arXiv Physics @arxiv_physics@qoto.org

Classical electrodynamics in material media, while essential, faces century-old inconsistencies regarding energy, momentum, and force. This work re-examines the theory from first principles to establish a rigorous description. Applying the force-energy consistency requirement, derived from the unambiguous Maxwell-Lorentz theory for free charges, reveals profound physical inconsistencies in conventional energy balances (using $\mathbf{D}, \mathbf{H}$) and major historical energy-momentum tensors (Minkowski, Abraham, Einstein-Laub). Their critical failure lies in accounting for energy dissipation, especially in stationary matter. This analysis justifies a unique formulation where the field's energy/momentum uses the vacuum tensor $T_{EM}(\mathbf{E},\mathbf{B})$, and interaction is solely the total Lorentz force $f_{Lorentz}$ acting on the total current $J_{total}$ (incorporating material polarization $\mathbf{P}$ and magnetization $\mathbf{M}$). This framework correctly treats $\mathbf{j}_{total} \cdot \mathbf{E}$ as the universal energy exchange gateway, handling storage and dissipation consistently, and clarifies $\mathbf{D}, \mathbf{H}$ as mathematical aids, not fundamental energy carriers. Addressing force density controversies, we demonstrate via spatial averaging analysis that microscopic force distributions are inherently indeterminable in any macroscopic theory. This justifies focusing on consistent energy/momentum accounting and provides a unified, physically sound, relativistically consistent foundation for electrodynamics in matter.

Lipid nanoparticles (LNPs) are precisely engineered drug delivery carriers commonly produced through controlled mixing processes, such as nanoprecipitation. Since their delivery efficacy greatly depends on particle size, numerous studies have proposed experimental and theoretical approaches for tuning LNP size. However, the mechanistic model for LNP fabrication has rarely been established alongside experiments, limiting a profound understanding of the kinetic processes governing LNP self-assembly. Thus, we present a population balance equation (PBE)-based model that captures the evolution of the particle size distribution (PSD) during LNP fabrication, to provide mechanistic insight into how kinetic processes control LNP size. The model showed strong agreement with experimentally observed trends in the PSD. In addition to identifying the role of each kinetic process in shaping the PSD, we analyzed the underlying mechanisms of three key operational strategies: manipulation of (1) lipid concentration, (2) flow rate ratio (FRR), and (3) mixing rate. We identified that the key to producing precisely controlled particle size lies in controlling super-saturation and lipid dilution to regulate the balance between nucleation and growth. Our findings provide mechanistic understanding that is essential in further developing strategies for tuning LNP size.

This work explores the two-dimensional Dirac oscillator (DO) within the framework of Amelino-Camelia doubly special relativity (DSR), employing a modified Dirac equation that preserves the first-order nature of the relativistic wave equation. By introducing non-minimal couplings, the system provides an exact analytical solution in terms of confluent hypergeometric functions, along with closed-form expressions for the energy spectrum (indulging a Landau-like signature along with accidental spin-degeneracies)-. In the low-energy limit, the results reproduce the well-known two-dimensional Dirac oscillator spectrum, and in the nonrelativistic regime, the results reduce the Schrödinger oscillator spectrum. First-order corrections in this DSR model introduce a mass-splitting term proportional to \(\pm \mathcal{E}_{\circ}/\mathcal{E}_p\), where \(\mathcal{E}_{\circ} = mc^2\) is the rest energy and \(\mathcal{E}_p\) is the Planck energy. These corrections preserve the symmetry between the energies of particles and antiparticles around zero energy, but induce a shift in the energy levels that becomes more significant for higher excited states (\(n > 0\)). By mapping the system to a DSR-deformed charged Dirac oscillator in the presence of an out-of-plane uniform magnetic field, we show that the leading-order Planck-scale corrections vanish at a critical magnetic field \(\mathcal{B}^{c}_{0}\), and as the magnetic field approaches this critical value, the relativistic energy levels approach \(\mathcal{E}_{n,\pm} = \pm \mathcal{E}_{\circ}\). Finally, we identify a previously undetermined feature in two-dimensional charged Dirac oscillator systems in a magnetic field, revealing that the corresponding modes manifest as spinning magnetic vortices.

This paper presents an improved approach for preserving sharp interfaces in multiphase Lattice Boltzmann Method (LBM) simulations using Physics-Informed Neural Networks (PINNs). Interface diffusion is a common challenge in multiphase LBM, leading to reduced accuracy in simulating phenomena where interfacial dynamics are critical. We propose a coupled PINN-LBM framework that maintains interface sharpness while preserving the physical accuracy of the simulation. Our approach is validated through droplet simulations, with quantitative metrics measuring interface width, maximum gradient, phase separation, effective interface width, and interface energy. The enhanced visualization techniques employed in this work clearly demonstrate the superior performance of PINN-LBM over standard LBM for multiphase simulations, particularly in maintaining well-defined interfaces throughout the simulation. We provide a comprehensive analysis of the results, showcasing how the neural network integration effectively counteracts numerical diffusion, while maintaining physical consistency with the underlying fluid dynamics.

Multiscale problems are ubiquitous in physics. Numerical simulations of such problems by solving partial differential equations (PDEs) at high resolution are computationally too expensive for many-query scenarios, e.g., uncertainty quantification, remeshing applications, topology optimization, and so forth. This limitation has motivated the application of data-driven surrogate models, where the microscale computations are $\textit{substituted}$ with a surrogate, usually acting as a black-box mapping between macroscale quantities. These models offer significant speedups but struggle with incorporating microscale physical constraints, such as the balance of linear momentum and constitutive models. In this contribution, we propose Equilibrium Neural Operator (EquiNO) as a $\textit{complementary}$ physics-informed PDE surrogate for predicting microscale physics and compare it with variational physics-informed neural and operator networks. Our framework, applicable to the so-called multiscale FE$^{\,2}\,$ computations, introduces the FE-OL approach by integrating the finite element (FE) method with operator learning (OL). We apply the proposed FE-OL approach to quasi-static problems of solid mechanics. The results demonstrate that FE-OL can yield accurate solutions even when confronted with a restricted dataset during model development. Our results show that EquiNO achieves speedup factors exceeding 8000-fold compared to traditional methods and offers an optimal balance between data-driven and physics-based strategies.

The Lorentz-Poincaré interpretation of special relativity (SR) keeps the classical concepts of separated space and time, at the price of postulating an indetectable preferred inertial frame or ``ether". But SR does not contain gravity. The presence of gravity could make the ether detectable. This is one idea behind the ``scalar ether theory of gravitation" (SET), which coincides with SR if the gravity field vanishes, and passes a number of tests. However, the coupling of SET with the Maxwell electromagnetic (EM) field needs to use the theory's dynamical equation for the energy tensor in a non-trivial way. It cannot be assumed that the energy tensors of the charged matter and the EM field add to give the total energy tensor, source of the gravitational field. Thus, an additional, ``interaction" energy tensor ${\bf T}_\mathrm{inter}$ has to be postulated. Asking that ${\bf T}_\mathrm{inter}$ is Lorentz-invariant in the situation of SR, fixes its form. It depends only on a scalar field $p$. ${\bf T}_\mathrm{inter}$ is an exotic kind of matter and is distributed in the whole space, hence it could contribute to dark matter. For a weak gravitational field, $p$ obeys a first-order partial differential equation (PDE) involving the EM field and the Newtonian potential. However, the EM field varies on the scale of the wavelength, which is extremely small. To get the field $p$ in a galaxy, some averaging has to be done. After several attempts based on the homogenization theory, a simpler way has been found recently: If the macro-averages of $p$ and the EM field vary smoothly, it can be shown that the PDE for $p$ remains valid in the same form with spacetime-averaged fields. The current stage of calculations will also been shown.

Non-destructive Schottky detectors are indispensable devices widely used in experiments at heavy-ion storage rings. In particular, they can be used to accurately determine the masses and lifetimes of short-lived exotic nuclear species. Single-ion sensitivity -- which enables highest sensitivity -- has been regularly achieved in the past utilizing resonant cavity detectors. Recent designs and analysis methods aim at pushing the limits of measurement accuracy by increasing the dimensionality of the acquired data, namely the position of the particle as well as the phase difference between several detectors. This work describes current methods and future perspectives of Schottky detection techniques focusing at their application for mass and lifetime measurements of the most rare and simultaneously short-lived radio nuclides.

The St. Benedict ion trapping system, which aims to measure the $β-ν$ angular correlation parameter in superallowed-mixed mirror transitions, is under construction at the University of Notre Dame. These measurements will provide much-needed data to improve the accuracy of the $V_{ud}$ element of the CKM matrix. One of the major components of this system is the radio frequency quadrupole cooler-buncher, which is necessary to create low-emittance ion bunches for injection into the measurement Paul trap. The off-line commissioning of the cooler-buncher, using a potassium ion source, determined that the device could produce cooled ion bunches characterized by a 50-ns full-width-half-maximum time width. The commissioning results also determined the trapping efficiency to be 93(1)$\%$ and the trapping half-life to be 20.0(5) s.

Earthquake catalog declustering is the procedure of separating event clusters from background seismicity, which is an important task in statistical seismology, earthquake forecasting, and probabilistic seismic hazard analysis. Several declustering methods have been introduced in the literature and operate under the supposition that background events occur independently while clusters are triggered by prior events. Here, we test the ability of Supervised Machine Learning (SML) on the declustering problem by leveraging two popular statistical methods. First, the Epidemic Type Aftershock Sequence (ETAS) model is fit to a target catalog and the parameters are used to generate synthetic earthquake data, which replicate the magnitude-space-time seismicity of the target catalog. Next, the Nearest-Neighbour Distance (NND) metrics are computed between each simulated event and used as features to train the SML algorithm. Finally, the trained algorithm is applied to decluster synthetic testing data and then the original target catalog. Our results indicate that SML method performs better than the NND-based and stochastic declustering methods on the test data and makes more nuanced selections of background and clustered events when applied to real seismicity. While the vast majority of the SML technique's predictive power appears to lie within the NND values of the ''first'' nearest-neighbours, a machine learning analysis reveals that predictive accuracy can be improved by additional ''next'' nearest-neighbours and differential magnitude features. The developed approach is applied to seismic catalogs in Southern California and Italy to decluster them.

Surface plasmons or phonons propagating on a two-dimensional (2D) material can exhibit coupling with resonant excitations in its substrate, and the resulting coupled modes were extensively studied. Similar coupling of edge modes propagating along a boundary of 2D material with the substrate excitations remains unexplored. This paper aims to bridge this gap by investigating the edge-substrate hybrid modes using the exact Wiener-Hopf analytical approach. We analyze dispersions, decay rate, and confinement of such hybrid modes both without magnetic field (plasmon-phonon and phonon-phonon hybrid modes) and in quantizing magnetic field where edge magnetoplasmons hybridize with substrate phonons. The hybrid modes are predicted to occur in THz and far-IR ranges for several combinations of quasi-2D materials (single- and bilayer graphene, quantum wells, thin-film semiconductors) and substrates with polar-phonon resonances.

Cellular development follows a stochastic yet rule-governed trajectory, though the underlying principles remain elusive. Here, we propose that cellular development follows paths of least action, aligning with foundational physical laws that govern dynamic systems across nature. We introduce a computational framework that takes advantage of the deep connection between the principle of least action and maximum entropy to model developmental processes using Transformers architecture. This approach enables precise quantification of entropy production, information flow curvature, and local irreversibility for developmental asymmetry in single-cell RNA sequence data. Within this unified framework, we provide interpretable metrics: entropy to capture exploration-exploitation trade-offs, curvature to assess plasticity-elasticity dynamics, and entropy production to characterize dedifferentiation and transdifferentiation. We validate our method across both single-cell and embryonic development datasets, demonstrating its ability to reveal hidden thermodynamic and informational constraints shaping cellular fate decisions.

This paper presents a novel pulse-reconstruction method well suited to sparsely sampled repetitive data, such as commonly arise from trains of ultrashort laser-pulses. Typically waveforms in such traces are fully instrument-limited by the detecting systems, for instance, the combination of detector and oscilloscope, and only the energy of each waveform is sought. The method applies whenever shape of the waveform is the same for every pulse and can be well-characterized, with only amplitude and relative peak-timing changing. Under such conditions this information, known in advance, can be used as a basis - an archetype - for very accurate pulse fitting. Our characterizations show that the method very accurately extracts pulse heights and relative pulse timing, even when sampling routinely misses the pulse peak, and entirely misses the rising or falling edge. We show this method to be adaptable in different detection systems, showing significant improvement in accuracy of measurement, in this class of problem.

Vanadium dioxide (VO2) is a thermochromic material that undergoes a phase transition from a monoclinic semiconducting state to a rutile metallic state at 68 degrees C, a temperature close to room temperature. This property makes VO2 particularly valuable in applications such as optical and electrical switches, data storage, neuromorphic computing, and remarkably dynamic smart windows for solar radiation control. VO2 typically needs to be synthesized for these applications as nanostructured thin films. Over the past few decades, significant efforts have been made to control the thermochromic properties of VO2 through crystal structure tuning, doping, and the development of VO2 nanocomposites. Additionally, introducing nano- and mesoporosity has been shown to enhance the optical properties of thermochromic VO2 films. This study presents a methodology for producing highly porous, aerogel-like V2O5 films, which can be thermally processed to form aerogel-like VO2 films. This process is based on sequential plasma polymerization and plasma etching to produce aerogel-like V2O5 films that are annealed to yield ultraporous nanocrystalline VO2 films. The sacrificial vanadium-containing plasma polymers are obtained by remote plasma-assisted vacuum deposition (RPAVD) using vanadyl porphyrin as a precursor and Ar as plasma gas. The aerogel-like VO2 films show exceptional thermochromic performance with luminous transmittances higher than 54%, solar modulation up to 18.8%, and IR modulation up to 35.5%. The presented plasma methodology is versatile, allowing both the synthesis of VO2 plasmonic structures to enhance the thermochromic response and the encapsulation of films to improve their stability in air dramatically. Additionally, this solvent-free synthetic method is fully compatible with doping procedures, scalable, and holds great potential for designing and optimizing smart window coatings.

To achieve high-resolution muography of compact targets in scenarios with complex logistical constraints, we are developing a portable muon detector system utilizing glass Resistive Plate Chambers (RPCs). Although RPCs are well understood and widely used, our work focuses on developing a gas-tight variant specifically tailored for a broad range of muography applications, with key design goals including portability, robustness, autonomy, versatility, safety, and cost-effectiveness. Our RPC detectors are designed with various configurations, each featuring unique characteristics and performance attributes. We investigate the temporal evolution of the surface resistivity of glass electrodes, as well as the detector efficiency at varying voltages and thresholds, over a span of several months. These RPCs have been utilized in a small-scale feasibility study on muon absorption using lead blocks.

The design of optimal wave energy parks, namely, arrays of devices known as wave energy converters (WECs) that extract energy from water waves, is an important consideration for the renewable transition. In this paper, the problem of simultaneously optimising the layout and device parameters of a wave energy park is considered within the framework of linear water wave theory. Each WEC is modelled as a heaving truncated cylinder coupled to a spring-damper power take-off. The single-WEC scattering problem is solved using an integral equation/Galerkin method, and interactions between the WECs are solved via a self-consistent multiple scattering theory. The layout of the array and power take-off parameters of its constituent devices are simultaneously optimised using a genetic algorithm, with the goal of maximising energy absorption under a unidirectional, irregular sea described by a Pierson--Moskowitz spectrum. When constrained to a rectangular bounding box that is elongated in the direction of wave propagation, the optimal arrays consist of graded pseudo-line arrays when the number of WECs is sufficiently large. Moreover, low-frequency waves propagate further into the array than high-frequency waves, which is indicative of rainbow absorption, namely, the effect wherein waves spatially separate in a graded array based on their frequency, and are preferentially absorbed at these locations. Arrays optimised for a square bounding box did not show strong evidence of grading or rainbow reflection, which indicates that more complicated interaction effects are present.

Monitoring $^{90}$Sr contamination in milk following a nuclear accident is critical due to its radiotoxicity and calcium-mimicking behaviour, leading to accumulation in bones and teeth. This study presents a high-efficiency protocol for quantifying$^{90}$Sr in cow milk by integrating freeze-drying, high-temperature calcination, ion exchange chromatography and liquid scintillation spectroscopy (LSC). The method was validated using reference milk samples with 0.45~Bq/mL of $^{90}$Sr, achieving a chemical yield of 100 $\pm$ 2\%, ensuring near-complete recovery and accurate quantification. The minimum detectable activity (MDA) was estimated at 0.33 Bq/L under optimal conditions, demonstrating the protocol's sensitivity for low-level detection. A comparative analysis with existing methods centrifugation-based approaches and Dowex resin techniques revealed that our protocol outperforms in both strontium recovery and organic matter elimination. Alternative methods showed lower recovery rates (68 $\pm$ 2\% for Guérin's method, 65 $\pm$6\% for Dowex resin) and suffered from procedural drawbacks, such as incomplete organic matter removal. Applying this methodology to compare samples from certified laboratories confirmed its robustness, with liquid scintillation spectroscopy radioactivity values doubling after 14 days, consistent with secular equilibrium between $^{90}$Sr and $^{90}$Y. While the protocol is optimized for milk, future research should explore its applicability to other food matrices. The high yield, reliability, and ease of implementation position this method as an effective tool for nuclear emergency response and routine radiological monitoring.

We explore the averaged fusion reactivity of the $p+{^{11}{\rm B}}$ reaction in tabletop laser experiments using a plasma expansion model. We investigate the energy distribution of proton beams accelerated by lasers as a function of electron temperature $T_e$ and the dimensionless acceleration time $ω_{pi} t_{\rm acc}$, where $ω_{pi}$ is the ion plasma frequency. By combining these distributions with the fusion cross-section, we identify the optimal conditions that maximize the fusion reactivity, with $\left\langle σv \right\rangle = 8.12 \times 10^{-16}\,{\rm cm^3/s}$ at $k_B T_e = 10.0\,{\rm MeV}$ and $ω_{pi} t_{\rm acc} = 0.503$. These findings suggest that an upper limit exists for the fusion reactivity achievable in laser-driven $p+{^{11}{\rm B}}$ fusion experiments, even under optimized conditions.

We present an explicit exact solution to the governing equations describing the vertical structure of the Arctic Ocean region centered around the North Pole. The solution describes a stratified water column with three constant-density regions: a motionless bottom layer, a top layer with uniform velocity and a middle layer - the halocline - described by nonhydrostatic, nearinertial Pollard waves.

This work studies the problem of predicting the loads and motions induced by wave systems on a ship in forward motion (seakeeping). Assuming that the hull is rigid, the motion of the ship is described by the equations of rigid body mechanics. The hydrodynamic phenomenon is analyzed using the inviscid fluid scheme in irrotational motion, leading to an initial value problem for Laplaces equation, coupled with the ship's motion, characterized by strong non-linearity due to the presence of moving boundaries (the hull surface and the air-water interface). Therefore, the mathematical problem has been further simplified by assuming small amplitude ship motion, resulting in a linear model for the fluid-dynamic problem. In this context, two approaches are developed: one in the frequency domain and the other in the time domain. Computational codes have been implemented for both formulations, and a wide range of results has been obtained for ship hulls of increasing geometric complexity. In the case of the frequency-domain model, several hull shapes were systematically studied, and the comparison with experimental data showed satisfactory agreement. The developed code was applied to the reference problem of the departure of a hull in the absence of waves, and the satisfactory comparison with experimental and numerical results from stationary codes indicates the good potential of the method.

Keratin composites enable animals to hike with hooves, fly with feathers, and sense with skin. These distinct functions arise from variations in the underlying properties and microscale arrangement of this natural polymer. One well-studied example is mammalian whiskers, elongated keratin rods attached to tactile skin structures that extend the animal's sensory volume. Here, we investigate the non-actuated whiskers that cover Asian elephant (Elephas maximus) trunks and find they are geometrically and mechanically tailored to facilitate tactile perception by encoding contact location in vibrotactile signal amplitude and frequency. Elephant whiskers emerge from armored trunk skin and shift from a thick, circular, porous, stiff root to a thin, ovular, dense, soft point. This smooth transition enables interaction with widely varying substrates, reduces wear, and increases the vibrotactile signal information generated during contact. The functionally graded geometry, porosity, and stiffness of elephant whiskers tune the neuromechanics of trunk touch, facilitating highly dexterous manipulation.