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Physics Physics

physics Watch Physics

Monday 24 November

Physics and Society

Misinformation Dynamics in Social Networks

Jeff Murugan

Information transmitted across modern communication platforms is degraded not only by intentional manipulation (disinformation) but also by intrinsic cognitive decay and topology-dependent social averaging (misinformation). We develop a continuous-fidelity field theory on multiplex networks with distinct layers representing private chats, group interactions, and broadcast channels. Our analytic solutions reveal three universal mechanisms controlling information quality: (i) groupthink blending, where dense group coupling drives fidelity to the initial group mean; (ii) bridge-node bottlenecks, where cross-community flow produces irreversible dilution; and (iii) a network-wide fidelity landscape set by a competition between broadcast truth-injection and structural degradation pathways. These results demonstrate that connectivity can reduce information integrity and establish quantitative control strategies to enhance fidelity in large-scale communication systems.
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Atomic Physics

Atomic magnetometry based on the ground-state Hanle effect in an elliptically polarized light wave

D.V. Brazhnikov, A.O. Makarov, K.S. Kozlova et al. (+1 more)

We investigate the ground-state Hanle effect in alkali-metal vapor irradiating by a resonant elliptically polarized light wave. The magneto-optical resonances are observed as a change in the ellipticity parameter of the light wave polarization when scanning the transverse magnetic field near zero. We use a miniature ($\approx\,$$0.125$ cm$^3$) glass cesium vapor cell heated to a relatively low temperature of $\approx\,$$85^\circ$C. Under the current experimental conditions, the sensitivity of magnetic field measurements is limited by a technical noise, reaching $180$ fT/$\surd$Hz in a $200$ Hz bandwidth. The ultimate photon-shot-noise-limited sensitivity of the method is estimated to be $\approx\,$$5$ fT/$\surd$Hz. The proposed scheme is promising for the development of a zero-field atomic magnetometer with reduced heat dissipation of the sensor head and relaxed requirements for magnetic shielding compared to counterparts operating in the spin-exchange relaxation-free regime. These features are of particular value for biomedical applications.
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All authors D.V. Brazhnikov, A.O. Makarov, K.S. Kozlova, A.N. Goncharov

Atomic Physics

Development of a projectile charge state analyzer and 10 kV bipolar power supply for MeV energy ion - atom/molecule collision experiments

Sandeep Bajrangi Bari, Sahan Raghava Sykam, Ranojit Das et al. (+2 more)

We have developed a post-collision projectile charge state analyzer (CSA) for detecting the charge state of the projectile ion following ion-atom/molecule collision. The design of the analyzer, based on electrostatic parallel plate deflector was simulated using SIMION ion optics package. We have also developed a 10 kV bipolar programmable power supply to bias the CSA electrodes. The CSA and the power supply, both, were tested in collision studies using MeV energy ion beam of proton and carbon ions at the 1.7 MV tandetron accelerator facility at IIT Kanpur.
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All authors Sandeep Bajrangi Bari, Sahan Raghava Sykam, Ranojit Das, Rohit Tyagi, Aditya H. Kelkar

Optics

Manipulation of photonic topological edge and corner states via trivial claddings

Hai-Xiao Wang, Li Liang, Shuai Shao et al. (+4 more)

Crystalline symmetry offers a powerful tool to realize photonic topological phases, in which additional trivial claddings are typically required to confine topological boundary states. However, the utility of the trivial cladding in manipulating topological waves is often overlooked. Here, we demonstrate two topologically distinct kagome photonic crystals (KPCs) based on different crystalline symmetries: \mathbit{C}_\mathbf{6}- symmetric KPCs exhibit a quantum spin Hall phase, while \mathbit{C}_\mathbf{3}-symmetric KPCs serve as trivial cladding. By tuning the geometric parameter of the trivial cladding, we observe that a pair of topological interface states featured with pseudospin-momentum locking undergoes a phase transition, accompanied by the appearance and disappearance of corner states in a finite hexagonal supercell. Such a geometry-induced band inversion is characterized by a sign change in the Dirac mass of the topological interface states and holds potential for applications such as rainbow trapping. Furthermore, we experimentally demonstrate the corner states, which is a hallmark of higher-order topology, also depend critically on the trivial cladding. Our work highlights the crucial role of trivial claddings on the formation of topological boundary states, and offers a novel approach for their manipulation.
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All authors Hai-Xiao Wang, Li Liang, Shuai Shao, Shiwei Tang, Junhui Hu, Yin Poo, Jian-Hua Jiang

Atmospheric and Oceanic Physics

A multiresolution weather dataset for the Southwestern South Atlantic (2017-2018)

Luan C. V. Silva, Lívia Sancho, Mauricio S. Silva et al. (+19 more)

The Southwestern South Atlantic (SWSA) is a key region for climate research and renewable energy assessment, yet high-resolution meteorological data are scarce. We present a multiresolution dataset spanning February 2017–November 2018, combining Weather Research and Forecasting (WRF) simulations with Sentinel-1A/B Synthetic Aperture Radar (SAR) wind fields processed using the CMOD5 model. WRF outputs were generated every 30 minutes for three nested domains (9 km, 3 km, 1 km) through 975 short-term simulations. SAR/CMOD5 wind fields are provided at 500 m and 1 km resolution across 104 acquisition dates. Validation shows strong agreement: daily spatial averages of 10 m wind speed yield RMSE and MAE below 3 m/s on over 93% of acquisition days, while more than 91.5% of pixel-level residuals fall within $\pm$3 m/s. In situ measurements from the Itajaí buoy further confirmed the reliability of both sources. The dataset supports regional climate studies, wind energy resource assessment, and machine-learning applications in forecasting and downscaling, with usage examples included to aid practical adoption.
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All authors Luan C. V. Silva, Lívia Sancho, Mauricio S. Silva, Elisa Passos, Larissa F. R. Jacinto, Rebeca S. Lyra, Nilton O. Moraes, Carina S. Bock, Douglas M. Nehme, Raquel Toste, Jacques Honigbaum, Rodrigo S. Luna, Carlos H. Beisl, Patricia M. Silva, Adriano O. Vasconcelos, Rian C. Ferreira, Cédric Eneau, Fernando A. Rochinha, Luiz P. F. Assad, Alvaro L. G. A. Coutinho, Laura Bahiense, Alexandre G. Evsukoff

Instrumentation and Detectors

Development of a dual-phase xenon time projection chamber prototype for the RELICS experiment

Lingfeng Xie, Jiajun Liu, Yifei Zhao et al. (+50 more)

The RELICS (REactor neutrino LIquid xenon Coherent elastic Scattering) experiment aims to detect coherent elastic neutrino-nucleus scattering from reactor antineutrinos using a dual-phase xenon time projection chamber. To validate the detector concept and ensure technical reliability for the full-scale experiment, a dedicated prototype was designed, constructed, and operated. This work presents an overview of the design, construction, and operational performance of the prototype, with emphasis on its major subsystems, including the TPC, cryogenic and xenon purification systems, slow control, and data acquisition. During operation, the detector demonstrated the capability to achieve a sub-keV energy threshold required for the RELICS physics program, as reflected by a measured single electron gain of 34.30~$\pm$~0.01~(stat.)~PE/e$^-$ and the successful detection of 0.27~keV L-shell decay events from $^{37}$Ar. In addition, essential data analysis techniques and simulation frameworks were developed and validated, establishing the methodological foundation for future RELICS operations. The successful construction and operation of this prototype confirm the feasibility of the core technologies and provide a crucial experimental basis for the final RELICS detector.
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All authors Lingfeng Xie, Jiajun Liu, Yifei Zhao, Chang Cai, Guocai Chen, Jiangyu Chen, Huayu Dai, Rundong Fang, Hongrui Gao, Fei Gao, Jingfan Gu, Xiaoran Guo, Jiheng Guo, Chengjie Jia, Gaojun Jin, Fali Ju, Yanzhou Hao, Xu Han, Yang Lei, Kaihang Li, Meng Li, Minhua Li, Ruize Li, Shengchao Li, Siyin Li, Tao Li, Qing Lin, Sheng Lv, Guang Luo, Yuanyuan Ren, Chuanping Shen, Mingzhuo Song, Lijun Tong, Yuhuang Wan, Xiaoyu Wang, Wei Wang, Xiaoping Wang, Zihu Wang, Yuehuan Wei, Liming Weng, Xiang Xiao, Yikai Xu, Jijun Yang, Litao Yang, Long Yang, Jingqiang Ye, Jiachen Yu, Qian Yue, Yuyong Yue, Tianyuan Zha, Bingwei Zhang, Yuming Zhang, Chenhui Zhu

Sunday 23 November

Optics

Bayesian Optimization and Convolutional Neural Networks for Zernike-Based Wavefront Correction in High Harmonic Generation

Guilherme Grancho D. Fernandes, Duarte Alexandrino, Eduardo Silva et al. (+2 more)

High harmonic generation (HHG) is a nonlinear process that enables table-top generation of tunable, high-energy, coherent, ultrashort radiation pulses in the extreme ultraviolet (EUV) to soft X-ray range. These pulses find applications in photoemission spectroscopy in condensed matter physics, pump-probe spectroscopy for high-energy-density plasmas, and attosecond science. However, optical aberrations in the high-power laser systems required for HHG degrade beam quality and reduce efficiency. We present a machine learning approach to optimize aberration correction using a spatial light modulator. We implemented and compared Bayesian optimization and convolutional neural network (CNN) methods to predict optimal Zernike polynomial coefficients for wavefront correction. Our CNN achieved promising results with 80.39% accuracy on test data, demonstrating the potential for automated aberration correction in HHG systems.
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All authors Guilherme Grancho D. Fernandes, Duarte Alexandrino, Eduardo Silva, João Matias, Joaquim Pereira

Instrumentation and Detectors

A Novel Apparatus For Particle-Particle Single Contact Electrification Experiments

Otome Obukohwo, Simon Jantač, Andrew Sowinski et al. (+2 more)

The experiment of a single contact between two sub-centimeter high-speed particles is often difficult to execute, especially if the collision must be physically and electrically isolated, as is the case for triboelectrification studies. Apparatuses designed for this type of experiment fall short of providing high-speed isolated collisions with a high probability of contact. In this article, we propose a novel apparatus that combines pneumatic conveying and acoustic levitation to provide an electrically and physically isolated, high impact speed collision between two sub-centimeter particles with a collision success rate of 93 %. We can control the pre-contact charge, material, and size of both particles, and the impact speed and angle. Test results show that charge transfer between two insulator particles is not solely driven by contact potential difference; it is a stochastic process that requires large datasets to resolve and understand. Our new apparatus can efficiently generate these datasets and provide new insights on the stochastic nature of charge transfer, and the effect of each of the collision parameters mentioned earlier on particle-particle charge transfer.
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All authors Otome Obukohwo, Simon Jantač, Andrew Sowinski, Poupak Mehrani, Holger Grosshans

Atmospheric and Oceanic Physics

Harmonic Extension for Multiscale Analysis and Modeling Near Boundaries, with an Ocean Application

Benjamin A. Storer, Mehrnoush Kharghani, Alistair Adcroft et al. (+1 more)

Treatment of fields near domain boundaries is a long-standing problem in signal processing that has come into renewed focus following recent efforts in convolution-based multiscale coarse-graining and in machine-learned parameterizations due to ocean boundary artifacts. Here, we propose a general method for extending fields beyond the domain boundaries by solving a Laplace boundary-value problem. Construction of the harmonic extension is well-posed, including uniqueness, and is consistent with the boundary conditions by design. The formulation applies to irregular boundaries such as discretized coastlines. The harmonic extension is physically desirable since it has minimum spatial variability among all admissible extensions satisfying the boundary conditions. The method is simple to implement using well-established numerical approaches, and is broadly applicable to extending oceanic variables over land boundaries. Other applications include machine learning parametrization and subgrid modeling of wall-bounded flows and multiphase flows. We demonstrate the method by extending sea-surface temperature (SST) over land using fixed temperature (Dirichlet) and no-flux (Neumann) boundary conditions: the land-filled solution is smooth with SST values between the coastal minimum and maximum.
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All authors Benjamin A. Storer, Mehrnoush Kharghani, Alistair Adcroft, Hussein Aluie

Chemical Physics

QuantumChem-200K: A Large-Scale Open Organic Molecular Dataset for Quantum-Chemistry Property Screening and Language Model Benchmarking

Yinqi Zeng and Renjie Li

The discovery of next-generation photoinitiators for two-photon polymerization (TPP) is hindered by the absence of large, open datasets containing the quantum-chemical and photophysical properties required to model photodissociation and excited-state behavior. Existing molecular datasets typically provide only basic physicochemical descriptors and therefore cannot support data-driven screening or AI-assisted design of photoinitiators. To address this gap, we introduce QuantumChem-200K, a large-scale dataset of over 200,000 organic molecules annotated with eleven quantum-chemical properties, including two-photon absorption (TPA) cross sections, TPA spectral ranges, singlet-triplet intersystem crossing (ISC) energies, toxicity and synthetic accessibility scores, hydrophilicity, solubility, boiling point, molecular weight, and aromaticity. These values are computed using a hybrid workflow that integrates density function theory (DFT), semi-empirical excited-state methods, atomistic quantum solvers, and neural-network predictors. Using QuantumChem-200K, we fine tune the open-source Qwen2.5-32B large language model to create a chemistry AI assistant capable of forward property prediction from SMILES. Benchmarking on 3000 unseen molecules from VQM24 and ZINC20 demonstrates that domain-specific fine-tuning significantly improves accuracy over GPT-4o, Llama-3.1-70B, and the base Qwen2.5-32B model, particularly for TPA and ISC predictions central to photoinitiator design. QuantumChem-200K and the corresponding AI assistant together provide the first scalable platform for high-throughput, LLM-driven photoinitiator screening and accelerated discovery of photosensitive materials.
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Space Physics · Cross-listed from cs.AI

Reasoning With a Star: A Heliophysics Dataset and Benchmark for Agentic Scientific Reasoning

Kevin Lee, Russell Spiewak and James Walsh

Scientific reasoning through Large Language Models in heliophysics involves more than just recalling facts: it requires incorporating physical assumptions, maintaining consistent units, and providing clear scientific formats through coordinated approaches. To address these challenges, we present Reasoning With a Star, a newly contributed heliophysics dataset applicable to reasoning; we also provide an initial benchmarking approach. Our data are constructed from National Aeronautics and Space Administration & University Corporation for Atmospheric Research Living With a Star summer school problem sets and compiled into a readily consumable question-and-answer structure with question contexts, reasoning steps, expected answer type, ground-truth targets, format hints, and metadata. A programmatic grader checks the predictions using unit-aware numerical tolerance, symbolic equivalence, and schema validation. We benchmark a single-shot baseline and four multi-agent patterns, finding that decomposing workflows through systems engineering principles outperforms direct prompting on problems requiring deductive reasoning rather than pure inductive recall.
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Physics and Society

Causal spillover effects of electric vehicle charging station placement on local businesses: a staggered adoption study

M. Mavin De Silva, Callie Clark, Tadachika Nakayama et al. (+1 more)

Understanding the economic impacts of the placement of electric vehicle charging stations (EVCSs) is crucial for planning infrastructure systems that benefit the broader community. Theoretical models have been used to predict human behavior during charging events, however, these models have often neglected the complexity of trip patterns, and have underestimated the real-world impacts of such infrastructure on the local economy. In this paper, we design a quasi-experiment using mobile phone GPS location and EVCS deployment history data to analyze the causal impact of EVCS placement on visitation patterns to businesses. More specifically, we leverage the staggered placement of EVCSs in New York City and California Bay Area to match treated and control businesses that share similar characteristics including the business sector, location, and pre-treatment visitation count. By comparing three alternative matching strategies, we show that staggered adoption avoids selecting controls from non-treated clusters, and yields greater spatial overlap in dense urban areas. We find that EVCS installations significantly increase customer traffic, with effects concentrated in recreational venues in New York City and routine destinations such as groceries, pharmacies, and cafes in California Bay Area. Our results suggest that the economic spillovers of EVCSs vary across urban contexts and highlight the effectiveness of leveraging the staggered nature of adoption timings for evaluating infrastructure impacts in heterogeneous urban environments.
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All authors M. Mavin De Silva, Callie Clark, Tadachika Nakayama, Takahiro Yabe

Instrumentation and Detectors

Performance of the High-Angle Time Projection Chambers in the Upgraded T2K Off-Axis Near Detector

K. Aivazelis, D. Attié, P. Billoir et al. (+94 more)

The off-axis magnetic near detector of the T2K experiment has undergone a significant upgrade, including the construction and installation of two new Time Projection Chambers featuring innovative resistive Micromegas technology and a field cage composed of thin composite walls. This paper provides a detailed description of the new components of the chambers, including the gas system, gas monitoring chambers, and data acquisition system. Additionally, it reports the results of extensive testing using both neutrino beams and cosmic rays, with comparisons between data and Monte Carlo simulations. The new detectors achieve improved spatial resolution and enhanced particle identification capabilities which are crucial for the precision goals of the T2K experiment.
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All authors K. Aivazelis, D. Attié, P. Billoir, A. Blanchet, G. Bortolato, S. Bolognesi, R. Boullon, N. F. Calabria, D. Calvet, M. P. Casado, M. G. Catanesi, M. Cicerchia, G. Cogo, G. Collazuol, P. Colas, D. Cotte, D. D'Ago, C. Dalmazzone, T. Daret, R. de Oliveira, A. Delbart, J. Dumarchez, K. Dygnarowicz, S. Emery-Schrenk, A. Ershova, G. Eurin, M. Feltre, C. Forza, A. N. Gacino Olmedo, A. Gambalonga, C. Giganti, F. Gramegna, P. Granger, R. Guida, M. Guigue, S. Hassani, D. Henaff, F. Iacob, C. Jesús-Valls, S. Joshi, R. Kurjata, M. Lamoureux, J. F. Laporte, M. Lehuraux, S. Levorato, A. Longhin, T. Lux, L. Magaletti, T. Marchi, D. Marchesini, L. Mareso, M. Mattiazzi, M. Mezzetto, B. Mehl, E. Miller, L. Mellet, L. Munteanu, Q. V. Nguyen, N. Ospina, Y. Orain, R. Palumbo, C. Pastore, J.-M. Parraud, E. Pierre, C. Pio, O. Pizzirusso, B. Popov, F. Pupilli, E. Radicioni, Ch. Riccio, L. Rinaldi, F. Rossi, S. Roth, L. Russo, S. Russo, A. Rychter, W. Saenz Arevalo, L. Scomparin, Ph. Schune, D. Smyczek, R. Spina, J. Steinmann, S. Suvorov, N. Thamm, D. Terront, A. Teixeira, F. Toussenel, V. Valentino, D. Vargas, M. Varghese, G. Vasseur, C. Vuillemin, U. Virginet, Ch. Winterstein, U. Yevarouskaya, M. Ziembicki, M. Zito

Computational Physics · Cross-listed from gr-qc

Dynamical interplay between coupled scalar dark sectors and gravity

Mihai Marciu

We explore a novel cosmological model based on coupled fields in the framework of scalar tensor theories, considering the specific interplay between gravity and scalar fields. The model further extends a recent axion-dilaton system by introducing viable couplings with the space–time geometry encoded into the scalar curvature. After briefly introducing the action and the corresponding field equations, we employ linear stability theory to investigate the physical properties. The analysis showed the compatibility of the current theoretical model with the recent history of the Universe, obtaining viable constraints for the model's parameters in some specific cases. In the present setup, the axion–dilaton system is non–minimally coupled with gravity in an independent manner, leading to distinct physical features in the phase-space structure, possible alleviating the cosmic coincidence problem.
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Applied Physics

High-Accuracy Material Classification via Reference-Free Terahertz Spectroscopy: Revisiting Spectral Referencing and Feature Selection

Mathias Hedegaard Kristensen, Paweł Piotr Cielecki and Esben Skovsen

We investigate how feature selection algorithms can enable accurate, reference-free classification of materials using sparse-frequency terahertz (THz) reflection spectroscopy. Three classes of feature selection strategies are evaluated. Namely, the filter-based mRMR (minimum Redundancy Maximum Relevance), the embedded LASSO (Least Absolute Shrinkage and Selection Operator), and the wrapper-based SFS (Sequential Forward Selection) algorithms. Each strategy is assessed using the Linear Logistic Regression, Naïve Bayes, and Support Vector Machine classifiers. Our results show that high classification accuracy can be achieved using only a small subset of frequencies. Particularly, when non-referenced spectra are applied. Furthermore, we show that the SFS-selected features align with the materials' absorption bands, confirming that the discriminative power arises from genuine spectroscopic contrasts. These findings highlights that reducing spectral dimensionality through data-driven selection eliminates the need for broadband sources and reference measurements, enabling compact, application-specific THz sensors. This approach offers robust material identification in real-world scenarios such as security screening, non-destructive testing, and environmental monitoring.
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Fluid Dynamics

Effect of subgrid-scale anisotropy on wall-modeled large-eddy simulation of turbulent flow with smooth-body separation

Di Zhou and H. Jane Bae

We examine the role of anisotropic subgrid-scale (SGS) stress in wall-modeled large-eddy simulation (WMLES) of flow over a spanwise-uniform Gaussian-shaped bump, with emphasis on predicting flow separation. The simulations show that eddy-viscosity-based SGS models often yield non-monotonic predictions of the mean separation bubble size on the leeward side under grid refinement, whereas models incorporating anisotropic SGS stress produce more consistent results. To identify where SGS anisotropy is most critical, we introduce anisotropic SGS stress in selected regions of the domain. The results reveal that the windward side, where a strong favorable pressure gradient (FPG) occurs, is crucial in determining downstream separation. Analysis of the Reynolds stress transport equation shows that fluctuations of anisotropic SGS stress modify SGS dissipation and diffusion in this region, thereby altering the Reynolds stress and the onset of separation. Examination of the mean streamwise momentum equation indicates that at coarse resolutions, the mean SGS shear stress dominates, and the differences between the eddy-viscosity-based and anisotropic models remain minor. With grid refinement, resolved Reynolds stresses increasingly govern the near-wall momentum transport, and the influence of SGS stress fluctuations grows as they determine the SGS dissipation and diffusion of Reynolds stresses. Component-wise analysis of the SGS stress tensor further shows that the improvement arises mainly from including significant normal stress contributions. An a priori study using filtered direct numerical simulation of turbulent Couette-Poiseuille flow confirms that wall-bounded turbulence under FPG is highly anisotropic and that anisotropic SGS models provide a more realistic SGS stress representation than eddy-viscosity-based models.
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Geophysics

Physically Informed Bayesian Retrieval of SWE and Snow Depth in Forested Areas from Airborne X And Ku-Band SAR Measurements

Siddharth Singh, Carrie Vuyovich and Ana P. Barros

This study presents a coupled physical statistical framework for retrieving snow water equivalent (SWE) in forested areas using dual frequency X and Ku band SAR observations. The method combines a multilayer snow hydrology model (MSHM) with microwave propagation and backscatter models, and includes a canopy parameterization based on a modified Water Cloud Model that accounts for canopy closure. The framework is applied to airborne SnowSAR measurements over Grand Mesa, Colorado, and evaluated against snow pit SWE and LiDAR snow depth from the SnowEx'17 campaign. Prior distributions of snowpack properties are generated with MSHM forced by numerical weather prediction, and vegetation and soil parameters are initialized from Ku HH observations under frozen conditions and interpolated from open to nearby forested areas using kriging. Successful SWE and snow depth retrievals in forested pixels are obtained where relative backscatter residuals are below 30% for incidence angles between 30 and 50 degrees, capturing both the mean and variance of snowpack distributions. For 90 m forested pixels, the snow depth RMSE is 0.033 m (less than 8% of maximum pit SWE), with improved spatial patterns relative to hydrology only simulations. Performance degrades in highly heterogeneous land cover such as mixed forest and wetlands and along canopy and water boundaries due to uncertainty in canopy closure, although absolute snow depth differences remain below 10% and 20% for about 62% and 82% of pixels, respectively. Retrievals at 30 m resolution for one flight further reduce spatial errors and increase the fraction of low error pixels by about 78% at a 10% absolute error threshold, demonstrating the feasibility of dual frequency Bayesian SWE retrievals in forested landscapes by combining physical modeling with SAR observations.
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Atomic and Molecular Clusters

Curvature-Dependent Polarity of Interfacial Energy Flow in Functionalized CNT Polymer Nanocomposites: A Reactive Molecular Dynamics Perspective

Mehedi Hasan, Khayrul Islam, Michael T. Kio et al. (+1 more)

Carbon nanotube (CNT)-polymer composites are widely engineered using surface coatings and chemical treatments to improve interfacial bonding and load transfer. It has been suggested in the nanocomposite literature that nanotube curvature, in conjunction with surface functionalization such as polydopamine (PDA) coating, could serve as an additional control knob for tuning interfacial bonding and energy dissipation in polymer-CNT systems. While experimental and simulation studies have demonstrated the benefits of PDA functionalization, the fundamental mechanism by which nanotube curvature modulates interfacial energy flow and mechanical polarity remains unresolved. This gap is sharpened by a persistent paradox: identical PDA functionalization strengthens some CNT-polymer systems while weakening others, a curvature-dependent inconsistency that has remained unexplained. Here, we employ reactive molecular dynamics (ReaxFF) simulations to resolve how curvature and PDA functionalization jointly govern interfacial energy evolution in CNT-polyvinyl alcohol (PVA) nanocomposites. Our investigation reveals that curvature and PDA functionalization jointly produce opposite regimes of interfacial energy flow: high-curvature CNTs generate dissipative, frictional interphases, whereas low-curvature CNTs confine energy in rigid, cohesive shells. This polarity inversion originates from a curvature-induced transition in PDA adsorption geometry that transforms the interphase from an energy-releasing to an energy-storing configuration. These results establish curvature as a fundamental design parameter for engineering polymer-nanotube interfaces, offering a predictive route to tune interfacial energy flow, mechanical resilience, and transport properties beyond the limits of conventional chemical functionalization.
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All authors Mehedi Hasan, Khayrul Islam, Michael T. Kio, AKM Masud

Fluid Dynamics

Quantum machine learning for efficient reduced order modelling of turbulent flows

Han Li, Yutong Lou and Dunhui Xiao

Accurately predicting turbulent flows remains a central challenge in fluid dynamics due to their high dimensionality and intrinsic nonlinearity. Recent developments in quantum algorithms and machine learning offer new opportunities for overcoming the computational barriers inherent in turbulence modeling. Here we present a new hybrid quantum-classical framework that enables faster-than-real-time turbulence prediction by integrating machine learning, quantum computation, and fluid dynamics modeling, in particular, the reduced-order modeling. The novel framework combines quantum proper orthogonal decomposition (QPOD) with a quantum-enhanced deep kernel learning (QDKL) approach. QPOD employs quantum circuits to perform efficient eigenvalue decomposition for low-rank flow reconstruction, while QDKL exploits quantum entanglement and nonlinear mappings to enhance kernel expressivity and dynamic prediction accuracy. The new method is demonstrated on three benchmark turbulent flows, our architecture achieves significantly improved predictive accuracy at reduced model ranks, with training speeds up to 10 times faster and parameter counts reduced by a factor of 1/N compared to classical counterparts, where N is the input dimensionality. Although constrained by current noisy intermediate-scale quantum (NISQ) hardware, our results demonstrate the potential of quantum machine learning to transform turbulence simulation and lay a solid foundation for scalable, real-time quantum fluid modeling in future quantum computers.
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Medical Physics

Towards Integrated Clinical-Computational Nuclear Medicine

Faraz Farhadi, Shadi A. Esfahani, Fereshteh Yousefirizi et al. (+7 more)

The field of Clinical-Computational Nuclear Medicine is rapidly advancing, fueled by AI, tracer kinetic modeling, radiomics, and integrated informatics. These technologies improve imaging quality, automate lesion detection, and enable personalized radiopharmaceutical therapy through physiologically based pharmacokinetic (PBPK) modeling and voxel-level dosimetry. Workflow automation and Natural Language Processing (NLP) further enhance operational efficiency. However, successful implementation and adoption of these tools require clinical oversight to ensure accuracy, interpretability, and patient safety. This paper highlights key computational innovations and emphasizes the critical role of clinician-guided evaluation in shaping the future of precision imaging and therapy.
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All authors Faraz Farhadi, Shadi A. Esfahani, Fereshteh Yousefirizi, Monica Luo, Pedro Esquinas Fernandez, Arkadiusz Sitek, Hamid Sabet, Babak Saboury, Arman Rahmim, Pedram Heidari

Fluid Dynamics · Cross-listed from math.AP

Sharp uniform-in-diffusivity mixing rates for passive scalars in parallel shear flows

Dallas Albritton and Rajendra Beekie

We consider the advection-diffusion equation describing the evolution of a passive scalar in a background shear flow. We prove the optimal uniform-in-diffusivity mixing rate $\| f \|_{H^{-1}} \lesssim \langle t \rangle^{-1/(N+1)}$, $t \geq 0$, where $N$ is the maximal order of vanishing of the derivative $b'(y)$ of the shear profile, e.g., $N=1$ for plane Pouseille flow. Our proof is based on the description of the solution in terms of resolvents and involves pointwise estimates on the resolvent kernel. In the non-degenerate case, we further give a rigorous asymptotic description of generic solutions in terms of shear layers localized around the critical points. This verifies formal asymptotics in [McLaughlin-Camassa-Viotti, Physics of Fluids, 22(11), 2010].
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Instrumentation and Detectors

From LIGO to Fiber- Compact Sagnac Interferometer for Gravitational-Wave Detection

Farhad Hakimi and Hosain Hakimi

Gravitational wave detection has transformed astrophysics, granting us direct access to black hole mergers, neutron star collisions, and the cataclysms of stellar death. Yet the great observatories of today, LIGO, Virgo, KAGRA, and the planned Einstein Telescope, rest on Michelson interferometers that, despite their triumphs, confront fundamental barriers of scale, cost, and environmental vulnerability. We envision a new path, a Sagnac-based fiber interferometer that leverages reciprocity and inherent robustness. Its meter-scale, modular design compact enough to fit within a small facility, offers dramatic gains in scalability and affordability over kilometer scale Michelson systems. Tunable to frequency bands where conventional detectors lose sensitivity, it opens the door to compact, versatile, and accessible GW observatories, empowering universities and research centers worldwide. Linked together in a global network, such facilities could transcend mere detection, they could localize cosmic sources and reconstruct them into images, much as black holes were first directly revealed, ushering in a new era of gravitational-wave astronomy and multi-messenger discovery.
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Chemical Physics

Neural network approximation of regularized density functionals

Andre Laestadius and Mathias Oster

We study neural-network approximation of density functionals in a Banach-space setting. Since the exact continuum functionals are discontinuous in the natural density topology, we apply $p$-Moreau-Yosida regularization. We construct finite-rank ReLU networks that preserve convexity and nonnegativity, lie below the regularized functional, and approximate it uniformly on compact sets. For energies obtained by minimization over compact density sets, we account exactly for the energy shift caused by regularization and bound the remaining error by the localization on these compact sets and network approximation errors. We apply this framework to canonical and grand-canonical density-functional theory at fixed particle number and fixed mean particle number, respectively. We obtain uniform energy convergence on norm-compact classes of external potentials, including molecular Coulomb potentials. We illustrate our results through numerical experiments on a simple model system.
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Fluid Dynamics

Deformation and stability of a gas bubble in a biaxial straining flow

Aliénor Rivière, David Fabre, Jacques Magnaudet et al. (+1 more)

Taking advantage of the recently developed L-ALE framework [Sierra-Ausin et al., Phys. Rev. Fluids {\bf{7}}, 113603 (2022)], we characterize the linear dynamics of an incompressible gas bubble immersed in a biaxial straining flow. We show that the system undergoes a saddle-node bifurcation with strongly different equilibrium shapes when varying the Ohnesorge number, $\Oh$, which compares viscous and capillary effects. Equilibrium shapes are found to be oblate for sufficiently large $\Oh$ while, counter-intuitively, they are prolate for low-enough $\Oh$. The bifurcation diagram is found to contain also two sets of disconnected branches that cannot be obtained by continuation starting from a spherical shape. One set corresponds to bubble shapes expected to be unstable, while the second set comprises a wide region exhibiting stable shapes that might be observed in practice. We then characterize the linear stability of the various branches. In addition to the unstable axisymmetric mode arising at the saddle-node bifurcation, two non-oscillating drift modes are also identified, together with a new unstable non-oscillating mode with azimuthal wave number $m=2$. This mode might be responsible for some type of bubble breakup observed in experiments.
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All authors Aliénor Rivière, David Fabre, Jacques Magnaudet, François Gallaire

Atmospheric and Oceanic Physics · Cross-listed from astro-ph.EP

The Albedo Problem and Cloud Cover on Hot Jupiters

Kevin Heng, Billy Edwards and Nicolas B. Cowan

Observations of transiting hot Jupiters have revealed a mismatch between the values of the Bond versus geometric albedos. In the planetary science literature, the ratio of these quantities is known as the phase integral. It has been extensively measured for the Solar System planets and shown to generally be non-unity in value. We use existing Cassini data of Jupiter to derive bandpass-integrated geometric albedos and phase integrals in the CHEOPS, TESS and Ariel bandpasses, demonstrating that these quantities vary markedly across these different wavelength ranges. By performing a population study of geometric albedos and phase integrals, we demonstrate that atmospheres with partial cloud cover may be identified using measurements of the phase integral if its measured uncertainty is $\sim 0.1$, which corresponds to an uncertainty of $\sim 3\%$ on the optical/visible secondary eclipse depth. The upcoming Ariel space mission will conduct an unprecedented statistical survey of cloud cover on hot Jupiters via the simultaneous measurement of $\sim 100$ infrared phase curves and optical secondary eclipses. Whenever available, the shape of optical phase curves of reflected light will directly constrain the phase integral, spherical albedo, degree of cloud cover and scattering asymmetry factor.
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Physics and Society

The Effects of Remote Working on Scientific Collaboration and Impact

Sara Venturini, Satyaki Sikdar, Martina Mazzarello et al. (+5 more)

The COVID-19 pandemic shifted academic collaboration from in-person to remote interactions. This study explores, for the first time, the effects on scientific collaborations and impact of such a shift, comparing research output before, during, and after the pandemic. Using large-scale bibliometric data, we track the evolution of collaboration networks and the resulting impact of research over time. Our findings are twofold: first, the geographic distribution of collaborations significantly shifted, with a notable increase in cross-border partnerships after 2020, indicating a reduction in the constraints of geographic proximity. Second, despite the expansion of collaboration networks, there was a concerning decline in citation impact, suggesting that the absence of spontaneous in-person interactions-which traditionally foster deep discussions and idea exchange-negatively affected research quality. As hybrid work models in academia gain traction, this study highlights the need for universities and research organizations to carefully consider the balance between remote and in-person engagement.
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All authors Sara Venturini, Satyaki Sikdar, Martina Mazzarello, Francesco Rinaldi, Francesco Tudisco, Paolo Santi, Santo Fortunato, Carlo Ratti

Computational Physics

A fast-converging and asymptotic-preserving method for adjoint shape optimization of rarefied gas flows

Yanbing Zhang, Ruifeng Yuan and Lei Wu

Adjoint based shape optimization is a powerful technique in fluid-dynamics optimization, capable of identifying an optimal shape within only dozens of design iterations. However, when extended to rarefied gas flows, the computational cost becomes enormous because both the six dimensional primal and adjoint Boltzmann equations must be solved for each candidate shape. Building on the general synthetic iterative scheme (GSIS) for solving the primal Boltzmann model equation, this paper presents a fast converging and asymptotic preserving method for solving the adjoint kinetic equation. The GSIS accelerates the convergence of the adjoint kinetic equation by incorporating solutions of macroscopic synthetic equations, whose constitutive relations include the Newtonian stress law along with higher order terms capturing rarefaction effects. As a result, the method achieves asymptotic preservation (allowing the use of large spatial cell sizes in the continuum limit) while maintaining accuracy in highly rarefied regimes. Numerical tests demonstrate exceptional performance on drag minimization problems for 3D bodies, achieving drag reductions of 34.5% in the transition regime and 61.1% in the slip-flow regime within roughly ten optimization iterations. For each candidate shape, converged solutions of the primal and adjoint Boltzmann equation are obtained with only a few dozen updates of the velocity distribution function, dramatically reducing computational cost compared with conventional methods.
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Physics and Society

The planar Lanchester model of insurgent warfare: Intricate Collateral Damage Functions and Global Bifurcation

Rouzbeh Aghaieebeiklavasani and Gholam Reza Rokni Lamouki

One of the most notable aspects of mathematical modeling is that it sheds light on the complexities arising from changes in parameters and their real-world implications, thus gaining better insight into the dynamics of economic, political, and security phenomena. Moreover, modifications to mathematical modeling will set the stage for embedding new features into the system and enriching the assessment of our analysis. In the case of the Lanchester model of warfare, the introduction of the collateral damage function was an attempt to turn the model into a more realistic framework for understanding counter-insurgencies and irregular battles. In this article, we focus on addressing the impact of sophisticated collateral damage functions in a modified Lanchester model of combat, which reflects the multifaceted nature of warfare. This analysis shows the possibility of global bifurcations and their repercussions for the combat situation between two players. Finally, we assess the codimension two bifurcation analysis of our system based on the interaction between collateral damage and the effectiveness of targeting insurgents.
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Applied Physics · Cross-listed from cond-mat.soft

Atomistic Framework for Glassy Polymer Viscoelasticity Across Twenty Frequency Decades

Ankit Singh, Vinay Vaibhav, Caterina Czibula et al. (+6 more)

Glassy polymers are central to engineering applications, yet their viscoelastic response over broad frequency and temperature ranges remains difficult to characterize. We extend non-affine deformation theory by incorporating a time-dependent memory kernel within the Generalized Langevin Equation for atomistic non-affine motions, yielding frequency-dependent mechanical response. Applied to poly(methyl methacrylate) (PMMA), the method captures the shear modulus and relaxation spectrum across more than twenty decades in frequency, from hundreds of terahertz to the millihertz regime, thus bridging polymer mechanics from ordinary to extreme scales. Our predictions show quantitative consistency with independent estimates from oscillatory-shear molecular dynamics, Brillouin scattering, ultrasonic spectroscopy, Split-Hopkinson testing, and dynamic mechanical analysis (DMA), demonstrating a unified theoretical-computational route for multiscale characterization of polymer glasses.
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All authors Ankit Singh, Vinay Vaibhav, Caterina Czibula, Astrid Macher, Petra Christoefl, Karin Bartl, Gregor Trimmel, Timothy W. Sirk, Alessio Zaccone

Optics

High-Spatial-Resolution Optical Correlation-Domain Reflectometry with 100-km Measurement Range

Takaki Kiyozumi, Soshi Yoshida, Yuta Higa et al. (+4 more)

In the maintenance of optical fiber networks, there is a growing demand for high-precision measurement of optical loss distribution and fault locations over long distances. In this study, we propose an OCDR method incorporating periodic pseudo-random modulation (PPRM), and demonstrate that it enables the acquisition of loss distribution based on Rayleigh scattering and the positions of reflection points in an approximately 100-km optical fiber, with a spatial resolution of about 19 cm and a measurement time of about 20 seconds.
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All authors Takaki Kiyozumi, Soshi Yoshida, Yuta Higa, Keisuke Motoda, Sze Yun Set, Shinji Yamashita, Yosuke Mizuno