photon-number projection measurement.The classical and nonclassical nature of the observed antibunching is analyzed. The results are helpful to understand the connection between classical and nonclassical correlation and may find applications in multiphoton interference and quantum imaging.
Matthias Roeper, Robin Buschbeck, Jakob Wetzel, Tobias Ritschel, Anna-Lena Hofmann, Vladyslav Kovtunovych, Mike N. Pionteck, Javier Taboada-Gutiérrez, Alexey B. Kuzmenko, Martina Basini, Vivek Unikandanunni, Iuliia Kiseleva, Jochen Geck, Susanne C. Kehr, Maximilian Lederer, Simone Sanna, Lukas M. Eng, Samuel D. Seddon Multiferroic domain walls in functional oxides exhibit properties distinct from the bulk and are increasingly exploited as active elements in nanoelectronic and photonic devices. Deterministic control of domain populations has typically remained limited to local control, or removal with temperature. Here we demonstrate continuous, reversible manipulation of the ferroelastic domain structure in single-crystal LaAlO$_3$ using in-situ uniaxial strain. Combining atomic force microscopy, X-ray diffraction, and Raman spectroscopy with first-principles calculations we map the complete microscopic evolution of the twin domain population through the strain-driven transition from the rhombohedral $R\bar{3}c$ ground state toward the predicted orthorhombic $Fmmm$ phase. Applied strains below $0.5\%$ produce pronounced surface flattening and large-scale domain reorganisation, establishing uniaxial strain as a technically accessible control parameter for ferroelastic domain engineering. These results open a route to active, real-time programming of domain architectures in LaAlO$_3$-based heterostructures, with implications for strain-tunable superconducting interfaces, nanoscale phonon-polariton optics, and ultrafast lattice control.
We present a minimal agent-based model of interacting agents characterized by their wealth to study taxation and inequality in a non-conservative economy. Wealth evolves through an extremal stochastic replacement process in which the poorest agent has its wealth replaced by a new random value, financed through a collective taxation mechanism. We explore taxation regimes ranging from regressive to progressive schemes and tune the overall redistribution strength. Under regressive taxation, the system self-organizes into two distinct stationary phases when changing the total tax collected: a non-ergodic, high-inequality regime characterized by wealth condensation in a subset of agents that permanently escape replacement, and a more homogeneous ergodic phase in which all agents participate in the dynamics. Increasing taxes drives an abrupt transition between these phases. The transition is discontinuous and exhibits hysteresis and bistability, consistently detected through the Gini index, the Top $1\%$ wealth share, the entropy, and the Binder cumulant. In contrast, neutral and progressive taxation suppress persistent wealth concentration, preventing the emergence of strongly unequal states and eliminating hysteretic behavior. These results show that minimal stochastic redistribution mechanisms alone can produce discontinuous transitions, metastability, and non-ergodicity, demonstrating that taxation structure can determine the emergence and stability of macroscopic inequality.
Finite Larmor radius magnetohydrodynamics (FLR-MHD) provides a hybrid model of plasma that explains how turbulent energy cascade extends to sufficiently small parallel length scales, potentially leading to perpendicular heating of the ions in the solar corona and the solar wind. In this work, we derive exact laws for the cascades of energy and generalized helicity in fully developed FLR-MHD turbulence. In large and small scale limits, we obtain the exact laws for reduced MHD and electron reduced MHD turbulence respectively. Unlike ordinary or reduced MHD turbulence, a global stationary state is shown to be absent in the case of a strong imbalance between the Elsasser variables. This is due to the so-called helicity barrier, which leads to two separate stationary energy cascades with different cascade rates. Our derived exact laws enable us to calculate these two cascade rates and therefore their difference, which effectively provides the heating rate of the ions. In addition, we also derive alternative Banerjee-Galtier forms for the exact laws and hence obtain the relaxed states of FLR-MHD turbulence using the framework of recently proposed principle of vanishing nonlinear transfer. The relaxed states show alignment between the velocity and magnetic field fluctuations. However, due to strong anisotropy, no Beltrami alignment is possible for velocity and magnetic fields. Similarly to the exact laws, the relaxed states of reduced and electron reduced MHD emerge in the large and small scale limits, respectively.
We introduce a refined immersed boundary (IB) methodology that is better-than-first-order accurate in practice, while preserving key properties of "continuous-forcing" IB approaches that retain a singular source term in the governing equations. Our method leverages a smoothed indicator (Heaviside) function, following ideas from multiphase flow and immersed layers formulations, to recast the IB solution as a composite of distinct interior and exterior fields. We demonstrate that, when cast through this composite-solution lens, prior continuous-forcing IB methods can be seen as neglecting terms in the governing and constraint equations that restrict the solution to first-order accuracy. We incorporate these terms to systematically improve accuracy without the need for heuristic corrections. In canonical Poisson problems, we empirically demonstrate second-order convergence, and in incompressible Navier-Stokes simulations the method achieves slightly sub-second-order performance. While our present study focuses on these cases, the framework suggests a path towards second-order accuracy or higher, with further extensions. This perspective reframes accuracy limitations typically attributed to IB schemes. Although continuous-forcing IB methods are often reported to be only first-order accurate, we show that neither smoothing nor interface interpolation inherently restricts attainable order. Moreover, we naturally incorporate this higher-order formulation into a projection-based solution process. The resulting algorithm simultaneously mitigates the spurious surface stresses produced by ill-conditioned linear systems and reduces sensitivity to geometric resolution, addressing both conditioning and accuracy concerns within a unified approach.
This work presents ThermoMesh, a passive thin-film thermoelectric mesh sensor designed to detect and characterize spatio-temporally sparse heat sources through conduction-based thermal imaging. The device integrates thermoelectric junctions with linear or nonlinear interlayer resistive elements to perform simultaneous sensing and in-sensor compression. We focus on the single-event (1-sparse) operation and define four performance metrics: range, efficiency, sensitivity, and accuracy. Numerical modeling shows that a linear resistive interlayer flattens the sensitivity distribution and improves minimum sensitivity by approximately tenfold for a $16\times16$ mesh. Nonlinear temperature-dependent interlayers further enhance minimum sensitivity at scale: a ceramic negative-temperature-coefficient (NTC) layer over 973--1273~K yields a $\sim14{,}500\times$ higher minimum sensitivity than the linear design at a $200\times200$ mesh, while a VO$_2$ interlayer modeled across its metal--insulator transition (MIT) over 298--373~K yields a $\sim24\times$ improvement. Using synthetic 1-sparse datasets with white boundary-channel noise at a signal-to-noise ratio of 40~dB, the VO$_2$ case achieved $98\%$ localization accuracy, a mean absolute temperature error of $0.23$~K, and a noise-equivalent temperature (NET) of $0.07$~K. For the ceramic-NTC case no localization errors were observed under the tested conditions, with a mean absolute temperature error of $1.83$~K and a NET of $1.49$~K. These results indicate that ThermoMesh could enable energy-efficient embedded thermal sensing in scenarios where conventional infrared imaging is limited, such as molten-droplet detection or hot-spot monitoring in harsh environments.
Zn(imidazolate)$_2$ metal-organic frameworks (MOFs) exhibit a remarkable degree of polymorphism. Because of their promising industrial applications, many research groups have investigated phase transitions, phase diagram and relative stability of these polymorphs. There is now wide consensus in the research community that these MOFs are solvothermally formed via non-classical nucleation mechanisms, in which pre-nucleation clusters are first formed, followed by an intermediate amorphous structure that subsequently reorganizes to yield the final crystalline MOF. However, no study up to date has uncovered which part of the synthesis process determines the final polymorph obtained. In this work, path collective variable metadynamics simulations performed with a partially reactive force field give insights into mechanistic and thermodynamic aspects of the self-assembly of these MOFs. Databases of transient and intermediate synthesis structures are built from the simulations. By developing and applying neural network classifiers over these databases, it is found that both pre-nucleation clusters and the amorphous intermediate structures are polymorph-dependent. These results suggest that polymorph selection happens as early as the pre-nucleation cluster stage.
Sensitive biomarker detection in physiological fluids is often limited by Debye screening, which suppresses electrostatic signals at sensor surfaces. Here we report a sensing approach based on flexoelectric resonance in silicon nanowire field-effect transistors. An applied radiofrequency field induces strain gradients in the nanowires, generating flexoelectric polarization that is amplified at resonant frequencies. This effect enhances the sensitivity of conductance measurements to small surface charge variations associated with biomolecular binding. Using C-reactive protein as a model biomarker, we observe an order-of-magnitude improvement in detection sensitivity compared to conventional operation, with a 62% conductance increase versus 30% without radiofrequency modulation. The high-frequency field also perturbs the electrical double layer, reducing Debye screening in high-ionic-strength environments. These combined effects enable direct biomarker detection without sample dilution. This work establishes flexoelectric resonance as a general strategy for improving nanoscale biosensing performance in physiologically relevant conditions.
This article presents a systematic review of theoretical and experimental findings for bound states of two and several dissipative solitons in fiber lasers. The theoretical basis underlying the formation and stabilization of soliton molecules in the fibers, which is provided by the complex Ginzburg-Landau equations and bound states of such equations, is presented in necessary detail, which is followed by a detailed presentation of experimental findings, including very recent ones. In particular, included are the results for the multi-soliton bound states in the fibers, as well as for the bound states in the temporal and frequency domains, single-component (scalar) and two-component (vector), two- and multi-soliton modes, as well as for bound states of spatiotemporal dissipative solitons in the lasers based on multimode fibers.
Semiconductor-based plasmonic nanostructures support localized surface plasmon modes in the infrared region. Unlike metallic nanostructures, they support both free electrons and holes, requiring a two-fluid hydrodynamic Drude equation (HDE) to accurately capture spatial dispersion effects and low-frequency acoustic plasmon modes that cannot be described by single-fluid models. In this work, a volume integral equation (VIE)-based solver is proposed for the analysis of electromagnetic scattering from semiconductor nanostructures. The proposed approach couples the VIE, formulated in terms of the electric flux density and the free-electron and hole polarization currents, with the two-fluid HDE. The coupled system is discretized using a tetrahedral mesh and solved efficiently using a two-level iterative solver. In contrast to finite-element-based methods, the proposed VIE-based approach does not require domain-wide meshing and inherently satisfies the radiation condition, thereby eliminating artificial absorbing boundaries. Numerical results for InSb-type semiconductor nanostructures demonstrate the accuracy and efficiency of the proposed VIE-based solver and its ability to capture unique optical phenomena, such as acoustic plasmon resonances and the blueshift of localized surface plasmon resonances, that cannot be described by the single-fluid HDE or classical Drude-based models.
To couple many independent modes from free space to on chip, the key challenge is not enhancing the many necessary coupling rates (scattering-matrix elements) between targeted mode pairs. Instead, the key is to avoid additional cross-couplings to undesired modes, due to the presence of multiple simultaneously satisfied phase-matching conditions. With this principle, we identify scaling laws for the maximum number of high-efficiency multi-mode couplings that may be achievable for a given refractive index and design region, which are strongly supported by extensive numerical inverse-design experiments in 2D (one-dimensional coupler patterns, scattering in 2D). For such couplers, typical mode counts of 5--10 appear achievable. Three-dimensional couplers (patterned across two dimensions) can be markedly better, with tens of Fourier components in a single-layer device offering the possibility of high-efficiency coupling of hundreds to thousands of modes in relatively compact form factors. Numerical simulations of such a device, without any parameter optimization, predict efficiencies on the order of 5\% for 100 modes -- a collective order-of-magnitude improvement over previous designs.
Although intersections are the most complex parts of the roadway network, pedestrian crashes at non-intersection locations are disproportionately frequent, highlighting a serious traffic safety concern. This study investigates non-intersection crashes involving pedestrians using a crash database (2017-2021) collected from Louisiana State. As the risk of pedestrian crashes tends to vary with distance from the intersection, the research team utilized a unique framework "distance to intersection" to capture the differences in crash patterns at non-intersection locations. The study identified that around 50% of non-intersection pedestrian crashes occurred within 198 ft. of the intersection. In the next step, the collected 3,135 pedestrian crashes at non-intersection locations during the study period were subdivided into three zones: D1 zone designates crashes occurring within 150 ft. of an intersection (1,277 crashes), D2 zone designates crashes occurring within 151 ft. to 435 ft. of an intersection (1,060 crashes) and D3 zone designates crashes occurring at 435 ft. or higher from an intersection (798 crashes). To explore the complex interaction of multiple factors, an intuitive data mining technique, Association Rules Mining was used. A total of the top 60 interesting association rules (20 for each zone) were identified by the algorithm (based on lift and support measures). In addition, a total of 124 rules were explored based on Lift Increase Criterion (LIC) measure. The findings of this research provide critical insights into pedestrian crash involvement at non-intersection locations and the variation in crash patterns according to the "distance to intersection". Based on the findings, some of the targeted problem-specific countermeasures are also recommended to address the crash patterns at non-intersection locations.
We apply a systematic inverse design approach to discover foundry-compliant, multilayer grating couplers that can efficiently couple a number of independent waves from free space to on-chip propagating modes. For visible- and near-infrared couplers, we find that minimum feature sizes are by far the most important constraint to tailor the design algorithms around. If, additionally, one forces the optimization to be robust to over- and under-etch errors, the resulting designs exhibit stable optimal efficiencies in the presence of other imperfections (critical dimension variations, overlay mismatch, and sidewall angle variation). The foundry-compliant designs exhibit moderate efficiency penalties as feature sizes increase, but no change to simple underlying scaling laws with respect to requisite numbers of layers and layer thicknesses. These results establish a practical, generalizable framework for high-efficiency multimode coupling within the constraints of modern semiconductor foundries.
Deep strong light-matter coupling represents an extreme non-perturbative regime of quantum electrodynamics, in which the interaction strength exceeds the bare frequencies of the uncoupled systems. The ground state features strong quantum correlations between photons and matter excitations, and new cavity-driven phase transitions are expected to occur. Whether a superradiant quantum phase transition, marked by spontaneous dipole ordering and photon condensation, is possible has remained a long-standing and controversial question. Such phenomena have been proposed to arise in exotic electronic systems hosting Dirac and Kane fermions, owing to the formal absence of an $A^2$ term in their low-energy Hamiltonian. Here we exploit the ultralow effective mass of Kane fermions to realise Landau polaritons in a bulk mercury cadmium telluride layer coupled to a Fabry-Perot resonator. Using thermally tunable carrier density, we continuously tune the coupling from the weak to the deep-strong regime, achieving a record normalised coupling ratio exceeding 1.6 above room temperature. The measured polariton spectra are in excellent agreement with a rigorous, gauge-invariant microscopic theory. Despite the nonlinear Landau level structure of relativistic Kane fermions, we show that a diamagnetic $A^2$ term naturally emerges and precludes a superradiant phase transition. These results resolve the long-standing controversy surrounding cavity quantum electrodynamics of relativistic-like matter systems, extend deep-strong-coupling physics to Kane fermions, and open new opportunities for polaritonic semiconductor devices operating in extreme light-matter coupling regimes.
The syntactic structure of a sentence can be represented as a tree where edges indicate syntactic dependencies between words. When that structure is a star, it has been demonstrated that the head should be placed in the middle of the linear arrangement according to the principle of syntactic dependency distance minimization. However, hubs of stars tend to be put at one of the ends, against that principle. Here we address two questions: (1) How difficult is it to minimize dependency distance? (2) Why anti dependency distance minimization effects have been found in star structures but not in path structures? The ease of optimization is determined by the shape of the optimization landscape. It was demonstrated that the landscape of star structures is quasiconvex (Ferrer-i-Cancho 2015, Language Dynamics and Change). As for (1), here we show that it is indeed convex (a particular case of quasiconvexity) both for star trees and quasistar trees and thus the distance-based optimization problem is simpler than previously believed. As for (2), we argue that (a) competing principles, rather than the difficulty of optimization, must be the actual reason for anti-dependency distance minimization effects and that (b) dependency distance minimization on star-like structures is less rewarding compared to other structures.
Cooking is a cultural expression of human creativity that transcends geography and time through the orchestration of ingredients and techniques, much like languages do through words and syntax. Yet, beneath the apparent diversity of culinary traditions, whether recipes obey statistical laws comparable to those of other symbolic systems remains unknown. Here we analyze a large corpus of traditional recipes spanning global cuisines, annotated using a state-of-the-art named entity recognition algorithm into ingredients, cooking techniques, utensils, and other culinary attributes. We find that ingredient usage exhibits Zipf-like rank-frequency scaling, that culinary diversity grows sublinearly with corpus size in accordance with Heaps' law, and that recipe complexity follows Menzerath-Altmann-type relations between the number and average information of constituent units. Consistent with observations in packaged foods, macronutrient concentrations across recipes also display a log-normal signature. Minimal generative models based on preferential reuse, constrained sampling, and incremental modification recapitulate these regularities, suggesting generic processes that shape recipe architecture across cultures. Together, these findings establish recipes as a compositional symbolic system in which complex structure emerges from simple, constrained generative processes.
For three-dimensional (3D) magnetic objects with linear size $L$ exceeding a few exchange lengths, the micromagnetic state exhibits pronounced informational sparsity: low-dimensional, high-gradient regions (e.g., domain walls) coexist with near-uniformly magnetized volumetric domains. Because standard micromagnetic simulation methods discretize the magnetization on near-uniform 3D grids with linear cell size $a$, they cannot take advantage of this sparsity. The computational problem scales as $\sim L^3$ and $\sim (1/a)^3$. In this Letter, we establish that direct tensor-train (TT) representations overcome these poor scalings by exploiting the spatial sparsity optimally, while preserving accuracy in a controlled way. Focusing on representative flux-closure configurations in soft-magnetic rectangular prisms, in the near-micrometer regime, we demonstrate that the parameter count of TT-compressed micromagnetic data scales approximately as $L^{1.8}$ and $(1/a)^{1.2}$. Hence the relative advantage over dense discretizations rapidly grows with the problem size and refinement level. These first results provide a strong motivation for future developments of micromagnetic solvers in TT format which could transcend the limitations of traditional simulators, with far reaching potential impacts on fundamental research and technology development.
Griffiths phases are typically associated with quenched disorder, while frustration gives rise to multistability and spin-glass behavior. Whether extended criticality can arise in other contexts remains an open question. Here, we show that synergistic interactions provide a distinct route to non-conventional critical phenomena. By combining spreading mechanisms that reinforce activity through complementary pathways, we uncover a broad distribution of relaxation rates, leading to Griffiths-like slow dynamics and extended criticality. We demonstrate that this mechanism is robust across networks and emerges both in systems with explicit higher-order interactions and in purely pairwise systems with nonlinear dynamics.
We demonstrate a technique for spatially resolved temperature measurement utilizing Rydberg Doppler broadening thermometry. This method employs two focused laser beams arranged perpendicularly to excite laser-cooled atoms from the ground state to a Rydberg state via two photon absorption process. Temperature is obtained through the Doppler broadening of the spectral line. The perpendicular configuration allows for selective probing of a specific position within the atomic cloud, enabling localized temperature measurement. This technique, in principle, offers a temperature resolution on the order of \SI\nano\kelvin, attributed to the exceptionally narrow natural linewidth of the involved rubidium Rydberg transition line. Furthermore, the setup enables the measurement of position-velocity correlations within the cold atom ensemble. The velocity information is extracted through the Doppler shift, whereas the spatial information is inferred from the arrival time of ions detected by a channel electron multiplier detector. Using this technique, for the first time, we observed the spatial temperature gradient within the atomic cloud, with higher temperature in the wings and lower temperature in the center.
Diffuse-interface (phase-field) models are widely used to describe multiphase mixtures and their interfacial dynamics. In multiphase settings, however, the constitutive closure should remain meaningful across different representations of the same mixture. Existing N-phase phase-field constructions commonly enforce reduction only when a phase is absent (restriction to a face of the Gibbs simplex), but do not address the natural requirement that physically identical phases can be merged without changing the governing equations. This requires characterizing thermodynamically admissible, mixture-aware constitutive closures that are consistent with merging identical phases at the PDE level. Here, we show that, under a small set of structural axioms, PDE-level reduction consistency uniquely fixes the admissible free-energy structure to an ideal-mixing contribution to an ideal-mixing contribution, a symmetric mean-field interaction term, and a constant-coefficient quadratic gradient penalty. yielding a thermodynamic closure that includes Maxwell--Stefan-type mobilities as a special case. The same requirement constrains the Onsager mobility matrix to a pairwise-exchange form with bilinear degeneracy in the volume fractions, yielding a thermodynamic closure that includes Maxwell--Stefan-type mobilities as a special case. These results provide a consistent closure for N-phase Navier--Stokes--Cahn--Hilliard mixture models and, in the bulk-only setting, for multiphase Maxwell--Stefan diffusion systems. Numerical experiments confirm the predicted mixture-aware reduction properties and illustrate the capabilities of the N-phase Navier--Stokes--Cahn--Hilliard framework in representative multiphase-flow computations.
Clustering is a fundamental collective phenomenon in agent-based models (ABMs) of opinion dynamics. To study clustering in systems with co-evolving social and opinion variables, we derive stochastic partial differential equation (SPDE) models that describe the evolution of clusters on a reduced state space. We consider two settings: one in which opinions do not affect social interactions, and another one in which a feedback mechanism couples the two. Our approach extends reduced PDE modelling to a stochastic framework, which is essential for capturing long-term cluster behaviour. Numerical experiments demonstrate that the proposed reduced SPDEs substantially decrease computational cost compared to full-state SPDE models, such as the Dean-Kawasaki equation, while still accurately reproducing the clustering behaviour of the underlying ABM. As a result, these reduced models provide an efficient tool for studying systems with large populations, including those arising in the analysis of real-world data: in particular, we provide an application related to the large-scale General Social Survey (GSS), which comprises opinion and social data of the US population since 1972.
Large-scale IoT weather sensing networks require incentive mechanisms to sustain participation, yet determining how much value individual data contributions bring to the network remains an open problem. Existing approaches address data quality but not data valuation; in operational meteorology, adjoint-based methods derive value from the forecast model itself but require full data assimilation infrastructure. We propose to utilise differentiable AI weather models to fill this gap and characterise gradient-based attribution on gridded GFS analysis inputs as a candidate value signal, evaluating fidelity, calibration, cost, and gaming vulnerability across more than 400 configurations. Attribution captures near-optimal sensor placement utility with monotonically faithful payments, but can be inflated by adversarial inputs, with detection requiring external baseline data. These findings establish gradient attribution as a computationally validated signal for model-informed reward allocation in participatory weather sensing.
T. Vozár, L. Čechová, J. Buday, M. Füleky, K. Molnárová, L. Lenža, J. Mužík, Y. Koshiba, M. Smrž, P. Pořízka, J. Kaiser The colonisation of extraterrestrial planets requires sustainable food production independent of Earth-based supplies. Due to the high costs and complicated logistics of food transport, in-situ cultivation will be essential. Growing plants directly in regolith offers a practical approach to achieve sustainable long-term human habitation beyond Earth. In this study, Laser-Induced Breakdown Spectroscopy (LIBS) technique was employed for bioimaging of broccoli (Brassica oleracea) and salad (Lactuca sativa) plants grown in Lunar regolith simulant and control substrate. For this purpose, the potential of the 2090 nm laser wavelength for bioimaging of plant tissue was studied compared to the conventional 1064 nm. The signal-to-noise ratio (SNR), total emissivity ($\epsilon_{\mathrm{tot}}$), and Mg II / Mg I intensity ratio (ionisation degree) were all higher when using the 2090 nm laser wavelength compared to 1064 nm. These findings indicate that the 2090 nm laser produces a hotter and more efficiently ionised plasma, supporting its feasibility for bioimaging of plant tissues. Additionally, bioimaging with both laser wavelengths confirmed higher uptake of key plant nutrients such as magnesium (Mg) and calcium (Ca) from Lunar regolith simulant. These results support the potential of LIBS as a diagnostic tool for plant growth monitoring in extraterrestrial environments.
Primates exhibit a robust deviation from canonical allometric scaling: at fixed body mass, their lifespans exceed those of non-primate mammals by factors of two to three. A rhesus macaque (8 kg) lives 25-40 years, whereas a cat of similar mass rarely exceeds 18 years. This statistically significant clade-level excess cannot be explained by standard metabolic or ecological models. We provide a thermodynamic explanation within the Principle of Biological Time Equivalence (PBTE), where lifespan is determined by a finite cycle budget governed by entropy production. We show that primates reduce entropy production per physiological cycle through increased neural energy allocation. The neural power fraction acts as a control parameter, extending the effective lifetime cycle count. Three mechanisms, predictive regulation, enhanced repair, and behavioral buffering, jointly suppress dissipation. This yields a quantitative neuro-metabolic multiplier that explains primate longevity and provides testable predictions linking brain energetics, entropy production, and lifespan.
In heterogeneous network systems such as ecological and social networks, structural stability depends on how connectivity changes under node removal, as different removal sequences can trigger distinct modes of systemic collapse. While robustness to random failures and targeted attacks has been extensively studied, most analyses have focused on connectivity loss or degree distribution, rather than on how scale-invariant organization emerges and evolves with system size. Here we examine how scale-free structure evolves under progressive degree-dependent node removal, systematically varying the hub-protection strength $\theta$. Starting from scale-free networks, we apply the recently developed finite-size scaling (FSS) analysis to node-removed networks and compare the results with those from Kullback-Leibler (KL) divergence-based classification. We find that under random ($\theta=0$) and hub-protecting removal ($\theta>0$), the two criteria largely agree, whereas under hub-preferential removal ($\theta<0$), networks may appear scale-free according to the KL criterion while failing the FSS test of scaling collapse. This discrepancy indicates that similarity to a reference degree distribution does not guarantee the persistence of scale-invariant organization across system sizes. The two diagnostics thus probe complementary aspects of network structure, and their joint use provides a more complete characterization of structural degradation.
Predictive simulation of electrochemical interfaces requires atomistic models that capture reactive bond rearrangements, long-range electrostatics, and charge distributions reflecting the electronic distinctness of electrode and electrolyte. Existing charge-aware machine-learned interatomic potentials (MLIPs) built on global charge equilibration (QEq) settle electrode and electrolyte at a common electrochemical potential, leaving no room for the interfacial gradient that the double layer requires and admitting spurious charge transfer between electronically disconnected regions. Per-fragment charge equilibration is the established remedy in classical molecular dynamics, but reliance on predefined molecular topology has confined it to non-reactive systems. We lift this restriction by making fragment identification itself a differentiable function of atomic geometry, yielding soft fragment-constrained charge equilibration (Soft-FQEq) -- a solver layer that restores fragment-resolved charge conservation in reactive MLIPs. The layer consumes four scalar MLP readouts from a shared atomic-feature network -- per-atom electronegativity, source charge, short-range energy, and a soft bond connectivity -- and returns equilibrated charges together with per-fragment chemical potentials. We implement Soft-FQEq as an extension of the hippynn framework on a HIP-NN feature network and train it on DFT energies, forces, and DDEC6 charges for IrO2/H2O/Na+/ClO4- interfaces. The trained model recovers a clear electrode-to-electrolyte gradient in the per-atom electrochemical potential. With the same trained weights but the fragment-constrained solver replaced by global QEq at inference, this gradient collapses to an essentially uniform profile, directly showing that the gradient cannot be sustained within global QEq while the fragment formulation recovers it.
Capturing ultrafast transient phenomena conventionally requires streak cameras or computational imaging based on compressed sensing, which lead to complex and costly systems. In this Letter, we demonstrate, to the best of our knowledge, the first fully passive single-shot ultrafast imaging architecture assembled entirely from off-the-shelf, low-cost components. A commercial microlens array combined with a stack of standard microscope cover glasses maps temporal information into multiple spatial channels, and a consumer-grade CMOS image sensor records all delayed replicas within a single camera exposure. The proposed system has a total hardware cost below US\$500 and captures the evolution of a picosecond laser pulse with a temporal sampling interval of 1.46~ps, an effective frame rate of 685~Gfps, and a sequence depth of ten frames. The temporal fidelity of the system is verified by recovering the expected Gaussian pulse profile, and the spatial resolution is characterized through a point-source measurement with a point spread function of 1.86 and 1.62 pixels full width at half maximum along the horizontal and vertical directions, respectively. The proposed architecture presents an alternative approach to single-shot ultrafast imaging with a simple, low-cost, computation-free, and fully passive design.
High-order harmonic generation (HHG) in solids - the frequency up-conversion of an optical signal - is governed by symmetries. At terahertz (THz) frequencies, HHG is a key technology to access high frequency spectral windows that are usually difficult to cover using conventional solid state laser technologies. This effect has been recently exploited in graphene where HHG has been demonstrated, albeit only at odd multiples of the driving frequency owing to its inherent centro-symmetry. In topological insulators (TIs), the combination of spin-orbit interaction and time-reversal symmetry create an insulating bulk state with an inverted band order, inseparably connected with conducting surface states. TIs have been predicted to support unconventional high harmonic generation from the bulk and topological surface, which are usually difficult to be distinguished. However, no experimental results have been provided, so far. Here, we exploit the strong optical field amplification provided by an array of single or double split ring resonators, with embedded Bi2Se3 or (InxBi(1-x))2Se3/ Bi2Se3 van der Waals heterostructures, to achieve up-conversion in the 6.4 (even) - 9.7 (odd) THz frequency range. This results from bulk centro-symmetry (odd states) and symmetry breaking in the topological surface states (odd and even).
A pygmy shrew (\textitSuncus etruscus, ${\approx}2$\u2009g) sustains a resting heart rate near $1{,}000$\u2009beats\u2009min$^{-1}$ and dies within two years; an African elephant (${\approx}4{,}000$\u2009kg) beats at $28$\u2009beats\u2009min$^{-1}$ and lives seven decades. Their chronological lifespans differ by a factor of 35, yet each accumulates close to $10^9$ cardiac cycles before death -- a near-constancy first noted by Rubner~(1908) and quantified by Lindstedt and Calder~(1981)~\citelindstedt1981, but never subjected to multi-clade statistical testing, phylogenetic correction, or explicit falsifiability criteria with a large modern dataset. We address this gap with a curated 230-species vertebrate dataset spanning non-primate placentals ($n=43$), primates ($n=18$), marsupials and monotremes ($n=19$), duty-cycle-corrected bats ($n=31$), dive-corrected cetaceans ($n=12$), birds ($n=78$), and Arrhenius-corrected ectotherms ($n=26$), and subject the log-invariant $\ell = \log_{10}(N^{\!\star})$ -- where $N^{\!\star} = f_H\,L\times 525{,}960$ cardiac cycles -- to four independent tests.
An innovative path for the detectors at future colliders to achieve higher performances is to use a Particle Flow approach, which requires highly granular calorimeters to image individual showers. The silicon-tungsten electromagnetic calorimeter (SiW-ECAL) aims at fulfilling all the expected physical and technical requirements. SiW-ECAL has been developed by the CALICE and ILD collaborations for more than two decades and is now reaching maturity, for linear machines. However, with the tendency towards circular machines, the progress of electronics and the rapid advancement of machine learning (ML) techniques, the SiW-ECAL design needs to be reoptimised to enhance its performance. This study develops ML-based reconstruction approaches for SiW-ECAL, achieving an approximate 20% improvement in energy resolution in the low-energy range and effectively correcting energy leakage in the high-energy range. Subsequently, the SiW-ECAL design is reoptimized based on this method.
We analyze the mixing, migration and spreading of a gravity current in a heterogeneous porous medium using high-fidelity numerical simulations. Heterogeneity is represented by log-normal permeability fields of varying correlation lengths and variance. Stable and unstable density stratification scenarios are considered through linear and non-monotonic density laws, respectively. Heterogeneity reduces dissolution and increases the speed of the gravity current proportionally to the Rayleigh number. In the unstable case, heterogeneity accelerates the onset of convection. Convection-driven dissolution slows down the gravity current and counteracts the dispersive effect of heterogeneity resulting in a narrower interface and higher dissolution than in the stable case. Permeability anisotropy reduces dissolution because of the barrier effect of low permeability regions, except when blobs of buoyant fluid are trapped in low permeability structures and rapidly dissolve. The variance of the log-permeability field enhances dissolution. However, the homogeneous case outperforms heterogeneous cases except when Rayleigh number is small. This suggest an interaction between the size of the instabilities, the correlation length of the permeability field and the dispersive and barrier effects of the permeability field that controls dissolution efficiency.
Ravi Kumar, Saksham Mahajan, Felix Donaldson, Leonardo Santoni, Aysha A. Riaz, Gediminas Seniutinas, Felipe Favaro de Oliveira, Anna Regoutz, Fabrizia Foglia, Siddharth Dhomkar, John J.L. Morton Degradation of near surface nitrogen vacancy (NV) centers in diamond under optical illumination has restricted their deployment in applications such as scanning NV magnetomety, particularly under harsh environment such as low temperatures and vacuum. Previously, alumina passivation of planar diamond samples has been shown to reduce the degradation of near surface ensemble NV centers in vacuum. Here, we expand this study to incorporate photonic nanostructures by analyzing the single photon emission characteristics of NV centers embedded in an array of alumina-coated diamond nanopillars in high vacuum and low temperature (6K, high vacuum) environments under non-resonant (522 nm) laser exposure. We find that, in contrast to the oxygen-terminated diamond nanopillars, NV centers in the alumina-coated nanopillars demonstrate negligible change in the single photon purity and brightness over the course of laser exposure in vacuum. At low temperature, NV centers under alumina termination demonstrate stable single photon emission, whereas under oxygen termination the single photon purity degrades under high intensity laser exposure. Alumina surface passivation is therefore shown as a viable path toward the realization of robust NV-diamond based nanoscale sensing under non-ambient atmospheric environments, including using diamond scanning probes.
The excitation of plasma wakefields driven by chirped laser pulses is investigated using a reduced relativistic fluid Poisson model supported by fully relativistic particle in cell (PIC) simulations. The study considers exponential, linear, quadratic, and unchirped phase-modulated laser drivers propagating in an underdense plasma. Numerical solutions of the governing equations demonstrate that exponential chirping produces enhanced wakefield amplitudes compared to polynomial and unchirped cases due to nonlinear phase variation across the pulse envelope. The analytical predictions are validated using quasi cylindrical PIC simulations performed under identical plasma and laser parameters. The simulations reveal strong chirp dependent wakefield modification, with positively chirped pulses generating peak accelerating fields exceeding 58 GV per m, accompanied by pronounced density compression and enhanced electron momentum gain. These results demonstrate that exponential chirping provides an effective mechanism for controlling wakefield strength and improving plasma based particle acceleration.
Julianna Winnik, Adam Walocha, Wojciech Ogonowski, Wiktor Forjasz, Piotr Arcab, Mikołaj Rogalski, Aleksandra Rutkowska, Marzena Stefaniuk, José Ángel Picazo-Bueno, Vicente Micó, Maciej Trusiak, Maria Cywińska We present YOSO (You Only Shot Once), a single-frame phase retrieval framework for digital in-line holographic microscopy (DIHM) in which supervised deep learning is used to numerically generate an additional hologram corresponding to different defocus distance, creating a so-called multi-height dataset, which is then conventionally processed with a well-established Gerchberg-Saxton (GS) algorithm. YOSO is trained on computer-generated data derived from natural images, enabling strong generalization. The selected multi-scale ResNet architecture enables rapid training in under two hours on a mid-range workstation, which is done only once, enabling efficient inference thereafter. We further show that YOSO network can process inputs of varying spatial dimensions, allowing training on small inputs and direct inference on full-sized holograms while bypassing patch-and-stitch procedure. A further advantage of YOSO is its physics-consistent hologram padding, which replaces conventional zero or edge-value padding with a physically grounded approach compatible with the GS framework. The YOSO framework is tested on various systems (lens-based and lensless DIHM) and diverse samples: a resolution test target, adherent and suspended biological cells, and a mouse brain slice. The results show that YOSO is compatible with 3D objects and correctly recovers defocused object wave features, enabling holographic postprocessing such as numerical refocusing. The results of this work are available publicly as software for end-to-end implementation.
Photoacoustic imaging is the leading technique for deep tissue optical imaging, allowing single-shot imaging at depths. However, its resolution may be limited by acoustic aberrations, caused by natural unknown heterogeneities in the tissue speed of sound. In recent years, reflection-matrix based scattering-compensation techniques have been successfully employed in ultrasound, optics, and seismology, to computationally correct such distortions. However, they have not been adapted to photoacoustic imaging since they rely on multiple acquisitions under different controlled excitations, such as input plane-wave illuminations, which do not result in signal changes in photoacoustics. Here, we introduce a framework that enables the direct application of the state-of-the-art reflection-matrix based aberration correction techniques to photoacoustic imaging of dynamic targets. Specifically, we show that a covariance matrix analysis of a conventional set of photoacoustic frames of dynamic targets, such as flowing red blood cells in blood vessels, yields a virtual reflection-matrix that is mathematically analogous to a pulse-echo reflection-matrix, and lends itself to direct processing by conventional reflection-matrix based scattering-compensation algorithms. We validate and demonstrate the approach for photoacoustic aberration correction of vessel-mimicking targets containing flowing absorbers in both simulations and experiments.
Toxic cyanobacterial blooms are a growing environmental concern that affects freshwater ecosystems, drinking water supplies, and public health. The cyanobacterium Microcystis is among the most important bloom forming species. It often grows in large colonies, which enhances its flotation, reduces grazing, and improves nutrient regulation. Microcystis cells are held together by a matrix of extracellular polymeric substances (EPS), making colony mechanics crucial for bloom formation. However, an analysis of the biomechanical properties of cyanobacterial colonies, and how these properties relate to environmental conditions like nutrient availability, remains largely missing. Here, we use micropipette force sensors to quantify the linear and non-linear mechanical properties of individual colonies at single-cell resolution. Bulk shear rheology complements these measurements by probing macroscopic properties. The measured tensile strength and yield stress are broadly comparable to those of bacterial biofilms and are far greater than the hydrodynamic stresses typically found in wind-mixed lakes. This implies that cyanobacterial colonies are highly resistant to fragmentation by natural mixing processes. We also show that low nutrient availability, particularly low phosphorus, produced stronger colonies, suggesting structural changes in the EPS. Overall, our results establish mechanical testing as a tool for a more complete and physically grounded understanding of cyanobacterial colony formation.
Multispectral polarisation imaging has a broad range of applications, from biological cell imaging to agricultural remote surveying. For such applications, especially involving lightweight unmanned aerial vehicles like drones, it is necessary to have compact, single-shot, efficient optical systems. We present a metasurface design that diffractively separates a scene into spectral and polarimetric measurements with a single optical component, operating for 532 nm and 700 nm in a single-shot imaging system. The polarisation imaging performance of the design is shown to be robust to both spectral and angular bandwidths, and multispectral polarimetry is demonstrated experimentally.
Using the path-integral formalism, we show that photons possess a nontrivial quantum metric in momentum space. We derive the semiclassical action and equations of motion by taking into account the quantum metric. In media with a spatially varying refractive index $n(\mathbf{x})$, the quantum metric induces a shift in the trajectory of light at second order in derivatives of $n$, which may be regarded as a nonlinear Hall effect of light. The quantum metric also gives rise to corrections to gravitational lensing in curved spacetime at the nonlinear order in wavelength. This gravitational nonlinear Hall effect results from the interplay between the geometry of position space and that of momentum space.
The polarization state of light plays a central role in strong-field light--matter interactions and is widely used to probe electronic structure in solids via high-order harmonic generation (HHG). In particular, helicity-resolved HHG has been interpreted as a fingerprint of crystal symmetry and topology. Here, we demonstrate deterministic and continuous control of harmonic helicity in solids using polarization-crafted beams, formed by two orthogonally polarized pulses with a controlled time delay. By tuning this delay, the polarization state of individual harmonics can be driven from linear to circular, independent of the material under investigation. We show that this behavior is robust across systems with distinct symmetry and topology, and originates from the sub-cycle modulation of the light--matter interaction mediated by the dipole coupling. Furthermore, the orthogonal configuration allows to break the dynamical symmetry of the light-matter interaction which is manifested in the generation of otherwise forbidden harmonics under standard selection rules.. These results establish harmonic helicity as a field-controlled observable rather than a direct material fingerprint.
Crowds have long held a paradoxical place in the human imagination, feared for their destructive potential yet essential for collective expression. This tension was tragically manifested in the 1919 Jallianwala Bagh massacre, when British colonial troops opened fire on a peaceful gathering in Amritsar, India. Although officially 379 deaths were recorded, eyewitnesses and historians have long challenged this figure. With this study, we critically revisit the events through the lens of the specific role of the crowd as a phenomenon, both regarding the physical and the socio-psychological dynamics. We show that even under conservative physical assumptions - moderate shooting cadence, crowd-shielding, and constrained escape routes - our agent-based simulations consistently yield fatality estimates well above the official death count. On the socio-psychological front, we explore how early 20th-century discourses, influenced by Le Bon's theory of crowd psychology, constructed the crowd as an inherently irrational and threatening entity, thus providing a rationale for the application of excessive force. Our findings show that acknowledging the socio-cultural construction of crowds as a relevant factor in how state power engage with and respond to collective gatherings brings to light contemporary parallels and the risks posed by their rhetorical framing. Furthermore, this study highlights the importance of interdisciplinary modelling for both historical accountability and current crowd safety, particularly in an era of growing political unrest, surveillance, and militarised crowd policing.
Fabricating brightly fluorescent layers with nanometric thickness and digitally controlled lateral structuration remains a challenge for next-generation photonic devices, optical calibration standards, and biocompatible interfaces. Here, we introduce Nano-Bead Emitters (NBEs), hydrogel nanoparticles covalently functionalized with fluorophores, as a universal, water-processable ink platform for fabricating programmable nanometric fluorescent architectures. By immobilizing fluorophores within a charged nanohydrogel scaffold, the platform entirely decouples film morphology from dye solubility. This molecule-independent strategy enables spectrally distinct, inherently water-insoluble dyes to be processed using a single, standardized aqueous ink formulation. Combined with laser-induced forward transfer (LIFT) printing, this additive approach yields highly uniform fluorescent layers (~7 nm thickness, sub-nanometric roughness). This structural invariance produces complex multicolor patterns sharing identical thickness and surface morphology across all spectral channels, a critical requirement for quantitative optical calibration. Furthermore, LIFT printing provides programmable, layer-by-layer control over fluorescence intensity via successive deposition cycles, yielding precisely tunable brightness without aggregation-caused quenching. This maskless technique enables rapid, high-fidelity printing of both monochromatic and multicolor patterns over macroscopic areas with absolute spatial resolution. Finally, these universally compatible NBE inks stably deposit onto diverse substrates (glass, polymers, semiconductors, metasurfaces), effectively bridging scalable manufacturing with high-performance integrated photonic systems.
Elastic wave propagation is intrinsically sensitive to the mechanical properties of the medium through which it travels. In soft elastomers, this makes guided elastic waves natural probes of viscoelastic and acoustoelastic behavior over a broad frequency range. In this work, we introduce a wave-based mechanical characterization method in which a thin elastomer strip acts as a waveguide supporting multiple in-plane guided modes. By combining stroboscopic measurements of monochromatic wave fields with a theoretical framework that couples frequency-dependent viscoelasticity and elongation-dependent acoustoelasticity, we extract complex-valued dispersion relations for guided modes under controlled static elongation. A dedicated numerical implementation allows these experimental dispersion curves to be quantitatively matched to theory, enabling identification of the material's rheological and hyperelastic parameters. Applied to several commercial silicone elastomers, the method yields mechanical parameters that are consistent with conventional plate-plate rheometry, while extending the accessible frequency range beyond that of conventional techniques. By exploiting the richness of guided-wave dispersion and the sensitivity of waves to both frequency and pre-stress, this approach provides a unified, broadband, and experimentally simple route to the mechanical characterization of soft elastomers.
This paper presents a physically-informed fuzzy clustering of vertical sounding ionograms for automatically separating the ionogram into tracks suitable for further interpretation and determining their optimal number. The model is designed for use not only in conditions where the number of tracks is known, but also in disturbed ionospheric conditions where the number of tracks is preliminary unknown. The method is based on an expectation-maximization algorithm, used for clustering, and on parametrically specified distributions of distances from points to parametrically specified curves. The curves used as track models are close to model tracks in the parabolic ionospheric layer model. The resulting model of each track has six parameters: three standard ones (the critical frequency, the lower boundary of the layer, and its half-width), and three additional ones to take into account possible underlying layer effects. By sequentially increasing the number of tracks and optimizing their parameters, the model finds the optimal number of tracks on the ionogram by minimizing the modified Bayesian information criterion. The Sequential Least Squares Quadratic Programming algorithm is used to find the parameters of a single track. The width of each single track is assumed to be unknown constant found during fitting process. To improve the quality of ionogram clustering, automatic adaptive noise filtering is performed before clustering. This filtering is based on a combination of the DBSCAN and Gaussian Mixture algorithms. Also, to improve clustering quality on an ionosonde without hardware separation of the ordinary and extraordinary components, a preliminary approximate removal of points belonging to the extraordinary mode is performed.
Room-temperature single-photon emission (SPE) resulting from a biexciton-exciton cascaded decay is demonstrated for the first time from chemically and photoelectrochemically etched site-controlled In0.14Ga0.86N quantum dots (QDs) embedded in vertical GaN nanowires. Diameter-dependent biexciton-exciton dynamics are analysed to determine the eligibility of QD as a single-photon emitter. The signal-to-noise ratio degrades with increasing QD diameter. Background noise photons pose a bottleneck to achieving SPE. This is also explained from a carrier dynamics perspective. Surface recombination contributes to inhomogeneous broadening at QD diameters larger than 35 nm. Below 35 nm, density-of-states-corrected Auger gradually becomes the principal biexciton-decay route with further reduction in QD diameter, thereby quenching the possibility of thermal broadening and setting a threshold for SPE. Below 9 nm, the Auger recombination rate becomes manyfold of other decay rates, causing multi-photon suppression via single Auger decay to form an exciton. Surface recombination probability of this exciton is minimized while biexciton state filling probability is maximized by reducing sidewall surface states through wet-treatment. These improve biexciton state preparation and enhance the single-photon purity of the exciton towards the exciton Bohr radius (3 nm) regime. Far away from this regime, higher-order autocorrelations to characterize quantum emission involving multi-photon events are discussed. This study establishes a generalized physical framework for predetermining SPE probability as a function of QD surface and geometry down to the exciton Bohr radius regime, with practical implementations. This work shows the pathway to design and develop next-generation semiconductor QDs for high-purity room-temperature SPE.
Braneshop, and
the US National Science Foundation.