Physics Education
Building Bridges in Quantum Information Science Education: Expert Insights to Guide Framework Development for Interdisciplinary Teaching and Evolution of Common Language
Liam Doyle, Fargol Seifollahi and Chandralekha Singh
The rapid growth of
quantum information science and technology (QIST) presents unique educational challenges as it brings together students and researchers from many disciplines. This work presents findings from in-depth interviews with leading
quantum researchers who are also educators, whose perspectives provide guidance for developing a framework for interdisciplinary QIST teaching and builds on our earlier paper that focused on QIST courses and curricula. We discuss their reflections on three critical aspects of QIST education: (1) the development of a common interdisciplinary
language, (2) determining appropriate levels of abstraction and physical detail for students from various disciplines, and (3) why students should pursue courses, degrees, and careers in this field. Our analysis reveals that the emergence of linguistic evolutions such as "
qubits" and "measurement bases", rather than a focus on measurement of physical observables and their corresponding Hermitian operators, has begun to create a unifying framework that transcends disciplinary boundaries. Nevertheless, educators face ongoing challenges in balancing the level of abstractness with physical details as well as mathematical rigor with conceptual accessibility. The experts emphasize that successful QIST education for an interdisciplinary student body not only requires a shift from traditional
quantum mechanics pedagogy for physics majors, but careful consideration of students' diverse prior conceptual and mathematical foundations. They highlighted that students have the unique historical opportunity to participate in creating transformative
quantum technologies while developing transferable skills for an evolving technological landscape. These findings provide valuable guidance for developing a framework for interdisciplinary QIST teaching especially useful for foundational courses.
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Fluid Dynamics
Particle Image Velocimetry Refinement via Consensus ADMM for Active Fluid Control
Alan Bonomi, Francesco Banelli and Antonio Terpin
Particle Image Velocimetry (PIV) is among the central modalities for measuring flow fields across laboratory, industrial and environmental setting. Traditional PIV approaches typically depend on tuning parameters specific to the imaging setup, making the performance sensitive to variations in illumination, flow conditions, and seeding density. Similarly, state-of-the-art machine learning methods for flow quantification are fragile outside their training set. In our experiments, we observed that flow quantification would improve if different tunings (or
algorithms) were applied to different regions of the same image pair. Motivated by this observation, we thus pose flow quantification as a multi-
estimator fusion problem: several heterogeneous
algorithms process the same image pair in parallel, and their dense flow fields are treated as complementary estimates. To fuse them, we adopt a consensus framework based on the alternating direction method of multipliers, incorporating priors such as smoothness and incompressibility. We perform several numerical experiments to demonstrate the benefits of this approach. For instance, we achieve a decrease in end-point-error of up to 20% of a dense-inverse-search
estimator at an inference rate of 60Hz, and we show how performance can be increased with outlier rejection. Our method is implemented in JAX and integrated into Flow Gym, enabling reproducible comparisons with the state of the art and systematic evaluation across different base
algorithms. Finally, we demonstrate successful deployment of our method in the same real-world active-fluids-control setup of Terpin and D'Andrea [1], where a reinforcement-learning
agent uses our flow estimates to learn to minimize drag (down by 36%) or maximize it (up to 32%) with only two minutes of real-world interaction. Hardware and software are made available at ActiveFluidControl.com.
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Computational Physics
Stable spectral neural operator for learning stiff PDE systems from limited data
Rui Zhang, Han Wan, Yang Liu et al. (+1 more)
Accurate modeling of spatiotemporal dynamics is crucial to understanding complex phenomena across science and engineering. However, this task faces a fundamental challenge when the governing equations are unknown and observational data are sparse. System stiffness, the coupling of multiple time-scales, further exacerbates this problem and hinders long-term prediction. Existing methods fall short: purely data-driven methods demand massive
datasets, whereas physics-aware approaches are constrained by their reliance on known equations and fine-grained time steps. To overcome these limitations, we introduce an equation-free learning framework, namely, the Stable Spectral Neural Operator (SSNO), for modeling stiff partial differential equation (PDE) systems based on limited data. Instead of encoding specific equation terms, SSNO embeds spectrally inspired structures in its architecture, yielding strong inductive biases for learning the underlying physics. It automatically learns local and global spatial interactions in the frequency domain, while handling system stiffness with a robust integrating factor time-stepping scheme. Demonstrated across multiple 2D and 3D
benchmarks in Cartesian and spherical geometries, SSNO achieves prediction errors one to two orders of magnitude lower than leading models. Crucially, it shows remarkable data efficiency, requiring only very few (2–5) training trajectories for robust generalization to out-of-distribution conditions. This work offers a robust and generalizable approach to learning stiff spatiotemporal dynamics from limited data without explicit a priori knowledge of PDE terms.
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All authors Rui Zhang, Han Wan, Yang Liu, Hao Sun
Geophysics
Machine-learned global glacier ice volumes
N. Maffezzoli, E. Rignot, C. Barbante et al. (+3 more)
We present a global
dataset of glacier ice thickness modeled with IceBoost v2.0, a machine learning model trained on 7 million ice thickness measurements and informed by physical and geometrical predictors. We model the distributed ice thickness for every glacier in the two latest Randolph Glacier Inventory releases (v6.0 and v7.0), totaling 215,547 and 274,531 glacier outlines, respectively, plus 955 ice masses contiguous with the Greenland Ice Sheet. We find a global glacier volume of $(149 \pm 38)\times 10^3$ km$^3$, consistent with the previous ensemble estimate of $(147 \pm 28)\times 10^3$ km$^3$. The corresponding sea-level equivalent, $323 \pm 91$ mm, is likewise consistent with the earlier value of $315 \pm 63$ mm. Compared to measurements, IceBoost error is 20-45% lower than the other solutions in the high Arctic, highlighting the value of machine-learning approaches. Confidence in our solution is highest at higher latitudes. Over mountainous terrain, small glaciers, and under-represented lower-latitude regions, confidence is lower. IceBoost v2.0 demonstrates strong generalization at ice sheet margins. On the Geikie Plateau (East Greenland), we find nearly twice as much ice as previously reported, highlighting the method's potential to infer bed topography in parts of the ice sheets. The quality of the solutions depends on the accuracy of the training data, the Digital Elevation Model, ice velocity fields, and glacier geometries, including nunataks. Using the Jensen Gap, we probe the model's
curvature with respect to input errors and find it is strongly concave over low-slope, thick-ice regions, implying a potential downward bias in predicted thickness under input uncertainty. The released
dataset can be used to model future glacier
evolution and sea-level rise, inform the design of glaciological surveys and field campaigns, as well as guide policies on freshwater management.
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All authors N. Maffezzoli, E. Rignot, C. Barbante, M. Morlighem, T. Petersen, S. Vascon
Optics
Arbitrary-order exceptional points in a nanomechanical cavity
Ning Wu, Kaiyu Cui, Ziming Chen et al. (+7 more)
Exceptional points (EPs), non-Hermitian spectral singularities, shape the
evolution dynamics ranging from topological mode control to high-precision sensing. Higher-order EPs unlock enriched and sharpened spectral
topology beyond the minimal second-order counterpart, thus expanding capabilities in those domains. However, the intrinsic topological fragility of higher-order EPs has long hindered their robust experimental realization. Here, we present a scalable architecture that implements arbitrary-order EPs via an optically mediated recurrent network comprising a single nanomechanical resonator and an ensemble of digital synthetic resonators. We experimentally validate this architecture's scalability for both linear and nonlinear mechanical EPs, demonstrating EPs up to the seventh order. Moreover, we reveal that the fundamental noise component, in contrast to some technical-noise contributions, shares the same system coupling channel and undergoes identical root-response amplification as the measured signal. The co-amplification mechanism sets the fundamental sensitivity limit near EPs of any order, as verified by direct signal-to-noise ratio measurements. Our work establishes a general platform for exploring higher-order EP-based phenomena while clarifying the fundamental boundary of non-Hermitian sensitivity enhancement. More broadly, it facilitates a connection between non-Hermitian physics and AI-inspired recurrent architectures across diverse physical systems.
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All authors Ning Wu, Kaiyu Cui, Ziming Chen, Chenxuan Wang, Xue Feng, Fang Liu, Wei Zhang, Hao Sun, Yongzhuo Li, Yidong Huang
Fluid Dynamics
The Persistent Clock of Turbulent Thermal Convection
Lázaro Martínez-Ortíz, Youri H. Lemm, Herman J. H. Clercx et al. (+1 more)
The large-scale circulation (LSC) of turbulent convection is a prominent feature of its dynamics and forms the basis for descriptive theories. We show, using experimental and numerical results from thermal convection in a cylindrical
cell, that the LSC possesses a persistent internal `clock': its pulsating velocity as a function of time is described by a constant value of the parameter U/(lf) where U is the mean velocity, f the pulsation frequency, and l the characteristic length scale. By introducing a narrow sidewall barrier, we can trip the LSC, forming a pair of interconnected rolls stacked above and below the barrier. They independently exhibit the same value for the ratio U/(lf), even for vertically asymmetric pairs, indicating signs of synchrony. Thus, this parameter establishes a direct connection between plume-shedding dynamics and the flow
topology.
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All authors Lázaro Martínez-Ortíz, Youri H. Lemm, Herman J. H. Clercx, Rudie P. J. Kunnen
Biological Physics
Marrying critical oscillators with traveling waves shapes nonlinear sound processing in the cochlea
Henri Ver Hulst, Carles Blanch Mercader, Frank Jülicher et al. (+1 more)
The cochlea's capacity to process a broad range of sound intensities has been linked to nonlinear amplification by critical oscillators. However, while the increasing sensitivity of a critical oscillator upon decreasing the stimulus magnitude comes with proportionally sharper frequency tuning and slower responsiveness – critical slowing down, the observed bandwidth of cochlear frequency tuning and the cochlear response time vary little with sound level. Because the cochlea operates as a distributed system rather than a single critical oscillator, it remains unclear whether criticality can serve as a fundamental principle for cochlear amplification. Here we tackle this challenge by integrating tonopically distributed critical oscillators in a traveling-wave model of the cochlea. Importantly, critical oscillators generically provide spatial buildup of energy gain from energy pumping into the waves and a key nonlinearity. In addition, our nonlinear model accounts for viscoelastic coupling between the oscillators. The model produces, with a single set of parameters, a family of cochlear tuning curves that quantitatively describe experimental data over a broad range of input levels. Overall, the interplay between generic nonlinear properties of local critical oscillators and distributed effects from traveling waves gives rise to a collective nonlinear response that preserves the power-law responsiveness afforded by criticality, but without paying the price of critical slowing down.
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All authors Henri Ver Hulst, Carles Blanch Mercader, Frank Jülicher, Pascal Martin
Applied Physics · Cross-listed from cond-mat.mes-hall
Impact of interdigitated electrodes design on the low frequency and random telegraph noise of single-layer graphene micro ribbons
Georgia Samara, Christoforos Theodorou, Alexandros Mavropoulis et al. (+3 more)
High performance devices consisting of interdigitated electrodes (IDEs) on top of single-layer
graphene (SLG) are candidates with favorable prospects for sensing applications.
Graphene micro ribbons (GMRs) of various widths and IDE design geometries were fabricated and experimentally examined regarding their low-frequency noise (LFN) behavior. Measurements revealed a 1/f behavior and different kinds of trap activity behind it, which were studied through the analysis of random telegraph noise (RTN) signals. Our investigation suggests that adjusting the geometrical characteristics of either the GMR width or the IDE
topology can significantly influence the signal-to-noise ratio (SNR) of SLG-based electronics. On the bright side, the results of our study can provide useful guidelines for fabrication decisions to maximize the SNR.
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All authors Georgia Samara, Christoforos Theodorou, Alexandros Mavropoulis, Nikolaos Vasileiadis, Konstantinos Papagelis, Panagiotis Dimitrakis
Instrumentation and Detectors
Design optimization of hadronic calorimeters for future colliders
Bruno Rodrigues, Inês Ochoa and Agostinho Gomes
Calorimeters are a crucial component in modern particle detectors. They are responsible for providing accurate energy measurements of particles produced in high-energy collisions. The demanding requirements set for next-generation
collider experiments impose new challenges on the design of new detectors, and a systematic approach to their optimization is increasingly necessary. The performance of calorimeters is primarily characterized by their energy resolution, parameterized by a
stochastic and a constant term, related to sampling fluctuations and non-uniformities respectively. To improve the reconstruction quality of physics objects in the calorimeter, both terms need to be taken into account. Changes in a longitudinally constrained design usually result in a trade-off between these terms, making optimization a non-trivial task. This work focuses on the optimization of a hadronic sampling calorimeter, based on the FCC-ee ALLEGRO detector concept. By controlling the absorber layer thickness in a Geant4 simulation, the impact of the passive to active material proportion on the deposited energy distribution and resolution can be analyzed. Our methodology aims at exploring the design space with practical considerations, paving the way for the development of a closed optimization framework that can evaluate multiple designs against physics performance targets.
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Plasma Physics · Cross-listed from astro-ph.IM
UK White Paper on Magnetic Reconnection
Alexander J. B. Russell, James A. McLaughlin, Philippa Browning et al. (+25 more)
Magnetic reconnection powers explosive releases of
magnetic energy, heating and particle acceleration throughout the
plasma universe. Knowledge of this universal process is vital to understanding the Heliosphere, as it plays a key role in
solar flares, coronal mass ejections, coronal heating,
solar wind acceleration, geomagnetic storms, and interactions between the
solar wind and planetary magnetospheres. As such, reconnection underpins multiple science objectives of multiple future space missions. The UK plays a leading role in this international field, through a combination of in situ measurements from Earth's magnetosphere and the
solar wind, observations of the
solar corona and chromosphere, and world-class numerical simulations and theory. This white paper identifies: Nine priority science objectives for reconnection research in the next decade; Recommendations to guide investment in theory, simulations and infrastructure; Mission priorities and required measurements to ensure the UK maintains and improves its world-class credentials in reconnection science.
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All authors Alexander J. B. Russell, James A. McLaughlin, Philippa Browning, Jennifer Carter, Luca Franci, Heli Hietala, Andrew Hillier, Gunnar Hornig, David MacTaggart, Sarah Matthews, James McKevitt, Eric Priest, Jack Reid, Ben Snow, Julia Stawarz, Anthony Yeates, Jeffersson Andres Agudelo Rueda, William Bate, Giulio Del Zanna, Jonathan Eastwood, Lucie Green, Anshu Kumari, Mike Lockwood, Thomas Neukirch, David Pontin, Andy Smith, Maria-Theresia Walach, Peter Wyper
Applied Physics
Thermochemical and elemental characterization of aromatic seed residues for solid biofuel applications in a circular economy context
Pablo Roig-Madrid, Miguel Carmona-Cabello, Alberto-Jesus Perea-Moreno et al. (+2 more)
This study investigates the energy valorization potential of 16 aromatic seed residues (ASW), a by-product generated after essential oil extraction from Mediterranean aromatic and medicinal plants. Driven by the increasing demand for natural bioactive ingredients and the expansion of aromatic crop production, large amounts of residual biomass remain underutilized. Their incorporation into thermochemical conversion routes aligns with circular economy strategies, offering opportunities for renewable energy generation, waste minimization, and the development of value-added bioenergy products. The objective of this work is to provide a comprehensive thermochemical, elemental, and structural assessment of ASW to determine their suitability for solid biofuel production (pellets, briquettes), pyrolysis for bio-oil generation, and biochar applications. The samples were analyzed under standardized ISO methodologies to ensure comparability and adherence to industrial fuel quality requirements.
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All authors Pablo Roig-Madrid, Miguel Carmona-Cabello, Alberto-Jesus Perea-Moreno, M.P. Dorado, David Munoz-Rodriguez
Optics · Cross-listed from quant-ph
Tailoring quantum walks in integrated photonic lattices
A. Raymond, P. Cathala, M. Morassi et al. (+4 more)
Unlike discrete photonic
circuits, which manipulate
photons step-by-step using a series of optical elements, arrays of coupled waveguides enable
photons to interfere continuously across the entire structure. When composed of a nonlinear material, such arrays can also directly generate
quantum states of light within the
circuit. To clarify the similarities and distinctions between these two approaches of
quantum walks, we conduct here a systematic comparison between linear waveguide arrays, injected with
photons produced externally, and nonlinear arrays, where
photon pairs are continuously generated via parametric down-conversion. We experimentally validate these predictions using III-V
semiconductor nonlinear waveguide
lattices with varied geometries, enabling us to tune the depth of the
quantum walks over an order of magnitude and reveal the gradual emergence of non-classicality in the output state. Finally, we demonstrate an inverse-design approach to engineer aperiodic waveguide arrays, whose optimized coupling profiles generate maximally entangled states such as the biphoton W-state. These results highlight the potential of continuously-coupled photonic systems to harness high-dimensional
entanglement within compact architectures.
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All authors A. Raymond, P. Cathala, M. Morassi, A. Lemaître, F. Raineri, S. Ducci, F. Baboux
Chemical Physics
Transfer learning of GW-Bethe-Salpeter Equation excitation energies
Dario Baum, Arno Förster and Lucas Visscher
A persistent challenge in machine learning for electronic-structure calculations is the sharp imbalance between abundant low-fidelity data like DFT or TDDFT results and the scarcity of high-fidelity data like many-body perturbation theory labels. We show that transfer learning provides an effective route to bridge this gap: graph neural networks pretrained on DFT and TDDFT properties can be finetuned with limited qs$GW$ and qs$GW$-BSE data to yield accurate predictions of quasiparticle and excitation energies. Assessing both full-model and readout-only finetuning across chemically diverse test sets, we find that pretraining improves accuracy, reduces reliance on costly qs$GW$ data, and mitigates large predictive outliers even for molecules larger or chemically distinct from those seen during finetuning. Our results demonstrate that multi-fidelity transfer learning can substantially extend the reach of many-body-level predictions across chemical space.
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Optics
Compact Eye Tracking for VR/AR Displays via Deep Learned MicroLED Projection and Single-Pixel Sensing
Graeme E. Johnstone, Catherine F. Higham, Aisha Kanwal et al. (+5 more)
Fast and accurate eye tracking in a virtual reality or augmented reality headset could lead to better display performance and enable novel methods of user interaction with the system. However, it remains a challenge for a system to combine the required operational speed and accuracy of eye tracking with a technology that has a small enough form factor and weight to be easily integrated into a user-friendly headset. By using small, lightweight hardware comprising a high frame rate microLED array and fast single pixel detector, we report a model eye tracking system based on single pixel tracking and a specially developed set of deep learned illumination patterns. This model system is used to demonstrate eye tracking with an angular accuracy of better than one degree and a measurement rate of up to $3.59 \,$ kHz.
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All authors Graeme E. Johnstone, Catherine F. Higham, Aisha Kanwal, Johannes Herrnsdorf, Robert K. Henderson, Martin D. Dawson, Roderick Murray-Smith, Michael J. Strain
Computational Physics
HPRMAT: A high-performance R-matrix solver with GPU acceleration for coupled-channel problems in nuclear physics
Jin Lei
I present HPRMAT, a self-contained, high-performance R-
matrix solver framework for coupled-channel scattering calculations in
nuclear physics. It provides the full R-
matrix propagation machinery with the same user-supplied-potential interface as standard R-
matrix packages, and is additionally a drop-in replacement for the linear
algebra routines of Descouvemont's package. It employs direct linear equation solving with optimized libraries instead of traditional
matrix inversion, achieving significant performance improvements. The package provides four solver backends: (1) double-precision LU factorization, (2) mixed-precision arithmetic with iterative refinement, (3) a Woodbury formula approach exploiting the kinetic-coupling
matrix structure, and (4) GPU acceleration.
Benchmark calculations demonstrate that the GPU solver achieves about 15x speedup over the optimized CPU direct solver, and 41x over the legacy inversion-based code, at N=25600. The mixed-precision strategy is particularly effective on consumer GPUs (e.g., NVIDIA RTX 3090/4090), where single-precision throughput exceeds double-precision by a factor of 64:1; by performing the factorization in single precision, with iterative refinement available to recover full double-precision accuracy where needed, HPRMAT overcomes the poor FP64 performance of consumer hardware while retaining the accuracy required for cross-section calculations. This makes large-scale continuum-discretized coupled-channels (CDCC) and coupled-channel calculations accessible to researchers using standard desktop workstations, without requiring expensive data-center GPUs.
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Fluid Dynamics
On the Markovian assumption in near-wall turbulence: The case of particle resuspension
David Ben-Shlomo, Ronen Berkovich and Eyal Fattal
We investigate the validity of the Markovian assumption in modeling near-wall
turbulence by analyzing the detachment of micron-sized particles from the viscous sublayer. By coupling direct numerical simulations with a fractional Ornstein-Uhlenbeck process, we demonstrate that while wall shear stress events follow Poissonian occurrence statistics, their internal dynamics exhibit strong temporal persistence (Hurst exponent $H \approx 0.84$), indicating non-Markovian
memory. We reveal that the successful predictions of Markovian resuspension models stems from their free parameter acting as a phenomenological surrogate for flow
memory. We further identify a critical regime transition governed by a wall shear stress events decay rate, $\lambda$. We identify a strong intermittency regime ($\lambda < 0.2$), where coherent structures exhibit extended temporal correlations that cannot be mimicked by white noise. Conversely, rapid decays ($\lambda > 0.2$) generate quasi-random fluctuations that justify the Markovian approximation. These findings offer a new perspective on the physical validity of classical
stochastic modeling in wall-bounded flows.
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Optics
Adiabatic and Deterministic Routes to Soliton Combs in Non-Hermitian Kerr Cavities
Salim B. Ivars, David Artigas, Carlos Mas Arabí et al. (+1 more)
We present a cardinal solution for the long-standing and fundamental problem associated with the adiabatic, reversible, and controlled excitation of both dark and bright
solitons in Kerr micro-resonators with normal group velocity dispersion. Our findings stem from the inclusion of a localised non-Hermitian potential, which we use to drastically reshape the characteristic collapsed snaking structure associated with such
solitons. Consequently, we demonstrate a novel snaking-free bifurcation landscape where
solitons of all possible widths are continuously connected via the dynamic change of the cavity detuning, and hence dissipative localised states of unprecedentedly high pump-to-comb conversion efficiencies can be excited in an adiabatic, deterministic, and reversible fashion. Our fundamental discovery has practical implications of paramount importance for frequency comb generation in all-normal dispersion cavities, which are key to comb generation in most spectral regions away from the telecom bands.
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All authors Salim B. Ivars, David Artigas, Carlos Mas Arabí, Carles Milián
Chemical Physics · Cross-listed from cond-mat.stat-mech
Exact fluctuation relation for open systems beyond the Zwanzig FEP equation
Mohammad Rahbar and Christopher J. Stein
We develop a fluctuation framework to quantify the free energy difference between two
equilibrium states connected by nonequilibrium processes under arbitrary dynamics and system-environment coupling. For an open system described by the
Hamiltonian of mean force (HMF), we show that the
equilibrium free energy difference between two canonical endpoints can be written as exponential averages of the HMF shift, divided by an explicit factor built from the chi-squared divergence between the initial and final system marginals. These relations hold at the endpoint level and, under an asymptotic equilibration postulate, admit trajectory representations for general driving and coupling protocols. A decomposition of the HMF increment along each trajectory separates the work-like contributions associated with changes in $\lambda(t)$ and $C(t)$, the heat-like exchange with the environment, and a feedback-like functional defined with respect to the initial protocol. In the frozen-driving regime with a noninteracting reference, the equalities reduce to new FEP-like expressions involving an environment functional and an explicit overlap correction, with the Zwanzig formula recovered as a limiting case. We validate the approach on an open system coupled to an environment and evolved under overdamped Langevin dynamics, where conventional Zwanzig FEP suffers from poor phase-space overlap and slow numerical convergence, while the present trajectory equality closely matches the exact free energy difference over a broad range of coupling strengths.
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Chemical Physics
Exceptional Alkaline Methanol Electrooxidation on Bi-modified Pt3M Intermetallics: Kinetic Origins and an OH Binding Energy Descriptor
Lecheng Liang, Hengyu Li, Shao Ye et al. (+7 more)
The exploration of advanced CO-free catalysts and clarifying the ambiguous kinetic origins and governing factors would undoubtedly open up opportunities to overcome the sluggish kinetics of methanol electrooxidation and promote the development of direct methanol fuel
cells. Herein, we constructed a family of Bi-modified Pt3M intermetallic catalysts (Bi-Pt3M/C, M=Cr, Mn, Co, Zn, In, Ga, and Sn) that follow CO-free dominated pathway and exhibit exceptional catalytic activity. More significantly, leveraging this platform, we have identified the pivotal factor governing the reaction kinetics in CO-free pathway, namely OH binding energy (OHBE). This arises because the rate-determining step (RDS) encompasses both C-H bond activation and water dissociation, whose respective barriers can be reflected by the OHBE. Accordingly, OHBE can act as an activity descriptor. Specifically, Bi-Pt3In/C stands out from other Bi-Pt3M/C and delivers the unprecedented mass activity of 36.7 A mgPt-1 at peak potential, far exceeding state-of-the-art Pt-based catalysts reported to date. Taking Bi-Pt3In/C as a proof of concept, we clearly elucidate the origin of enhanced MOR activity by combining theoretical calculations, kinetic isotope effects, and formaldehyde electrooxidation. Moreover, there exhibits a volcano-type trend between OHBE and the activity of Bi-Pt3M/C. Beyond the discovery of ultrahigh-performance catalysts, these findings provide a detailed mechanistic picture of RDS and offer an innovative design principle for advanced catalysts.
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All authors Lecheng Liang, Hengyu Li, Shao Ye, Peng Li, Kaiyang Xu, Jinhui Liang, Binwen Zeng, Bo Shen, Taisuke Ozaki, Zhiming Cui
Classical Physics
Recovering long-range cumulative response to geometric frustration in quasi-1d systems, mediated by constitutive softness
Snir Meiri and Efi Efrati
Cumulative
geometric frustration can drive self-limited assembly and morphology selection through size-dependent energetic costs. However, the slenderness of quasi-one-dimensional systems generally suppresses the formation of long-range longitudinal gradients. We show that the suppression of longitudinal gradients can be overcome by tuning the ratio between the longitudinal and transverse (shear)
moduli. We demonstrate the recovery of cumulative frustration across distinct quasi-one-dimensional systems, each frustrated through a different mechanism, by the introduction of a soft response mode. The resulting saturation length scale is universal and is dictated by the local constitutive information of the system.
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Fluid Dynamics
Capillary Filling Dynamics in Polygonal Tubes
Chen Zhao, Yu Huang, Tingxuan Chen et al. (+3 more)
We study the dynamics of capillary filling in tubes of regular polygon cross-section. Using Onsager variational principle, we derive a coupled ordinary differential equation and partial differential equation, which respectively describe time
evolution of the bulk flow and the saturation profile of the finger flow. We obtain both numerical solution and self-similar solution to the coupled equations, and the results indicate that the bulk flow and the finger flow both follow the $t^{1/2}$ time-scaling. We show that due to the coupling effect of the finger flow, the prefactor for the bulk flow is smaller than that of the Lucas-Washburn prediction. The reduction effect is more pronounced when the side number $n$ of the regular-polygon is small, while as $n$ increases, the prefactor approaches Lucas-Washburn prediction.
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All authors Chen Zhao, Yu Huang, Tingxuan Chen, Jiaxuan Li, Jiajia Zhou, Masao Doi
Geophysics
Physics-Informed Cross-Learning for Seismic Acoustic Impedance Inversion and Wavelet Extraction
Junheng Peng, Xiaowen Wang, Yingtian Liu et al. (+2 more)
Seismic acoustic impedance inversion is one of the most challenging tasks in geophysical exploration. Many studies have proposed the use of deep learning for processing; however, most of them are limited by factors such as seismic wavelets and low-frequency initial models. Furthermore, self-supervised frameworks constructed entirely using deep learning models struggle to form direct and effective physical constraints to unlabeled outputs during the multi-model concatenation, which leads to instability in inversion. In this work, we introduced innovations in both the deep learning framework and training strategy. First, we designed a deep learning framework to perform acoustic impedance inversion and seismic wavelet extraction simultaneously. Building on this foundation, considering the scarcity of well data, we proposed a physics-informed cross-learning strategy to impose effective constraints on the framework. We conducted comparative experiments and ablation experiments on both synthetic
datasets and field
datasets. The results demonstrate that the proposed method achieves a significant improvement compared with semi-supervised learning methods and can extract seismic wavelets with relatively high accuracy. Finally, to ensure the reproducibility of this work, we have made the code open-source.
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All authors Junheng Peng, Xiaowen Wang, Yingtian Liu, Yong Li, Mingwei Wang
Chemical Physics · Cross-listed from cond-mat.soft
Heat capacity of dense liquids: A link between two-phase model and melting temperature scaling
S. A. Khrapak and A. G. Khrapak
Generalized Rosenfeld-Tarazona scaling predicts the power-law dependence of the excess heat capacity of simple
liquids on temperature. The two-phase model treats a
liquid as a superposition of gas- and solid-like components whose relative abundance is quantified by a
liquid rigidity parameter. We demonstrate here that the generalized Rosenfeld-Tarazona scaling emerges naturally in the two-phase model from the scale invariance of the
liquid rigidity parameter.
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Physics Education
Exploring Students' Understanding of Linear and Quadratic Relationships in a Projectile Motion Context
Yosep Dwi Kristanto, Teo Paoletti, Russasmita Sri Padmi et al. (+4 more)
Previous research has shown that students often struggle to develop an understanding of linear and quadratic relationships. Covariational
reasoning has been identified as a way to support this development. This study aims to investigate how covariational
reasoning supports students in developing understandings of linear and quadratic relationships within a projectile motion context. A teaching experiment was conducted with two middle school students who engaged in a digital task exploring the relationship between height and time. The analysis characterizes how the students' covariational
reasoning evolved as they interpreted the changing quantities in the task. The findings suggest that
prompts encouraging students to compare linear and quadratic relationships can foster more sophisticated forms of covariational
reasoning. The discussion highlights how specific features of the task design, including the affordances of technology, the emphasis on conceiving graphs as representations of covarying quantities, and the use of non-canonical graphing tasks, can support covariational
reasoning.
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All authors Yosep Dwi Kristanto, Teo Paoletti, Russasmita Sri Padmi, Serli Evidiasari, Zsolt Lavicza, Tony Houghton, Houssam Kasti
Fluid Dynamics
Oscillating electroosmotic flow in channels and capillaries with modulated wall charge distribution
A. Shrestha, E. Kirkinis and M. Olvera de la Cruz
Electrolyte-filled channels with modulated wall charge distribution subjected to an applied DC electric field, form time-independent
vortices whose sense of circulation is determined by the field direction [Physical Review Letters $ \mathbf{75}, 755, (1995)$]. In this paper we show that an electrolyte in a channel or cylindrical capillary subjected to an external alternating (AC) electric field gives rise to various laminar flow structures, including
vortices whose sense of circulation changes with the period of oscillation of the applied AC field. The introduction of a period of oscillation lifts certain degeneracies associated with its time-independent counterpart. Although, in general, the mass flux vanishes, the charge flux is nonzero. The flow is accompanied by a longitudinal (oscillating) advective current that displays hysteresis accompanied by a diverging and negative self-similar conductance that depends on the applied voltage [Nano Letters $\mathbf{10}, 2674, (2010)$]. We show that this behavior can be interpreted with respect to a “
memory retention time”, that depends on frequency, viscosity and the Debye length and could thus form the impetus for investigating control protocols of signal carriers.
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Applied Physics
Response times of two-dimensional photodetectors limited by intrinsic resistance and capacitance
Ilya Safonov and Dmitry Svintsov
Most contemporary architectures of photodetectors based on two-dimensional materials include global gates for carrier density control and local p-n junctions in the channel. We study the dependence of photocurrent in such detectors on the light modulation frequency, fully taking into account the effects of distributed resistance and gate-channel capacitance. The decay of photocurrent with modulation frequency governs the response time. We find that the maximum modulation frequency is largely determined by the position of light-sensitive junction with respect to the middle of the channel. Largest modulation frequency is achieved for junctions in immediate vicinity of either source or drain contacts, while fast roll-off of the modulation characteristic is observed for junction in the middle of the channel.
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Plasma Physics
Numerical investigation of kinetic instabilities in BGK equilibria under collisional effects
Sofia Zanelli, Gabriele Celebre, Sergio Servidio et al. (+1 more)
An unstable one-dimensional Bernstein-Greene-Kruskal (BGK) mode has been studied through high-precision numerical simulations. The initial turbulent, periodic
equilibrium state is obtained by solving a Vlasov-Poisson system for initially thermalized electrons, with the addition of an external electric field able to trigger undamped, high-amplitude electron acoustic waves (EAWs). Once the external field is turned off, resonant particles are trapped in a stationary two-hole phase-space configuration. This
equilibrium scenario is perturbed by some large-scale density noise, leading to an electrostatic instability with the merging of
vortices into a final one-hole state. Numerical runs investigate several features of this regime, focusing on the dependence of the instability trigger time and growth rate on the rate of short-range collisions and grid resolution. According to Landau theory for weakly inhomogeneous
equilibria, we observe that the growth rate of the instability depends only on the slope of the distribution function in the resonant region. Conversely, the onset time of the instability is affected by the collisional rate, which is able to postpone the onset of the instability. Moreover, by extending the simulations to a long-time scale, we investigate the saturation stage of the instability, which can be analyzed through the Hermite spectral analysis. In collisionless simulations where grid effects are negligible, the Hermite spectrum follows a power law typical of a constant enstrophy flux scenario. Otherwise, if collisional effects become significant, a cutoff is observed at high Hermite modes, leading to a decaying trend.
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All authors Sofia Zanelli, Gabriele Celebre, Sergio Servidio, Francesco Valentini
Medical Physics
Mastering Preclinical Fast MRSI: From Setup to Execution
Gianna Nossa, Eloïse Mougel, Brayan Alves et al. (+6 more)
MR experiments are essential for studying
brain metabolism, yet preclinical 1H-MRSI remains underdeveloped, with significant limitations in SNR, acquisition speed, and automated data processing. Although recent advances-such as accelerated sequences, denoising strategies, and ultra-high-field systems-have begun to reduce these barriers, preclinical MRSI still lags far behind the human research field in accessibility and routine use. Based on our expertise, we have created this guide that outlines a complete workflow for acquiring and analyzing high-quality fast MRSI data in rodent
brains at ultra-high fields (9.4T and 14.1T), enabling novice users to perform reliable experiments using optimized MRSI sequences (FID-MRSI, SE-MRSI, and PRESS-MRSI) and standardized processing pipelines, while also highlighting strategies to further improve acquisition speed, coverage, and reproducibility. Overall, this paper provides a strong foundation for future methodological advances that will expand metabolic imaging capabilities and deepen insights into
brain function and disease.
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All authors Gianna Nossa, Eloïse Mougel, Brayan Alves, Tan Toi Phan, Alessio Siviglia, Thi Ngoc Anh Dinh, Thanh Phong Lê, Bernard Lanz, Cristina Cudalbu
Chemical Physics · Cross-listed from cond-mat.stat-mech
A mini-review on combinatorial solutions to the Marcus-Lushnikov irreversible aggregation
Michał Łepek, Agata Fronczak and Piotr Fronczak
Over the past decade, a combinatorial framework for discrete, finite, and irreversibly aggregating systems has emerged. This work reviews its progress, practical applications, and limitations. We outline the approach's assumptions and foundations, based on direct enumeration of system states, contrasting with classical Smoluchowski and Marcus-Lushnikov methods. Using the constant kernel as an example, we derive combinatorial expressions for the average number of clusters of a given size and their standard deviation, and present the complete probability distribution for cluster counts. The method is then extended to several kernels (additive, product, linear-chain, condensation) by explicitly enumerating ways to form clusters of a given size. For general kernels, approximate solutions are obtained via recursive expressions, enabling predictions without explicit solutions. Applications to aerosol growth and planetesimal formation are demonstrated, with comparisons to numerical results. We summarize issues of validity and precision and propose open problems. The appendix includes partial Bell
polynomials, generating functions, Lagrange inversion, potential applications, and links between combinatorial and scaling solutions of the Smoluchowski equation.
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Instrumentation and Detectors
Multichannel SiPM test readout system for gamma-ray measurements with monolithic inorganic CeBr_{3}
Veronika Asova, Galin Bistrev, Valentin Buchakchiev et al. (+1 more)
Energy resolution and the detection efficiency for gamma quanta are fundamental properties in the construction of detectors for ionizing radiation. In this study, a SiPM-based photodetector coupled to a monolithic inorganic CeBr_{3}
crystal is exposed to gamma rays in order to study the performance of the CeBr_{3}
crystal. Measurements are made using three different radioactive sources - ^{137}Cs, ^{22}Na, and ^{60}Co. For each source, the measurements are conducted at several SiPM bias voltages. Furthermore, two CeBr_{3}
crystals with different thicknesses are used in order to study how detector efficiency is affected by
crystal dimensions. A preliminary analysis of the data is presented.
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All authors Veronika Asova, Galin Bistrev, Valentin Buchakchiev, Venelin Kozhuharov