Fluid Dynamics
Two-phase stratified MHD flows in rectangular ducts
Subham Pal, Ilya Barmak, Arseniy Parfenov et al. (+2 more)
The characteristics of two-phase stratified magnetohydrodynamic (MHD) flow in horizontal rectangular ducts are investigated for a system consisting of a conductive
liquid and a non-conductive gas. Numerical and analytical solutions of the governing equations for the velocity and induced
magnetic field intensity of fully developed laminar MHD flow are obtained for various combinations of bottom- and side-wall conductivities and for different orientations of an externally applied transverse
magnetic field. The relevant set of dimensionless parameters governing the problem is identified. Unlike in single-phase MHD flows, the presence of a non-conductive gas layer breaks the flow
symmetry, leading to a significantly different dependence of the flow characteristics on duct aspect ratio, wall-conductivity configuration, and the strength and orientation of the applied
magnetic field. Using mercury-air flow as a representative test case, the solutions are employed to quantify the influence of the gas phase on the in-situ
liquid holdup, velocity field, pressure gradient, flow lubrication, and pumping-power requirements. It is shown that, regardless of the
magnetic Reynolds number, these characteristics are strongly affected by the wall-conductivity configuration and by the orientation of the external
magnetic field.
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All authors Subham Pal, Ilya Barmak, Arseniy Parfenov, Alexander Gelfgat, Neima Brauner
Optics
Spatiotemporal Optical Vortices From All-Dielectric Bilayer Metagratings
Ken Qin, Shijie Kang, Aoning Luo et al. (+9 more)
Spatiotemporal optical
vortices (STOVs) carry transverse orbital angular momentum within the space-time domain, rendering them powerful tools for constructing high-dimensional and
quantum optical fields. However, most existing approaches rely on highly lossy metallic structures or complex pulse-shaping systems. Here, we propose an all-dielectric route to STOV generation based on
symmetry-protected bound states in the continuum (BICs) in a
bilayer metagrating and provide proof-of-concept validation of its key momentum-frequency signatures. By simply introducing a lateral shift between the upper and lower layers of the vertical slots on the dielectric metagrating, the {\Gamma}-point BIC transforms into a quasi-BIC (qBIC) with momentum-dependent directional radiation and asymmetric coupling. This qBIC further leads to an isolated zero-transmission dip associated with a clear phase singularity and branch cut in the momentum-frequency response, enabling stable STOV generation under excitation by a spatiotemporal Gaussian pulse. The multipole analysis of the qBIC reveals the key role of the Kerker-type interference between its electric and
magnetic dipoles and associated asymmetric coupling in the STOV generation. Experimentally, free-space transmission measurements reveal a transmission zero and a branch cut that agree excellently with theoretical analysis. Therefore, our work provides an experimentally validated, scalable route for manipulating spatiotemporal optical fields on low-loss all-dielectric metasurfaces via only gliding offsets, with potential applications in directional coupling of
quantum light sources and spatiotemporal shaping of single-
photon wave packets.
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All authors Ken Qin, Shijie Kang, Aoning Luo, Yiyi Yao, Xiexuan Zhang, Hanchuan Chen, Yahan Xiao, Yangsong Ye, Junqing Shi, Xusheng Xia, Haitao Li, Xiaoxiao Wu
Optics
Obituary for Augustin Fresnel
Alphonse Duleau and Gavin R. Putland
Annotated English translation of Duleau's "Notice sur A. Fresnel" in Revue encyclopédique, vol.39, pp.558-67 (September 1828), and of the shorter obituary for Fresnel in id., vol.37, pp.316-7.
Chemical Physics
ChemFlow:A Hierarchical Neural Network for Multiscale Representation Learning in Chemical Mixtures
Jinming Fan, Chao Qian, Wilhelm T. S. Huck et al. (+2 more)
Accurate prediction of the physicochemical properties of molecular mixtures using graph neural networks remains a significant challenge, as it requires simultaneous
embedding of intramolecular interactions while accounting for mixture composition (i.e., concentrations and ratios). Existing approaches are ill-equipped to emulate realistic mixture environments, where densely coupled interactions propagate across hierarchical levels - from atoms and functional groups to entire molecules - and where cross-level information exchange is continuously modulated by composition. To bridge the gap between isolated molecules and realistic chemical environments, we present ChemFlow, a novel hierarchical framework that integrates atomic, functional group, and molecular-level features, facilitating information flow across these levels to predict the behavior of complex chemical mixtures. ChemFlow employs an atomic-level feature fusion module, Chem-embed, to generate context-aware atomic representations influenced by the mixture state and atomic characteristics. Next, bidirectional group-to-molecule and molecule-to-group
attention mechanisms enable ChemFlow to capture functional group interactions both within and across molecules in the mixture. By dynamically adjusting representations based on concentration and composition, ChemFlow excels at predicting concentration-dependent properties and significantly outperforms state-of-the-art models in both concentration-sensitive and concentration-independent systems. Extensive experiments demonstrate ChemFlow's superior accuracy and efficiency in modeling complex chemical mixtures.
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All authors Jinming Fan, Chao Qian, Wilhelm T. S. Huck, William E. Robinson, Shaodong Zhou
Fluid Dynamics
A finite element formulation for incompressible viscous flow based on the principle of minimum pressure gradient
Julian J. Rimoli
We present a finite element formulation for incompressible viscous flow based on the principle of minimum pressure gradient (PMPG). This variational principle, recently established by Taha, Gonzalez & Shorbagy (Phys. Fluids, vol. 35, 2023), states that the Navier-Stokes equations are equivalent to determining the rate of change of velocity at each instant by minimizing the L2 norm of the implied pressure gradient, subject to incompressibility and boundary conditions. We discretize the PMPG functional directly using Q9 biquadratic finite elements and minimize over the nodal velocity rates (Rayleigh-Ritz). No pressure degrees of freedom appear; incompressibility and boundary conditions are enforced as linear equality constraints through a monolithic saddle-point system, whose Lagrange multipliers provide wall forces without pressure reconstruction. We verify the formulation against exact Poiseuille flow (machine-precision recovery) and the Kovasznay solution (convergence rate ~3.3), and validate it against published
benchmarks for the lid-driven cavity, the backward-facing step, and flow past a circular cylinder. The formulation produces smooth, oscillation-free solutions on coarse meshes in the convection-dominated regime without stabilization. We further show that the element-wise PMPG functional density serves as a built-in error indicator for adaptive mesh refinement, and that the stationarity condition can be read backwards to estimate the kinematic viscosity directly from velocity field measurements.
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Applied Physics · Cross-listed from cond-mat.mtrl-sci
HERB: a unified framework for the evaluation of Hydrogen Embrittlement mechanisms driven by the Rice-Beltz concept
Kai Zhao
The multiscale picture of hydrogen embrittlement (HE) mechanisms has been under controversy for a long time. Here I report a thermomechanically-consistent HERB framework driven by the Rice-Beltz concept meanwhile incorporating the hydrogen transport near the crack-tip and void growth within the plastic zone. Triggered solely by dislocation emission from the crack tip, the HERB theory unifies multiple HE mechanisms, such as HEDE, HELP, NVC and HESIV within a single framework. Specifically, a generalized model for predicting the hydrogen-informed dislocation emission is established by incorporating the Rice-Beltz model with the transition state theory. Accounting for the dynamic variation of the trapping energy of spherical inclusions, hydrogen transport is modeled in the dislocation free zone in front of the crack tip. Semi-analytical expressions of the density of geometrically necessary dislocations are obtained by incorporating the Hutchinson-Rice-Rosengren solution with the conventional theory of mechanism-based strain gradient plasticity model. By exploring the feasibility of
stochastic analysis, the present theory demonstrates that the hydrogen-informed void dynamics is dominated by the dislocation density between the limits of Lifshitz-Allen-Cahn and Lifshitz-Slyozov-Wagner laws, even though individual events remain unpredictable. These insights fundamentally reshape hydrogen/dislocation interactions across multiple scales, including the core width, short-range and long-range levels.
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Fluid Dynamics
Improved Stability-Based Transition Transport Model for Airships Incorporating Wall Heating Effects
Yayun Shi, Qiyun Wang, Xiaosong Lan et al. (+3 more)
Laminar drag reduction is a critical technology for enhancing the endurance and station-keeping capabilities of airship platforms. However, existing transport-based transition models fail to account for the premature transition induced by wall heating, a limitation that significantly hinders the robust engineering application of laminar-flow technology in realistic thermal environments.To address this deficiency, this study first develops stability-based correction for transition modeling that explicitly incorporates wall-to-freestream temperature ratios. Leveraging the Falkner–Skan–Cooke (FSC) equations and linear stability theory (LST) with the $e^N$ method, we derive physics-based correlations for the transition criteria as functions of the temperature ratio, pressure gradient, and
turbulence intensity. These corrections are integrated into a simplified stability-based transition transport model proposed by \citet{franccois2023simplified} and validated against the classic Schubauer and Klebanoff flat-plate experiments, demonstrating accurate prediction of transition locations under adiabatic, heated, and cooled conditions. Crucially, wind-tunnel experiments on a heated airship model show that wall-heating sensitivity is strongly influenced by local pressure-gradient variations, which is due to Reynolds-number-driven transition-location shifts. The proposed model successfully reproduces the experimentally observed transition advancement caused by wall heating. This framework, covering both heating and cooling regimes, provides a capability to support future laminar-flow control technologies based on wall-temperature modulation.
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All authors Yayun Shi, Qiyun Wang, Xiaosong Lan, Bo Wang, Tihao Yang, Yifu Chen
Instrumentation and Detectors
Capacitively Coupled GaAs p-i-n/Substrate Photodetector with Ohmic Contacts on Lightly Doped n-GaAs for Hard X-Ray Imaging
V.G. Harutyunyan, S.D. Zilio, M. Colja et al. (+5 more)
In this work, we present a capacitively coupled GaAs p +-i-n/substrate photodetector (CC-GaAs PIN/S PD), which also represents a preliminary step toward 3D detection (x, y, time) of high-energy X-ray pulses. Although the final 3D detector will be based on a separate absorption and multiplication avalanche photodiode (SAM APD) design, the present device exhibits characteristics that offer valuable insights into the performance expected once a multiplication layer is incorporated into the final device. In particular, we present a fabrication strategy that employs multi-step annealing in the low-temperature range of 280 330C to achieve Cr/Au ohmic contacts on lightly doped n-GaAs, which is also required for the photodetector architecture. Simultaneously, the same contact preparation process was applied to p + GaAs. Furthermore, the fabricated CC-GaAs PIN/S PD includes an additional contact designed to reduce leakage current by applying the same bias as that of the anode. Measurements performed using an 80 MHz
laser demonstrated the photodetector's ability to detect pulses corresponding to 10^6 electrons per pulse.
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All authors V.G. Harutyunyan, S.D. Zilio, M. Colja, M. Cautero, G. Cautero, L. Sbuelz, D. Curcio, G. Biasiol
Chemical Physics
Hybrid Machine Learning for Enhanced Prediction of Diffusion Coefficients in Liquids
Jens Wagner, Zeno Romero, Kerstin Münnemann et al. (+4 more)
Diffusion coefficients are key thermophysical properties for modeling mass transport in
liquids, but experimental data are scarce, making reliable prediction methods indispensable. In the present work, we introduce a new method for predicting
diffusion coefficients of molecular components at infinite dilution in pure
liquid solvents by integrating the Stokes-Einstein (SE) equation with machine learning (ML). Unlike previous ML approaches, the resulting hybrid Enhanced Stokes-Einstein (ESE) model provides strictly physically consistent predictions for
diffusion coefficients as a function of temperature across a broad range of binary mixtures. Trained and validated using an extensive compilation of literature data for infinite-dilution
diffusion coefficients in binary
liquid systems, ESE achieves significantly higher prediction accuracies than the previous state-of-the-art model, SEGWE, while requiring only the SMILES strings encoding of the molecular formulae of the components of interest as additional inputs, which are always available. This simplicity makes ESE broadly applicable, e.g., for process design and optimization. The ESE model and its source code are fully disclosed and are directly accessible via an interactive web interface at https://ml-prop.mv.rptu.de/.
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All authors Jens Wagner, Zeno Romero, Kerstin Münnemann, Sebastian Schmitt, Thomas Specht, Hans Hasse, Fabian Jirasek
Fluid Dynamics
Large-eddy simulations of a lean hydrogen premixed turbulent jet flame with tabulated chemistry
Emiliano M. Fortes, Eduardo J. Pérez-Sánchez, Temistocle Grenga et al. (+3 more)
Large-eddy simulations (LES) of a planar turbulent lean hydrogen-air jet flame at Re = 11000 are performed using a tabulated flamelet model based on mixture-averaged
diffusion that incorporates detailed transport, including differential and preferential
diffusion, wall heat loss, and thermodiffusion. The approach is extended to turbulent combustion in LES using a presumed-shape probability density function formulation that accounts for sub-filter effects. The flame exhibits a highly corrugated front, driven by local variations of mixture fraction induced by strong thermodiffusive transport. These effects significantly alter both the flame structure and morphology. The LES results are systematically compared to a reference direct numerical simulation across varying LES filters through different mesh resolutions to evaluate the predictive capability of the model. The LES accurately reproduces instantaneous flow structures and thermodiffusive effects. Global flame characteristics including flame length, surface area, and consumption speed, are well captured and show limited sensitivity to mesh resolution. The role of thermodiffusion is also examined, showing that its incorporation leads to a more reactive flame and should not be neglected in the formulation. Heat losses are incorporated into the tabulated chemistry framework for completeness but are found to have a negligible impact, consistent with the walls weak influence in the present configuration. Overall, the results demonstrate that the proposed approach provides reliable predictions of the main flame characteristics, with remaining discrepancies primarily associated with unresolved sub-filter effects that deserve further investigation.
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All authors Emiliano M. Fortes, Eduardo J. Pérez-Sánchez, Temistocle Grenga, Michael Gauding, Heinz Pitsch, Daniel Mira
Optics
Hardware Implementation of Photonic Spiking Hash Retrieval
Shangxuan Shi, Shuiying Xiang, Xintao Zeng et al. (+5 more)
Hashing
retrieval is a pivotal technology for large-scale similarity search, widely applied in
retrieval-augmented generation (RAG) for large
language models (LLMs), massive image repositories, and bioinformatics sequence matching. However, traditional electronic hashing implementations face severe bottlenecks in power consumption and latency when processing high-dimensional data, while existing photonic neural networks often lack robust mechanisms for direct binary code generation under analog noise. To address these challenges, we propose a hardware-software co-designed photonic spiking hashing framework. We utilize the nonlinear thresholding dynamics of a distributed feedback
laser with saturable absorber (DFB-SA) to realize the final binarization of a single-step spiking neural network (SNN). Crucially, a hardware-aware quantization margin loss is introduced to maximize the decision margin, effectively mitigating bit flips caused by optical intensity fluctuations. Validated on MNIST (image) and 20 Newsgroups (text)
datasets, our system demonstrates robust binary code generation and high
retrieval accuracy comparable to digital baselines. Most significantly, the proposed photonic architecture exhibits superior efficiency with an encoding latency of 2.294 ns/query and an energy consumption of 73.70 pJ/query. This work offers a robust and viable path for ultra-fast, energy-efficient optoelectronic neuromorphic computing in high-throughput information
retrieval tasks.
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All authors Shangxuan Shi, Shuiying Xiang, Xintao Zeng, Yonghang Chen, Wanting Yu, Yahui Zhang, Xingxing Guo, Yue Hao
Atomic Physics
Geometric mechanisms enabling spin- and enantio-sensitive observables in one photon ionization of chiral molecules
Philip Caesar M. Flores, Stefanos Carlström, Serguei Patchkovskii et al. (+3 more)
We examine
spin-resolved photoionization of randomly oriented
chiral molecules via circularly polarized light, and revisit earlier predictions of Cherepkov (J. Phys. B: Atom. Mol. Phys. 16, 1543, 1983). We will show that the dynamical origin of
spin- and enantio-sensitive observables arise from two intrinsic mechanisms that are quantified by two pseudovectors stemming from the
geometric properties of the photoionization dipoles in
spin space and in real space, and an extrinsic mechanism which is a directional bias introduced by the well-defined direction of light polarization. These mechanisms arise solely from electric dipole interactions. Consequently, this means that the ten independent parameters that was earlier predicted by Cherepkov to fully describe
spin-resolved photoionization of
chiral molecules can be reduced as moments of these three pseudovectors. We also find that the molecular pseudoscalars describing the
spin- and enantio-sensitive components of the yield can be described by the flux of these pseudovectors through the energy shell, which changes sign upon switching enantiomers. Our results provide compact expressions for these observables which provide an intuitive picture on what determines the strength of these
spin- and enantio-sensitive observables. The approach can be readily generalized to photoexcitation, multiphoton processes, and arbitrary field polarizations. Regardless of the specific driving conditions, the resulting
spin- and enantio-sensitive observables are still controlled by the same three pseudovectors, underscoring their universal role as the primary generators of chirality-induced
spin asymmetries, emphasizing their fundamental
geometric origin and the universality of the mechanism identified here.
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All authors Philip Caesar M. Flores, Stefanos Carlström, Serguei Patchkovskii, Misha Ivanov, Andres F. Ordonez, Olga Smirnova
Applied Physics
Non-Volatile Vortex MTJs with Opto-Electrical and Spin-Diode Nonlinearities as Multifunctional Neuromorphic Platforms
Felix Oberbauer, Tristan Joachim Winkel, Clara C Wanjura et al. (+6 more)
The human
brain achieves exceptional energy efficiency by co-locating
memory and processing, yet reproducing this principle in hardware remains challenging because many neuromorphic devices require standby power, offer limited programmability, or separate state storage from nonlinear computation. Here we demonstrate a multifunctional spintronic platform based on storage-layer-enabled
vortex magnetic tunnel junctions (MTJs) that unifies non-volatile weight storage, optoelectrically driven nonlinear computation, and multilevel readout within a single nanopillar. A thermally programmable FM/AFM storage layer retains analog synaptic weights with zero standby power and enables non-volatile tuning of the
vortex gyrotropic resonance over ${\sim}15$~MHz. Under optoelectrical operation, combined
laser heating and dc bias drive the junction into the bias-enhanced tunnel magneto-Seebeck (bTMS) regime, where the thermoelectric response exhibits a pronounced cubic nonlinearity providing a compact, hardware-native transfer function for weighted analog computation. The electrical and thermoelectric channels switch at matched coercive fields but with distinct amplitudes, yielding an effective four-level readout space. Crossbar-array simulations parameterized by measured device response maps evaluate two neuromorphic modes – a bTMS mode (optical input, dc-bias weights) and a
spin-diode mode (RF-frequency input, RF-power weights) – achieving image-classification accuracies of $95.4\%$ and $94.9\%$, comparable to a digital single-layer network with sigmoid activations. Smaller 600~nm devices consistently outperform larger ones, identifying nonlinear-response engineering as a key device-level lever. Because bTMS and
spin-diode rectification coexist in the same junction, a combined regime could enable nonlinear multi-input interactions, including quadratic cross-terms, within a single nanoscale element.
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All authors Felix Oberbauer, Tristan Joachim Winkel, Clara C Wanjura, Maksim Steblii, Jakob Walowski, Tim Böhnert, Ricardo Ferreira, Markus Münzenberg, Tahereh Sadat Parvini
Plasma Physics
Energization of Proton via Beam-Driven Ion Bernstein Waves in p11B Plasmas
Yangchun Liu, Hairong Huang, Dong Wu et al. (+6 more)
Energizing background ions plays a pivotal role in all forms of thermal
nuclear fusion, as it can increase the fusion reaction rate without affecting the overall mechanical
equilibrium. This is particularly critical for p11B fusion due to its exceptionally high operating temperature and substantial energy losses from bremsstrahlung radiation. Here, we report a nonlinear mechanism that efficiently transfers the energy of injected heating beams to background
protons in p11B mixed
plasmas, via fully kinetic Particle-In-
Cell (PIC) simulations. When a
proton neutral beam is injected into p11B
plasmas, it triggers the excitation of ion Bernstein waves (IBWs) at harmonics of the
proton cyclotron frequency. In the initial linear stage, the energy channels to background electrons and
protons might be comparable, consistent with theoretical model for the energy transfer. However, in the latter nonlinear stage, the dominant channel transfers to background
protons, generating a non-Maxwellian population of energetic
protons. This transition is driven by a nonlinear spectral cascade of IBWs toward lower frequencies and longer wavelengths, which strengthens wave
proton coupling while suppressing wave electron coupling.
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All authors Yangchun Liu, Hairong Huang, Dong Wu, Tianxing Hu, Huasheng Xie, Bing Liu, Zhengmao Sheng, Jiaqi Dong, Yueng-Kay Martin Peng
Chemical Physics · Cross-listed from quant-ph
Correction scheme for molecular total energies from quantum phase estimation under limited qubit resources
Nobuki Inoue and Hisao Nakamura
We propose a practical method for accurately evaluating molecular total energies using a hybrid approach that integrates fault-tolerant
quantum computers with classical computing. Our scheme consists of two complementary components:
quantum dominant orbital selection (QDOS) and subspace dynamical correlation (SDC). QDOS extracts only the essential active orbitals from the complete active space (CAS) configuration interaction (CI) state on a
quantum computer, yielding a compact active space suitable for classical CASCI calculations. SDC then evaluates dynamical-correlation corrections for the CASCI energy using this compact state, which remains tractable on classical machines. To demonstrate that the CAS energy obtained on a
quantum computer can be post-corrected by SDC, we examine two frameworks: multireference perturbation theory and tailored coupled-cluster theory. Our scheme enables effective treatment of relatively large molecular systems by combining limited
quantum and classical resources.
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Biological Physics
All-optical intracellular thermal profiling using nanodiamond-based "thermal radar"
Jiahua Zhang, Yong Hou, Xinhao Hu et al. (+4 more)
The local thermal conductivity (ąppa}) is a pivotal biophysical parameter, governing intracellular heat flux and underlying functional processes like metabolic regulation and stress response. However, label-free mapping with sub-micron resolution in living
cells remains challenge. Here, we present frequency-domain fluorescence thermometry (FD-FTM), an all-optical method based on a hybrid nanodiamond-on-gold-membrane platform, which enables quantitative mapping of ąppa} in biological systems. Fluorescence nanodiamonds (FNDs) are deposited on substrates coated with a 50 nm gold membrane, where FNDs function as nanoscale thermometers, and the gold membrane serves as a photothermal heat source. We validate FD-FTM across reference materials and biological media, with fitting uncertainties of ~10%. By varying the modulation frequency, we tune the thermal penetration depths, enabling controlled heat propagation from the substrate to the
cell nucleus. The method delivers sensitivity sufficient to resolve changes in biofluid thermal conductivity on the order of 16% relative to water. Using these capabilities, we demonstrate non-invasive thermal profiling across scales: at the cellular level,
nuclear chromatin packing yields ąppa} higher by ~10% relative to the cytoplasm; at the organelle level, we resolve ąppa} variations associated with
protein aggregates formed during
liquid-
liquid phase separation in an amyotrophic lateral sclerosis disease model. Temporal measurements in living
cells over 30 minutes further reveal spatially resolved intracellular responses to osmotic stress, linking nanoscale thermal dynamics to biomolecular condensates. These results establish FD-FTM as a label-free, robust, and quantitative platform for thermally decoding intracellular processes, opening avenues for studying metabolic heterogeneity, disease mechanisms, and therapeutic responses.
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All authors Jiahua Zhang, Yong Hou, Xinhao Hu, Yicheng Wang, Madoka Suzuki, Bo Gao, Zhiqin Chu
Plasma Physics
PanoMHD: Multimodal Modelling of Plasma Dynamics towards Tokamak Control
Hyeongjun Noh, Chweeho Heo, Xiaotian Gao et al. (+1 more)
Modelling the dynamics of complex physical systems is a fundamental challenge, particularly where nonlinear dynamics and multi-scale interactions render traditional simulations computationally prohibitive.
Nuclear fusion
plasma represents a complex system where accurately predicting the
plasma state, encompassing both performance and stability, is a prerequisite for active control required for sustained energy production. However, existing approaches are limited in providing a comprehensive solution as they largely focus on predicting isolated indicators such as binary stability labels. To overcome this, we present Panoramic MagnetoHydroDynamics (PanoMHD), a self-supervised
multimodal framework designed to model
plasma dynamics. By utilising a causal
Transformer operating on tokenised representations of
multimodal physical signals, PanoMHD is able to model the dynamics of high-dimensional
magnetic fluctuation signals, which serve as a direct signature of
plasma stability. This shifts the prediction paradigm from isolated indicators to
multimodal signals. We pioneer the direct prediction of
magnetic fluctuation signals for the first time, and demonstrate that this comprehensive representation enables state-of-the-art performance on KSTAR
nuclear fusion plant experimental data. Our model outperforms baselines in future
plasma performance prediction ($R^2=0.987$ vs. $0.957$) and surpasses dedicated classifiers in the downstream classification of distinct
plasma states (L/H mode) with 97.3% vs. 94.5% accuracy.
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All authors Hyeongjun Noh, Chweeho Heo, Xiaotian Gao, Yong-Su Na
Fluid Dynamics
Experimental and Computational Analysis of the Hydrodynamics of Droplet Generation in a Cylindrical Microfluidic Device
Pratibha Dogra, Ram Prakash Bharti and Gaurav Sharma
This study investigates the hydrodynamics of droplet formation in a T-shaped cylindrical microfluidic device using micro-PIV experiments and CFD simulations. Devices of 150 micro-m internal diameter were fabricated from PDMS via a cost-effective embedded templating method. Flow visualization was conducted using immiscible silicone oil and deionized water, forming water-in-oil droplets. A mathematical model coupling the Navier-Stokes and conservative level-set equations was solved using the finite element method. Detailed flow fields (velocity, pressure, and phase distribution) were obtained over a wide range of flow-rate ratios (0.1-10) and capillary numbers (0.001-0.1) to characterize droplet formation mechanisms. Phase
evolution revealed distinct breakup stages (lag, filling, necking, and pinch-off) and multiple regimes (squeezing, dripping, sausage flow, and parallel flow with tip streaming). A regime map delineating droplet and non-droplet regions was developed. Droplet size,
curvature, and internal flow profiles exhibited strong dependence on Ca and Qr. Scaling analysis showed linear dependence of droplet size on Qr in the squeezing regime, with
curvature nearly independent of Qr. In contrast, both size and
curvature followed power-law dependence on Ca and Qr in the dripping regime. Velocity fields inside droplets were laminar and
parabolic in the core. Fully developed plug-like profiles appeared in squeezing, whereas front and rear regions remained developing in dripping. Correlations for droplet length,
curvature, and film thickness, including a novel thin-film model incorporating visco-inertial and capillary effects, enable predictive design within the studied range. These findings advance fundamental understanding of confined droplet dynamics and provide quantitative guidelines for optimizing droplet-based microfluidic systems.
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Biological Physics · Cross-listed from q-bio.QM
Stochastic modeling of long-legged ant A. gracilipes locomotion in laboratory experiments
Jack Featherstone, Anouk Béraud, Meta Virant-Doberlet et al. (+2 more)
Stochastic modeling of movement behavior provides a valuable way to understand how complex motion can be generated from relatively simple building blocks. Ants demonstrate sophisticated social behavior ranging from foraging to nest relocation; while emphasis is often placed on the communication methods used to synchronize individuals, the movement paradigms of those individuals are of tantamount importance. Here, we apply a
stochastic modeling approach to better understand the movement of isolated long-legged ant (A. gracilipes) specimens, informed by extensive laboratory tracking experiments. We find that a combination of active Brownian and run-and-tumble models reproduces the trajectory statistics observed in experiments, both qualitatively and quantitatively. We identify reproducible probability distributions for the turn angles, run times, and waiting times across specimens, and find good agreement between analytical predictions and quantities empirically measured from the trajectories. Having such a model allows for a better understanding and predictions of movement ecology from both simulations and analytics, and even can give insight into the underlying generative mechanisms of motion and the ants' sensory systems.
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All authors Jack Featherstone, Anouk Béraud, Meta Virant-Doberlet, Antonio Celani, Mahesh Bandi
Space Physics · Cross-listed from astro-ph.SR
Polarity-Resolved Far-Side Magnetograms Based on Helioseismic Measurements
Amr Hamada, Kiran Jain, Hanna Strecker et al. (+2 more)
Understanding and monitoring
solar active regions is essential for operational space-weather
forecasting and improved
solar dynamo modeling. This requires comprehensive 360-degree observations of the Sun. While space-weather
forecasting has long relied successfully on high-quality observations of the Earth-facing hemisphere, a critical gap remains due to the lack of direct, continuous
magnetic field measurements of far-side active regions, particularly
magnetic field strength, polarity configurations, and related parameters. We present a methodology for inferring
magnetic field distributions of active regions in helioseismic maps of the far hemisphere. The analysis focuses on identifying the
magnetic polarities of opposing components of a helioseismic signature and applying stable, continuous polarity assignment to large-scale
magnetic structures derived from such maps. These helioseismic signatures reliably resolve strong active regions, especially those that later appear as major rotation regions when they rotate into Earth view. Polarity boundaries are identified by analyzing the bimodal longitudinal variance profile of the seismic signal within each region, after which Hales law is applied to establish east-west ordering consistent with the
solar cycle. The method produces polarity-resolved far-side magnetograms suitable for integration with near-side observations, enabling construction of full-Sun
magnetic boundary conditions for coronal and
solar wind modeling and providing a critical step toward improved heliospheric simulations and operational
forecasting.
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All authors Amr Hamada, Kiran Jain, Hanna Strecker, Charles Lindsey, David Orozco Suarez
Atomic Physics
Relativistic calculations of electron impact excitation cross-sections of neutral tungsten
Ritu Dey, Ayushi Agarwal, Reetesh Kumar Gangwar et al. (+4 more)
Reliable tungsten spectroscopy is essential for modelling and diagnosing tungsten-containing
plasmas, particularly in edge/divertor conditions where neutral tungsten (WI) is produced by sputtering and contributes to visible/near-UV emission. In this work, we present fine-structure-resolved EIE cross sections for W I computed using the relativistic distorted-wave (RDW) method. Target states are described by multi-configurational Dirac-Fock (MCDF)wave functions with an extensive configuration-interaction (CI) expansion including valence-valence and core-valence correlation through hole and deeper-core configurations. The resulting energies are bench marked against available theoretical results and the recommended values from the NIST database. Fine-structure-resolved cross sections are reported for excitations from the ground level 5d4 6s2 (5 D0) and six metastable levels 5d4 6s2 (5D1 , 5D2 , 5D3, 5D4 , 3P0) and 5d5 (6S)6s (7S3 ) into excited levels belonging primarily to the 5d4 6s(6 D)6p and 5d5 (6S)6p configurations, over incident electron energies from threshold to 500 eV. With the exception of a limited set of previously studied transitions, most of the reported fine-structure-resolved cross sections are presented here for the first time. The results show a strong dependence on the initial level and indicate that excitation from the metastable state yields the largest cross sections among the states considered, highlighting the importance of metastable populations in W I modelling. In addition, radiative transition probabilities for selected prominent transitions are calculated and compared with existing data. The present
dataset is expected to be valuable for collisional-radiative modelling and spectroscopic diagnostics of tungsten
plasmas in the 1-50 eV range.
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All authors Ritu Dey, Ayushi Agarwal, Reetesh Kumar Gangwar, Deepti Sharma, M. B. Chowdhuri, Rajesh Srivastava, Joydeep Ghosh
Chemical Physics
Bayesian Optimization in Chemical Compound Sub-Spaces using Low-Dimensional Molecular Descriptors
Yun-Wen Mao and Roman V. Krems
Efficient optimization of molecules with targeted properties remains a significant challenge due to the vast size and discrete nature of chemical compound space. Conventional machine-learning-based optimization approaches typically require large
datasets to construct accurate surrogate models, limiting their applicability in data-scarce settings. In this study, we present a
Bayesian optimization (BO) framework that identifies optimal molecular structures with high precision using fewer than 2,000 training data points within a chemical subspace containing more than 133,000 molecules. The framework employs a low-dimensional and physics-informed molecular descriptor vector that facilitates data-efficient surrogate modelling and optimization. A key innovation of the proposed framework is a reliable inverse mapping scheme that translates optimized points in the descriptor space back into chemically valid molecular structures, thereby bridging continuous optimization and discrete molecular design. We demonstrate the effectiveness of our approach on the QM9
benchmark dataset, where the framework successfully identifies organic molecules with the target
entropy and zero-point vibrational energy (ZPVE) values.For
entropy optimization, our approach achieves a 100% success rate while requiring fewer than 1,000 molecular evaluations in more than 80% of test cases. For ZPVE, the success rate exceeds 80% for molecules containing more than two heavy atoms. These results highlight the critical role of low-dimensional, interpretable descriptors in enabling data-efficient optimization and robust inverse molecular design, and establish
Bayesian optimization as a practical tool for molecular discovery in small-data regimes.
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Biological Physics · Cross-listed from cond-mat.soft
Dynamic Instabilities and Pattern Formation in Chemotactic Active Matter
Hongbo Zhao, Qiwei Yu, Andrej Košmrlj et al. (+1 more)
Collectives of actively-moving particles can spontaneously segregate into dilute and dense phases through a process known as motility-induced phase separation (MIPS). This captivating phenomenon is well-studied for randomly-moving particles with no directional bias. However, many active systems perform collective chemotaxis – directed motion along a chemical gradient collectively generated by the particles themselves through consumption or production. Here, we use linear stability analysis, amplitude equations, and numerical simulations to study how MIPS is influenced by collective chemotaxis. We find that chemotaxis can either arrest or entirely suppress MIPS, or give rise to novel dynamic instabilities such as traveling waves and spirals. We predict the stability region of the stationary and oscillatory patterns and identify four types of bifurcation that can arise: pitchfork, saddle-node, infinite period, and supercritical
Hopf. We also derive analytical expressions for the amplitude of the pattern and traveling wave velocity, yielding excellent quantitative agreement with simulations. Furthermore, we generalize our model to study particles that either consume or produce chemoattractant or chemorepellent, as well as mixtures of particles with different chemotactic behaviors. By establishing quantitative principles describing the competition between MIPS and chemotaxis, our study helps deepen understanding of the rich physics underlying chemically-responsive active matter systems.
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All authors Hongbo Zhao, Qiwei Yu, Andrej Košmrlj, Sujit S. Datta
Optics
Ultra-low loss piezo-optomechanical low-confinement silicon nitride platform for visible wavelength quantum photonic circuits
Mayank Mishra, Gwangho Choi, Wenhua He et al. (+7 more)
Realizing photonic
quantum computing at scale requires integrated
circuits that combine ultra-low loss with fast, low-power, low-hysteresis, and low-crosstalk reconfiguration. These requirements are particularly challenging at visible wavelengths, where many
quantum resource-state generators, including single-
photon sources and
quantum memories, naturally operate. Low-confinement silicon nitride waveguides offer the requisite loss performance, but conventional thermo-optic modulators dissipate significant static power, driving thermal crosstalk and precluding cryogenic operation. Visible-wavelength piezo-optomechanical
circuits avoid these drawbacks, but existing demonstrations rely either on high-confinement waveguides with propagation losses of 35-100 dB/m, or on low-confinement platforms using foundry-incompatible PZT. Here we combine piezo-optomechanical actuation with a CMOS-foundry-fabricated, low-confinement silicon nitride platform, achieving 2.6 dB/m propagation loss at 780 nm, megahertz-scale modulation bandwidth, a half-wave voltage-length product of 2.8 Vm, and negligible hysteresis. We demonstrate reconfigurable Mach-Zehnder interferometers with 0.63 dB loss per spiral phase shifter, enabling deep, actively reconfigurable visible-wavelength
quantum photonic
circuits.
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All authors Mayank Mishra, Gwangho Choi, Wenhua He, Gina M. Talcott, Katherine Kearney, Michael Gehl, Andrew Leenheer, Daniel Dominguez, Nils T. Otterstrom, Matt Eichenfield
Physics and Society
Higher-order interactions at scientific conferences influence team formation
Emma Zajdela and Nicholas W. Landry
Cooperation enables teams to solve complex problems that one individual alone cannot address. In science, collaborative teams have become the predominant way through which progress is achieved. These scientific collaborations arise though various mechanisms, among which interactions at conferences. The Scialog conferences, which comprise a series of small, interdisciplinary scientific workshops held over several years, are an ideal laboratory to study the network mechanisms leading to team formation. Building on existing work studying team formation from a pairwise perspective, we present a higher-order network perspective generalizing this framework. We provide a formalization for the notion of group interaction over time by defining a taxonomy of synchronous and asynchronous group interactions. We apply this framework to the Scialog case study using a stepwise selection logistic model and find evidence that all interaction types described in our taxonomy are highly significant for team formation. This higher-order network perspective provides a new framework for the study of collective behavior and group formation.
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Optics
A Disordered Photonic Medium Enabling Ultrabroadband Light Scattering and Selective Longwave Infrared Emission
Zhenpeng Li, Mathis Degeorges, Nithin Jo Varghese et al. (+1 more)
A surface that selectively emits heat in the long-wave infrared (LWIR) can enable passive cooling in hot environments while retaining partial radiative insulation in cold conditions. However, its cost-effectiveness, practical deployment, and fundamentally, the optical design, remain limited by the reliance on metal reflectors. To overcome this limitation, here we use an absorption-scattering competition factor to establish design guidelines for enhancing reflection or absorption in disordered media across the ultrabroadband ultraviolet-to-far-infrared range. Based on electromagnetic simulations and the optical constants of real materials, we then propose disordered photonic media with a layered, multiscale scattering architecture which, unlike typical scattering designs, simultaneously attains ultraviolet-to-far-infrared reflection and selective LWIR emission. We validate this approach by developing a metal-free selective emitter that exhibits high LWIR emittance (0.88), strong
solar reflectance (0.97), and low thermal emittance outside the LWIR (0.49), independent of substrates. Field tests, supported by theoretical modelling, show both enhanced radiative cooling and seasonal thermoregulation performance relative to a state-of-the-art broadband radiative cooler. By expanding the spectral functionality of disordered scattering media as a scalable and low-cost optical materials platform across the
solar-to-thermal infrared waveband, this work provides a pathway towards improved energy savings and thermal comfort through passive radiative thermal management.
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All authors Zhenpeng Li, Mathis Degeorges, Nithin Jo Varghese, Jyotirmoy Mandal
Optics
High-Stress Si3N4 Reflective Membranes Monolithically Integrated with Cavity Bragg Mirrors
Megha Khokhar, Lucas Norder, Paolo M. Sberna et al. (+1 more)
High-stress silicon nitride (Si3N4) membranes represent the state-of-the-art for cavity optomechanics, combining ultralow dissipation, optical transparency, and full compatibility with wafer-scale nanofabrication. Yet their integration into high-finesse optical cavities has remained difficult, typically requiring bonding or
alignment-sensitive assembly that limits scalability and long-term stability. Here, we introduce a monolithic, wafer-level integration strategy that directly suspends high-stress Si3N4 photonic-
crystal membranes above thermally compatible SiN/SiO2 distributed Bragg reflectors (DBRs) capable of withstanding the high temperatures required for stoichiometric Si3N4 growth. A defect-free amorphous-silicon sacrificial layer and stiction-free
plasma undercut yield vertically coupled cavities with sub-micron spacing-forming self-aligned resonators within seconds of release. Owing to the intrinsic tensile stress, the suspended membranes exhibit atomic-scale sagging, ensuring near-ideal cavity parallelism and long-term stability. Optical reflectivity measurements reveal cavity finesse exceeding 800 with nanoscale gaps between mirrors. Mechanical ringdown measurements show Q > 10^5, indicating that DBR integration preserves the low-dissipation character of high-stress Si3N4. This demonstrates that the integration process preserves the material's exceptional dissipation dilution, supporting straightforward extension to high-Q nanomechanical architectures reported in the literature. The resulting Si3N4-DBR platform unites optical and mechanical coherence with high fabrication yield and design flexibility, enabling scalable optomechanical devices for precision sensing and
quantum photonics.
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All authors Megha Khokhar, Lucas Norder, Paolo M. Sberna, Richard A. Norte