arXiv Physics @arxiv_physics@qoto.org

The universe is not locally realistic. Abandoning causality often appears more palatable than giving up on realism. This paper proposes two novel experimental setups to test realism's failure using the conservation of electric charge. The first employs weak measurements of charge density in a double-slit interference setup. The second uses entangled charged particles in a Bell-type experiment, measuring electric field correlations to detect non-local charge distribution. Both leverage charge conservation to explore whether charge location remains indefinite until measured. These experiments offer a new perspective on quantum foundations, using charge as a probe to question whether the universe assigns definite properties only upon observation. A discussion on why charge is more appropriate for such experiments than mass is also present.

Blood accounts for 7-8% of total body weight, with an average adult containing 4.5 to 6 quarts. It delivers oxygen and nutrients to cells, removes waste products, supports immunity, and regulates body temperature. Comprising over 4,000 components, including plasma, platelets, erythrocytes, and leukocytes, blood presents a challenge for isolating specific cell types due to its heterogeneity. In autologous therapies, target cells may be as rare as one per million background cells. This study presents the development of a compact disc (CD)-based microfluidic device for high-throughput, label-free separation of blood components. The system integrates three main modules on a single disk: cell sorting, fluid control, and cell lysis. The initial module utilizes Pinched Flow Fractionation (PFF) to separate red blood cells, white blood cells, and platelets based on size, achieving 99.99% efficiency. A capillary valve directs the sorted cells to the lysis module, where a chemical reagent is used to rupture cell membranes for downstream analysis. To enhance lysis efficiency, a micro mixer was incorporated using two zigzag geometries with internal barriers. The mixing performance of both designs was evaluated to determine optimal fluid interaction. This integrated, multifunctional CD platform offers a compact and effective solution for isolating and processing specific blood cells, with potential applications in diagnostics and personalized medicine.

Many viscous liquids behave effectively as incompressible under high pressures but display a pronounced dependence of viscosity on pressure. The classical incompressible Navier-Stokes model cannot account for both features, and a simple pressure-dependent modification introduces questions about the well-posedness of the resulting equations. This paper presents the first study of a second-gradient extension of the incompressible Navier-Stokes model, recently introduced by the authors, which includes higher-order spatial derivatives, pressure-sensitive viscosities, and complementary boundary conditions. Focusing on steady flow down an inclined plane, we adopt Barus' exponential law and impose weak adherence at the lower boundary and a prescribed ambient pressure at the free surface. Through numerical simulations, we examine how the flow profile varies with the angle of inclination, ambient pressure, viscosity sensitivity to pressure, and internal length scale.

Ejectors are used in various engineering systems, including steam and vapor compression cycles. Optimizing the performance of ejectors requires understanding and analysis of multiphase and turbulent flow structures associated with their internal flow fields. This approach yields higher fidelity but at a high computational cost. Lower-fidelity one-dimensional (1D) models offer lower computational costs; however, 1D models are often empirical and provide limited understanding of the internal flow fields, overlooking possibilities of optimization. Ejector flows can be categorized into four regimes: Regime 1 (R1), which is compressibility dominated; Regime 2 (R2), which is interface instability driven; Regime 3 (R3), which is buoyancy dominated; and Regime 4 (R4), which is a wall-bounded turbulent jet expansion. Among these, the buoyancy-dominated regime is the most complex and least understood. This work discusses an approach to develop a reduced-order model utilizing a self-similarity framework to capture the internal flow field of the jet within the buoyancy-dominated regime under quasi-steady, compressible, and isothermal flow conditions, where density variations arise only from mixing. The density variation is captured through the Favre-averaging approach. The model captures the expansion of a central jet influenced by momentum diffusivity and a constant streamwise pressure gradient. Interaction of the central jet with the cylindrical wall induces a counterflow annular wall jet due to the combined effects of negative radial density gradients and shear stress imposed by the wall. Initially, the discussion focuses on flow topology inside the ejector, followed by the self-similarity methodology and implementation of asymptotic analysis. Finally, the resemblance...

Magnetic holes are structures commonly observed in various space plasma environments throughout the solar system, including the solar wind. These structures are characterized by a localized decrease in magnetic field strength, coincident with an increase in plasma density. Previous observational studies in the solar wind link the presence of Langmuir waves to magnetic holes, suggesting a strong correlation between these phenomena. We develop a model based on magnetic-moment conservation and its violation to explain the excitation of Langmuir waves in magnetic holes. Our model illustrates that magnetic holes induce changes in the electron velocity distribution function that emit electrostatic Langmuir waves due to the bump-on-tail instability. Using data from the Solar Orbiter spacecraft, we provide a comprehensive analysis of this process and test our predictions with observations. The consistency between the model and observations indicates that our proposed process is a viable mechanism for producing Langmuir waves in magnetic holes in the solar wind.

The present work investigates numerically the transport of colloidal particles within an evaporating sessile droplet and their deposition on the substrate in unfavourable conditions. The coupled phenomena of fluid flow and heat and mass transfer with phase change are modelled by taking into account for the evaporative cooling effect, the surface tension gradient effect at liquid air interface and the particles-substrate interaction effect due to DLVO attractive forces.The results show that in presence of the particles-substrate interaction, the outward radial internal flow is the main responsible for the coffee ring effect giving rise a ring like pattern with inner traces, while the thermal Marangoni internal flow reduces this effect giving rise a uniform deposit with a dark periphery.

Our numerical study aims to investigate particle deposit patterns from the evaporation of a sessile colloidal droplet. An in house finite volume code is developed to simulate the coupled phenomena of flow and heat and mass transfer with phase change of the evaporating droplet. The numerical model takes into account evaporative cooling effect, surface tension gradient effect at the liquid-air interface, thermal buoyancy effect inside the droplet, thermosolutal buoyancy effect in the surrounding air and electrical double layer and Van der Waals interactions between substrate and colloidal particles. Three models are used in this study: (a) a model that takes into account only the strong evaporation near the pinned contact line (b) a model that takes into account in addition the thermo capillary effect and (c) a comprehensive model that takes into account all effects. The results show that without heating the thermal buoyancy has a negligible effect on the formation of particle deposit. In presence of substrate-particle interactions, dominant radial flow is the main responsible for the coffee ring effect giving a ring-like pattern with inner traces, while the Marangoni flow reduces the coffee ring effect giving a uniform deposit with a dark periphery

The interaction between light's angular momentum (AM) and material systems has unlocked new avenues in structured photonics, including in magneto-optical (MO) materials. While spin angular momentum (SAM) effects in MO systems are well-established, orbital angular momentum (OAM) introduces novel opportunities for new nonreciprocal light-matter interactions. In this study, we demonstrate a unique optical phenomenon where OAM states undergo state-specific nonreciprocal operation within an MO medium, reducing Faraday rotation. This effect arises from transverse momentum transfer into the material, inducing spin-orbit coupling (SOC) at a perturbed electronic transition rate. The resulting OAM-dependent optical SOC modifies the material's refractive index, directly linking structured light and MO response. Our findings extend previous observations of paraxial beams and reveal a deeper fundamental mechanism governing OAM-driven nonreciprocal interactions. These insights pave the way for OAM-selective nonreciprocal photonic devices, chiral optical logic, quantum memory elements, and ultrafast spintronic architectures. This work advances MO integration with structured light for enhanced control over photonic and spintronic systems.

We derive an analytic solution for the electromagnetic vector potential in any gauge directly from Maxwell's equations for potentials for an arbitrary time-dependent charge-current distribution. No gauge condition is used in the derivation. Our solution for the vector potential has a gauge-invariant part and a gauge-dependent part. The gauge-dependent part is related to the scalar potential.

We present a hybrid machine learning framework that combines Physics-Informed Neural Operators (PINOs) with score-based generative diffusion models to simulate the full spatio-temporal evolution of two-dimensional, incompressible, resistive magnetohydrodynamic (MHD) turbulence across a broad range of Reynolds numbers ($\mathrm{Re}$). The framework leverages the equation-constrained generalization capabilities of PINOs to predict coherent, low-frequency dynamics, while a conditional diffusion model stochastically corrects high-frequency residuals, enabling accurate modeling of fully developed turbulence. Trained on a comprehensive ensemble of high-fidelity simulations with $\mathrm{Re} \in \{100, 250, 500, 750, 1000, 3000, 10000\}$, the approach achieves state-of-the-art accuracy in regimes previously inaccessible to deterministic surrogates. At $\mathrm{Re}=1000$ and $3000$, the model faithfully reconstructs the full spectral energy distributions of both velocity and magnetic fields late into the simulation, capturing non-Gaussian statistics, intermittent structures, and cross-field correlations with high fidelity. At extreme turbulence levels ($\mathrm{Re}=10000$), it remains the first surrogate capable of recovering the high-wavenumber evolution of the magnetic field, preserving large-scale morphology and enabling statistically meaningful predictions.

Signal detection is one of the main challenges of data science. According to the nature of the data, the presence of noise may corrupt measurements and hinder the discovery of significant patterns. A wide range of techniques aiming at extracting the relevant degrees of freedom from data has been thus developed over the years. However, signal detection in almost continuous spectra, for small signal-to-noise ratios, remains a known difficult issue. This paper develops over recent advancements proposing to tackle this issue by analysing the properties of the underlying effective field theory arising as a sort of maximal entropy distribution in the vicinity of universal random matrix distributions. Nearly continuous spectra provide an intrinsic and non-conventional scaling law for field and couplings, the scaling dimensions depending on the energy scale. The coarse-graining over small eigenvalues of the empirical spectrum defines a specific renormalization group, whose characteristics change when the collective behaviour of "informational" modes become significant, that is, stronger than the intrinsic fluctuations of noise. This paper pursues three different goals. First, we propose to quantify the real effects of fluctuations relative to what can be called "signal", while improving the robustness of the results obtained in our previous work. Second, we show that quantitative changes in the presence of a signal result in a counterintuitive modification of the distribution of eigenvectors. Finally, we propose a method for estimating the number of noise components and define a limit of detection in a general nearly continuous spectrum using the renormalization group. The main statements of this paper are essentially numeric, and their reproducibility can be checked using the associated code.

Sea ice is a key component of the Earth's climate system, making its aging process an essential focus of current research. The age of sea ice is closely linked to its thermal and mechanical properties, which govern its interactions with the surrounding environment. In this study, we combine experimental techniques and modeling to explore the full dynamical process of mushy ice growth and spontaneous aging in saline water, within a natural convective flow system. We show that the aging of newly formed mushy ice in the present system, characterized by a gradual long-term reduction in porosity, is controlled by diffusion-driven desalination. Moreover, we observe that the system eventually transits into a dense freshwater ice layer adjacent to a well-mixed saline water region. The shape of the ice layer in this asymptotic state is well captured by numerical simulations of non-porous ice. Our findings improve the understanding of the complex physics governing phase changes in aqueous systems and provide a framework for studying sea ice aging in laboratory settings, with implications spanning diverse natural and industrial applications.

Multiple Mach-Zehnder interferometers are constructed using fiber-Bragg grating couplers, y-branches, silicon waveguides, and/or broadband splitters in silicon on insulator strip waveguides to test the effect of variation in waveguide length between consecutive bends on the transmission gain spectrum, free-spectral range, group refractive index, and dispersion. Dispersion is mitigated by increasing the path length between consecutive bends of minimum radius. With long linear waveguide sections, a free spectral range of 0.41 nm is achieved.

Time-varying group interactions constitute the building blocks of many complex systems. The framework of temporal hypergraphs makes it possible to represent them by taking into account the higher-order and temporal nature of the interactions. However, the corresponding datasets are often incomplete and/or limited in size and duration, and surrogate time-varying hypergraphs able to reproduce their statistical features constitute interesting substitutions, especially to understand how dynamical processes unfold on group interactions. Here, we present a new temporal hypergraph model, the Emerging Activity Temporal Hypergraph (EATH), which can be fed by parameters measured in a dataset and create synthetic datasets with similar properties. In the model, each node has an independent underlying activity dynamic and the overall system activity emerges from the nodes dynamics, with temporal group interactions resulting from both the activity of the nodes and memory mechanisms. We first show that the EATH model can generate surrogate hypergraphs of several empirical datasets of face-to-face interactions, mimicking temporal and topological properties at the node and hyperedge level. We also showcase the possibility to use the resulting synthetic data in simulations of higher-order contagion dynamics, comparing the outcome of such process on original and surrogate datasets. Finally, we illustrate the flexibility of the model, which can generate synthetic hypergraphs with tunable properties: as an example, we generate "hybrid" temporal hypergraphs, which mix properties of different empirical datasets. Our work opens several perspectives, from the generation of synthetic realistic hypergraphs describing contexts where data collection is difficult to a deeper understanding of dynamical processes on temporal hypergraphs.

Many reactions in chemistry and biology involve multiple electronic states, rendering them nonadiabatic in nature. These reactions can be formally described using Fermi's golden rule (FGR) in the weak-coupling limit. Nonadiabatic instanton theory presents a semiclassical approximation to FGR, which is directly applicable to molecular systems. However, there are cases where the theory has not yet been formulated. For instance, in many real-world reactions including spin-crossover or proton-coupled electron transfer, the crossing occurs near a barrier on a diabatic state. This scenario gives rise to competing nonadiabatic reaction pathways, some of which involve tunneling through a diabatic barrier while simultaneously switching electronic states. To date, no rate theory is available for describing tunneling via these unconventional pathways. Here we extend instanton theory to model this class of processes, which we term the ``non-convex'' regime. Benchmark tests on model systems show that the rates predicted by instanton theory are in excellent agreement with quantum-mechanical FGR calculations. Furthermore, the method offers new insights into multi-step tunneling reactions and the competition between sequential and concerted nonadiabatic tunneling pathways.

Solid-state $^{229}$Th nuclear clocks are set to provide new opportunities for precision metrology and fundamental physics. Taking advantage of a nuclear transition's inherent low sensitivity to its environment, orders of magnitude more emitters can be hosted in a solid-state crystal compared to current optical lattice atomic clocks. Furthermore, solid-state systems needing only simple thermal control are key to the development of field-deployable compact clocks. In this work, we explore and characterize the frequency reproducibility of the $^{229}$Th:CaF$_2$ nuclear clock transition, a key performance metric for all clocks. We measure the transition linewidth and center frequency as a function of the doping concentration, temperature, and time. We report the concentration-dependent inhomogeneous linewidth of the nuclear transition, limited by the intrinsic host crystal properties. We determine an optimal working temperature for the $^{229}$Th:CaF$_2$ nuclear clock at 195(5) K where the first-order thermal sensitivity vanishes. This would enable in-situ temperature co-sensing using different quadrupole-split lines, reducing the temperature-induced systematic shift below the 10$^{-18}$ fractional frequency uncertainty level. At 195 K, the reproducibility of the nuclear transition frequency is 280 Hz (fractionally $1.4\times10^{-13}$) for two differently doped $^{229}$Th:CaF$_2$ crystals over four months. These results form the foundation for understanding, controlling, and harnessing the coherent nuclear excitation of $^{229}$Th in solid-state hosts, and for their applications in constraining temporal variations of fundamental constants.

We theoretically analyze the nonlinear dynamics and routes to chaos in a broad-area vertical cavity surface-emitting laser (BA-VCSEL) in free-running operation. Accounting for the onset of higher order transverse modes (TMs) unveils additional bifurcations at higher currents not found for single-mode VCSELs (SM-VCSELs). The resulting dynamics involves competition between polarization modes with different transverse profiles and shows good qualitative agreement with recent experimental observations.

In this study, we investigated the propagation pattern and the site-to-site correlation function in a PT-symmetric waveguide array with different input quantum states. Recognizing the stark difference in propagation pattern before and after the PT symmetry-breaking point, we have developed a novel, straightforward intensity-based criterion to determine the exceptional point (EP). This new criterion shows excellent agreement with those obtained by directly computing the Hamiltonian's eigenvalues. Given the computational complexity of Hamiltonian diagonalization, our proposed criterion provides a highly efficient and valuable alternative for identifying the PT symmetry-breaking point.

Imaging techniques such as functional near-infrared spectroscopy (fNIRS) and diffuse optical tomography (DOT) achieve deep, non-invasive sensing in turbid media, but they are constrained by the photon budget. Wavefront shaping (WFS) can enhance signal strength via interference at specific locations within scattering media, enhancing light-matter interactions and potentially extending the penetration depth of these techniques. Interpreting the resulting measurements rests on the knowledge of optical sensitivity - a relationship between detected signal changes and perturbations at a specific location inside the medium. However, conventional diffusion-based sensitivity models rely on assumptions that become invalid under coherent illumination. In this work, we develop a microscopic theory for optical sensitivity that captures the inherent interference effects that diffusion theory necessarily neglects. We analytically show that under random illumination, the microscopic and diffusive treatments coincide. Using our microscopic approach, we explore WFS strategies for enhancing optical sensitivity beyond the diffusive result. We demonstrate that the input state obtained through phase conjugation at a given point inside the system leads to the largest enhancement of optical sensitivity but requires an input wavefront that depends on the target position. In sharp contrast, the maximum remission eigenchannel leads to a global enhancement of the sensitivity map with a fixed input wavefront. This global enhancement equals to remission enhancement and preserves the spatial distribution of the sensitivity, making it compatible with existing DOT reconstruction algorithms. Our results establish the theoretical foundation for integrating wavefront control with diffuse optical imaging, enabling deeper tissue penetration through improved signal strength in biomedical applications.