Papers

Four out of 10 adults aged 30 years or older in the USA are impacted by periodontal diseases which span a spectrum of inflammatory conditions. Currently, a subjective, invasive and a semi-quantitative approach, termed bleeding on probing, is employed in clinics for inflammation assessment. The long-term goal of this study is to fill the current clinical diagnostic gap in dentistry by proposing quantitative ultrasound (QUS)-based biomarkers for inflammation diagnosis and monitoring. Here, as one of the early works in this area, we investigated two QUS parameters for characterizing periodontal inflammation in gingival tissues using a longitudinal preclinical porcine study: attenuation coefficient slope (ACS) and backscatter intensity (BSI). Our study included eight pigs imaged intraorally (24 MHz) at four bi-weekly timepoints from week 0 (healthy) to week 6 (post inflammation inoculation) at their interproximal sites of the third premolars (PM3-Mes) from all quadrants. Moreover, we compared gingival tissues surrounding a tooth at lingual/palatal side versus buccal side at the second molar (M2-Dis) in healthy condition. Our results showed that gingival ACS at all inflammation timepoints were significantly lower than healthy gingival ACS (1.69 \pm 0.53 dB/cm.MHz) using a mixed effect analysis (week 6: 1.08 \pm 0.25 dB/cm.MHz). Longitudinal comparison of BSI did not demonstrate any statistical significance. Gingival ACS and BSI at lingual/palatal versus buccal sides of M2-Dis did not exhibit any statistical significance. These ACSs were linearly correlated with R-squared = 0.89 and a correlation slope of 0.87. For BSI, Bland-Altman analysis demonstrated no difference in mean BSI, although variability was high. These findings highlight the promising potential of ultrasonography paired with QUS to complement current standard of care in dentistry.

We present a framework to advance analytical theory in the area of soft matter and multiphysics using assistance from AI. The framework emphasizes (i) setting "honesty rules", (ii) focusing on a few key papers that are used to formulate a problem of interest, (iii) specifying checks a priori, (iv) using only one task per session and keeping a running log of tasks, checks, corrections, and sessions so that a mistake is not propagated through a project, and (v) ensuring all decisions are approved by humans while AI focuses primarily on execution. The framework is applied to two distinct problems to create new knowledge, including (i) diffusiophoresis of a spherical particle in the presence of an arbitrary number of electrolytes at arbitrary Debye lengths, and (ii) analytical description of I-V curves, including overlimiting current, in bipolar membranes without including the water dissociation kinetics. Both of these problems are advanced analyses and would each typically require several months to a year's worth of effort, but were solved in a matter of a few days. However, the increased efficiency should be cautiously navigated to avoid cognitive offloading and ensure that AI-assisted results are used to extract human-readable insights instead of just reporting a new mathematical formula. The usage of AI is transparently presented. Finally, we also share brief thoughts on how to integrate AI tools in theoretical research during graduate education.

A self-consistent treatment of strong-field laser - plasma interactions suggests a heuristically valuable extension of the notion of nonlinear-optical susceptibility to perturbative regimes of stimulated light scattering in plasmas, offering powerful insights into stimulated-scattering-induced cross-beam energy transfer processes. These findings lay grounds for a unified treatment of stimulated light scattering across a vast area of optical physics, spanning from fiber optics and neutral gases to laser - plasma physics.

Generative AI tools are being widely taken up by students in their physics courses and beyond, often before instructors and institutions can develop policies and effective approaches for the use of these tools. Building on a framework for change in the era of AI, we developed and implemented a faculty learning community to help a university physics department address these challenges collectively. Over six biweekly sessions, faculty worked through course policies, classroom conversations about AI, AI-integrated coursework tasks, and assessment. Each session shared a common structure: we presented local data and department-sourced materials, tested them in small groups, and discussed them together, emphasizing durable pedagogical approaches over specific tools and platforms, and leading with evidence of students' own AI use. The series produced a shared, evolving repository of resources for faculty to draw on. This workshop provides an adaptable, theoretically informed model for a faculty learning community that departments can build on.

On 7 and 8 September 2026 programmatic search produced singularities: a forced Navier-Stokes singularity at every fixed viscosity, statements (C) and (D) of the Clay problem, and two Euler singularities. The unforced problem, statements (A) and (B), stands open, and a search for it needs a target. This paper fixes one: the positive defect problem, that a Leray-Hopf solution from smooth data on the periodic cube loses energy on a finite window, at fixed viscosity, beyond what viscosity removes. A positive defect implies blowup and so a negative answer to statement (B); the converse is not known. The paper proves the target equivalent to a floor on the energy flux through the Littlewood-Paley shells, the Fourier-side form of the coarse-grained flux of the Onsager theory of turbulence, averaged over the window; states necessary conditions on a candidate: a singular time of Type II in velocity, energy concentrating on a set of zero length, a pressure outside L^2, a velocity outside the Onsager-critical class L^3_t B^{1/3}_{3,c_0}, an obstruction to collapse onto a fixed steady Euler profile; and states what cannot certify one: no finite computation witnesses a Galerkin-uniform ceiling, and selection and forcing return the question to a positive defect. A pseudo-spectral search at 128^3 and 256^3 shows which condition of the reduction binds: the fine-shell flux floor holds to within 1 to 6 percent of the ceiling for a third of a turnover time, and fails in scale at the Kolmogorov wavenumber, so a candidate must differ from generic turbulence in the depth of its cascade, not in its timing. Every implication not marked otherwise is a theorem in Lean 4 over Mathlib; the library contains no Navier-Stokes object, and the equation enters only through hypotheses.

The continuity equation serves as a fundamental principle for mass transport in continuous media. While its mathematical structure mirrors that of probability transport and the Liouville equation, an informational interpretation of macroscopic fluid flow is less commonly explored in engineering contexts. This paper treats fluid density as a spatial probability density function, modeling macroscopic motion as the continuous transport of uncertainty. I derive the temporal evolution of Shannon entropy under general flow conditions, establishing how entropy generation depends on macroscopic compressibility and microscopic diffusion. Microscopic diffusion is shown to act as a strictly positive entropy source governed exactly by local Fisher Information. Furthermore, the framework yields an explicit algebraic scaling law characterizing the equilibrium thickness of compressive mixing layers at a local Peclet number of unity. The theoretical model is computationally validated through finite-difference simulations of one-dimensional canonical flows and advection-diffusion within a three-dimensional, spatially varying Arnold-Beltrami-Childress (ABC) flow, yielding a mean relative error of $0.001$. The framework provides an analytical perspective on thermodynamic irreversibility, with potential applications to turbulence modeling, thermal entropy generation in heat exchangers, in-cylinder mixing in internal combustion engines, and aerodynamic flows.

We employ equation-driven, physics-constrained deep learning to solve the fixed-boundary Grad-Shafranov (GS) equilibrium problem, constructing axisymmetric magnetohydrodynamic equilibria with tokamak-relevant characteristics. Equilibria across linear (Solov'ev) and nonlinear profile functions are constructed using Physics-Informed Kolmogorov-Arnold Networks (KANs) that approximate GS solutions while satisfying appropriate boundary conditions. A highly nonlinear pressure profile recreating high-confinement mode phenomenology, such as pressure pedestals and significant bootstrap current components, is also considered. To enable efficient convergence, guided training schemes are employed, specifically homotopy-based continuation curriculum learning and transfer learning via pretrained networks. While computing nonlinear equilibria employing standard Multi-Layer Perceptrons under unguided physics-informed training remains an elusive or computationally inefficient task, our framework overcomes this limitation. Specifically, we demonstrate that the combination of three key elements, namely KAN architecture, guided training schemes, and the self-scaled Broyden optimization method, enables stable, efficient, and accurate equilibrium computation with simultaneous profile parameter identification in view of equilibrium constraints.

A physics-informed latent neural operator framework is proposed for three-dimensional compressor cascade flow prediction. The proposed method combines a Transolver-based latent encoder, which extracts compact global latent representations of flow conditions from point-cloud CFD data, with a coordinate-based PINNs decoder to reconstruct continuous flow fields while incorporating physics-informed residual constraints during training. The results show that the proposed framework can accurately reconstruct pressure and velocity distributions of complex three-dimensional cascade flows and maintains good prediction capability under previously unseen operating conditions. The present method demonstrates promising potential for efficient CFD surrogate modelling and aerodynamic prediction in turbomachinery applications.

Radiation drag from inverse-Compton scattering of an external radiation field changes the acoustic dispersion relation of a relativistic fluid. We derive the linear dispersion relation and show that, depending on the electron distribution and fluid sound speed, radiation drag either damps both modes or damps one while driving the other unstable. The unstable growth is robust at short wavelengths but slow and overdamped at long wavelengths, where the linear analysis is limited by the background acceleration. The resulting growth and damping rates are of order the bulk radiative acceleration rate, so whenever radiation drag is important for the background flow, its effects on fluid perturbations are also important. A purely damped region exists for a wide range of parameters. Radiation drag also makes acoustic waves dispersive: The phase speed deviates from the ordinary sound speed at longer wavelengths, and approaches the sound speed at short wavelengths. We validate the derived rates using special-relativistic hydrodynamic simulations. We apply the derived relation to astrophysical jets such as gamma-ray bursts, blazars in the broad-line region of the host active galactic nucleus, and quasar jets traveling through the CMB and galactic radiation field. We find that the derived instability, damping and dispersion may be relevant across a range of relativistic outflow conditions.

The flux of protons slow-extracted from the CERN Super Proton Synchrotron (SPS) to the North Area experiments is limited by the induced radioactivity of the beam lost on the electrostatic septum during the third-integer resonant extraction. Crystal shadowing, in which a thin bent silicon crystal deflects the portion of beam that would otherwise impinge on the septum wires, has been in operation at the SPS since 2021, and a single crystal at a non-local position halved the extraction losses, short of the fourfold reduction that the activation budget of the future high-intensity operation calls for. In this paper, the design, deployment and operation of a 3-crystal Multi-Volume Reflection Array (MVRA) at the non-local position in the fourth long straight section (LSS4) are presented. The array geometry was designed by Multi-Fidelity Bayesian Optimization (MFBO), combining a multilayer-perceptron surrogate with full particle tracking, which predicts a fourfold loss reduction with three crystals and up to tenfold with an ideally aligned array of four to five. Measurements with beam confirm the prediction: at its optimum the as-built array reduces the losses by the predicted factor of four, and beam dynamics simulations matched to the measured position and angle scans reproduce them to a few per cent. The array has since been used in physics production, held on its operating plateau by an extremum-seeking controller, with a median loss reduction by a factor of 3.6 over 0.1 million extraction cycles relative to the pre-shadowing baseline. The remaining margin to the target, the limitations of the present installation and the upgrade to a 4- or 5-crystal array are discussed.

Twin-beam (TWB) states generated by parametric down-conversion exhibit strong photon-number correlations that can be exploited for quantum communication. In the mesoscopic regime, these correlations can be directly investigated using photon-number-resolving detectors, such as silicon photomultipliers (SiPMs). Their nonclassical nature is quantified by the noise reduction factor (NRF), which provides a witness of quantum correlations and a means of monitoring channel security. In this work, a quantum communication protocol based on TWB states, originally proposed for binary encoding, is extended to a four-state scheme capable of transmitting two bits per use. Information is encoded by combining two mean photon-number values and two mode numbers of thermal noise superimposed on one arm of the twin beam, defining four distinguishable states in the (m,R) plane. While the mean detected photon number enables the retrieval of one bit, the NRF provides discrimination of the second bit and a security check against intercept-and-resend attacks, which introduce additional noise and degrade the quantum correlations. The protocol is investigated through two experimental measurement campaigns. First, the twin-beam source is characterized in terms of photon-number statistics, NRF, and detection efficiency to establish the operating conditions of the communication channel. Subsequently, the four-state protocol is implemented and evaluated through the analysis of state distributions and their 95% confidence regions, error probability as a function of sample size, and a comparative assessment of machine learning classifiers for state discrimination. Finally, the response of the NRF to an intercept-and-resend attack is investigated to assess the protocol's ability to detect eavesdropping.

Classical electrodynamics including Lorentz-invariant classical electromagnetic zero-point radiation leads to a ground state and resonant excited states for a charged particle in a Coulomb potential. These resonant states correspond to integer values of the action variables analogous to those appearing in the Bohr theory of the hydrogen atom. Thus there is a purely classical electromagnetic derivation of the Bohr energy levels. According to classical electromagnetic theory, it is not that there are states where the charged particle does not radiate, but rather in these states there is a resonant balance between radiation emission and absorption The work on classical zero-point radiation reported here is a continuation of the analysis published in 1975, but with the addition of the ideas of relativity and resonance between the charged-particle orbit and classical zero-point radiation.

A signed pairwise graphlet encoding of the velocity gradient tensor $\bm{A}$ is introduced as a minimal discrete representation of its off-diagonal structure, and its statistical relationship with the PQR topology of homogeneous isotropic turbulence is investigated using direct numerical simulation data at $Re_\lambda \approx 433$. Each off-diagonal component is assigned a ternary label based on a local threshold, mapping the thresholded off-diagonal sign pattern to one of $3^6=729$ discrete states. The imbalance $\Delta n=n_W-n_S$ between rotation-promoting and strain-promoting pairs is shown to act as a robust proxy for the sign of $Q$: $P(Q>0\mid\Delta n)$ increases monotonically from $0\%$ at $\Delta n\leq-2$ to $100\%$ at $\Delta n=+3$, and graphlets with a single rotation-promoting pair $(\Delta n=+1)$ predict the stable-focus-stretching region with probability approximately $65\%$ -nearly double the global base rate of $36.5\%$- despite discarding all magnitude information. This predictive power can be largely attributed to the off-diagonal contribution of $\bm{A}$, which dominates the sign of $Q$ in a majority of flow states. The graphlet–PQR association remains statistically correlated over several Kolmogorov time scales and exhibits spatial coherence comparable to that of the $Q$ field at dissipative scales, providing a consistency check that the discrete thresholding preserves the short-range organisation inherited from the velocity-gradient field. An out-of-sample predictive log-loss test further shows that $\Delta n$ captures most, but not all, of the predictive content of the complete $729$-state graphlet code: the full code retains a small, reproducible gain of order $10^{-3}$ bits per observation in predicting future PQR topology beyond $\Delta n$ and the continuous invariants, present at finite lag and absent at zero lag.

Plasma transport produced by electrostatic fluctuations is studied using a test particle model in a toroidal geometry. A model wave spectrum is assumed which introduces a time dependence in the Hamiltonian description of particle motion in the strong magnetic field. This allows for the existence of chaotic particle motion which gives rise to transport processes. Another way of producing transport is by introducing a stochastic process inside the wave spectrum. The two possibilities are explored and it is shown that both cases give rise to diffusive transport. The presence of chaotic motion is corroborated by computing approximate Lyapunov exponents. Additionally the presence of zonal flow is considered which can give rise to a transport barrier by suppressing radial particle fluxes, in the cases that show chaotic transport. This effect gives support to the existence of chaotic motion as responsible for transport since it reproduces the behavior of the robust invariant torus resilience to chaos, acting as a transport barrier, observed in Hamiltonian models of chaos.

The intricate procedure of mass renormalization in quantum field theory has a simpler precursor in classical electromagnetism. There, the renormalized mass of a charged body includes both its bare mass and its electromagnetic mass. For a body moving at constant low velocity, the electromagnetic mass can be calculated either from the total momentum of the electromagnetic field or from the total energy of the electromagnetic field in the body's rest frame. For a spherically symmetric charge distribution, these methods appear to disagree by a factor of 4/3. This 4/3 problem has a long history. Here, we see that the concept of field velocity can be used to quickly resolve the 4/3 problem. The two methods for finding the electromagnetic mass agree after noting that the average velocity of energy flow in the electromagnetic field is 4/3 the velocity of the body. The energy in the electromagnetic field moves faster than the charged body because, in addition to moving with the body, the field energy must make its way from the back of the body (where it is being emitted) to the front (where it is being absorbed).

A polarimeter does not report stress. It reports a retardance and an azimuth, and converting those to a stress pair fixes both the calibration that is required and the covariance that the subsequent inference must carry. We set out that observation chain for planar viscoelastic flow, combine it with velocimetry, and ask what the combined measurement can resolve. The calibration constant is fixed by three separately measurable quantities (path length, stress-optic coefficient and wavelength) rather than being fitted. Propagating the polarimetric errors to first order gives a stress covariance that is anisotropic and site dependent even for independent homoscedastic inputs, and whose conditioning degrades as the retardance approaches zero, where the linearization itself stops describing the measurement. Wrapping imposes a separate design bound. Two numerical studies follow, both in ideal calibrated stress coordinates under a prescribed covariance rather than the propagated polarimetric one. Holding the total scalar count fixed and varying the split between velocimetric and optical sites, an unequal allocation favoring optical sites outperforms either pure configuration. We then ask what the measurement resolves between constitutive models compatible with the same velocity data. In self-consistent pressure-driven flow of the finitely extensible nonlinear elastic Peterlin model, at extensibility L^2=50 and 3% noise, the optical channel rejects the velocity-compatible Oldroyd-B family in 78.1% of realizations at the Deborah number De=2, the ratio of the relaxation time to the flow timescale, and in 100% at De=4, while rejecting only about 5% below De=0.3. The resolving power of a design is therefore a strong function of Deborah number and must be quoted with it.

We demonstrate a simple and effective approach for realizing extended volumetric optical trapping using a single linearly polarized Gaussian beam propagating through a stratified medium with engineered refractive index gradients. On modifying the gradients, the focal field undergoes axial elongation and develops multiple localized intensity maxima, enabling simultaneous trapping of particles at distinct axial planes. Both on-axis and off-axis trapping configurations are experimentally observed, forming a volumetric particle distribution without the need for complex holographic beam shaping. A rigorous theoretical framework based on the Debye Wolf diffraction formalism combined with Generalized Lorenz-Mie Theory is employed to model the focused field and the resulting optical forces. The analysis, substantiated with numerical simulations, shows that spherical aberration arising from refractive-index discontinuities plays a crucial role in redistributing optical energy, leading to the formation of multiple stable trapping regions. The axial trapping potential decreases monotonically with increasing mismatch, indicating a trade-off between multi-site trapping capability and trapping potential, which is in quantitative agreement with experimental observations. This work establishes refractive index engineering as a powerful tool for tailoring three-dimensional optical force landscapes using minimal optical complexity, thus offering a scalable alternative to conventional holographic optical tweezers towards applications in colloidal assembly, micromanipulation, and biophotonics.

This study examines the impact of processing conditions, such as thermalprocessing, on the resulting microstructure and mechanical properties. Spe-cial emphasis is placed on microstructural features obtained from three-dimensional phase-field simulations, which provide detailed insights into bai-nite morphology, phase distribution and retained austenite content. Thesesimulated microstructures are correlated with changes in yield strength undermultiaxial load, as represented in the yield surface of the material. The re-sults demonstrate that optimized processing routes can refine the microstruc-ture, enhance mechanical properties and significantly alter the yield surfacecharacteristics. These findings provide valuable insights for the design andapplication of bainitic steels, as well as for the development of predictivemodels linking process-structure-property relationships.

This paper deals with the historical development and physical mechanisms of pyroelectricity, a phe-nomenon whose roots date back over 2000 years to ancient times. This paper is aimed at students and engineers as a concise introduction to the subject area. While the attractive effect of heated tourma-line was already described by Theophrastus, scientific systematization did not occur until the 18th century by researchers, such as Aepinus and Canton, who identified the effect of electrical polarization resulting from temperature changes. This essay highlights the path from early analogies to magnetism to the modern crystallographic description by Haüy and Thomson. In the physics section, pyroelectricity is defined as the temperature dependence of the spontaneous polarization in anisotropic solids. At the microscopic level, the permanent dipole moment of an elementary cell is described by the vector sum of individual moments. It is mathematically demonstrated that the macroscopic spontaneous polarization correlates with the surface charge density and is linked to the temperature change via the pyroelectric cofficient. A distinction was made between the primary pyroelectric effect and the secondary effect resulting from the thermal deformation of the crystal. Finally, the renaissance of this field of research through the development of modern infrared detectors and ferroelectric materials in the 20th century was highlighted.

Two-round legislative elections are decided not only by first-round vote shares, but by how voters whose preferred party did not advance redistribute their votes among the surviving candidates. We develop a principled model of this transfer process, grounded in multi-dimensional ideological embeddings derived from the Chapel Hill Expert Survey, and apply it to French legislative elections. Calibrated on the 2017 and 2022 elections, the model is evaluated on the unusually complex 2024 contest, in which three ideologically distinct blocs reached the second round simultaneously, achieving 90.34 percent constituency-level accuracy. We show that ideological proximity alone explains the large majority of vote transfers, that a simple left-right axis is insufficient to capture the relevant distances, and that the structural configuration of the 2024 electorate was far less favorable to the far right than pre-election forecasts suggested.

Accurate prediction of ionospheric Total Electron Content (TEC) during geomagnetically disturbed conditions remains challenging, particularly when empirical models perform poorly during storms and neural network (NN) approaches rely explicitly on geomagnetic indices such as Kp/Ap or Dst/SYM-H. In this study, we develop an NN-based model to predict TEC variations over the Indian longitude sector without incorporating geomagnetic indices as inputs. The model is trained on the full-year (2024) Global Ionospheric Map (GIM) TEC dataset at a 15-minute cadence, together with solar wind parameters: Interplanetary Magnetic Field (IMF) $B_z$, velocity ($V_{sw}$), and density ($N_p$), along with the F10.7 cm solar radio flux. Seasonal and diurnal variability are represented using sinusoidal transformations of Day of Year (DOY) and hour of day (HOD). An optimized architecture with 96 hidden neurons yields strong training performance ($R^2$ $\sim$ 0.96, RMSE $\sim$ 5.4, and MAE $\sim$ 3.8 TECU). Independent predictions for the year 2025 demonstrate robust generalization under both quiet and geomagnetically active conditions. Quiet-time residuals are generally confined within $\pm$10 TECU, while during moderate and strong storm-time conditions, the model maintains comparable error bounds across most of the Indian region, despite not being trained on geomagnetic indices. The results indicate that solar wind parameters, combined with the F10.7 solar flux and cyclical temporal encoding, contain sufficient information to reproduce geomagnetic storm-time TEC variability. This study therefore demonstrates the applicability of the proposed approach for regional TEC prediction and ionospheric nowcasting, with potential applications in GNSS positioning, satellite navigation, radio communication systems, and the future development of operational ionospheric forecasting tools.

This study investigates storm-to-storm variability and hemispheric differences in magnetosphere-ionosphere (MI) coupling during the extreme (G5) geomagnetic storm of May 10-11 and the severe (G4) storm of October 10-11, 2024. Global field-aligned current (FAC) patterns derived from the Active Magnetosphere and Planetary Dynamics Response Experiment (AMPERE), together with conjugate observations from ground-based magnetometers within the SuperMAG network and Global Positioning System (GPS)-derived total electron content (TEC), are analyzed to examine high-latitude electrodynamic and ionospheric responses in both hemispheres. The May event exhibits broad and relatively organized Region 1/Region 2 FAC systems encircling the polar caps across multiple local time sectors, accompanied by intervals of correspondence in conjugate magnetic perturbations and structured TEC enhancements with temporal offsets between hemispheres. In contrast, the October event shows more localized, asymmetric, and uneven FAC morphology with pronounced hemispheric and dawn-dusk asymmetries, together with greater divergence in conjugate magnetic responses and spatially heterogeneous TEC variability. These differences are consistent with enhanced mesoscale variability and asymmetric current closure under storm-time conditions. Overall, the results highlight that even under similarly strong solar wind driving, the coupled MI system can exhibit substantially different spatial organization and interhemispheric coupling, reflecting the combined influence of FAC morphology, ionospheric conductance, and local electrodynamic conditions.

Kinetic Ising models are widely used to describe binary opinion dynamics, but their microscopic validity has rarely been tested empirically. Here, we infer the transition probabilities governing opinion updates from a year-long online social network and show that they are accurately described by an Ising heat-bath dynamics. The inferred parameters admit a direct sociological interpretation: the external field quantifies intrinsic bias, the coupling strength measures social influence, and a persistence term captures temporal inertia. We further show that persistence is positively correlated with node degree, while both persistence and interaction strength are strongly correlated with global network heterogeneity and clustering. Using the inferred time-dependent parameters in Monte Carlo simulations on the empirical temporal networks, we accurately reproduce the observed response functions, flip probabilities, and macroscopic opinion dynamics. Within the Twitter climate debate, our results provide direct empirical support for a kinetic Ising description of online opinion formation and establish a quantitative link between microscopic social behavior and evolving network topology.

A rigorous implementation of energy-dependent ion- and electron-induced electron emission in a continuum-kinetic framework is used to reveal their effects on the scaling of plasma properties in the sheath with an applied bias potential to the walls. The approach comes with a novel methodology for modeling particle-induced electron emission (PIEE) that includes 1) improved fitting functions for the yield and spectra, 2) the use of SRIM and a summation of the Lindhard formula and a modified Bethe formula for obtaining accurate stopping powers, and 3) a binding energy correction to the PIEE spectra and ion-induced yield based on density functional theory (DFT) calculations. The emission models are implemented as a fully energy dependent and dynamic boundary condition. For this investigation, tungsten and graphite walls are studied for their relevance in magnetic fusion experiments. Equations are derived from fluid theory that predict the relative importance of ion- and electron-induced emission on the structure of the sheath. The simulations provide evidence for the theoretical predictions, showing that a transition from a classical to space-charge limited (SCL) sheath depends primarily on electron-induced emission. Furthermore, claims in previous literature of increased heat and particle loads to the walls due to electron emission are supported, however differing mechanisms for the increase are observed. The increased thermal and particle fluxes due to PIEE are primarily driven by collisional transfer of energy from emitted electrons in the presheath. Finally, quantitative predictions for the device modeled in this study coincide with previous modeling efforts and experimental measurements.

Adulteration of milk with water, urea, calcium compounds and starch is still a widespread food safety problem, especially in areas where there is no access to laboratory based chemical testing, and is a global threat to human food safety and security, especially for children and the elderly. We present the development of a reagent free screening method, based on droplet evaporative deposition method, optical microscopy, and quantitative image analysis, for consistent detection and classification of milk adulteration. Droplets of Single Toned ST, 3% fat, and Double Toned DT, 1.5% fat milk samples, adulterated with water, urea, calcium, and starch, respectively, at different concentrations were tested. Deposition patterns were characterized by image processing using radial intensity profile descriptors area under the curve, and edge decay slope and gray level co occurrence matrix GLCM texture features contrast, correlation, energy, homogeneity, and entropy. The descriptors exhibit consistent adulterant specific trends: water and urea adulteration resulted in increasingly smooth, more homogeneous deposits decreasing contrast, increasing homogeneity, while calcium and starch adulteration resulted in structurally rougher deposits increasing contrast, decreasing homogeneity. Urea was further distinguished in the two groups by a significant increase in homogeneity and entropy collapse at higher concentrations, whereas calcium and starch were distinguished by diverging area under curve AUC trends. Milk type ST vs. DT was resolved by a combined multivariate signature of the descriptors at baseline. Our findings show that a simple, two level feature based framework: first resolving milk type, then adulterant family, then specific adulterant identity can be realized entirely from optical microscopy data without additional chemical reagents.

Extending the orbital angular momentum (OAM) degree of freedom to slow electrons would open a distinct regime of low-energy electron-matter interactions. Using time-dependent density-functional theory, we demonstrate that laser-driven armchair carbon nanotubes (CNTs) can emit OAM-carrying slow electrons. A circularly polarized OAM-writing pulse promotes circumferential electronic motion and generates OAM about the tube axis; a delayed linearly polarized extraction pulse releases slow electrons carrying the injected angular momentum. We find much higher ionization yields and net emitted axial OAM in representative CNTs than in oriented CO2 and benzene under identical laser conditions. Increasing the tube diameter enhances the ionization yield and OAM substantially, and moreover, reversing the helicity of the OAM-writing pulse flips the sign of the emitted OAM. Together, these findings establish CNTs as a platform for slow-electron emission with optically controllable OAM.

Exponential potentials were used since Kramers, Hellman, Glauberman, Yukhnovskii and Kelbg for solving problems of quantum chemistry, ionic solutions and plasmas. Here we develop the theory further and add new results, in particular with respect to the quantum-statistical theory. In particular, we derive the Kelbg quantum potential for the exponential interactions and discuss screening as well as the problem of thermodynamic stability. Further we give new applications to a qualitative theory of alkali plasmas and fusion plasmas. We show that the exponential potential and the early analytical results of Yukhnovskii and Kelbg allow a qualtative analytical treatment of such difficult problems as the phase transitions in electrolytes and alkali plasmas.

We investigate spin-dependent transport in a non-collinear helical antiferromagnet in the presence of spin-orbit coupling within a tight-binding framework. Using the Landauer-Büttiker formalism, we analyze the generation of spin polarization arising from the combined effects of the helical magnetic texture and spin-orbit interaction. We find that finite spin polarization can be generated simultaneously along the $\hat{x}$, $\hat{y}$, and $\hat{z}$-directions, in contrast to the predominantly single-axis spin polarization commonly observed in conventional spin-filtering systems. The multidirectional spin polarization originates from the symmetry breaking introduced by the non-collinear magnetic order. We further show that the spin polarization is modified significantly in the presence of spin-orbit coupling and it has strong dependence on the parameters associated with the helical system. In particular, long-range hopping significantly enhances the spin-filtering effect, leading to large spin polarization components along all three spatial directions.

Attosecond charge migration (CM) is an ultrafast process occurring when a molecule is irradiated by ultrashort laser pulses, creating a localized hole. The hole propagates rapidly through the molecule, generating electric currents and transferring charge across the molecular backbone. CM is a key ingredient in solar energy conversion, photosynthesis, and radiation damage. Despite its importance and intensive research, the fundamental physical and chemical mechanisms of CM remain not fully understood. Especially, a deeper insight into the role correlations play in the dynamics, and which chemical attributes determine CM timescales, is needed. Here we study with ab-initio time-dependent density functional theory CM in the benchmark molecule, BrC$_4$H. We thoroughly explore CM under different initial conditions at the electronic and structural levels, including with theories of varying degrees of electronic correlations. We uncover a universal behavior where the hole moment (connecting to experimental observables) dominant frequency is roughly independent of all of these characteristics. In contrast, the timescales of the hole density evolution do vary with the chemical conditions and level of correlations. Employing a semi-analytical theory that reconstructs the hole moments in the cationic reference frame, we show that the attosecond timescale of CM is determined by molecular dipoles that filter out specific frequency responses with an analogy to optical selection rules. Our results provide essential insight into CM physics, which should be useful for interpreting attosecond experiments and engineering CM timescales by tailoring transition dipoles.

Phase change materials (PCMs) can enable non-volatile optical memory through the large refractive index and extinction coefficient contrast between crystalline and amorphous states. Among them, $\mathrm{Sb}_{2}\mathrm{Se}_{3}$ combines low optical attenuation at telecom wavelengths, making it promising for low-loss programmable phase shifters. Its non-volatility is particularly useful for cryogenic systems, where power dissipation and thermal load constrain scalability. However, its optical properties at cryogenic temperatures remains poorly understood. Here, we report the first cryogenic optical characterization of $\mathrm{Sb}_{2}\mathrm{Se}_{3}$ integrated on a foundry silicon photonic platform from 4K to 300K for both crystalline and amorphous states. We observe that the magnitude of the thermo-optic coefficient decreases upon cooling, whereas the optical attenuation changes only weakly for both the amorphous and crystalline phases. We report the stability of the material upon repeated thermal cycling. These measurements provide the material parameters and stability required to design low-loss, non-volatile photonic memory elements for scalable cryogenic information processing.