Physics Education
A Reproducible Inertia Dynamometer for Vocational Schools: Design, Fabrication, and Pedagogy
Stylianos A. Tsilioukas
We report the design, construction, and classroom use of an inertia dynamometer, built as a year-long project-based learning (PBL) activity with adult students at a Greek Evening Vocational High School (EPAL). The apparatus consists of a machined steel drum of calculated moment of inertia $I = 0.6507~\mathrm{kg\,m^2}$, mounted on a student-welded frame and instrumented with a green-
laser / light-dependent resistor (LDR) optical interrupter. The analogue output is sampled at 44.1\,kHz by the microphone input of a laptop computer, which is used as an opportunistic analogue-to-digital converter; torque and power curves are then reconstructed in software from the inter-pulse intervals via $\tau = I\alpha$ and $P = \tau\omega$. The drum's moment of inertia is cross-checked by an inclined-plane rolling experiment. A wide-open-throttle test with a 50\,cc scooter reproduces the expected flat-power / falling-torque signature of a continuously variable transmission in the low-to-moderate RPM range; the LDR's millisecond-scale recovery time imposes an upper bandwidth limit that provides an unplanned but pedagogically rich lesson in sensor physics. The project integrated industrial-lathe fabrication, arc welding, analogue electronics, and numerical differentiation into a single coherent workflow. We describe the apparatus, the physics, the signal-processing pipeline (for which MATLAB and Python/Octave code are provided as supplementary material), and reflect on the pedagogical outcomes for a student population traditionally disengaged from abstract physics.
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Fluid Dynamics
Drag penalty during relaminarization and Kelvin-Helmholtz-promoted retransition in an accelerating turbulent boundary layer over initially drag-reducing riblets
Benjamin Savino and Wen Wu
Direct numerical simulations of an accelerating turbulent boundary layer (TBL) over a smooth wall and a wall fully covered with streamwise-aligned riblets are performed to investigate drag modulation and its underlying mechanisms. The riblet-scale flow is resolved using an immersed boundary method. Starting from a zero-pressure-gradient (ZPG) TBL at Re=6800, the flow undergoes a threefold freestream acceleration over seventy-five boundary-layer thicknesses, matching the development reported by Warnack and Fernholz (1998), and consequently experiences relaminarization followed by retransition farther downstream. The riblets, defined by a sinusoidal spanwise profile with initial s+=15.2 and lg+=10.5, correspond to near-optimal drag-reducing size in ZPG flows. However, even modest acceleration renders them drag-increasing, showing that the conventional ZPG interpretation based on total-drag viscous scaling does not apply directly in this non-
equilibrium flow. During relaminarization, the drag penalty arises primarily from geometry-determined concentration of viscous shear near the riblet crest, with negligible direct Reynolds- and dispersive-stress contributions prior to retransition. Despite the drag increase, the overlying TBL remains statistically similar to the smooth-wall case when scaled with the total shear stress at the groove opening, demonstrating that this shear sets the relevant scaling for the TBL, while the additional drag generated within the grooves remains largely decoupled from the outer-layer
turbulence dynamics. This partial decoupling persists until the onset of retransition, when spanwise Kelvin-Helmholtz rollers develop near the riblet crest and promote earlier, stronger retransition through their interaction with the residual near-wall streaks. These findings provide a revised physical picture of riblet performance in non-
equilibrium turbulent flows.
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Biological Physics
Wave physics as a choreographic notation for partner dance
Fernando Ramiro-Manzano
The wave is considered a paradigm in dance and connects bodily expression with nature. Although wave concepts such as propagation and phase have proven to be powerful tools for dance analysis, many aspects of bodily expression, including partner dance, have been investigated using numerical approaches and neural networks. Complementarily, compact analytical models have been especially successful for describing human motion, particularly gait. Here, we show that expressive-motion trajectories in partner dance can be reconstructed using physically interpretable analytical wave and oscillator models. We apply this framework to Bachata Sensual, a dance style in which the wave is the leitmotif. We analyse three couples (Phase I) performing five fundamental sequences and one composite. The sequences exhibit multiple wave-related phenomena, including time-dependent interference and harmonic-like modal relations. As an illustrative acoustic analogy, coupled modes with a frequency ratio remarkably close to 3:1, analogous to a harmonic relation, can be mapped to audible frequencies, forming musical dyads. Not rigidly constrained by body morphology, modal response can be tuned to underpin fluid motion, adapting across musical timescales and movement patterns. Overall, within this wave-physics perspective, the formalism can be viewed as a choreographic motion notation highlighting connections between partner-dance expressivity and harmonic nature.
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Optics · Cross-listed from quant-ph
A Universal Quantum Information Preserving Photonic Switch for Scalable Quantum Networks
Jiapeng Zhao, Stéphane Vinet, Amir Minoofar et al. (+6 more)
Quantum networks are a keystone of the
quantum internet. However, existing implementations remain largely confined to static point-to-point links due to the absence of a switching paradigm capable of dynamically routing fragile
quantum entanglement without introducing decoherence. Here, we propose the Universal
Quantum Switch, a foundational building block allowing on-demand, non-blocking, and encoding-agnostic routing of
quantum information, as well as seamless modality conversion between disparate
quantum platforms. We develop a prototype in thin-film lithium niobate and experimentally demonstrate robust switching with $\le 4\%$ decoherence via thermo-optic modulation and high-speed electro-optic switching of arbitrary entangled states at 1 MHz. Moreover, we show that our platform can support reconfiguration speeds up to 1 GHz. To our knowledge, this work represents the first demonstration of multi-node dynamic
entanglement distribution at these speeds. Complementing these experimental results, we project the architecture's scalability, showing dimension-independent decoherence, and provide a scalable, interoperable building block for heterogeneous
quantum network fabrics.
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All authors Jiapeng Zhao, Stéphane Vinet, Amir Minoofar, Michael Kilzer, Lucas Wang, Galan Moody, Vijoy Pandey, Ramana Kompella, Reza Nejabati
Plasma Physics
Ion Channel Dynamics in Temperature-Dependent Weibel Instability Saturation
Vivek Shrivastav, Mani K Chettri, Hemam D. Singh et al. (+2 more)
We present 1X2V continuum Vlasov-Maxwell simulations of interpenetrating
plasma beams with mobile ions. While the early-time
evolution is similar to the stationary-ion case, the late-time dynamics are dominated by the ion-Weibel instability. As ion channels merge, the
magnetic energy increases and the
magnetic structures extend further along the beam direction. Electrons rapidly reach thermal
equilibrium, whereas ions retain distinct bulk velocities for much longer and thermalize more slowly. These results are relevant to collisionless shock formation in astrophysical compact objects and
laser-
plasma experiments. Wind/SWE observations place all four simulated cases in the firehose/Weibel-unstable region of the
proton temperature anisotropy diagram, and MMS1 observations of a quasi-perpendicular bow shock ($\theta_{Bn}\approx83^\circ$, $M_A\approx27$) show a qualitatively similar electron-ion thermalization disparity.
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All authors Vivek Shrivastav, Mani K Chettri, Hemam D. Singh, Britan Singh, Rupak Mukherjee
Physics Education
Locating acts of mechanistic reasoning in student team conversations with mechanistic machine learning
Kaitlin Gili, Mainak Nistala, Kristen Wendell et al. (+1 more)
STEM education researchers are often interested in identifying moments of students' mechanistic
reasoning for deeper analysis, but have limited capacity to search through many team conversation transcripts to find segments with a high concentration of such
reasoning. We offer a solution in the form of an interpretable machine learning model that outputs time-varying probabilities that individual students are engaging in acts of mechanistic
reasoning, leveraging evidence from their own utterances as well as contributions from the rest of the group. Using the toolkit of intentionally-designed probabilistic models, we introduce a specific inductive bias that steers the probabilistic dynamics toward desired, domain-aligned behavior. Experiments compare trained models with and without the inductive bias components, investigating whether their presence improves the desired model behavior on transcripts involving never-before-seen students and a novel discussion context. Our results show that the inductive bias improves generalization – supporting the claim that interpretability is built into the model for this task rather than imposed post hoc. We conclude with practical recommendations for STEM education researchers seeking to adopt the tool and for ML researchers aiming to extend the model's design. Overall, we hope this work encourages the development of mechanistically interpretable models that are understandable and controllable for both end users and model designers in STEM education research.
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All authors Kaitlin Gili, Mainak Nistala, Kristen Wendell, Michael C. Hughes
Fluid Dynamics
Meshless $h$-adaptive Solution for non-Newtonian Natural Convection in a Differentially Heated Cavity
Miha Rot and Gregor Kosec
One of the main challenges in numerically solving partial differential equations is finding a discretisation for the computational domain that balances the accurate representation of the underlying field with computational efficiency. Meshless methods approximate differential operators based on the values of the field in computational nodes, offering a natural approach to adaptivity. The density of computational nodes can either be increased to enhance accuracy or decreased to reduce the number of numerical operations, depending on the properties of the intermediate solution. In this paper, we utilise an adaptive discretisation approach for the numerical simulation of natural convection in non-Newtonian fluid flow. The shear-thinning behaviour is interesting both due to its numerous occurrences in nature, blood being a
prime example, and due to its properties, as the decreasing viscosity with increasing shear rate results in sharper flow structures. We focus on the de Vahl Davis test case, a natural convection driven flow in a differentially heated rectangular cavity. The thin boundary layer flow along the vertical boundaries makes this an ideal test case for refinement. We demonstrate that adaptively refining the node density enhances computational efficiency and examine how the parameters for adaptive refinement affect the solution.
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Accelerator Physics
Orbital angular momentum radiation and polarization of relativistic electrons in magnetic fields
Ziqiang Huang, Qi Meng, Xuan Liu et al. (+6 more)
While
spin polarization from synchrotron radiation is well established, the polarization of orbital angular momentum (OAM) in such radiative processes remains elusive. We study radiation and polarization of relativistic electrons in a uniform
magnetic field, focusing on OAM polarization radiation for
vortex electrons which carry intrinsic OAM. The results illustrate that transition rates are asymmetric in the low-
photon-energy regime, favoring OAM decrease, analogous to the
spin-flip asymmetry in the Sokolov-Ternov effect. Under these conditions, synchrotron radiation can polarize the OAM. The characteristic relaxation time and stationary-state OAM distribution are obtained analytically. The polarization of
spin about \(\mathcal{P}_{\text{spin}}\) reaches \(92.38\%\), while that of \(\mathcal{P}_{\text{OAM}}\) can even approach almost unity for a large OAM; however, their polarization behaviors are different. For typical storage ring parameters, the OAM polarization time is orders of magnitude shorter than the
spin polarization time. Thus, synchrotron radiation offers a mechanism for controlling
vortex electron beams which carry OAM for high-energy accelerator applications.
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All authors Ziqiang Huang, Qi Meng, Xuan Liu, Wei Ma, Zhen Yang, Liang Lu, Alexander J. Silenko, Pengming Zhang, Liping Zou
Physics and Society
Generative artificial intelligence reduces social welfare through model collapse
Fabian Baumann, Erol Akçay and Joshua B. Plotkin
Generative artificial intelligence (genAI) is rapidly reshaping how knowledge and culture are produced and consumed. Yet generative models are vulnerable to model collapse: when trained on data generated by earlier versions of themselves, their outputs can lose diversity and accuracy. This creates a social dilemma, because delegating tasks to genAI can be individually beneficial in the short term even as widespread adoption degrades future model performance. Here we develop a parsimonious model of behavior in collaborative interactions in which individuals can either exert human effort, rely on genAI, or refrain from work altogether. The welfare consequences of genAI are organized by a simple two-dimensional taxonomy: the strength of the incentive to perform the task without AI, and the severity of model collapse. Within this framework, the introduction of genAI – while initially beneficial at the individual level – will reduce social welfare for the most important types of tasks. In addition, habit formation around genAI use can couple otherwise separate domains, so that adoption in low-stakes tasks spills over into high-value tasks and amplifies welfare losses. Together, these results identify a general pathway by which, in the absence of intervention, individually rational adoption of genAI will assuredly and profoundly reduce collective welfare.
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Accelerator Physics · Cross-listed from astro-ph.IM
Broad-band High-Energy Resolution Hard X-ray Spectroscopy using Transition Edge Sensors at SPring-8
Shinya Yamada, Yuto Ichinohe, Hideyuki Tatsuno et al. (+39 more)
We have succeeded in operating a transition-edge sensor (TES) spectrometer and evaluating its performance at the SPring-8 synchrotron X-ray light source. The TES spectrometer consists of a 240 pixel National Institute of Standards and Technology (NIST) TES system, and 220 pixels are operated simultaneously with an energy resolution of $4$~eV at 6~keV at a rate of about 1~c/s/pixel. The tolerance for high count rates is evaluated in terms of energy resolution and live time fraction, leading to an empirical compromise of about 2 x 10^3 c/s/all pixels with an energy resolution of 5 eV at 6 keV. By utilizing the TES's wide-band spectroscopic capability, simultaneous multi-element analysis is demonstrated for a standard sample. We conducted X-ray absorption near-edge structure (XANES) analysis in fluorescence mode using the TES spectrometer. The excellent energy resolution of the TES enabled us to detect weak fluorescence lines from dilute samples and trace elements that have previously been difficult to resolve due to the nearly overlapping emission lines of other dominant elements. The neighboring lines of As K alpha and Pb L alpha2 of the standard sample were clearly resolved and the XANES of Pb L alpha2 was obtained. Moreover, the X-ray spectrum from the small amount of Fe in aerosols was distinguished from the spectrum of a blank target, which helps us to understand the targets and the environment. These results are the first important step for the application of high resolution TES-based spectroscopy at hard X-ray synchrotron facilities.
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All authors Shinya Yamada, Yuto Ichinohe, Hideyuki Tatsuno, Ryota Hayakawa, Hirotaka Suda, Takaya Ohashi, Yoshitaka Ishisaki, Tomoya Uruga, Oki Sekizawa, Kiyofumi Nitta, Yoshio Takahashi, Takaaki Itai, Hiroki Suga, Makoto Nagasawa, Masato Tanaka, Minako Kurisu, Tadashi Hashimoto, Douglas Bennett, Ed Denison, William Doriese, Malcolm Durkin, Joseph Fowler, Galen O'Neil, Kelsey Morgan, Dan Schmidt, Daniel Swetz, Joel Ullom, Leila Vale, Shinji Okada, Takuma Okumura, Toshiyuki Azuma, Toru Tamagawa, Tadaaki Isobe, Satoshi Kohjiro, Hirofumi Noda, Keigo Tanaka, Akimichi Taguchi, Yuki Imai, Kosuke Sato, Tasuku Hayashi, Teruhiko Kashiwabara, Kohei Sakata
Biological Physics · Cross-listed from q-bio.NC
Hierarchical organization of critical brain dynamics
Gustavo G. Cambrainha, Daniel M. Castro, Leonardo L. Gollo et al. (+2 more)
The hierarchical organization of the
brain is a fundamental structural principle, while
brain criticality is a leading hypothesis for its collective dynamics. However, the connection between structure and signatures of criticality remains an open question. Here, we address this issue by applying phenomenological renormalization group approaches to large-scale neuronal spiking activity from the mouse visual cortex and hippocampus. We find that signatures of criticality are not uniform, but instead vary systematically along the known anatomical hierarchy in both
brain systems. Strikingly, the direction along this gradient is inconsistent across different criticality exponents, revealing a nontrivial, measure-dependent organization: exponents based on static properties point to a gradient in one direction, while the exponent based on dynamic properties points in the opposite direction. Moreover, the signatures across the visual system are strongly modulated by the engagement in a visual task. We show that the correlations among criticality markers of different
brain regions during active engagement are sufficient to reconstruct the anatomical hierarchy from the dynamics. Scaling exponents closely follow a theoretically predicted scaling relation among them, and covary with the hierarchical position. Our findings provide a direct link between the collective dynamics of
neurons and the macroscopic architecture of the
brain.
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All authors Gustavo G. Cambrainha, Daniel M. Castro, Leonardo L. Gollo, Pedro V. Carelli, Mauro Copelli
History and Philosophy of Physics · Cross-listed from quant-ph
The clock ambiguity problem: extended or extinguished?
Ovidiu Cristinel Stoica
I show that the clock ambiguity cannot be solved by a purely relational condition like the noninteraction condition, and it is even stronger, extending to
evolution laws. The ambiguity is solved by specifying the physical meaning of observables. Page and Wootters (1983) showed how time and dynamics can emerge from
entanglement within a stationary
quantum system containing a clock. The clock ambiguity problem is that, from a purely relational stance and without fixing a clock-world split, the emergence is ambiguous, resulting in any possible history (Albrecht 1995). I show that the ambiguity is stronger than previously recognized. Under the relational stance, it extends from histories to the
evolution laws themselves. The spectrum of any ideal clock uniformizes the spectra of the world's
evolution operators, leaving only the dimension of the
Hilbert spaces as invariant information. Fixing the clock-world split can solve the ambiguity, but this would block spacetime
symmetries. One might want to remove the ambiguity up to a
unitary equivalence by imposing noninteraction, as in Marletto and Vedral (2017). But once the clock spectrum uniformizes the world spectrum,
unitary equivalence becomes too coarse to distinguish any two possible world dynamics, which is the result proved here. Thus a purely relational condition such as noninteraction is insufficient. Nor can all different decompositions be regarded as equally valid perspectives, since then records would not be correlated with the events they record, and empirical knowledge would be impossible. The resolution is therefore not to embrace the ambiguity, but to recognize what a bare reading of the Page-Wootters structure omits: the physical meaning of the operators.
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Instrumentation and Detectors
Agentic AI Detector Co-design and Optimization in Vertically Integrated Differentiable Full Simulations
Wonyong Chung, Qibin Liu, Liangyu Wu et al. (+1 more)
We present the first implementation of AI
agents into the design and optimization of detectors in high-energy physics experiments via a bi-level optimization framework that vertically integrates detector geometry, front-end digitization, and high-level reconstruction
algorithm parameters in differentiable full simulations. Using the example of a dual-readout, segmented
crystal EM calorimeter with a baseline resolution of $3\%/\sqrt{E}$, we investigate the capabilities and value propositions of AI
agents in the identification and reduction of key detector parameters and in the nonlinear traversal of design space. We find that frontier LLM
reasoning-models today, without being given additional experiment-specific context, are able to effectively execute complex workflows and proactively suggest generic but relevant avenues for further study or improvement. Here, we demonstrate an AI
agent's ability to find an optimal design point amidst three competing performance criteria, showing that effective integration of
agents into the complex workflows of frontier research areas can yield higher performance for key physics goals while reducing labor and compute. This study establishes the foundation for a future demonstration of the first fully AI-designed detector for future scientific facilities.
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All authors Wonyong Chung, Qibin Liu, Liangyu Wu, Julia Gonski
Fluid Dynamics
Turbulent mixing of a hydrogen jet in crossflow: direct numerical simulation and model assessment
Yiqing Wang, Chao Xu, Riccardo Scarcelli et al. (+3 more)
A numerical study for a hydrogen (H2) jet in an air crossflow (JICF) was performed using direct numerical simulation (DNS), large eddy simulation (LES), and Reynolds-averaged Navier-Stokes (RANS) approaches, based on a geometry representative of key aspects of port fuel injection (PFI) in a H2-fueled heavy-duty internal combustion engine. The focus was placed on the H2 mixing process and the turbulent species flux model used in the latter two approaches. Based on the DNS data, the performance of LES and RANS on predicting the turbulent flow fields and mixing process was comprehensively evaluated. Results showed that LES performs very well in predicting both the mean velocity and the Reynolds stress. In contrast, RANS significantly under-predicts all Reynolds stress components, while predicting the mean flow field relatively well. Regarding the H2 mixing prediction, LES shows an excellent agreement with DNS, while RANS significantly under-predicts the mixing process. The underlying reasons for the poor performance of RANS were identified by extracting turbulent transport properties used in RANS approach from DNS data. It was found that the turbulent diffusivity used in RANS is much smaller than that derived from DNS, which is attributed to the over-prediction on turbulent Schmidt number (Sct), as well as the under-prediction on turbulent viscosity. By further analyzing the anisotropic components of Sct and the misalignment angle between turbulent species fluxes directly obtained from DNS and those predicted by the RANS mixing model, the commonly used assumption of isotropic turbulent diffusivity in RANS was demonstrated to be invalid for the present configuration. This study provided a unique DNS
dataset for H2 jet in a crossflow relevant to H2 PFI engines and generated new insights on improved modeling of turbulent mixing.
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All authors Yiqing Wang, Chao Xu, Riccardo Scarcelli, Ben Cantrell, Jon Anders, Sameera Wijeyakulasuriya
Computational Physics · Cross-listed from cs.MA
Agentic AI-Enabled Framework for Thermal Comfort and Building Energy Assessment in Tropical Urban Neighborhoods
Po-Yen Lai, Xinyu Yang, Derrick Low et al. (+2 more)
In response to the urban heat island effects and building energy demands in Singapore, this study proposes an agentic AI-enabled
reasoning framework that integrates large
language models (LLMs) with lightweight physics-based models. Through
prompt customization, the LLMs interpret urban design tasks, extract relevant policies, and activate appropriate physics-based models for evaluation, forming a closed-loop
reasoning-action process. These lightweight physics-based models leverage core thermal and airflow principles, streamlining conventional models to reduce computational time while predicting microclimate variables, such as building surface temperature, ground radiant heat, and airflow conditions, thereby enabling the estimation of thermal comfort indices, e.g., physiological equivalent temperature (PET), and building energy usage. This framework allows users to explore a variety of
climate-resilient building surface strategies, e.g., green façades and cool paint applications, that improve thermal comfort while reducing wall heat gain and energy demand. By combining the autonomous
reasoning capacity of LLMs with the rapid quantitative evaluation of lightweight physics-based models, the proposed system demonstrates potential for cross-disciplinary applications in sustainable urban design, indoor-outdoor environmental integration, and
climate adaptation planning. The source code and data used in this study are available at: https://github.com/PgUpDn/urban-cooling-
agent.
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All authors Po-Yen Lai, Xinyu Yang, Derrick Low, Huizhe Liu, Jian Cheng Wong
Fluid Dynamics
Exact formulas for arbitrary order velocity-gradient moments in isotropic turbulence
Tong Wu, Chensheng Luo, Le Fang et al. (+1 more)
Statistical moments of velocity gradients provide fundamental information on the small-scale properties of
turbulence. In this work, we propose a systematic method to derive exact expressions for statistical moments of arbitrary order for both longitudinal and transverse velocity gradients in isotropic
turbulence. The approach is applicable to both compressible and incompressible flows and expresses the moments in terms of invariants of the velocity gradient tensor. The derivation combines isotropic tensor theory, orientational averaging, and an algorithmic implementation, enabling the computation of high-order moments in a unified framework. We show that longitudinal velocity gradient moments of order higher than three depend not only on $\mathrm{tr}(\boldsymbol{S}^2)$, which is proportional to the dissipation rate, but also on $\mathrm{tr}(\boldsymbol{S}^3)$, which reflects strain self-amplification, where $\boldsymbol{S}$ denotes the strain-rate tensor. The resulting theoretical expressions are validated through comparisons with existing theoretical results and direct numerical simulations.
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All authors Tong Wu, Chensheng Luo, Le Fang, Michael Wilczek
Computational Physics · Cross-listed from cond-mat.mtrl-sci
Nearly Complete Charge–Spin Conversion via Strain-Eliminated Fermi Pockets in $d$-Wave Altermagnets
Wancheng Zhang, Fangqi Liu, Yong Liu et al. (+4 more)
Ideal $d$-wave altermagnets with nearly orthogonal flat
Fermi surfaces enable complete
spin-channel separation and 100% theoretical charge–
spin conversion efficiency (CSE). The metallic altermagnet $\mathrm{KV_2Se_2O}$ exemplifies this, but realistic samples host residual elliptical
Fermi pockets that enhance charge conductivity while suppressing
spin conductivity, drastically reducing CSE. Here we show that in-plane equibiaxial tensile strain systematically eliminates these parasitic pockets, restoring the flat-band geometry. Our first-principles calculations reveal that CSE increases monotonically with strain, reaching a record $\sim$96% at 4% strain. An effective tight-binding model confirms that pocket suppression, governed by reduced next-nearest-neighbor hoppings, is the dominant mechanism. We further identify an unconventional out-of-plane
spin current component with CSE $\sim$55% at optimal orientations, enabling field-free perpendicular magnetization switching. Moreover, the same strain-driven removal of parasitic pockets yields
giant TMR enhancement in $\mathrm{KV_2Se_2O}$-based
magnetic tunnel junctions, from $10^{5}%$ to $10^{9}%$, and the
giant TMR persists over a wide energy window near the
Fermi level. These findings establish strain engineering as a clean, widely applicable strategy to maximize CSE and magnetoresistance in $d$-wave altermagnets, and provide predictive descriptors for screening high-efficiency spintronic materials.
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All authors Wancheng Zhang, Fangqi Liu, Yong Liu, Jingguo Hu, Zhihong Lu, Rui Xiong, Zhenhua Zhang
Chemical Physics
Molecular dynamics simulations of Nafion thin films at a platinum catalyst surface: Correlating structure with charging behaviour
Dustin Vivod, Binny A. Davis, Tobias Binninger et al. (+1 more)
Electrocatalysis is greatly influenced by the local reaction environment, which is governed by the structure of the catalyst, the distribution of the electrolyte, and the local electric field. In catalytic systems comprised of complex molecular species like ionomers, the distribution of electrolyte can vary substantially, resulting in divers local reaction environments. In order to gain atom-scale insight into this micro-environment we construct a model system consisting of a platinum surface, varying levels of water, and a Nafion thin film and conduct molecular dynamics simulations. We employ a construction based on Voronoi tesselation to assemble a dense film of ionomer that fully covers the platinum substrate. An energy analysis reveals that water film configurations with thickness of less then 1.3 nm are stable. Simulations with charged platinum surfaces are analysed in view of electrostatic conditions and differential capacitance of the interface configuration. Trends observed in these properties can be interpreted in view of the crowding of hydronium ions or the Nafion film at the platinum surface. The presented workflow can be easily applied to investigate novel ionomers for use in PEMFCs.
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All authors Dustin Vivod, Binny A. Davis, Tobias Binninger, Michael Eikerling
Computational Physics · Cross-listed from cs.LG
Transferable Physics-Informed Representations via Closed-Form Head Adaptation
Jian Cheng Wong, Isaac Yin Chung Lai, Pao-Hsiung Chiu et al. (+3 more)
Physics-informed neural networks (PINNs) have garnered significant interest for their potential in solving partial differential equations (PDEs) that govern a wide range of physical phenomena. By incorporating physical laws into the learning process, PINN models have demonstrated the ability to learn physical outcomes reasonably well. However, current PINN approaches struggle to predict or solve new PDEs effectively when there is a lack of training examples, indicating they do not generalize well to unseen problem instances. In this paper, we present a transferable learning approach for PINNs premised on a fast Pseudoinverse PINN framework (Pi-PINN). Pi-PINN learns a transferable physics-informed representation in a shared
embedding space and enables rapid solving of both known and unknown PDE instances via closed-form head adaptation using a least-squares-optimal pseudoinverse under PDE constraints. We further investigate the synergies between data-driven multi-task learning loss and physics-informed loss, providing insights into the design of more performant PINNs. We demonstrate the effectiveness of Pi-PINN on various PDE problems, including Poisson's equation, Helmholtz equation, and Burgers' equation, achieving fast and accurate physics-informed solutions without requiring any data for unseen instances. Pi-PINN can produce predictions 100-1000 times faster than a typical PINN, while producing predictions with 10-100 times lower relative error than a typical data-driven model even with only two training samples. Overall, our findings highlight the potential of transferable representations with closed-form head adaptation to enhance the efficiency and generalization of PINNs across PDE families and scientific and engineering applications.
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All authors Jian Cheng Wong, Isaac Yin Chung Lai, Pao-Hsiung Chiu, Chin Chun Ooi, Abhishek Gupta, Yew-Soon Ong
Medical Physics
OptoCENTAL: a standardised, bench-testing platform based on phantoms for validating optical systems aimed at clinical monitoring of the placenta
Luca Giannoni, Uzair Hakim, Fréderic Lange et al. (+6 more)
Optical imaging and spectroscopy solutions, such as near-infrared spectroscopy (NIRS) and diffuse optical
tomography (DOT), have the potential to provide compact, bedside monitoring of the placenta in the clinic, thanks to recent advancements in miniaturisation and wireless wearability. This would provide neonatologist with continuous assessment of the pregnancy status in real-time, as well as tools to possibly predict delivery outcomes. We present here OptoCENTAL, a standardized platform based on multiple optical phantoms, from digital, through solid to
liquid, for a comprehensive bench-testing, characterisation and validation of any photonics solution and instrumentation that aims at in vivo, clinical monitoring of the human placenta. Results: Exemplary applications of the OptoCENTAL platform on different types of optical systems, from wearable, continuous-wave devices to broadband and time-domain NIRS systems, demonstrate the flexibility of its procedures to be implemented with any setup, allowing users to compare performances across different solutions. The results also show the capability of OptoCENTAL to provide quantitative assessment of the major features required by any photonic solution for providing effective and efficient monitoring of the placenta, including basic instrument performances, quantification of monitoring accuracy, as well as depth sensitivity. OptoCENTAL represent the first-of-a-kind effort in standardising bench-testing and validation of optical imaging and spectroscopy methods in the framework of placental clinical applications, further advancing the translation of such modalities into the hospitals, as well as towards future certification and commercialisation of such technologies.
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All authors Luca Giannoni, Uzair Hakim, Fréderic Lange, Musa Talati, Darshana Gopal, Angelos Artemiou, Niccole Ranaei-Zamani, Subhabrata Mitra, Ilias Tachtsidis
Optics
High-speed hyperspectral 3D ghost imaging LiDAR
Jing Hu, Wending Huang, Tianjian Lv et al. (+7 more)
Light detection and ranging (
LiDAR) is widely used in autonomous systems and industrial metrology; however, the simultaneous acquisition of three-dimensional (3D) structure and broadband spectral information remains challenging, as conventional hyperspectral
LiDAR relies on wavelength-scanning or spectrometer-based detection that limits speed. Here, we demonstrate a hyperspectral 3D ghost imaging
LiDAR that eliminates these bottlenecks. By combining a
stochastic broadband
laser with single-pixel detection, and integrating spatiotemporal encoding with spectral ghost imaging in a time-of-flight framework, the system enables pulse-resolved recovery of spatial and spectral information. Consequently, we achieve a line-scanning rate of 60.5 MHz (point rate 1.8 GHz) and a ranging precision of 0.02 mm within a 10 {\mu}s integration time. Each voxel contains a 1.4 nm resolution spectrum over 1100-1250 nm, enabling simultaneous 3D imaging and chemical identification. This approach provides a route to high-speed hyperspectral
LiDAR for environmental monitoring, precision agriculture, and industrial inspection.
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All authors Jing Hu, Wending Huang, Tianjian Lv, Ming Yan, Zhaoyang Wen, Zijian Wang, Yuan Chen, Zhuoren Wan, Mei Yang, Heping Zeng
Optics
Real-Space Imaging of Guided Exciton Polaritons in Free-standing Monolayer WSe2
Manuka Suriyage, Hao Qin, Xueqian Sun et al. (+5 more)
Monolayers of transition metal dichalcogenides (TMDCs), known for their strong excitonic states with high binding energies in the visible spectrum at room temperature, offer great potential for polariton-driven devices. While polariton guided modes in bulk TMDCs have been reported the real space experimental observation of 2D
exciton-polariton guided modes in a monolayer remains challenging due to various mode cut-off conditions that arise as the TMDC layer becomes thinner, including cut-off frequency, mode confinement and boundary conditions. Here using scanning near-field optical microscopy (s-SNOM), we directly visualized the real-space propagation of these guided modes for the first time in an angstrom-thick, suspended monolayer of WSe2. Through numerical simulations we have also validated that the guided mode can only exist in a monolayer WSe2 when symmetric cladding conditions are closely applied. By tuning the excitation
laser energy and analysing the guided mode distribution, we observed a pronounced back-bending dispersion around the A
exciton, indicating strong light-matter interactions, and confirmed the existence of the fundamental TE0
exciton polariton (EP) propagation mode. The unique dispersion characteristics of these modes were further validated through theoretical modelling of the mode in free-standing monolayer WSe2. Our findings provide crucial experimental evidence of guided mode EPs in atomically thin TMDCs, opening new possibilities for nanoscale photonic applications.
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All authors Manuka Suriyage, Hao Qin, Xueqian Sun, Wenkai Yang, Shuyao Qiu, Qingyi Zhou, Zongfu Yu, Yuerui Lu
Atomic Physics · Cross-listed from quant-ph
Entanglement of two optical emitters mediated by a terahertz channel
Yanis Le Fur, Diego Martín-Cano and Carlos Sánchez Muñoz
Quantum technologies in the terahertz (THz) require a coherent interface between addressable
qubits and THz
quantum channels – a capacity that so far, remains largely underdeveloped. Here, we propose and demonstrate the generation of steady-state
entanglement between polar
quantum emitters, mediated by THz
photons. We exploit strong visible-light driving of the emitters to create Rabi-split dressed eigenstates whose energy separation can be optically tuned into the THz regime. The polar nature of the emitters activates THz transitions within these eigenstates, allowing them to couple to a THz photonic mode that induces collective dissipative dynamics. A coherent driving and control of these effective THz emitters is achieved by using a sideband optical drive with detuning close to the THz transition frequency. The resulting interplay of collective dissipation and driving activates a mechanism to generate steady-state
entanglement with high values of the concurrence ($C>0.9$), attainable under experimentally feasible parameters. Crucially, both coherent manipulation and
quantum state
tomography are implemented entirely through optical means, avoiding direct THz control and detection. This establishes a hybrid visible-THz
quantum interface in which a THz channel mediates
qubit-
qubit entanglement (a key operational requirement for THz
quantum technologies) while remaining optically accessible.
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Plasma Physics
Towards hybrid kinetic/drift-kinetic simulations in 6d Vlasov codes
M. Pelkner, K. Hallatschek and M. Raeth
Simulating fully kinetic, two-species
plasmas is computationally challenging due to the stiff multiscale dynamics of electrons and ions. While enforcing a quasi-neutral time
evolution mitigates this stiffness, it requires an electric potential that consistently maintains this constraint. In this work, we present an implicit approach to determine this electric field self-consistently within the semi-Lagrangian, fully kinetic BSL6D code. We employ a hybrid two-species model that couples kinetic ions with massless, drift-kinetic electrons, enabling an implicit treatment of the latter. Notably, the model captures the generation of ion-scale zonal flows. Beyond the algorithmic description, we provide a proof of second-order time-splitting error convergence under specific regularity assumptions. A key feature of our approach is an error-balancing mechanism: we demonstrate that the field solver achieves the required accuracy of the electric field by automatically adjusting the error of certain moments of the distribution function. Furthermore, we provide a comprehensive analysis of semi-Lagrangian interpolation errors to ensure robustness against the steep density and temperature gradients characteristic of tokamak edge
plasmas.
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Instrumentation and Detectors
Performance characterisation of the Hamamatsu R760 photomultiplier tube for the PLUME detector
A. Bellavista, A. Carbone, V. Chaumat et al. (+5 more)
The Probe for Luminosity Measurement detector is a novel luminometer designed to monitor the luminosity and beam conditions of the Large
Hadron Collider at the interaction point of the LHCb experiment, starting from Run 3. The detector is based on a hodoscope composed of 48 Hamamatsu R760 photomultiplier tubes, which detect the Cherenkov light produced by charged particles originating from the interaction region. The accurate and stable operation of these sensors is essential to ensure reliable luminosity measurements throughout the full data-taking period. This paper presents a detailed characterisation of the photomultiplier tubes currently installed in the detector. In particular, their absolute gain, transit-time drift, linearity, dark current, and ageing behaviour are systematically studied under controlled laboratory conditions. The results provide a comprehensive assessment of the performance of the detection modules and establish the optimal operating conditions required to ensure stable and precise measurements throughout Run 3 and Run 4.
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All authors A. Bellavista, A. Carbone, V. Chaumat, F. Ferrari, T. Nguyen-Trung, V. Puill, L. Toscano, A. Villa
Medical Physics
Flexible Piezoresistive Yarn Sensor for Human Physiological Signal Measurement
Rizal Maulana, Ádám Rák, Sándor Földi et al. (+1 more)
Continuous monitoring of physiological signals is essential for the early detection of health problems. A measurement system that ensures high sensitivity, accuracy, and user comfort is needed. In this study, we designed and optimized a flexible piezoresistive yarn (FPY) sensor to achieve a high sensitivity and wide working range for detecting physiological signals. The representative sensor design was constructed by applying an FPY bonding pattern, utilizing tightly arranged triangular patterns and using minimal FPY. The prototype sensor operates in two measurement modes, strain and pressure, and was evaluated for measuring neck motion, finger bending, respiratory signals, and arterial blood pressure (ABP) waveforms. A qualitative evaluation, performed by comparing the characteristics of the measurement results of each physiological signal with those from related studies, indicates a high similarity in its morphological characteristics. Then, a quantitative evaluation through baseline drift analysis demonstrates that the FPY sensor displays high measurement stability. The ABP waveform measurement shows the most stable baseline, with a mean absolute error (MAE) of $0.0051 \pm 0.0029$ in terms of baseline drift, using normalized values from 0 to 1. Based on our results, the prototype sensor can be used as an innovative solution for physiological signal monitoring and can be further enhanced for personalized healthcare and sports applications.
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All authors Rizal Maulana, Ádám Rák, Sándor Földi, György Cserey
Biological Physics
Shaping nematic order in bacterial films with single-cell resolution patterning
Matthias Le Bec, Guillem Pérez Martín, Cameron Boggon et al. (+7 more)
Bacterial colonies composed of elongated
cells form active nematic fluids that spontaneously self-organise into ordered domains of aligned
cells and exhibit self-generated chaotic flows powered by
cell growth. While their dynamics have attracted significant
attention, the role of initial conditions remains largely unexplored due to a lack of precise patterning methods. Here, we harness the precision of capillary assembly to pattern Bacillus subtilis endospores into arrays with controlled positions and orientations at single-
cell resolution. Upon germination and growth of
cell chains, we quantify the dynamics and morphologies of the resulting bacterial films. While orthogonally seeded spores lead to chaotic dynamics, seeding them with parallel orientations yields films with high nematic order across millimetres, which subsequently synchronously buckle upon further growth. Our observations are captured by numerical simulations and a model that describes the buckling dynamics starting from the mechanical properties of individual filaments. By programming local
cell orientation with single-
cell precision, we finally harness nematic
alignment to create macroscopic bacterial films with local optical anisotropy, via structural colouration and light polarisation. Our findings demonstrate that initial conditions play a key role and offer exciting opportunities to control the spatio-temporal organization of bacterial assemblies towards addressing open biological questions and realizing living materials with tailored properties.
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All authors Matthias Le Bec, Guillem Pérez Martín, Cameron Boggon, Yiyao Hu, Leonardo Puggioni, Rosa Heydenreich, Alexander Mathys, Luca Giomi, Eleonora Secchi, Lucio Isa
Optics
A microwave super-resolution imaging approach towards breast cancer margin mapping
Harry Penketh, Sonal Saxena, Michal Mrnka et al. (+10 more)
Accurate characterisation of margins in excised breast cancer tumours is critical to the success of
surgical interventions. Yet margin status is typically confirmed post-operatively using histopathology. Here we present a microwave single pixel imaging technique designed for use in intraoperative margin assessment. By leveraging the photo-induced change in microwave transparency of a silicon modulator placed under the sample, we map the microwave reflectivity of tissue-mimicking phantoms with deeply sub-wavelength resolution, allowing hydration mapping across large areas (10 x 10 cm) at ~1 mm resolution. We evaluate the discriminatory capability of our method using gelatine-based tumour phantoms with water-content variations designed to mimic the contrast between malignant tissue and tumour margins in resected breast specimens. We demonstrate the capability to identify, locate and quantify inadequate margins up to the typically targeted minimum thickness of 2 mm. Furthermore, using numerical modelling, we show that our approach is expected to be resilient to patient-specific tissue differences. These results establish microwave single-pixel imaging as a promising route towards real-time intraoperative assessment of margins in excised breast tumours.
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All authors Harry Penketh, Sonal Saxena, Michal Mrnka, Cameron P. Gallagher, Caitlin Lloyd, Diksha Garg, Christopher R. Lawrence, Nicholas E. Grant, John D. Murphy, David B. Phillips, Ian R. Hooper, Nick Stone, Euan Hendry
Optics
Hybridization of Kerr Solitons in Coupled Microresonators
Alena Kolesnikova, Ivan Pshenichnyuk and Andrey Gelash
Recent advances in manufacturing photonic integrated devices enable efficient coupling between high-Q microresonators in both linear and nonlinear regimes, creating a tunable, complex, hybridized optical system. Considering two coupled microresonators with normal and anomalous dispersion and equal free spectral range (FSR), we theoretically predict a novel nonlinear phenomenon: fully coherent hybridization of dissipative Kerr
solitons (DKS) and propose a realistic integrated photonic design for its experimental observation. Using the Lugiato-Lefever equations in the supermode basis, we show that the emergent picture of inter-resonator DKS interactions can be understood as the formation of coherent structures in both supermodes generated by an unusual four-wave mixing process. The found hybridized DKS states can exhibit a broad, flat spectral profile near the pumped mode and remarkable oscillatory features in the spectral wings, promising broad applications in the generation and control of optical Kerr frequency combs.
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Chemical Physics
Reaction-Diffusion Processes with Surface Autocatalysis
Denis S. Grebenkov
Autocatalytic processes underlie diverse systems in which replication is triggered at interfaces, including heterogeneous catalysis on solid substrates, enzyme activity at membranes, viral infections, biofilm growth, and spatially structured ecosystems. In a typical scenario, particles move in a bulk medium and interact with surface regions, where they may either disappear or reproduce through branching, splitting or fission. Here, we develop a general theoretical framework to understand such surface-mediated autocatalytic processes. We show that the interplay between loss and replication at surfaces gives rise to rich population dynamics. For this purpose, we derive a renewal-type nonlinear integral equation for the generating function of the population size, providing access to its full probability distribution and statistical moments. We further establish an equivalent description in terms of a Fokker-Planck equation with nonlinear Robin-type boundary conditions that encode surface reactions. Our results identify distinct dynamical regimes and universal scaling laws, and provide a unified framework to predict when surface activity promotes extinction or explosive growth. These findings offer quantitative insight into catalytic efficiency, metabolic regulation, and population persistence in spatially heterogeneous environments.
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