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Physics Physics

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Tuesday 19 May

Fluid Dynamics · Cross-listed from math.AP

A conservation-consistent boundary condition for nonlinear models of soluble-surfactant-laden falling films

Sanghasri Mukhopadhyay, Séverine Millet, Bastien Di Pierro et al. (+1 more)

A conservation-consistent boundary condition is proposed for nonlinear models of soluble-surfactant-laden falling films, ensuring exact conservation of total surfactant mass. The formulation resolves an inconsistency in widely used reduced models, Pascal et al. (PRF, 2019), D'Alessio et al. (JFM, 2020), which exhibit a gradual drift of mass during nonlinear evolution in a closed periodic domain. We show that this originates from an inconsistency in the surface transport reduction and derive a corrected boundary condition that removes this defect. As the discrepancy appears only at the nonlinear order, linear stability results remain unaffected, explaining why the issue has remained unnoticed.
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All authors Sanghasri Mukhopadhyay, Séverine Millet, Bastien Di Pierro, Asim Mukhopadhyay

Optics

Ultrafast Nano-Imaging and Optical Control of Hyperbolic Phonon Polaritons at hBN/WS$_2$ Heterojunctions

Kazuki Kamada, Keisuke Shinokita, Fanyu Zeng et al. (+7 more)

Manipulating nanoscale light-matter interactions on ultrafast time scales is indispensable for future polaritonic devices. Hyperbolic phonon polaritons (HPhPs) in van der Waals materials enable deep subwavelength confinement of electromagnetic fields in the infrared region and long-distance propagation of polaritonic waves. However, achieving ultrafast imaging and optical control of HPhPs remains a major challenge. Here, we demonstrate the direct observation of transient modulation of HPhPs induced by local photocarrier generation in WS$_2$/hBN heterostructures using ultrafast infrared scanning near-field optical microscopy. We implement grating-based spectral filtering of broadband near-field scattering to simultaneously achieve nanoscale and femtosecond spatiotemporal resolution together with fine spectral selectivity. This ultrafast nano-imaging technique reveals that photocarriers in WS$_2$ modulate the polaritonic field amplitudes and wavelengths of HPhPs in hBN. Theoretical simulations corroborate that these changes arise from photoinduced changes in WS$_2$ dielectric properties. This approach offers a versatile platform for exploring ultrafast polaritonic dynamics at the nanoscale.
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All authors Kazuki Kamada, Keisuke Shinokita, Fanyu Zeng, Ryo Kitaura, Kenji Watanabe, Takashi Taniguchi, Alexander Paarmann, Masahiro Shibuta, Takashi Kumagai, Jun Nishida

Biological Physics

Xenon Anesthesia and Nuclear Spin Effects in Chiral Systems

Allan Wang and S. Furkan Ozturk

A general mechanism for anesthetic function is not fully understood. Similarly, the mechanism by which xenon, a chemically inert noble gas, can produce anesthetic effects remains ambiguous. However, a previous study reported a surprisingly strong nuclear-spin-dependent variation in anesthetic potency in mice, although no rigorous molecular mechanism was proposed. This perspective examines that observation and explores a potential connection to the chiral-induced spin selectivity (CISS) effect, a phenomenon that can account for spin-dependent processes in chiral systems. Here we propose a mechanism that links spin-dependent charge organization with chiral molecular systems through a kinetic model that reproduces the reported nuclear spin dependence of xenon anesthesia. The model is based on the nuclear spin-dependent permeability of isotopes through homochiral media, which modulates biological function through ligand-receptor binding in analogy with the Hill-Langmuir equation. Unlike mechanisms that require long-range quantum coherence, our framework remains robust under physiological, room-temperature conditions because it relies on the intrinsic stability of the CISS effect in dissipative biological environments. Our analysis motivates further experimental investigation of spin-dependent processes, not limited to anesthesia, in complex living systems where chirality is pervasive.
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Chemical Physics

Prebiotic magnetite enables chirality-magnetic surface feedback

Jose A. P. M. Devienne, Ziwei Liu, Clancy Z. Jiang et al. (+5 more)

The emergence of biomolecular homochirality requires both an initial symmetry-breaking event and a mechanism to amplify and preserve a chiral imbalance. Magnetic minerals have been shown to function as chiral agents through the chiral-induced spin selectivity (CISS) effect and may have enabled homochirality on early Earth, yet the magnetic properties of magnetite formed under realistic prebiotic conditions remain unexplored. Here we show that magnetite synthesized through two geochemically plausible pathways - UV-driven photo-oxidation and nitrite-mediated oxidation of Fe(II) - produces particles dominated by single-vortex and multi-vortex magnetic domain states. Magnetic measurements and electron microscopy confirm that these populations differ markedly from the nano-fabricated thin-film substrates conventionally used in previous CISS experiments. Using 3D micromagnetic simulations, we demonstrate that single-domain and vortex-state grains undergo irreversible, exchange-driven re-magnetization when interacting with spin-polarized homochiral compounds. This magnetic irreversibility provides a robust mechanism for storing and reinforcing weak chiral bias, suggesting that prebiotic magnetite could have contributed to the emergence and stabilization of persistent chiral bias on the early Earth.
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All authors Jose A. P. M. Devienne, Ziwei Liu, Clancy Z. Jiang, Nicholas J. Tosca, Thomas Ginnis, Dimitar D. Sasselov, Richard J. Harrison, S. Furkan Ozturk

Fluid Dynamics

Graph-based Automated Discovery of Concise Soil Hydraulic Functions from Data: Beyond Empirical Formulations

Hao Xu, Jinshen Sun, Yuntian Chen et al. (+1 more)

Soil hydraulic functions underpin water flow and transport modeling in the vadose zone, yet they are commonly represented by expert-designed formulations such as the Mualem-van Genuchten (MvG) model. Because these formulations rest on predefined functional assumptions, balancing predictive accuracy and parametric compactness across soil textures remains challenging. Here we introduce a graph-based equation discovery framework that distills soil hydraulic functions from experimental data. Reapplied to the original datasets that motivated the MvG parameterization, the framework recovers a concise water retention function together with an analytical unsaturated hydraulic conductivity expression whose mathematical structure departs from classical empirical forms. Evaluated across soils of contrasting texture, the discovered model predicts unsaturated hydraulic conductivity more accurately than MvG and its common variants, while its fitted parameters retain interpretable correlations with measurable soil physical properties. These findings suggest that data-driven discovery can yield compact, analytically tractable constitutive relations that move beyond fixed empirical formulations.
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All authors Hao Xu, Jinshen Sun, Yuntian Chen, Dongxiao Zhang

Optics

Microwave photonic radar jamming and target detection integration based on advanced waveform editing, forwarding, and self-squaring reception

Fangyi Yang, Hang Yang, Xin Zhong et al. (+2 more)

The integrated radar and jamming (IRAJ) system provides a promising solution that meets the demands for miniaturization, integration, and multifunctionality in complex warfare environments. However, traditional electronic-domain IRAJ systems face limitations in operating frequency and bandwidth. In this paper, we propose and experimentally demonstrate a microwave photonic IRAJ system based on pseudo-random binary phase modulation and segmented frequency shifting. By modulating pseudo-random binary coding sequence and frequency-shifting signals onto linearly frequency-modulated (LFM) pulses, an IRAJ waveform is generated to achieve noise-like jamming against the adversary radar. To overcome the random {\pi}-phase jumps introduced by pseudo-random binary modulation in the de-chirped signal, a time-domain squaring operation is implemented during de-chirped reception, restoring the radar detection ability of our system and enabling accurate target sensing without prior knowledge of the coding sequence. Experimental results demonstrate that the system can generate IRAJ waveforms with a bandwidth of up to 4 GHz, covering both 10-28 GHz. The proposed system achieves effective jamming against adversary radars employing either de-chirped reception or pulse compression, with the generated jamming results exhibiting an irregular and random distribution of false targets. Meanwhile, the system maintains radar performance with a ranging error of around 5 cm and a radial velocity measurement error below 4 cm/s.
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All authors Fangyi Yang, Hang Yang, Xin Zhong, Taixia Shi, Yang Chen

Chemical Physics

Translating Spin-Adapted RPA to Spin-Adapted TDDFT

Xiaoyu Zhang and Tai Wang

Linear-response TDDFT based on open-shell references may produce spin-mixed excited states, particularly when the response space is not spin complete. Spin-adapted RPA was developed using tensor equation-of-motion and applying the Wigner-Eckart theorem with tensor decoupling. Casting the RPA Fock matrix and kernels as energy derivatives gives a TDDFT extension. To restore spin-component degeneracy and eliminate multi-counting of spin correlation in pure exchange-correlation functionals, we use hybrids combining HF exchange with spin-unpolarized pure XC parts. Given that the mapping from spin-adapted RPA to spin-adapted TDDFT is ad hoc, we introduce a first-principles framework for evaluating the expectation value $\langle \hat{S}^2 \rangle$ of excited states in order to rigorously prove their spin purification. In addition, we derive explicit working equations for the computation of the single-particle density matrix of excited states, thereby enabling a natural orbital analysis of their electronic structure. Benchmarks, diradicals/triradicals, Cr$_2$ dissociation, and phenol O-H conical intersections demonstrate the method.
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Applied Physics · Cross-listed from cs.CV

Scalable, Energy-Efficient Optical-Neural Architecture for Multiplexed Deepfake Video Detection

Parnian Ghapandar Kashani, Shiqi Chen and Aydogan Ozcan

The rapid proliferation of AI-generated visual media has created an urgent need for efficient, trustworthy deepfake detection systems. However, existing deep learning-based detection methods rely on computationally intensive and energy-demanding inference algorithms, limiting their scalability. Here, we present a hybrid digital-analog deepfake video detection framework that combines a lightweight digital front-end with a spatially multiplexed optical decoding back-end for massively parallel analog inference through a programmable spatial light modulator. By simultaneously processing 15 or more video streams within a single optical propagation pass, the system enables high-throughput and accurate video-level authenticity prediction at reduced computational cost compared with purely digital methods. We validated this hybrid deepfake video processor using different datasets spanning classical face-swapping, real-world deepfake recordings, and fully AI-generated videos. Using a spatially multiplexed experimental set-up operating in the visible spectrum, we achieved average deepfake detection accuracy, sensitivity and specificity of 97.79%, 99.86% and 95.72%, respectively, on the Celeb-DF video dataset with 15 videos tested in parallel in a single optical pass per inference. The multiplexed optical decoder also demonstrates resilience against various types of video degradation, noise, compression, experimental misalignments and black-box adversarial attacks. Our results show that integrating optical computation into AI inference enables simultaneous gains in throughput, energy efficiency, and adversarial robustness - three properties that are difficult to achieve together in purely digital systems.
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Atomic Physics

High-stability offset-frequency locking of two lasers using a balanced filter discriminator

Sang Eon Park, Meung Ho Seo, Young-Ho Park et al. (+6 more)

We demonstrate a high-stability laser offset-frequency locking technique based on a balanced filter discriminator. The beat note between two 852 nm external-cavity diode lasers is down-converted in two parallel arms using local-oscillator frequencies placed symmetrically around the desired offset frequency. After low-pass filtering and RMS detection, differential subtraction of the two detector outputs produces a dispersive frequency-error signal with a zero crossing primarily defined by the reference local-oscillator frequencies. This balanced configuration reduces sensitivity to common beat-power fluctuations and can improve the effective error-signal signal-to-noise ratio. The system was implemented for an 8.653 GHz offset corresponding to the cesium repumping frequency difference used in our laser-cooling setup. Measurements with different low-pass filters reveal a trade-off between discrimination sensitivity and feedback bandwidth. With an SLP-1.9+ filter, the locked beat frequency reached a fractional instability of $4\times10^{-15}$ at 10 s when referred to the 852 nm optical carrier. The residual dependence on photodetector optical power was also characterized, showing that amplitude-to-frequency conversion remains small in the optimized differential configuration. This approach provides a practical frequency-only offset-locking method for atomic-physics experiments requiring stable and tunable microwave-scale laser frequency offsets.
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All authors Sang Eon Park, Meung Ho Seo, Young-Ho Park, Hyun-Gue Hong, Sang-Bum Lee, Sangwon Seo, Jae Hoon Lee, Seji Kang, Taeg Yong Kwon

Optics · Cross-listed from cond-mat.mtrl-sci

Cryogenically Enhanced Laser-Induced Amorphous Phase Transitions in Crystalline Silicon

Conrad Kuz, Andy Lee, Shashu Tomar et al. (+6 more)

Amorphization of silicon is crucial to applications in photonics, microelectronics and solar cell technologies. Ultrafast lasers have been used to generate amorphous silicon from crystalline silicon using rapid nonthermal melting and solidification in room temperature. As material temperature can affect cooling rates significantly, adding temperature control in ultrafast laser modification of silicon may allow a new degree of freedom in ultrafast laser modification. In this work, we investigate the role of cryogenic temperature in governing ultrafast damage pathways via single-shot femtosecond laser irradiation of silicon from room temperature down to 24K at 1030nm. Across this temperature range, we observe a pronounced enhancement of amorphization at lower temperatures, revealed through optical microscopy, Raman spectroscopy, and Kelvin probe force microscopy (KPFM). Raman analysis identifies this ring as an amorphous surface layer, while complementary AFM and SEM imaging show temperature-dependent changes in surface morphology, including localized melt redistribution and refrozen material. To elucidate the physical origins of this behavior, we implement a carrier dependent two-temperature model (nTTM). The simulations reproduce the experimentally observed trends and indicate that reduced phonon population, modified absorption pathways, and altered lattice relaxation dynamics at cryogenic temperatures collectively promote amorphous freezing over recrystallization. This study represents the first detailed examination of silicon under ultrafast irradiation below the liquid-nitrogen regime and reveals temperature-governed mechanisms relevant for advanced silicon microstructuring.
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All authors Conrad Kuz, Andy Lee, Shashu Tomar, Ravleen Kaur, Mohamed Yaseen Noor, Justin Twardowski, Liam Clink, Roberto C. Myers, Enam Chowdhury

Fluid Dynamics

Prescribed Wall-Heat-Flux Control of Blockage and Impulse in a Rarefied Micro-Nozzle

Amirmehran Mahdavi and Ehsan Roohi

Prescribed wall heat flux provides an active route for controlling rarefied micro-nozzle flows, but its effect is governed by the coupled wall–bulk thermal response rather than by the imposed flux alone. This work uses direct simulation Monte Carlo (DSMC) simulations to study nitrogen flow in a converging–diverging micro-nozzle with cooling, adiabatic, and heating applied on the diverging wall. The imposed heat flux is scaled by the inlet kinetic-energy flux, $E=0.5\rho_i U_i^3$, giving $Q_w/E$ from $-10.5\%$ to $97.3\%$; this range spans moderate cooling, weak-to-intermediate heating, and a near-unity thermal-forcing regime. Wall and mass-flux-weighted bulk temperature profiles, film-temperature-based Nusselt and local-viscosity Brinkman-type diagnostics, gradient-length Knudsen indicators, mass-flux thickness, thrust decomposition, and proper orthogonal decomposition (POD) of signed numerical schlieren are analyzed. The results show that heating creates strong wall–bulk stratification: the wall temperature exceeds five times the inlet value, while the bulk temperature responds more gradually. Cooling cases contain locations where $T_w-T_b$ changes sign, making the local Nusselt-type response singular; the raw singular behavior is retained for diagnosis and a validity mask is used only for comparative plotting. Heating contracts the effective mass-carrying core, increasing aerodynamic blockage and reducing mass flow rate. However, strong heating increases the specific impulse from $156$ s to $201$ s because thermal and pressure-thrust augmentation outweigh the mass-flow penalty. The internal compression feature evolves into a finite viscous–thermal compression zone, and its heat-flux-parametric response remains low-dimensional, with the first two POD modes capturing more than $97\%$ of the fluctuation energy.
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Atomic Physics · Cross-listed from quant-ph

Entangling gate performance and fidelity limits with neutral atom Förster resonances

S. A. Norrell, Y. Shen, M. Saffman et al. (+1 more)

Neutral-atom entangling gates are commonly analyzed with a single effective Rydberg-pair state, but near Förster resonances the pair manifold contains resonantly coupled interaction channels that change both the control landscape and the achievable fidelity. We develop a two-eigenstate model for this regime and show that when allowing for coupling to both pair states in the resonance, the gate fidelity is bounded by $\mathcal{F}\leq 1-(\pi/2)/(V\tau_R)$, for interaction strength $V$ and Rydberg lifetime $\tau_R$. We construct a gate protocol that saturates this bound in the large-Rabi-frequency limit, improving the existing fidelity limit by approximately $40\%$. We also evaluate common gate protocols near Förster resonances and find that retaining the exchange dynamics increases predicted fidelities by up to two orders of magnitude over earlier treatments.
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All authors S. A. Norrell, Y. Shen, M. Saffman, M. Otten

Fluid Dynamics

Multiresolution analysis on tessellation graphs for inertial particle dynamics

Keigo Matsuda, Thibault Maurel-Oujia and Kai Schneider

A multiresolution technique on tessellation graphs for particle dynamics is proposed. This allows to split spatial field data given on millions of discrete particle positions into scale-dependent contributions. The Delaunay tessellation is used to define the graph, and Voronoi cell volumes are used to satisfy volume conservation. Our approach enables computation of the scale-dependent statistics of particle dynamics by leveraging a wavelet transformation of Lagrangian point particle data and is useful for characterizing particle clustering in turbulent flows. The technique is systematically verified by using synthetic data of randomly distributed particles in a two-dimensional plane. Then the applicability of the technique is demonstrated by extracting the scale-dependent particle velocity divergence of inertial particles in homogeneous isotropic turbulence from direct numerical simulation data. The result is verified by comparing the energy spectrum of the divergence with that obtained by a Fourier-based approach. Finally, the wavelet-based filtering to the particle velocity divergence is demonstrated to extract the effect of caustics in inertial particle clustering.
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Atomic Physics

Elastic electron scattering from Zn, Cd, and Hg

Mehrdad Adibzadeh and Constantine E. Theodosiou

We present an extensive set of theoretical results for differential, integrated, and momentum transfer cross sections for the elastic scattering of electrons by zinc, cadmium, and mercury. This study extends the application of our method of calculations, previously employed for stable inert gases and alkaline-earth-metals. Our approach is a self-consistent calculation, with a semi-empirical element in the adjustable cut-off radius of the polarization potential. Our method is expected to provide a set of accurate data for Zn, Cd and Hg, based on the satisfactory agreement in our previous investigations with experimental values and other precise theoretical results.
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Atomic Physics

Spin asymmetry for the elastic scattering of polarized electrons from Zn, Cd, and Hg

Mehrdad Adibzadeh and Constantine E. Theodosiou

We present an extensive set of theoretical results for spin asymmetry in the form of Sherman functions for the elastic scattering of electrons by zinc, cadmium, and mercury. This study extends the application of our earlier method of calculations, which we previously employed for stable inert gases and alkaline-earth-metals to these three atoms. Our predictions are in adequate agreement with experimental values and precise theoretical results.
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Instrumentation and Detectors

Design and Initial Test of the Weighing Unit for Tsinghua Tabletop Kibble Balance

Weibo Liu, Nanjia Li, Lushuai Qian et al. (+4 more)

This paper presents a customized weighing unit developed for the Tsinghua tabletop Kibble balance. The system is based on a flexure hinge mechanism sourced from a commercial weighing cell, with a major modification to the feedback control loop. The redesigned loop incorporates a capacitive displacement sensor for high-resolution position detection and a novel PID control strategy that ensures both fast dynamic response and high static stability. Initial characterization results demonstrate a repeatability better than 0.1 mg in air for 1 kg mass exchanges, validating the system's potential for high-accuracy mass metrology in Kibble balances.
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All authors Weibo Liu, Nanjia Li, Lushuai Qian, Wei Zhao, Lisha Peng, Songling Huang, Shisong Li

Monday 18 May

Applied Physics · Cross-listed from cs.CE

Physics-Informed Neural Networks for Radial Consolidation of Combined Electroosmotic, Vacuum and Surcharge Preloading Considering Smear Effects

Dong Li, Yapeng Cao, Shuai Huang et al. (+4 more)

This study develops a dimensionless multi-domain physics-informed neural network (PINN) framework for electro-osmotic radial consolidation considering smear effects and combined vacuum and surcharge loading. Three PINN-based models are investigated: a standard soft-constrained PINN (Std-PINN), a modified gated PINN (Mod-PINN), and a modified gated PINN with hard-constraint boundary encoding (Mod-HC-PINN). The models are evaluated against FEM reference solutions under four loading cases, including constant vacuum, exponential vacuum, exponential vacuum with ramp surcharge, and exponential vacuum with cyclic haversine surcharge. The results indicate that the gated architecture applied in Mod-PINN improves the resolution of steep pressure gradients near the cathode and smear-zone interface under constant vacuum loading. Under time-dependent loading, the soft-constrained Mod-PINN shows reduced accuracy because it must learn multiple competing objectives simultaneously. The Mod-HC-PINN mitigates this issue by embedding the cathode boundary and initial conditions into the output structure, thereby reducing the optimization burden and improving physical consistency. The Mod-HC-PINN achieves MAE values of 0.43, 0.41, and 0.27 kPa for the exponential vacuum, ramp surcharge, and cyclic surcharge cases, respectively. Sensitivity analyses further demonstrate that the proposed framework remains robust across practical ranges of network architecture, collocation density, and permeability contrast.
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All authors Dong Li, Yapeng Cao, Shuai Huang, Yujun Cui, Haiping Fu, Lu Yang, He Wei

Fluid Dynamics

Viability of Tensor Train Methods for Geophysical Fluid Dynamics

Jeremy Lilly, Derek DeSantis and Mark R. Petersen

Tensor train (TT) methods have recently gained popularity for accelerating the solving of systems of PDEs. Here, we evaluate the performance of TT methods in the context of geophysical fluid dynamics (GFD) using the shallow water equations and a discretization scheme employed by the ocean component of the Energy Exascale Earth System Model (E3SM). Through a suite of four test cases of increasing complexity, we evaluate TT methods in terms of how much TT is able to compress the model state, the error incurred by the TT approximation, and the speedup obtained by TT versus an optimal standard non-TT implementation in a representative subproblem. We show that though TT is able to effectively compress and speed up simple flows, it struggles to efficiently represent more complex states that are common in realistic GFD applications.
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Medical Physics

Rapid online deep artifact suppression for real-time spiral bSSFP CMR with blipped-CAIPI simultaneous multi-slice imaging at 1.5 T

Julius Åkesson, Iulius Dragonu, Einar Heiberg et al. (+6 more)

Purpose: Real-time (RT) bSSFP MRI enables fast free-breathing cardiovascular imaging but requires 10-16 slices for functional assessment, resulting in prolonged scan times. Simultaneous multi-slice (SMS) imaging can reduce acquisition time but when combined with non-Cartesian trajectories, it relies on iterative reconstructions that preclude online use. This study investigates deep artifact suppression to facilitate rapid, online reconstruction of RT-SMS. Methods: A spiral bSSFP SMS RT sequence with two simultaneously acquired slices was implemented at 1.5 T. Reconstruction used slice separation in k-space, followed by deep artifact suppression in image space using a 3D U-Net. Ten healthy volunteers were imaged. RT-SMS image quality and reconstruction time were compared between deep artifact suppression and compressed sensing (CS) reconstructions. Left (LV) and right (RV) ventricular volumes at end diastole (EDV) and end systole (ESV) and LV mass (LVM) were compared between RT-SMS with deep artifact suppression and reference-standard breath-hold (BH) imaging. Results: The RT-SMS acquisition was ~13x faster than BH imaging (15 s vs 3 min 15 s). RT-SMS reconstruction using deep artifact suppression was ~50x faster than CS (30 s vs 24 min 55 s). Deep artifact suppression consistently outperformed CS in quantitative and qualitative image quality (p<0.001). Functional agreement between BH and RT-SMS with deep artifact suppression was good (LVEDV: -7.5 +/- 6.8 ml, LVESV: -0.9 +/- 4.2 ml, RVEDV: -6.4 +/- 8.4 ml, RVESV: 0.2 +/- 10.7 ml, LVM: -10.3 +/- 11.0 g). Conclusion: Online deep artifact suppression reconstruction for RT-SMS bSSFP CMR enables free-breathing short-axis coverage with a substantial reduction in acquisition and reconstruction time while maintaining diagnostic image quality.
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All authors Julius Åkesson, Iulius Dragonu, Einar Heiberg, Tina Yao, Rebecca Baker, Ruta Virsinskaite, Daniel Knight, Vivek Muthurangu, Jennifer Steeden

General Physics

Non-Extensive Generalization of Double Ionization by Electron Impact: Calculations of Both Cross Sections and Rate Coefficients for $\text{H}^-$, $\text{He}$, and $\text{Li}^+$

Abdelmalek Boumali

We generalize Tweed's seminal Born~I formalism for the electron-impact double ionization of two-electron atomic systems ($\text{H}^-$, $\text{He}$, $\text{Li}^+$) to non-extensive plasma environments described by Tsallis $q$-statistics. The bare Coulomb interaction is replaced by a $q$-deformed screened potential, which introduces a unique form factor envelope into the momentum-transfer integrations. We rederive the differential, partial, and total cross sections, demonstrating that this screening regularizes the classical high-energy Bethe logarithm and, in the $q < 1$ regime, induces diffraction-like oscillations in the angular cross sections. The model is calibrated against digitized historical experimental data to quantify how non-extensive screening deforms the vacuum baseline. Finally, coupling these cross sections to a $q$-Maxwellian distribution yields the macroscopic rate coefficients and reveals a novel subextensive critical-temperature threshold below which ionization is energetically forbidden.
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Chemical Physics

Harnessing AtomisticSkills for Agentic Atomistic Research

Bowen Deng, Bohan Li, Matthew Cox et al. (+20 more)

Computational materials science and chemistry span vast knowledge domains and fractured software ecosystems. Although large language models (LLMs) have demonstrated research capabilities, scaling monolithic agents to manage the rigor and complexity of atomistic research remains a challenge. Here, we introduce AtomisticSkills, an open-source harness framework that empowers general-purpose AI coding agents to conduct atomistic research across materials science, chemistry, and drug discovery. By hierarchically decomposing scientific workflows into agent skills and tools, AtomisticSkills provides agents with modular, extensible, and plug-and-play research capabilities. The framework integrates more than 100 human-curated multidisciplinary skills, including database access, thermodynamics and kinetics modeling, and diverse simulation engines employing machine learning interatomic potentials (MLIPs) and density functional theory (DFT). We validate its functional coverage against scientific literature and demonstrate robust orchestration capabilities across diverse scientific campaigns: generative design of Li-ion solid-state electrolytes, high-throughput screening of metal-organic frameworks for CO2 capture, autonomous MLIP benchmarking and fine-tuning, multi-stage structure-based virtual screening for drug design, multimodal X-ray diffraction pattern analysis, and screening of Fe-oxide catalysts for oxygen evolution reaction. AtomisticSkills provides a critical agent infrastructure towards building fully autonomous AI scientists.
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All authors Bowen Deng, Bohan Li, Matthew Cox, Hoje Chun, Juno Nam, Artur Lyssenko, Sathya Edamadaka, Jurgis Ruza, Xiaochen Du, Nofit Segal, Jesus Diaz Sanchez, Mingrou Xie, Ty Perez, Yu Yao, Miguel Steiner, Sauradeep Majumdar, Charles B. Musgrave III, Anirban Chandra, Abhirup Patra, Detlef Hohl, Connor W. Coley, Ju Li, Rafael Gómez-Bombarelli

Chemical Physics

Modelling the photocatalytic oxidation of methane and other air pollutants for applications in ventilation systems

Samuel D. Tomlinson, Aliki Marina Tsopelakou, Tzia Ming Onn et al. (+3 more)

Photocatalytic oxidation (PCO) is a promising strategy for indoor air purification and outdoor pollutant abatement, potentially offering treatment for climate- and health-relevant pollutants such as methane (CH$_4$), nitrogen oxides (NO$_\text{x}$) and volatile organic compounds (VOCs). In this work, we present experiments evaluating the PCO of CH$_4$ (2 to 10 ppm) under varying UV-C light intensities (4 to 59 W/m$^2$), using titanium dioxide (TiO$_2$) as the photocatalyst. At 2 ppm CH$_4$, TiO$_2$ achieves a maximum conversion efficiency of 24.4% and a maximum apparent quantum yield of $0.013$% over the tested UV-C light intensities, demonstrating activity at environmentally relevant concentrations. We develop a model to interpret the experimental results and assess the potential of PCO for ventilation applications. The model is validated against our CH$_4$ data and literature results for formaldehyde (HCHO) and NO$_\text{x}$. While laboratory-scale configurations achieve high conversions (e.g., 24.4% for CH$_4$), ventilation-scale performance is predicted to be limited by thin concentration boundary layers and short residence times, with conversion efficiencies dropping to around $0.017$%. Finally, we estimate the climate impact of CH$_4$ removal in terms of CO$_2$e emission rates, demonstrating that TiO$_2$-based PCO in ventilation applications can yield a net climate benefit (i.e., a net-negative CO$_2$e emissions rate) when the modelled CO$_2$e removal rate exceeds the emissions from catalyst material production and UV operation, particularly when pre-existing UV-C irradiation is leveraged.
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All authors Samuel D. Tomlinson, Aliki Marina Tsopelakou, Tzia Ming Onn, Steven R. H. Barrett, Adam M. Boies, Shaun Fitzgerald

Physics and Society

The NetMob26 Dataset: A High-Resolution Multi-Source View of Public Bus Mobility in Niterói

Felipe Domingos, Humberto T. Marques-Neto, Bruno Pereira et al. (+3 more)

The NetMob Data Challenge releases a comprehensive public transportation dataset from Niterói, addressing the lack of high-quality mobility and passenger demand data. Based on operational records from March 2026, the dataset combines four main sources: GPS telemetry from buses, approximately 7.2 million ticketing transactions, auxiliary transit data (routes, stops, and weather), and urban infrastructure and socio-demographic information. Together, these sources provide a detailed view of both transit supply and passenger demand. The data were preprocessed, cleaned, and anonymized to preserve privacy and improve reliability, including the removal of operational inconsistencies and anonymization of passenger identifiers. Access is restricted to challenge participants who accept the Terms and Conditions and sign an NDA. The paper describes the data collection and preprocessing pipeline, dataset organization, and mobility patterns observed in the system. The dataset supports research on topics such as public transportation efficiency, demand forecasting, accessibility analysis, service reliability, and the influence of external factors like weather on urban mobility.
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All authors Felipe Domingos, Humberto T. Marques-Neto, Bruno Pereira, Clayson Celes, Steffen Knoblauch, Vinícius F. S. Mota

Plasma Physics

Production of Nuclear Battery $\beta^{-}$ Emitters Driven by Fusion Neutrons

J. F. Parisi

Nuclear batteries require radioisotopes with specific combinations of half-life, decay mode, and radiation properties, yet most candidate fuels lack scalable production routes. We show how the future availability of deuterium-tritium (D-T) fusion neutrons could enable manufacturing nuclear battery radioisotopes at many orders of magnitude higher rate than at present. We assess the capability of 14 MeV D-T fusion neutrons to produce nuclear battery radioisotopes by simulating feedstock material irradiation with neutrons. Promising radioisotope candidates include ${}^{147}$Pm, ${}^{63}$Ni, ${}^{39}$Ar, and ${}^{137}$Cs. Some feedstocks allow a radioisotope to be produced at scale while also closing the tritium fuel cycle, resulting in hundreds to over one thousand kilograms of high specific activity radioisotope per gigawatt thermal year of D-T fusion irradiation. We perform OpenMC simulations of an enriched ${}^{148}$Nd blanket for a tokamak, demonstrating that tritium self-sufficient designs can produce over one ton of ${}^{147}$Pm per gigawatt thermal year, equivalent to $\sim$one billion Curies per year of ${}^{147}$Pm. Operation of such a blanket would represent an unprecedented increase of nuclear battery radioisotope production capability.
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Biological Physics

Morphology-Driven optimization of Double Nanohole-based Plasmonic Optical Tweezers

Pau Molet, Edona Karakaçi, Maria Sanz Paz et al. (+2 more)

Plasmonic Optical Tweezers based on Double Nanohole (DNH) structures are an emerging tool for trapping and interrogating single proteins under physiologically relevant conditions without the need for labeling or tethering. Nevertheless, their current performance is hindered by low signal-to-noise ratios for small proteins, fabrication variability, and photothermal instabilities, in part due to the required high laser power. To address these limitations, we present a comprehensive optimization of DNH parameters using parametric simulations and morphological characterization of experimentally fabricated DNHs. This approach ensures that the optimized geometries are experimentally realizable and compatible with established fabrication processes. We evaluate critical structural features, including gap width, gap length, cusp curvature, wedged tapers, adhesion layer and gold layer thicknesses, and the inclusion of pillars inside the nanoholes. By tailoring these variables, we aim to maximize the transmission variation upon trapping single nanoparticles or proteins and the local electric-field enhancement, thereby maximizing the optical force, while minimizing the required optical power. Two optimized DNH designs are proposed and simulated using a heuristic approach and a differential evolution algorithm. Both substantially outperform the reference structure and deliver 6.5- and 22-fold increases in sensitivity and 2.4- and 1.4-fold improvement in electric-field enhancement, respectively. These refinements provide a framework for developing efficient nanostructures to study single-protein dynamics with Plasmonic Optical Tweezers.
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All authors Pau Molet, Edona Karakaçi, Maria Sanz Paz, Mariano Barella, Michael Mayer

Computational Physics · Cross-listed from cs.LG

Physics-informed convolutional neural networks for fluid flow through porous media

Rafał Topolnicki, Paweł Dłotko and Maciej Matyka

Accurate simulation of fluid flow in porous media is challenging due to complex pore-space geometries and the computational cost of solving the Navier-Stokes equations. This difficulty is particularly important when repeated simulations are required, as standard numerical solvers may converge slowly in intricate porous domains. We present a neural-network-based framework for predicting pore-scale velocity fields directly from sample geometry. The method uses a convolutional encoder-decoder architecture with skip connections to preserve spatial detail while extracting multi-scale features. Physical consistency is encouraged through a custom loss function combining velocity reconstruction with incompressibility, no-flow conditions inside solids, periodicity constraints, and agreement with the global tortuosity index. We analyze the influence of the corresponding loss weights and quantify the contribution of individual loss components to prediction accuracy. Several CNN backbones are evaluated to identify architectures providing accurate and robust predictions. The generalization ability of the trained model is tested on samples outside the training distribution, including changes in obstacle geometry, boundary conditions, porosity, and realistic porous structures. Finally, we demonstrate a practical use of the predicted velocity fields as initial conditions for Lattice-Boltzmann simulations. This warm-start strategy accelerates solver convergence, reducing the number of iterations in over 90% of tested cases.
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Chemical Physics · Cross-listed from cs.LG

LEAP: A closed-loop framework for perovskite precursor additive discovery

Xin-De Wang, Zhi-Rui Chen, Ze-Feng Gao et al. (+3 more)

Efficient discovery of precursor additives is essential for improving the performance of perovskite solar cells, yet the large chemical space makes conventional trial-and-error screening inefficient. We develop LEAP(LLM-driven Exploration via Active Learning for Perovskites), an expert-in-the-loop closed framework that couples a domain-specialized large language model(LLM) with active learning for iterative additive prioritization. The LLM is trained to extract mechanism-relevant knowledge from the perovskite additive literature and to represent candidate molecules through interpretable descriptors, which are further integrated into a Bayesian optimization workflow for uncertainty-aware prioritization under low-data conditions. Benchmark results on unseen literature show that the domain-specialized model outperforms general-purpose models in mechanism-consistent reasoning. Experimental validation in an expert-in-the-loop proof-of-concept study suggests improved additive prioritization across three screening rounds, leading to average device PCEs of 20.13% and 20.87% for the later-round 6-CDQ- and 2-CNA-treated devices, respectively, compared with 19.25% for the control, with a champion PCE of 21.32%. These results provide preliminary evidence that literature-grounded mechanistic descriptors, when coupled with Bayesian optimization and expert feasibility review, can support mechanism-aware additive prioritization in perovskite photovoltaics.
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All authors Xin-De Wang, Zhi-Rui Chen, Ze-Feng Gao, Peng-Jie Guo, Cheng Mu, Zhong-Yi Lu

Instrumentation and Detectors

Discrete Wavelet Transform for Serial X-ray Crystallography Image Segmentation

Dionisio Doering, Noemie Claret, Guilherme Paulino et al. (+10 more)

Upcoming LCLS-II/II-HE operation at repetition rates approaching 1MHz demands on-detector data reduction to manage the resulting data volumes. We present a 2D discrete wavelet transform (DWT) pre-processing algorithm that segments background scatter from crystal diffraction in serial crystallography images, enabling early data analysis and, when combined with peak finding, lossy compression by transmitting only the identified diffraction peaks. The method zeroes the approximation (LL) coefficients of a multi-level Haar wavelet decomposition and reconstructs from detail subbands only, exploiting the natural separation of smooth background and sharp Bragg peaks in the wavelet domain. Evaluated on 100 simulated nanoBragg frames with known ground truth, the pipeline achieves $F1 \approx 0.96$ at four decomposition levels ($J = 4$), substantially outperforming the established peakfinder8 algorithm ($F1 \approx 0.37$) in both precision ($P \approx 1.00$ vs.\ $0.94$) and recall ($R \approx 0.92$ vs.\ $0.24$). A comparison of 12 wavelet families confirms that Haar is optimal for Bragg-peak detection due to its minimal filter support. Downstream crystallographic analysis performed on real ePix10kA data shows that CC* and $R_\mathrm{split}$ converge at $J = 4$ and track the unprocessed baseline through the practical resolution limit. Under added noise exceeding $\sim$50 ADU, the current pipeline's precision degrades significantly more than that of the pf8 algorithm, exposing a limitation of the proposed strategy. We also demonstrate an FPGA implementation of the DWT filters on an Alveo U200 at 200MHz, with a projected resource footprint compatible with integration into the upcoming ePixUHR firmware and a path to on-detector ASIC implementation in SparkPix detector family.
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All authors Dionisio Doering, Noemie Claret, Guilherme Paulino, Luca Scomparin, Frederic Poitevin, Eric Darve, Conny Hansson, James Russell, Abhilasha Dave, Lorenzo Rota, Antonino Miceli, Ryan Herbst, Angelo Dragone

Optics

Open-source segmentation and biometry dataset using spectrally-multiplexed whole-eye optical coherence tomography

Ruobing Qian, Catherine Fromm, Pushkar Anand et al. (+6 more)

Whole-eye optical coherence tomography (WEOCT) has emerged as a transformative imaging modality capable of simultaneously capturing the anterior and posterior segments of the human eye. WEOCT enables comprehensive ocular biometry, which is critical for a wide range of clinical and research applications-from intraocular lens power calculation, myopia progression monitoring, and refractive surgery planning to the precise measurement of the visual and optical axes and the generation of personalized eye models for eye tracking in virtual, augmented and mixed reality(VR/AR/MR). However, existing WEOCT systems often face trade-offs between signal-to-noise ratio, imaging speed, and the ability to capture dynamic processes without motion artifacts. To address these limitations, we present a novel spectrally-multiplexed WEOCT system that utilizes two synchronized 200 kHz swept sources at 1310 nm and 1060 nm. Coupled with an automated end-to-end processing pipeline involving deep learning-based surface segmentation, 3D distortion correction, surface fitting and ray-tracing refraction correction, our system enables anatomically accurate 3D reconstruction of the segmented ocular layers. Through a 300+ participant user study and comprehensive phantom studies, we demonstrate that our system can provide simultaneous accurate measurements of cornea topography and 3D pupil center. While labeled retinal OCT data is abundantly available in open-source repositories, labeled B-scan or volumetric anterior segment data remains significantly limited. Consequently, research groups working in related domains must often acquire their own data using custom imaging systems. To help bridge this gap, we are releasing as open-source a comprehensive dataset comprising 6,621 processed volumes from 276 unique participants with corresponding segmentation and calibrated 3D anterior point clouds.
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All authors Ruobing Qian, Catherine Fromm, Pushkar Anand, Kyle Johnson, Zach Willms, Yimin Ding, Weihan Zhang, Ali Behrooz, Mohamed El-Haddad

Fluid Dynamics

Unitary discretization of the Koopman-von Neumann equation for quantum simulation of fluid and plasma dynamics

Aleksandar Jemcov and Scott C. Morris

The Koopman–von Neumann (KvN) formulation of spectrally truncated fluid and plasma dynamics is considered as a potential approach for quantum computation. The KvN framework embeds the Liouville equation into a Hilbert space with norm-preserving, unitary evolution. Here, we propose a Weyl-ordered KvN generator along with a summation-by-parts discretization, which ensures that the resulting operators are exactly unitary as required for quantum computers. The Weyl-ordered KvN generator is derived as the unique anti-Hermitian operator symmetrization for real velocity fields. The formulation operates directly in the physical amplitude space without phase-space doubling, so the Heisenberg uncertainty principle does not constrain the grid resolution during evolution. This limitation re-enters only at the measurement stage on a quantum computer. Exact discrete unitarity is proved as a purely algebraic identity that holds regardless of grid resolution or stencil order. To manage boundaries, a split-step Kraus absorbing layer is introduced via a Stinespring dilation requiring only one ancilla qubit. Validation on three test cases spanning dissipative and Hamiltonian regimes (a viscous Navier–Stokes triad, an incompressible Euler triad, and a Hasegawa–Mima drift-wave triad) confirms fourth-order convergence and machine-precision unitarity.
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