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Physics Physics

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Tuesday 3 February

Fluid Dynamics

Causal structures of turbulent skin-friction drag in wall-bounded turbulent flows

Yunchao Zhao, Yitong Fan and Weipeng Li

Understanding the mechanism of turbulent skin-friction drag (TSD) generation is of fundamental and practical importance for designing effective drag reduction strategies. However, many previous studies adopted correlation analysis to reveal the causal map between turbulent motions and TSD generation, an approach that is potentially risky as correlation does not necessarily imply causation. In this study, a novel causal inference method called Liang-Kleeman information flow (LKIF) is utilized for the first time to identify the velocity-induced causal structures related to TSD generation in a turbulent channel flow. The statistical properties of the causal structures are comprehensively investigated. The positive and negative causal structures, defined by their signs and respectively associated with an increase and decrease in TSD information entropy, promote and suppress the generation of extreme TSD. Particularly, we find that the underlying physics of causal structures is essentially associated with the processes of streamwise streaks and rolls approaching or receding from the extreme events. Results indicate that the physics-informed LKIF framework can reveal a more explicit and interpretable causal relationship than correlation analysis.
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Fluid Dynamics

Impulse-induced liquid jets from bubbles with arbitrary contact angles

Hiroyuki Miyoshi, Hiroya Watanabe, Ishin Kikuchi et al. (+1 more)

This paper investigates the relationship between the contact angle of a spherical bubble attached to a tube submerged in a container and the jet speed induced by an impulsive acceleration at its base. While it has been well established that bubble geometry strongly influences the ejection speeds of liquid jets, mathematical studies of liquid jets with arbitrary bubble shapes remain limited. In this work, we derive a pressure impulse in the small-cavity limit as a tractable integral of classical Legendre functions. It is shown that the jet speed can be divided into two components: (i) the velocity induced by the hydrostatic pressure impulse distribution created by the curvature of the bubble, and (ii) the velocity induced by the distribution of the submersion of the tube in a container. This decomposition reveals that an optimal bubble curvature emerges only when the tube is submerged: the optimality is absent for non-submerged configurations, where the jet speed increases monotonically with bubble depth. Experiments confirm this non-monotonicity and quantitatively support the predicted shift of the optimal geometry with submersion depth.
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All authors Hiroyuki Miyoshi, Hiroya Watanabe, Ishin Kikuchi, Yoshiyuki Tagawa

Computational Physics · Cross-listed from math.NA

Fully Automated Adaptive Parameter Selection for 3-D High-order Nyström Boundary Integral Equation Methods

Davit Aslanyan and Constantine Sideris

We present an adaptive Chebyshev-based Boundary Integral Equation (CBIE) solver for electromagnetic scattering from smooth perfect electric conductor (PEC) objects. The proposed approach eliminates manual parameter tuning by introducing (i) a unified adaptive quadrature strategy for automatic selection of the near-singular interaction distance and (ii) an adaptive computation of all self- and near-singular precomputation integrals to a prescribed accuracy using Gauss-Kronrod (h-adaptive) or Clenshaw-Curtis (p-adaptive) rules and singularity-resolving changes of variables. Both h-adaptive and p-adaptive schemes are explored within this framework, ensuring high-order accuracy and robustness across a broad range of geometries without loss of efficiency. Numerical results for canonical and complex CAD geometries demonstrate that the adaptive solver achieves accuracy and convergence rates comparable to optimally tuned fixed-grid CBIE implementations, while offering automation and scalability to electrically large, geometrically complex problems.
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Optics

Intrinsically Design-Rule-Compliant Nanophotonic Inverse Design via Learned Generative Manifolds

Bahrem Serhat Danis, Demet Baldan Desdemir, Enes Akcakoca et al. (+6 more)

Inverse design has enabled the systematic design of ultra-compact and high-performance nanophotonic components. Yet enforcing foundry design rules during inverse design remains a major challenge, as optimized devices frequently violate constraints on minimum feature size and spacing. Existing fabrication-constrained approaches typically rely on penalty terms, projection filters, or heuristic binarization schedules, which restrict the accessible design space, require extensive hyperparameter tuning, and often fail to guarantee compliance throughout the optimization trajectory. Here, we introduce a framework for nanophotonic inverse design with intrinsic enforcement of design rules through a generative reparameterization of the design space, restricting optimization to a learned manifold of DRC-compliant geometries. We validate this paradigm by designing representative silicon photonic components including broadband power splitters, spectral duplexers, and mode converters operating across the 1,500-1,600 nm band for both electron-beam lithography and photolithography platforms. Across all devices, the manifold-based formulation reaches state-of-the-art performance metrics with over a 5-fold reduction in computational cost compared to pixel-based representations, while ensuring fabrication-compatible geometries throughout the entire design process. By treating fabrication constraints as a fundamental property of the design representation rather than an external penalty, this work establishes a direct pathway toward broadly applicable, platform-agnostic, and intrinsically DRC-compliant nanophotonics.
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All authors Bahrem Serhat Danis, Demet Baldan Desdemir, Enes Akcakoca, Zeynep Ipek Yanmaz, Gulzade Polat, Ahmet Onur Dasdemir, Aytug Aydogan, Abdullah Magden, Emir Salih Magden

Optics

Opposite amplitude phase entropy responses at a non Hermitian avoided crossing

Kyu-Won Park, Soojoon Lee and Kabgyun Jeong

Avoided crossings (A.C.) in open resonators arise from non-Hermitian mode interaction, where leakage produces complex spectra and biorthogonal eigenmodes. Intensity-based entropies are robust markers of mode mixing but discard the phase structure of the complex field. Here we introduce a field-level information-theoretic analysis based on the joint statistics of local amplitude and phase under Born-weighted sampling on the cavity grid. For an open elliptical microcavity in the strong-interaction A.C. regime, we find a distinctive sector-resolved response: amplitude statistics tighten while phase statistics broaden maximally at the mixing point, and conditioning reveals strong amplitude-phase dependence. By introducing a coarse position label and the associated co-information, we further show that the enhancement of global amplitude-phase coupling is strongly shaped by spatial heterogeneity across the cavity.
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Fluid Dynamics

An effective correction method for droplet volume conservation in direct numerical simulation of droplet-laden turbulence

Cheng Peng, Xuming Li, Chunhua Zhang et al. (+7 more)

Accurately preserving the volume of the dispersed droplets remains a significant challenge in phase-field simulations of droplet-laden turbulence, especially under conditions that feature strong interfacial deformation and breakup. While modified phase-field equations have been developed to mitigate volume loss, their effectiveness has not been systematically assessed in the context of fully developed turbulent flows. In this work, we first evaluate the performance of several representative volume-corrected phase-field models in direct numerical simulations of droplet-laden homogeneous isotropic turbulence. Our results reveal that, at sufficiently high Weber numbers, none of the existing models provides satisfactory droplet-volume preservation. To address this limitation, we then propose a simple yet effective modification of the conservataive Allen-Cahn equation by incorporating a curvature-dependent counter-diffusion correction. Direct numerical simulations in turbulent regimes demonstrate that the proposed model achieves conservation of droplet volume in a statistical sense, while avoiding common adverse effects, such as numerical instability, violation of global mass conservation, increased computational cost, artificial coarsening, or enhanced spurious velocities.
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All authors Cheng Peng, Xuming Li, Chunhua Zhang, Lian-Ping Wang, Xinnan Wu, Cheng Peng, Xuming Li, Chunhua Zhang, Lian-Ping Wang, Xinnan Wu

Optics

Inverse Design of Tunable Infrared Metasurface Absorbers via a Conditional Wasserstein Generative Adversarial Network

H. Shen, T. Wang, X. Yao et al. (+5 more)

Narrowband perfect absorbers are interesting for spectrum sensing, molecular detection, and infrared imaging. However, their design remains constrained by intuitive, iterative methods that lack flexibility, while also facing challenges in multi-objective optimization. Here, we introduce a deep learning-enabled inverse-design framework that overcomes these limitations through a conditional Wasserstein Generative Adversarial Network (WGAN). The main contribution of this work is a dual-channel image encoding scheme that jointly represents the geometry and thickness of a Si$_3$N$_4$ meta-layer, facilitating the network to learn the distribution of viable structures for a target optical response. This approach naturally solves the inherent “one-to-many” design issue, giving a diverse portfolio of functional candidates from a single input spectrum. The designed absorbers achieve exceptional spectral fidelity, with resonance peak errors below 5 nm, a mean squared error (MSE) on the order of $10^{-3}$, and the capacity to produce over 10 distinct, high-performance designs per target. Furthermore, we demonstrate the model's robustness under oblique illumination, showing that it can be efficiently fine-tuned to maintain spectral accuracy across incidence angles from $10^\circ$ to $40^\circ$ by transfer learning, thus extending its practical utility to non-normal operating conditions. Full-wave simulations confirm that the generated geometries support a hybrid plasmonic-dielectric resonance, leading to near-perfect absorption and strong near-field enhancement. Our study provides a robust, physics-aware design paradigm that moves beyond conventional parametric optimization. The introduced framework establishes a versatile platform for the on-demand inverse design of advanced photonic devices for sensing, spectroscopy, and optical signal processing.
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All authors H. Shen, T. Wang, X. Yao, O. Wu, C. Xie, C. Qian, H. Chen, T. Wang

Chemical Physics

Impact of Local Descriptors Derived from Machine Learning Potentials in Graph Neural Networks for Molecular Property Prediction

Ryoichi Uchiyama, Yuya Nakajima, Yuta Tanaka et al. (+1 more)

In this study, we present a framework aimed at enhancing molecular property prediction through the integration of local descriptors obtained from large-scale pretrained machine learning potentials into three-dimensional graph neural networks (3D GNNs). As an illustration, we developed an EGNN-PFP model by integrating descriptors derived from the preferred potential (PFP) features, acquired through Matlantis, into an equivariant graph neural network (EGNN), and evaluated its effectiveness. When tested on the QM9 dataset, comprising small organic molecules, the proposed model demonstrated superior accuracy compared to both the original EGNN models and the baseline models without PFP-derived descriptors for 11 out of the 12 molecular properties. Furthermore, when evaluated on the tmQM dataset, which encompasses transition metal complexes, notable enhancements in performance were observed across all five target properties, indicating the significance of the local atomic environment surrounding transition metals. In essence, the proposed methodology is adaptable to any 3D GNN architecture, and further enhancements in prediction accuracy are anticipated when integrated with continually evolving GNN architectures.
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All authors Ryoichi Uchiyama, Yuya Nakajima, Yuta Tanaka, Junji Seino

Optics

Plasmonic Spin Meron Lattices with Height-Sensitive Topology Evolution

Anand Hegde, Komal Gupta and Chen-Bin Huang

We demonstrate height-controlled topological switching of plasmonic spin meron lattices above a metallic square coupling structure under circularly polarized illumination. Near the interface, an evanescent surface plasmon polariton (SPP) channel yields a Néel-type meron lattice with $\pm\frac{1}{2}$ like effective site charges. At larger heights, diffracted fields from the square edges dominate and convert the lattice into a Bloch-type configuration. Over a range of intermediate heights, crossover between the evanescent SPP and edge diffraction gives rise to rich rapid topology evolutions. The switching is accompanied by nucleation of off-boundary vortex-anti vortex pairs in the in-plane spin phase, producing height-dependent fractional site charges. Our findings are analytically formulated by linear superposition of SPPs in the plasmonic regime and Stratton-Chu model in diffraction regime and confirmed via full-wave finite-difference time-domain simulations.
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Atmospheric and Oceanic Physics · Cross-listed from econ.TH

A Bridge Between Climate Science and Economics: OPTiMEM and the Heat Conjecture for Estimation of Social Cost of Greenhouse Gases

Brian Hanley, Pieter Tans, Edward A.G. Schuur et al. (+3 more)

We present an entirely new physics founded approach to estimating the social cost of carbon (SCC). For this, we developed our Ocean-Heat-Content Physics and Time Macro Economic Model (OPTiMEM) to estimate future heat content. The heat conjecture assumes that weather damages curves are stochastically proportional to ocean heat increase. We model carbon combustion, validate to datasets for greenhouse gas (GHG), temperature, and ocean heat content (OHC). We show that the social cost of 4 GHGs: CO2, CH4, N2O and halogenated hydrocarbons, cannot be single values, but must be represented by a kind of economic phase space. We propose very long-term carbon bonds to implement real discounting. This obviates the Gordian knot of the descriptivist versus prescriptivist discount disagreement that is unsolvable. Implementing these bonds leads to a new monitoring metric: real-dollar spending and bond discount rates compared to SC-GHG cost with variation on the discount scale, where the discount has no relationship to the pure rate of time preference (PRTP). This heat conjecture is based on OPTiMEM. OPTiMEM initiates from a fossil fuel consumption function to produce CO2, with 18 scenarios implemented to provide the uncertainty range. We provide 1:N year loss risk models (1:10, 1:100, 1:1000) that government, engineers, and actuaries should find useful. A scenario implementing DICE family of models carbon and growth assumptions shows +18{\deg} C is breached by 2210 CE, and +110{\deg} C by 2300 CE – both of which outcomes are obviously not compatible with the fairly rosy conclusions of DICE models. Concerns are raised about having enough low-cost fossil fuel for conversion to minimal CO$_2$ maximal energy return on energy invested (EROEI) power if nations wait too long, and low EROEI power is questioned because monetary value is dependent on energy.
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All authors Brian Hanley, Pieter Tans, Edward A.G. Schuur, Geoffrey Gardiner, Steve Keen, Adam Smith

Optics

Ultralow radiative heat flux by Anderson localization in quasiperiodic plasmonic chains

Yizhi Hu, Kun Yan, Wei-Hua Xiao et al. (+1 more)

Anderson localization, arising from wave interference in disordered systems, profoundly hinders energy transport, yet its impact on radiative heat flux in many-body thermophotonic systems remains unclear. Here, we demonstrate a three-order-of-magnitude suppression of radiative heat transfer, resulting in ultralow radiative heat transfer, in a one-dimensional quasiperiodic chain of plasmonic nanoparticles. This suppression in radiative heat transfer is directly correlated with mode localization, as revealed by the mode decomposition of the transmission coefficient, which serves as evidence of Anderson localization. Furthermore, we elucidate the dependence of radiative thermal conductance reduction on interparticle spacing and material damping rates, uncovering the interplay between intrinsic Ohmic losses, mode localization, and long-range many-body interactions. Our findings advance the understanding of wave-mediated thermal transport in disordered photonic structures and suggest strategies for tailoring nanoscale heat management via engineered disorder.
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All authors Yizhi Hu, Kun Yan, Wei-Hua Xiao, Xiaobin Chen

Optics

Rotating fluorescent nanodiamond assemblies with focused Laguerre-Gaussian beams

Adam Stewart, Anthony J. El-Helou, Ying Zhu et al. (+3 more)

Optical tweezers which utilize structured light fields enable the rotation of trapped nanoparticles through the transfer of orbital angular momentum (OAM) from holographically generated Laguerre-Gaussian (LG) modes. In this research we use OAM transfer to demonstrate controlled rotation of bright fluorescent nanodiamond clusters assembled in a focused higher-order LG beam. We find that the assemblies can be effectively rotated in a two-dimensional optical trap with orbital frequencies of up to 5 Hz. We use video tracking to explore the Brownian dynamics of such a trapping arrangement and look at the impact of orientation stability on measurements of optically detected magnetic resonance (ODMR) with an applied weak external magnetic field. By collecting ODMR spectra at multiple points along the orbit, we show that the constrained two-dimensional motion can provide additional insights for vector magnetic field reconstruction.
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All authors Adam Stewart, Anthony J. El-Helou, Ying Zhu, David McGloin, David A. Simpson, Peter J. Reece

Geophysics

Variational and Monte Carlo Methods for Bayesian Inversion of Dynamic Subsurface Flow Simulations Using Seismic and Fluid Pressure Data

Zhen Zhang, Xuebin Zhao and Andrew Curtis

In order to predict future performance of subsurface fluid reservoirs under possible operating scenarios, a dynamic, porous-medium flow simulation model must be tuned to include representative properties of the reservoir. Estimating subsurface reservoir properties given remotely sensed or borehole-based observations typically involves finding the solution to a challenging inverse problem. We compare Monte Carlo random sampling to variational inference methods which use optimisation to constrain parametrised uncertainties in nonlinear Bayesian inversions. We use them to estimate the posterior probability distribution of reservoir permeability given fluid pressure and seismic measurements. The methods include automatic differentiation variational inference (ADVI), Stein variational gradient descent (SVGD), and a Monte Carlo method called stochastic SVGD (sSVGD), all of which we benchmark against results from Metropolis-Hastings McMC. We also test an ADVI variant called physically structured variational inference (PSVI): in our implementation this method estimates only spatially-local correlations between model parameters based on the intuition that such correlations are strong in remote sensing problems in which data only inform about spatial-averages of local dynamics. We apply the methods to two- and three-dimensional inverse problems of carbon dioxide storage, inspired by the Endurance field, located in the UK North Sea. Results show that PSVI achieves a good balance between mean-field ADVI and full-rank ADVI in terms of accuracy of the posterior approximation and computational efficiency. SVGD and sSVGD offer more accurate approximations of the target posterior distribution, but at far higher computational cost. Between them, sSVGD outperforms SVGD, exhibiting better computational efficiency and mitigating the problems of mode collapse and spurious correlations.
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Fluid Dynamics

Nonlinear electrohydrodynamics of a surfactant-laden leaky dielectric drop

Michael A. McDougall, Stephen K. Wilson and Debasish Das

A nonlinear three-dimensional small-deformation theory is presented for a leaky dielectric drop coated with a dilute monolayer of insoluble apolar surfactant and subjected to a uniform DC electric field. The theory is developed within the framework of the Taylor–Melcher leaky dielectric model, and builds on previous work by retaining surface charge convection in the charge conservation equation. Solving the problem in three dimensions and retaining charge convection allows us to capture the transition to Quincke rotation, a symmetry breaking instability wherein a drop begins rotating at a steady angular velocity when the applied electric field strength exceeds a critical value. We derive a system of coupled nonlinear ordinary differential equations for the drop shape, dipole moment, and surfactant distribution, which we solve numerically. We discuss the combined effects of charge convection and surfactant in the Taylor regime – in which the field strength is too weak to induce Quincke rotation and the drop adopts an axisymmetric spheroidal shape. In the Quincke regime, we find that the presence of a weakly-diffusing surfactant results in a lower critical electric field than that for a drop with uniform surfactant coverage. Varying the elasticity number, which quantifies the variation of the surface tension as a function of the surfactant concentration, can either increase or decrease the critical field strength depending on the diffusivity of the surfactant. Additionally, we find that the experimentally observed hysteresis in the angular velocity of the drop can disappear when surfactant diffusion is sufficiently weak.
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Optics

Deterministic Control of Extreme Events in a semiconductor VCSEL via Polarization-Engineered Optical Feedback

T. Wang, Z. Li, Y. Ma et al. (+6 more)

Extreme events, or rogue waves, are high-amplitude, rare occurrences that emerge across diverse physical systems and often defy conventional statistical predictions. While optical systems provide a controlled setting for studying these phenomena, achieving deterministic control over their generation remains challenging. Here, we demonstrate a novel approach to induce and precisely modulate extreme events in a semiconductor VCSEL using polarization-controlled optical feedback. By integrating a $\lambda$/2-waveplate into a polarization-selective external cavity, we regulate the nonlinear interaction between TE and TM modes. This setup triggers high-intensity, heavy-tailed fluctuations in the TM mode, exhibiting clear signatures of extreme events. We show that these events arise from deterministic energy exchange between modes, as evidenced by strong bipolar correlations and long-range temporal memory. The waveplate angle serves as an effective external parameter, enabling non-monotonic tuning of the event rate, intensity, and temporal clustering. Our study establish a platform for exploring extreme events in dissipative systems, with implications for nonlinear photonics and optical technologies.
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All authors T. Wang, Z. Li, Y. Ma, J. Huang, Y. Li, Z. Tu, S. Xiang, G. Ruocco, Y. Hao

Physics and Society

Influence Mechanism Of Environmental Stimulus And Consumer Ethnocentrism On Purchasing Wuliangye: Applications Of Extended Theory Of Planned Behavior (ETPB) And Stimulus-Organism-Response (SOR) Theory

Ruofeng Rao and Sarana Photchanachan

Environmental stimuli play a pivotal role in triggering impulsive purchases among consumers,while consumers from Sichuan Province, China, exhibit strong ethnocentric tendencies, impacting their decision-making process, particularly regarding Wuliangye liquor, a local product. Through an online survey of 453 Wuliangye consumers from Sichuan, an analysis was conducted using structural equation modeling rooted in the ETPB and SOR theory. This analysis revealed the favorable impact of environmental stimuli and consumer ethnocentrism on purchasing behavior. This influence was found to be partially mediated through perceived value, attitudes, and purchase intention, forming a chain-mediated effect. Notably, purchase intention doesn't always translate to actual buying behavior, with environmental stimuli, consumer ethnocentrism, perceived behavioral control and purchase intention all being robust predictors of purchase behavior. Finally, several management strategies were proposed, aimed at bolstering Wuliangye sales, with a focus on platform development, mid-to-low range product creation, and appealing to Generation Z consumers.
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Atomic Physics

Efficient Three-Dimensional Sub-Doppler Cooling of $^{40}$Ca$^+$ in a Penning Trap

Brian J. McMahon and Brian C. Sawyer

We demonstrate efficient sub-Doppler laser cooling of the three eigenmodes of a $^{40}$Ca$^+$ ion confined in a compact Penning trap operating with a magnetic field of 0.91 T. Using the same set of laser beams as required for the initial Doppler laser cooling operation, we detune the laser frequencies to produce a narrow two-photon dark resonance. The process achieves a 1/e cooling time constant of 108(8) $\mu$s, ultimately reducing the mean thermal axial mode occupation from 72(23) to 1.5(3) in 800 $\mu$s as measured by resonantly probing an electric quadrupole transition near 729 nm. A parametric drive is applied to the trap electrodes which coherently exchanges the axial mode occupation with that of each radial mode, allowing for three-dimensional sub-Doppler cooling using only the axially-propagating laser beams. This sub-Doppler cooling is achieved for an axial oscillation frequency of $\omega_z = 2\pi~\times~$221 kHz, which places the motion well outside of the Lamb Dicke confinement regime at the Doppler laser cooling limit. Our measured cooling rate and final mode occupation are in good agreement with a semiclassical model which combines a Lindblad master equation solution for ion-photon interactions with classical harmonic oscillator motion of the trapped ion.
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Monday 2 February

Applied Physics

The effect of graphene orientation on permeability and corrosion initiation under composite coatings

Majid Shaker, Erfan Salahinejad, Weiqi Cao et al. (+3 more)

The unique anisotropic properties of graphene, particularly impermeability, have made it a promising candidate for further advances in corrosion prevention applications. Despite the large number of experimental works divulging the use of graphene in anticorrosion coatings, there is no report on the numerical modelling and simulation of the relationships between the orientation of graphene sheets in composite coatings and the introduced corrosion protection efficiency, to our knowledge. Herein, it is tried to model the influence of the orientation of graphene sheets dispersed in organic coatings on the diffusivity and flux of corrosive substances besides the corrosion initiation time of the protected substrates. To discover the relationship between the graphene orientation and corrosion-related phenomena, this study introduces a novel model consisting of a trigonometric factor named unprotected projected surface area proportion, which calculates corrosion-related parameters based on the principal Fick's laws. The model reveals that the decrease in the angle between graphene sheets and the substrate is highly beneficial for postponing the corrosion onset. It is accordingly estimated that a mismatch angle of 10 degree can slow down the diffusion process significantly and delay the corrosion initiation by around 65 times in a 100 micron thick epoxy/graphene composite coating in comparison to the counterpart with the perpendicular alignment. The predicted corrosion parameters were in a good agreement with the experimental data, indicating the merit of the proposed model. Thus, this model can be further employed as the fundamental of future research on the optimum graphene orientation in anticorrosion composite coatings.
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All authors Majid Shaker, Erfan Salahinejad, Weiqi Cao, Xiaomin Meng, Vahdat Zahedi Asl, Qi Ge

Applied Physics

Characterizing Secondary Neutrons at BLIP for Isotope Production Applications

Wilson Lin, Michael A. Skulski, Cathy S. Cutler et al. (+2 more)

Fast secondary neutrons created at the Brookhaven Linac Isotope Producer (BLIP) facility following proton irradiation were characterized by the foil activation technique and compared with FLUKA Monte Carlo simulations. The FLUKA-simulated neutron flux was spectrally adjusted following the maximum entropy formalism using the International Reactor Dosimetry and Fusion File (IRDFF-II), with predictions agreeing with experimental measurements to within 9% following the adjustment procedure. A multitude of degrader configurations were simulated to assess the feasibility of improving the fast (En > 20 MeV) secondary neutron yield at the proposed neutron target position (N-slot). A configuration where the N-slot is closest to the proton degrader produced the highest fast neutron yield, with tungsten degraders achieving the best performance. Assuming the optimized target-degrader configuration proposed in this work, we discuss potential isotope production opportunities with secondary neutrons. In most cases the yields are in the order of several mCi.
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All authors Wilson Lin, Michael A. Skulski, Cathy S. Cutler, Dmitri G. Medvedev, Jonathan T. Morrell

Physics Education

Exploring Students perceptions of their learning experience and self efficacy in physics online class with project based learning

Mutmainna, Edi Istiyono, Haryanto et al. (+1 more)

Project Based Learning (PBL), recognized as an active learning strategy, has been linked to self efficacy of student in prior studies, including those within Physics Education Research. Meanwhile, technological advancements have significantly facilitated the optimization of diverse learning modes, including online learning. However, comprehensive investigations addressing questions such as how students perceive PBL and their self efficacy, what forms of PBL design they prefer, and what benefits and challenges they encounter during its implementation remain underexplored in existing literature. This study sought to uncover the experiences of ten students through longitudinal observation in an online PBL Physics class, focusing on its influence on their self efficacy within a phenomenological study. Data were collected via semi structured, in depth interviews. Social Cognitive Theory, Self Efficacy theory of Bandura, and theoretical framework for the impact of project based learning on educational outcomes were employed as guiding frameworks to shape the interpretation of students experiences and perspectives. Through Interpretative Phenomenological Analysis (IPA), the researchers identified four primary themes representing the experience of students. There are, variety of unique learning experiences based on students perceptions about their character, pivotal experience as a catalyst in the learning process, student self efficacy in Project Based Learning, and self reflection based on prior experience. The findings contribute novel insights for educators in implementing PBL, particularly in online contexts, by highlighting strategies to accommodate diverse student personality characteristics. Moreover, the study provides helpful references for longitudinal research on the interplay between PBL and self efficacy.
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All authors Mutmainna, Edi Istiyono, Haryanto, Beta Wulan Febriana

Fluid Dynamics

Validation and extension of an analytic momentum availability model for the two-scale momentum theory of wind farm flows

Mads Baungaard, Takafumi Nishino and Andrew Kirby

A key parameter in the two-scale momentum theory of wind farm flows is the momentum availability, which quantifies the supply of momentum to a wind farm from various different momentum transport mechanisms (advection, pressure gradient, Coriolis, turbulence and unsteadiness). In this study, the contribution of each of these mechanisms to the momentum availability is evaluated directly from large-eddy simulation (LES) data in order to validate an analytic momentum availability model (Kirby, Dunstan, & Nishino, J. Fluid Mech., vol. 976, 2023, A24). Application of the model to six wind farm cases, three with different atmospheric boundary-layer (ABL) heights and three with different turbine layouts, shows that the full model performs well across all cases, but that its linearized version increasingly overpredicts the momentum availability for increasing ABL heights. It is found that the overprediction is related to the ABL Rossby number, and based on this observation, we propose an extension of the original linear model, which improves its accuracy for the considered cases and makes it more generally applicable, in particular to cases with tall ABL heights or strong Coriolis forcing.
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Optics

Planar Scale Invariant Waveguides and Resonators with Uniform Air Confined Modes

Mohammad Enjavi, Amin Khavasi, Ashkan Zandi et al. (+4 more)

We demonstrate a planar metamaterial based resonator and waveguide with strong light confinement in air based on a silicon-on-insulator (SOI) platform that exhibits scale invariance in the lateral direction. By embedding a sub wavelength grating (SWG) region between two silicon ridges, the waveguide maintains a nearly constant effective index across varying widths while sustaining a uniform field distribution. Simulations and experimental measurements using Mach Zehnder interferometers confirm scale invariance, and racetrack resonators fabricated from the same structure exhibit an intrinsic quality factor of 40000. The ability of the resonance based structures for confining light in air, providing large interaction regions with high quality factors along with compatibility with CMOS fabrication processes and robustness against fabrication imperfections make them excellent candidates for enhanced light matter interaction applications with improved power handling, offering a promising platform for integrated photonics.
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All authors Mohammad Enjavi, Amin Khavasi, Ashkan Zandi, Saeed Javadizadeh, Reza Marzban, Devin K Brown, Ali Adibi

Optics

Time-Resolved dynamics of semiconductor nanolaser via four-wave mixing gating

Federico Monti, Guilhem Madiot, Giuseppe Modica et al. (+5 more)

We experimentally demonstrate the direct time-domain characterization of photonic-crystal nanolasers at telecom wavelengths using a nonlinear optical gating technique based on four-wave mixing. This approach enables the temporal characterization of the ultrafast emission dynamics under short-pulse excitation with picosecond time resolution. When a weak continuous-wave component is added to the pulsed pump, the emission becomes deterministic and the build-up time is considerably reduced. The difference between purely pulsed and hybrid excitation regimes points to the influence of pulse-to-pulse timing fluctuations. To elucidate this effect, we perform Langevin-based simulations that reproduce the experimentally observed broadening and confirm that time jitter, originating from spontaneous-emission noise near threshold, dominates the temporal dispersion. These results establish four-wave-mixing gating as a powerful method to probe nanolaser dynamics with picosecond precision.
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All authors Federico Monti, Guilhem Madiot, Giuseppe Modica, Grégoire Beaudoin, Konstantinos Pantzas, Isabelle Sagnes, Alejandro M. Yacomotti, Fabrice Raineri

Applied Physics · Cross-listed from cond-mat.mes-hall

Switching Characteristics of Electrically Connected Stochastically Actuated Magnetic Tunnel Junction Nanopillars

Dairong Chen, Ahmed Sidi El Valli, Jonathan Z. Sun et al. (+3 more)

We investigate the stochastic dynamics of nanoscale perpendicular magnetic tunnel junctions (pMTJs) and the correlations that arise when they are electrically coupled. Individual junctions exhibit thermally activated spin-transfer torque switching with transition probabilities that are well described by a Poisson process. When two junctions are connected in parallel, circuit-mediated redistribution of voltages that occurs in real time as the junction resistances change leads to correlated switching behavior. A minimal stochastic model based on single-junction statistical switching properties and Kirchhoff's laws captures the coupled switching probabilities, while a Markov-chain formalism describes nonequilibrium steady states under multi-pulse driving. Further, these circuit-mediated interactions can be mapped onto the parameters of an Ising Hamiltonian, providing an interpretation in terms of effective spin-spin interactions. Our results demonstrate how simple electrical connections can generate Ising-like couplings and tunable stochastic dynamics in nanoscale magnets.
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All authors Dairong Chen, Ahmed Sidi El Valli, Jonathan Z. Sun, Flaviano Morone, Dries Sels, Andrew D. Kent

Biological Physics · Cross-listed from quant-ph

Quantum Information Flow in Microtubule Tryptophan Networks

Lea Gassab, Onur Pusuluk and Travis J.A. Craddock

Networks of aromatic amino acid residues within microtubules, particularly those formed by tryptophan, may serve as pathways for optical information flow. Ultraviolet excitation dynamics in these networks are typically modeled with effective non-Hermitian Hamiltonians. By extending this approach to a Lindblad master equation that incorporates explicit site geometries and dipole orientations, we track how correlations are generated, routed, and dissipated, while capturing both energy dissipation and information propagation among coupled chromophores. We compare localized injections, fully delocalized preparations, and eigenmode-based initial states. To quantify the emerging quantum-informational structure, we evaluate the $L_1$ norm of coherence, the correlated coherence, and the logarithmic negativity within and between selected chromophore sub-networks. The results reveal a strong dependence of both the direction and persistence of information flow on the type of initial preparation. Superradiant components drive the rapid export of correlations to the environment, whereas subradiant components retain them and slow their leakage. Embedding single tubulin units into larger dimers and spirals reshapes pairwise correlation maps and enables site-selective routing. Scaling to larger ordered lattices strengthens both export and retention channels, whereas static energetic and structural disorder suppresses long-range transport and reduces overall correlation transfer. These findings provide a Lindbladian picture of information flow in cytoskeletal chromophore networks and identify structural and dynamical conditions that transiently preserve nonclassical correlations in microtubules.
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Accelerator Physics

Monochromation of pulsed electron beams with terahertz radiation at a planar mirror

Cecilia Abbamonte, Adam Bartnik and Jared Maxson

Exquisite control of electron beam energy is required for many electron spectroscopy and imaging applications. For both continuous and pulsed beams, the beam energy spread is fundamentally limited by the electron source, and is typically a sizable fraction of an electron-volt. In this paper, we present a means to reduce electron beam energy spread after emission to the level of a few 10s of meV rms using femtosecond photoemission and an interaction with laser-derived single- to few-cycle terahertz (THz) radiation. We show analytically and in particle tracking simulations that this interaction can remove energy spread stored in both the transverse and longitudinal degrees of freedom. We analytically formulate the limit of energy spread that this technique can achieve, and map the non-ideal affects arising at high frequencies. The interaction is mediated by the beam's passage through a mirror which is reflective to terahertz radiation but allows transmission of the majority of the electron beam (e.g. a wire mesh). This method then only requires beam current losses of a few tens of percent, far smaller than what is achieved in prism and slit-based electron monochromators.
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Fluid Dynamics

Direct power spectral density estimation from structure functions without Fourier transforms

Mark A. Bishop, Sean Oughton, Tulasi N. Parashar et al. (+1 more)

Second-order structure functions and power spectral densities are popular tools in the study of statistical properties across scales, particularly for the analysis of turbulent flows. Although intimately related, analyses primarily use one or the other. We introduce a framework for estimating the power spectrum using the second-order structure function without applying Fourier transforms – enabling one to take advantage of the real-space structure function calculations. We validate and showcase this method, comparing it to classical Fourier power spectrum estimates determined from analytical calculations, fractional Brownian motion, turbulence simulations, and space-physics and astrophysical observations of turbulence. We show that this method is able to robustly obtain the expected power law behaviour where we use turbulence ranges as test-cases.
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All authors Mark A. Bishop, Sean Oughton, Tulasi N. Parashar, Yvette C. Perrott

Fluid Dynamics · Cross-listed from cs.LG

Invertible continuous latent dynamic for long-term data assimilation in complex physical systems

Rares Grozavescu, Etienne Meunier, Pengyu Zhang et al. (+1 more)

Forward forecasting and data assimilation are the two important aspects in physical simulation: one propagates the state forward, the other recovers unknown states from sparse observations. Learned surrogates are normally built and benchmarked for forward forecasting, however, whether a surrogate could attain good performance in data assimilation tasks is valuable as well, as inverse problems are of paramount importance in the scientific domain. In this paper, we propose a continuous-time Koopman autoencoder whose latent dynamics obey $\frac{dz}{dt} = \mathbf{K}_{\mathrm{cont}} z$, yielding closed-form inference via $z(\tau) = \exp(\mathbf{K}_{\mathrm{cont}} \tau) z(0)$ at any horizon $\tau$ in a single step. This decouples forecast cost from forecast length at inference time, showing long-term stability and high efficiency in forward simulation, and also supports data assimilation as gradient-based optimization with cost independent of the assimilation window. Experiments are performed on the Kuramoto–Sivashinsky equation and a transient flow, and we compare our method against a range of baselines on the forward problem, including diffusion models and operator-learning models, and obtain a 110x inference speedup over strong diffusion baselines. We further test these baselines on an initial-state inference data assimilation task, and find that a strong forecaster does not guarantee a strong assimilator, while the continuous-time Koopman autoencoder achieves both higher accuracy and efficiency than surrogates of comparable forward performance.
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All authors Rares Grozavescu, Etienne Meunier, Pengyu Zhang, Mark Girolami

Plasma Physics

Plasma Confinement State Classification in Fusion Power Plants: Profile Reflectometer and Ensemble Diagnostics

Randall Clark, Vacslav Glukhov, Georgy Subbotin et al. (+4 more)

As Fusion Pilot Plants (FPPs) are increasingly viewed as within reach, many engineering challenges remain. Not many diagnostics are expected to be available in a reactor environment. Survivability, maintainability, and limited port space substantially restrict the number of FPP-relevant diagnostics. One remaining challenge is developing tools and devices to extract plasma state information necessary for controlling an FPP from a limited subset of diagnostics. This work is part of an overarching project to address this challenge. The specific diagnostic subset to be used in FPPs is still under debate. We take the approach of developing machine-learning-based tools for different significant plasma state parameters, using already known FPP-viable diagnostics. Previously we developed a plasma confinement mode classifier utilizing the Electron Cyclotron Emission (ECE) diagnostic. Here, we expand on this by developing a Profile Reflectometer (PR) based classifier with 97% test accuracy, and an ensemble model that combines the ECE and PR models into a single model, achieving 99% test accuracy.
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All authors Randall Clark, Vacslav Glukhov, Georgy Subbotin, Maxim Nurgaliev, Aleksandr Kachkin, Lei Zeng, Dmitri M. Orlov

Applied Physics

Temperature-dependent acoustic loss at microwave frequencies in thin-film lithium niobate

Qixuan Lin, Yue Yu, Alejandra Guedeja-Marrón et al. (+7 more)

Thin-film lithium niobate (TFLN) has emerged as a versatile platform for phononic and photonic devices with applications ranging from classical signal processing to quantum technologies. However, acoustic loss fundamentally limits the performance of acoustic devices on TFLN platforms, yet its physical origin remains insufficiently understood. Here, we systematically investigate acoustic propagation loss in various TFLN platforms, including lithium niobate on insulator (LNOI), lithium niobate on sapphire (LNOS), suspended LN thin films, and bulk LN at gigahertz frequencies over temperatures ranging from 4 K to above room temperature. Using a delay-line method, we extract frequency- and temperature-dependent losses for Rayleigh, shear-horizontal, and Lamb modes. We observe an anomalous non-monotonic temperature dependence in LNOI that closely resembles acoustic loss in amorphous materials, indicating a dominant loss channel associated with the buried oxide layer at low temperatures. At elevated temperatures, the loss converges to the Akhiezer damping governed by phonon-phonon interactions. High-resolution electron microscopy further reveals nanoscale interfacial crystal impurities that may contribute to the increased acoustic loss in TFLN platforms relative to bulk LN. These results elucidate the acoustic loss mechanisms in TFLN and provide guidelines for designing low-loss acoustic devices.
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All authors Qixuan Lin, Yue Yu, Alejandra Guedeja-Marrón, Catalina Scolnic, Haoqin Deng, Shucheng Fang, Yibing Zhou, Bingzhao Li, Juan Carlos Idrobo, Mo Li