Physics and Society
Signed Networks: theory, methods, and applications
Fernando Diaz-Diaz, Elena Candellone, Miguel A. Gonzalez-Casado et al. (+4 more)
Signed networks provide a principled framework for representing systems in which interactions are not merely present or absent but qualitatively distinct: friendly or antagonistic, supportive or conflicting, excitatory or inhibitory. This polarity reshapes how we think about structure and dynamics in complex systems: a negative tie is not simply a missing positive one but a constraint that generates tension, and possibly asymmetry. Across disciplines, from sociology to neuroscience and machine learning, signed networks provide a shared
language to formalise duality, balance, and opposition as integral components of system behaviour. This review provides a comprehensive and foundational summary of signed network theory. It formalises the mathematical principles of signed graphs and surveys signed-network-specific measures, including signed degree distributions, clustering, centralities, motifs, and
Laplacians. It revisits balance theory, tracing its cognitive and structural formulations and their connections to frustration. Structural aspects of signed networks are examined, analysing key topics such as null models, node
embeddings, sign prediction, and community detection. Subsequent sections address dynamical processes on and of signed networks, such as opinion dynamics, contagion models, and data-driven approaches for studying evolving networks. Practical challenges in constructing, inferring and validating signed data from real-world systems are also highlighted, and we offer an overview of currently available
datasets. We also address common pitfalls and challenges that arise when modelling or analysing signed data. Overall, this review integrates theoretical foundations, methodological approaches, and cross-domain examples, providing a structured entry point and a reference framework for researchers interested in the study of signed networks in complex systems.
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All authors Fernando Diaz-Diaz, Elena Candellone, Miguel A. Gonzalez-Casado, Emma Fraxanet, Antoine Vendeville, Irene Ferri, Andreia Sofia Teixeira
Optics · Cross-listed from quant-ph
Optical entanglement facilitated by a hot mechanical oscillator
Aleksandr V. Karpenko, Andrey B. Matsko and Sergey P. Vyatchanin
Optomechanical generation of entangled optical beams is usually hindered by thermal noise. We present a theoretical study of low frequency
entanglement generation between two optical harmonics emitted from a cavity optomechanical system operating in the resolved-sideband regime. The system comprises three nearly equidistant optical modes in a high-finesse cavity, with the central mode coherently driven. This configuration enables radiation-pressure interactions that generate strong
quantum correlations between the two sideband modes. Remarkably, these correlations persist even at large numbers of thermal quanta if one properly engineers the optical cooling rate of the mechanical mode. Our findings demonstrate the feasibility of robust
entanglement under ambient conditions, opening new avenues for hybrid
quantum technologies based on mechanical interfaces and continuous-variable
quantum information processing.
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Chemical Physics · Cross-listed from cs.LG
Generating transition states of chemical reactions via distance-geometry-based flow matching
Yufei Luo, Xiang Gu and Jian Sun
Transition states (TSs) are crucial for understanding reaction mechanisms, yet their exploration is limited by the complexity of experimental and computational approaches. Here we propose TS-DFM, a flow matching framework that predicts TSs from reactants and products. By operating in molecular distance geometry space, TS-DFM explicitly captures the dynamic changes of interatomic distances in chemical reactions. A network structure named TSDVNet is designed to learn the velocity field for generating TS geometries accurately. On the
benchmark dataset Transition1X, TS-DFM outperforms the previous state-of-the-art method React-OT by 30% in structural accuracy. These predicted TSs provide high-quality initial structures, accelerating the convergence of CI-NEB optimization. Additionally, TS-DFM can identify alternative reaction paths. In our experiments, even a more favorable TS with lower energy barrier is discovered. Further tests on RGD1
dataset confirm its strong generalization ability on unseen molecules and reaction types, highlighting its potential for facilitating reaction exploration.
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Accelerator Physics
A Short Introduction to Metal Forming
Charbel Moussa and Pierre Montmitonnet
The present paper is a short introduction to metal forming. The paper highlights the different disciplines that should be considered when analyzing metal forming. Hence, mechanical flow, heat flow, mechanical and thermal contact, and microstructure
evolution are presented within the perspective of metal forming. For every aspect, well-established knowledge is presented in addition to scientific open questions on which active research is still done.
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Physics Education
Teaching quantum computing to computer science students: Review of a hands-on quantum circuit simulation practical
Florian Krötz, Xiao-Ting Michelle To, Korbinian Staudacher et al. (+1 more)
We present a practical course targeting graduate students with prior knowledge of the basics of
quantum computing. The practical aims to deepen students' understanding of fundamental concepts in
quantum computing by implementing
quantum circuit simulators. Through hands-on experience, students learn about different methods to simulate
quantum computing, including state vectors, density
matrices, the stabilizer formalism, and
matrix product states. By implementing the simulation methods themselves, students develop a more in-depth understanding of fundamental concepts in
quantum computing, including superposition,
entanglement, and the effects of noise on
quantum systems. This hands-on experience prepares students to do research in the field of
quantum computing and equips them with the knowledge and skills necessary to tackle complex research projects in the field. In this work, we describe our teaching approach and the structure of our practical, and we discuss evaluations and lessons learned.
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All authors Florian Krötz, Xiao-Ting Michelle To, Korbinian Staudacher, Dieter Kranzlmüller
History and Philosophy of Physics · Cross-listed from math.HO
Mathematics: the Rise of the Machines
Yang-Hui He
We argue how AI can assist mathematics in three ways:
theorem-proving,
conjecture formulation, and
language processing. Inspired by initial experiments in geometry and theoretical physics in 2017, we summarize how this emerging field has grown over the past years, and show how various machine-learning
algorithms can help with pattern detection across disciplines in the mathematical sciences. At the heart is the question how does AI help with theoretical discovery, and the implications for the future of mathematics.
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Applied Physics
Ferroelectric Nematic Liquid Crystals as Charge Boosters for Triboelectric Nanogenerators
Jia-Yao Ye, Susanta Chakraborty, Karthick Subramani et al. (+3 more)
Driven by growing demand for clean energy, triboelectric nanogenerators (TENGs) have emerged as promising self-powered systems, yet achieving high charge density remains a critical challenge. In
polymer dielectrics, triboelectricity can be further amplified by incorporating high-dielectric and polar materials for functional adaptability. Conventional dielectrics, including
liquid crystals (LCs), offer limited improvement for triboelectrification, whereas the breakthrough ferroelectric nematic
liquid crystals (NF-LCs), with
giant spontaneous polarization and high dielectric constant, act as highly effective charge boosters. Here, we introduce NF-LC (DIO) as a functional filler in a PVDF-based TENG. Increasing DIO content progressively grows the electroactive phase and effective polarization in PVDF and defines a marked improvement in TENGs electrical performances through favourable dipolar
alignment and strengthened charge-trap effects. The optimized composite film achieves an impressive open-
circuit voltage of 1.1 kV, short-
circuit current of 50 micro Amp, and power density of 110 W/m2 - seven times higher than pure PVDF. The device exhibits excellent charge-storage capability, powers over 500 LEDs without power management. This work establishes NF-LC-based TENGs as a new platform for high-performance self-powered energy harvesting, linking soft matter physics with applied energy technology.
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All authors Jia-Yao Ye, Susanta Chakraborty, Karthick Subramani, Xing-Zhou Tang, Yan-Nan Xie, Bing-Xiang Li
Applied Physics
From Cantilevers to Membranes: Advanced Scanning Protocols for Magnetic Resonance Force Microscopy
Nils Prumbaum, Christian L. Degen and Alexander Eichler
Magnetic Resonance Force Microscopy (MRFM) enables three-dimensional imaging of
nuclear spin densities in nanoscale objects. Based on numerical simulations, we evaluate the performance of strained SiN resonators as force sensors and show that their out-of-plane oscillation direction improves the quality of the reconstructed sample. We further introduce a multislice, compressed-sensing scan protocol that maximizes the information obtained for a given measurement time. Our simulations predict that these new scanning protocols and optimized
algorithms can shorten the total acquisition time by up to two orders of magnitude while maintaining the reconstruction fidelity. Our results demonstrate that combining advanced scanning protocols with state-of-the-art resonators is a promising path toward high-resolution MRFM for volumetric imaging of biological nanostructures.
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Atomic Physics · Cross-listed from quant-ph
Collective early-time spontaneous decay of a strongly driven cold atomic ensemble
Daniel Benedicto Orenes, Naudson Lucas Lopes Matias, Apoorva Apoorva et al. (+3 more)
In this work we present a numerical and experimental investigation of the collective early-time decay rates of a strongly driven and optically dense cold atomic cloud. We prepare the atomic ensemble by driving the system to its steady state with varying Rabi frequencies $\Omega$ that go from the weak $\Omega \ll \Gamma$ to the strong driving regime $\Omega \gg \Gamma$, where $\Gamma$ is the single-atom decay rate. We investigate the early-time dynamics in the transition between the strong and weak driving regimes using: i) angular-dependent observables such as the light emitted by the cloud, and ii) global observables, i.e., the excited state population. When driving the cloud on-resonance, we find that as a function of the driving frequency, the behavior of the collected light at certain angles transitions from the single-
photon subradiant regime to a superradiant regime while the behavior of the excited state population does not show superradiance. The experiment shows good agreement with numerical predictions in the regime of parameters under study.
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All authors Daniel Benedicto Orenes, Naudson Lucas Lopes Matias, Apoorva Apoorva, Antoine Glicenstein, Raphaël Saint-Jalm, Robin Kaiser
Optics
Orbital rotation of spheroidal Mie particles driven by counter-propagating circularly-polarized beams
E. N. Bulgakov, A. E. Ershov, V. Kimberg et al. (+2 more)
We theoretically consider orbital rotation of a spheroidal submicron particle in the field of two counter-propagating circularly polarized Gaussian beams. We derived equations connecting the parameters of the circular orbits centered on the beams axis to the optical force and torque. The equations show that, besides orbital rotation, the spheroidal particle simultaneously rotates around its equatorial axis. We found that two distinct dynamic regimes are possible. The orbital motion can be accompanied by a rapid proper rotation with angular velocity an order of magnitude larger than the angular velocity of the orbital rotation. Alternatively, the orbital and proper rotations can be synchronized. The direction of orbital rotation can either coincide with or be opposite to the direction of rotation of the electric vector. The findings are confirmed by direct numerical simulations. The results can be of use in development of nano-scale gyroscopes as well in shape-selective sorting of submicron particles.
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All authors E. N. Bulgakov, A. E. Ershov, V. Kimberg, V. S. Gerasimov, D. N. Maksimov
Atmospheric and Oceanic Physics
On the Predictive Skill of Artificial Intelligence-based Weather Models for Extreme Events using Uncertainty Quantification
Rodrigo Almeida, Noelia Otero, Miguel-Ángel Fernández-Torres et al. (+1 more)
Accurate prediction of extreme weather events remains a major challenge for artificial intelligence-based weather prediction systems. While deterministic models such as FuXi, GraphCast, and SFNO have achieved competitive forecast skill relative to numerical weather prediction, their ability to represent uncertainty and capture extremes is still limited. This study investigates how state-of-the-art deterministic artificial intelligence-based models respond to initial-condition perturbations and evaluates the resulting ensembles in
forecasting extremes. Using four perturbation strategies (Gaussian, Perlin noise, Hemispheric Centered Bred Vectors, and Huge Ensembles), we generate 50 member ensembles for the August 2022 Pakistan floods and China heatwave, and complement these case studies with a global threshold-based evaluation. Ensemble skill is assessed against ERA5 and compared with IFS ENS and the AIFS ENS probabilistic model using deterministic and probabilistic metrics. Results show that simpler perturbations like Gaussian and Perlin noise produce similarly realistic ensemble spread and probabilistic skill as flow-based approaches like HCBV and HENS, narrowing but not closing the performance gap with numerical weather prediction ensembles, or native probabilistic models which retain the highest probabilistic skill across variables. Model choice is the dominant factor for ensemble performance, not perturbation method. Across variables, models capture temperature extremes more effectively than precipitation. These findings demonstrate that simple input perturbations can extend deterministic models toward probabilistic
forecasting in hardware-constrained settings, supporting artificial intelligence-driven early warning systems.
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All authors Rodrigo Almeida, Noelia Otero, Miguel-Ángel Fernández-Torres, Jackie Ma
Physics and Society
Thermonuclear Explosions for Large-Scale Carbon Sequestration: A Call for Exploration
Andy Haverly, So Yeon Kim and Ju Li
Climate change is a rapidly accelerating problem that requires fast and large-scale carbon sequestration to prevent catastrophe. This paper proposes a novel approach to use explosives for large-scale carbon sequestration. Combining the long-practiced method of explosive mining with newer enhanced rock weathering techniques, we propose a faster, greener, and profitable method of large-scale carbon sequestration. This method is applicable for all explosives, including thermonuclear, and can be done safely with minimal anthropological and ecological impact. We estimate a cost of $0.68/ton of CO2 sequestered.
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Medical Physics
Exploring the added value of pretherapeutic MR descriptors in predicting breast cancer pathologic complete response to neoadjuvant chemotherapy
Caroline Malhaire, Fatine Selhane, Marie-Judith Saint-Martin et al. (+13 more)
Objectives: To evaluate the association between pretreatment MRI descriptors and breast cancer (BC) pathological complete response (pCR) to neoadjuvant chemotherapy (NAC). Materials & Methods: Patients with BC treated by NAC with a breast MRI between 2016 and 2020 were included in this retrospective observational single-center study. MR studies were described using the standardized BI-RADS and breast edema score on T2-weighted MRI. Univariable and multivariable logistic regression analyses were performed to assess variables association with pCR according to residual cancer burden. Random forest classifiers were trained to predict pCR on a random split including 70% of the database and were validated on the remaining cases. Results: Among 129 BC, 59 (46%) achieved pCR after NAC (luminal (n=7/37, 19%), triple negative (TN) (n=30/55, 55%), HER2+ (n=22/37, 59%). Clinical and biological items associated with pCR were BC subtype (p<0.001), T stage 0/I/II (p=0.008), higher Ki67 (p=0.005) and higher tumor-infiltrating lymphocytes levels (p=0.016). Univariate analysis showed that the following MRI features, oval or round shape (p=0.047), unifocality (p=0.026), non-spiculated margins (p=0.018), no associated non-mass enhancement (NME) (p = 0.024) and a lower MRI size (p = 0.031) were significantly associated with pCR. Unifocality and non-spiculated margins remained independently associated with pCR at multivariable analysis. Adding significant MRI features to clinicobiological variables in random forest classifiers significantly increased sensitivity (0.67 versus 0.62), specificity (0.69 versus 0.67) and precision (0.71 versus 0.67) for pCR prediction. Conclusion: Non-spiculated margins and unifocality are independently associated with pCR and can increase models performance to predict BC response to NAC. Clinical Relevance Statement: A
multimodal approach integrating pretreatment MRI features with clinicobiological predictors, including TILs, could be employed to develop machine learning models for identifying patients at risk of non-response. This may enable consideration of alternative therapeutic strategies to optimize treatment outcomes
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All authors Caroline Malhaire, Fatine Selhane, Marie-Judith Saint-Martin, Vincent Cockenpot, Pia Akl, Enora Laas, Audrey Bellesoeur, Catherine Ala Eddine, Melodie Bereby-Kahane, Julie Manceau, Delphine Sebbag-Sfez, Jean-Yves Pierga, Fabien Reyal, Anne Vincent-Salomon, Herve Brisse, Frederique Frouin
Instrumentation and Detectors
Distributed Acoustic Fiber Sensing for Research Campuses and Large Scientific Infrastructures – The Hamburg WAVE proto-network
Oliver Bölt, Luigia Cristiano, Sandy Croatto et al. (+17 more)
Here, we demonstrate and investigate how Distributed Acoustic Sensing (DAS) can be utilized on research campuses and in large scientific infrastructures to study environmental vibrations and reduce their impact on high-precision experiments. We first discuss the potential of DAS in the context of particle accelerators, gravitational wave detection experiments and research campuses. Next, we present the results of our seismic measurement campaign conducted with our proto-network, which involved the probing of over 12 km of fiber, in May 2021. This campaign was conducted by the Hamburg WAVE initiative in Science City Hamburg Bahrenfeld and included DESY, the European XFEL, PETRA III and the University of Hamburg. Our proto-network confirms the ability to observe natural, anthropogenic, and infrastructural vibrations and how and where these couple into different parts of the heterogeneously set up fiber network. We also present results on a study of noise and motion coupling aspects of DAS probing double-redundant fiber loops in a unique environment, the European XFEL. Our results show that DAS can greatly benefit research campuses and large scientific infrastructures and they highlight the opportunities and challenges of implementing and operating such seismic networks.
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All authors Oliver Bölt, Luigia Cristiano, Sandy Croatto, Dirk Gajewski, Erik Genthe, Oliver Gerberding, Céline Hadziioannou, Conny Hammer, Markus Hoffmann, Katharina-Sophie Isleif, Antonia Kiel, Charlotte M. Krawczyk, Regina Maass, Ingra Barbosa, Norbert Meyners, Reinhardt Rading, Holger Schlarb, Roman Schnabel, Wanda Vossius, Christopher Wollin
Optics · Cross-listed from eess.IV
Towards Blind Lens Aberration Correction via Large LensLib Pre-training and Discrete Degradation Priors
Xiaolong Qian, Qi Jiang, Yao Gao et al. (+8 more)
Emerging deep-learning-based lens library pre-training (LensLib-PT) pipeline offers a new avenue for blind lens aberration correction by training a universal neural network, demonstrating strong capability in handling diverse unknown optical degradations. This work proposes FoundCAC, a universal foundational framework that resolves two challenges hindering the generalization of existing pipelines: the difficulty of scaling training data and the absence of prior guidance characterizing optical degradation. To improve data scalability, we expand the design specifications to increase degradation diversity and construct AODLibpro, a large-scale lens library using stratified sampling over spatial-variation patterns and degradation severity. In terms of model design, to leverage Point Spread Functions (PSFs) as guidance while maintaining the blind paradigm, we propose a multi-stage vector-quantized representation learning scheme. This paradigm is specifically designed to construct a Latent PSF Representation (LPR), explicitly encoding complex continuous PSFs into a discrete degradation prior to regularize the highly ill-posed restoration process. Through a simple yet effective codebook-freezing strategy, our framework leverages the discrete prior to elevate full-shot restoration performance and unlock highly efficient few-shot adaptation for unseen lenses. Experiments on synthetic LensLib, real-design simulations, and real-captured lenses show that our framework achieves state-of-the-art zero-shot performance under complementary evaluation protocols, while enabling highly efficient few-shot adaptation for specific lenses. The source code and
datasets will be made publicly available at https://github.com/zju-jiangqi/FoundCAC.
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All authors Xiaolong Qian, Qi Jiang, Yao Gao, Lei Sun, Kailun Yang, Xian Wang, Zhonghua Yi, Wenyong Li, Ming-Hsuan Yang, Luc Van Gool, Kaiwei Wang
Fluid Dynamics
Transport and removal of a passive tracer in porous media employing surface washing
Georgia Ioannou, Francesco Paolo Cont`o, Merlin A. Etzold et al. (+2 more)
This experimental study investigates the dynamics of surface washing to remove a passive tracer from a porous plate by a
gravity-driven
liquid film across its surface. A disodium fluorescein tracer is introduced at the surface of a water-saturated porous plate and allowed to diffuse into the plate for a number of hours before a film of water solution flows over its surface to extract and transport the tracer away. The removal rate of the tracer is monitored quantitatively by using fluorescence measurements to determine the concentration in the effluent from the washing process. These measurements are supplemented by dye-attenuation imaging, which provides mainly qualitative insights about the tracer's concentration distribution on the porous plate surface. Our findings reveal a three-stage mass-transport process consisting of an initial period of rapid removal of the tracer found within the surface roughness, followed by a period of slower removal, which appears to be limited by vertical
diffusion, and a third stage of accelerated advection-dominated removal when the tracer-rich region that was transported downstream during the second stage reaches the downstream boundary of the porous plate. A parametric study explores the influence of the characteristics of the washing film, the permeability of the porous plate, the amount and initial spatial extent of the tracer on the porous plate and the tracer's diffusive penetration depth on the mass removal rates. Our insights offer practical guidance for optimising surface washing protocols for porous systems in industrial and environmental applications.
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All authors Georgia Ioannou, Francesco Paolo Cont`o, Merlin A. Etzold, Julien R. Landel, Stuart B. Dalziel
Optics · Cross-listed from cond-mat.mtrl-sci
Effect of temperature and excitation power on down-conversion process in Tb3+/Yb3+-activated silica-hafnia glass-ceramic films
S. E. Amrani, M. Sun, S. Valdueza-Felip et al. (+4 more)
Transparent glass ceramics, when activated by rare earth ions, are excellent photonic materials. Regarding photonic glass-ceramics based on silicates, hafnia and silica in a binary system has proved to be an excellent
matrix to incorporate rare earth ions in the hafnia nanocrystals, resulting in important luminescence enhancement and, consequently, allowing a large spectrum of critical applications. Here we will focus on the downconversion mechanism driven by the couple Tb3+/Yb3+, largely exploited in photovoltaic systems. The research presented here has been performed on 70SiO2-30HfO2 silica-hafnia glass-ceramic films activated with 19 % rare earth ions: [Tb + Yb]/[Si + Hf] = 19 %. Two main results will be discussed: (a) the intensity and the broadening of the Yb3+ emission band at 975 nm were found to be temperature-dependent, as shown in the figure; (b) the energy transfer mechanism Tb3+ >> Yb3+ will be discussed, referring to the mechanisms that have been proposed in the literature. In relation to the latter topic, the power dependence spectra for the luminescence of Yb3+: 2F5/2 >> 2F7/2 will be discussed.
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All authors S. E. Amrani, M. Sun, S. Valdueza-Felip, F. B. Naranjo, M. R. Britel, M. Ferrari, A. Bouajaj
Physics and Society
Modeling memory in time-respecting paths on temporal networks
Silvia Guerrini, Ciro Cattuto and Lorenzo Dall'Amico
Human close-range proximity interactions are the key determinant for spreading processes like knowledge
diffusion, norm adoption, and infectious disease transmission. These dynamical processes can be modeled with time-respecting paths on temporal networks. Here, we propose a framework to quantify
memory in time-respecting paths and evaluate it on several empirical
datasets encoding proximity between humans collected in different settings. Our results show strong
memory effects, robust across settings, model parameters, and statistically significant when compared to memoryless null models. We further propose a generative model to create synthetic temporal graphs with
memory and use it to show that
memory in time-respecting paths decreases the
diffusion speed, affecting the dynamics of spreading processes on temporal networks.
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Chemical Physics
Calculation and analysis of exciton couplings via a subsystem formulation of the $GW$-Bethe-Salpeter Equation
Sarathchandra Khandavilli, Arno Förster and Lucas Visscher
We present a fragment-based framework for analyzing
exciton couplings within the $GW$-Bethe-Salpeter Equation formalism using localized molecular orbitals, and assess how excitonic states in molecular dimers can be decomposed into local and charge-transfer (CT) sectors. Our localization procedure preserves orbital orthonormality via a block-diagonal
unitary transformation, enabling a simple and interpretable analysis of excitonic interactions. Using ethylene and pyrene dimers as model systems, we identify key effects of excitonic basis truncation and coupling approximations on excitation energies. We then extend the method to chlorophyll dimers, where weak CT asymmetries emerge due to
geometric distortions. This framework offers a tractable route to analyze excitonic behavior in complex systems and paves the way for future fragment-based reconstruction of full
exciton coupling
matrices in large molecular assemblies.
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Fluid Dynamics
Collapse of turbulence in optimised curved pipe flow
Eman Bagheri, Stefan Becker and Philipp Schlatter
The increased friction caused by
turbulence is a significant contributor to energy consumption in the fluid-transport and piping industries. Here we describe a passive approach to reduce friction: we show that a local increase in streamwise flow
curvature, combined with changing the circular cross-section to an oval, relaminarizes turbulent flow in curved pipes. We exemplify this effect in a $180^\circ$ bend at $Re_D = 10\,000$ and $20\,000$, well above the linear-stability limit.
Curvature inhibits streamwise Reynolds stresses, and cross-sectional modifications weaken the secondary flow, together disrupting the near-wall regeneration cycle and collapsing
turbulence. Simulations and experiments confirm that these
geometric modifications suppress
turbulence and reduce pressure loss by 53% and 36% compared with the baseline $180^\circ$ bend and an equal-length fully developed straight pipe, respectively. The results establish a passive, mechanism-based route to relaminarization in curved pipes with implications for energy-efficient control in other wall-bounded flows with
curvature.
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Chemical Physics
Density and excess molar enthalpy of (2-propanol + glyme) liquid mixtures. Application of the Flory model
João Victor Alves-Laurentino, Fatemeh Pazoki, Luis Felipe Sanz et al. (+3 more)
For glymes of general formula CH3O(CH2CH2O)uCH3, with u = 1, 2, 3, 4, the densities of the (2-propanol + glyme) systems at temperatures ranging from (293.15 to 303.15) K and at pressure 0.1 MPa were determined using a DSA 5000 densimeter (from Anton Paar). The corresponding excess molar volumes were calculated from these density measurements. In addition, excess molar enthalpies at 298.15 K and 0.1 MPa were measured using a Tian-Calvet micro-calorimeter. The results show that alkanol-ether interactions are strong but do not contribute significantly to the excess molar enthalpy, as the values are large and positive, and comparable to those of (glyme + n-heptane) systems. The excess molar volumes are small or even negative (in the case of the mixture with u = 4), indicating that they are mainly governed by structural effects. Mixtures with 1-propanol or 2-propanol behave similarly, although interactions between unlike molecules become slightly stronger when 1-propanol is involved. On the other hand, effects related to alcohol self-association play a decisive role in the thermodynamic properties when glymes are replaced by di-n-propyl ether. This is supported by the application of the Flory model, which shows that orientational effects are weak in the studied glyme-containing mixtures but become significantly stronger when di-n-propyl ether is considered.
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All authors João Victor Alves-Laurentino, Fatemeh Pazoki, Luis Felipe Sanz, Juan Antonio González, Fernando Hevia, Daniel Lozano-Martín
Chemical Physics
Asymmetry and coverage dependence in two-pulse correlation measurements of CO photodesorption from Pd(111): Insights from theory
Raúl Bombín, Alberto S. Muzas, Alfredo Serrano Jiménez et al. (+2 more)
Two-pulse correlation experiments performed using pulses of different intensities on Pd(111) with different CO coverages showed that the CO photodesorption probability depends on whether the strong or the weak pulse arrives first to the surface, being this difference particularly large for the low-covered surface. Motivated by these experiments, we perform molecular dynamics simulations using a multicoverage potential energy surface that was previously constructed with the embedded atom neural network method. The process is modeled by combining the two-temperature model (2TM)–to describe the
laser-excited electrons and
phonons–and Langevin dynamics with electronic time-dependent temperature $T_\textrm{e}$–to model the coupling of the
nuclei degrees of freedom with the
laser-excited electrons. We show that improving the energy balance description in 2TM–by including ab-initio $T_\textrm{e}$-dependent electronic heat capacity and electron-
phonon coupling constant–is key to reproduce the asymmetry of the photodesorption probability $P_{\rm des}$ between positive and negative time delays. Furthermore, we also explore possible reasons for the usual underestimation of $P_{\rm des}$ at zero delay given by state-of-the-art calculations. In particular, we improve the description of the energy exchange between CO and the metal surface at high $T_\textrm{e}$ by including in the simulations a $T_\textrm{e}$-dependent friction coefficient. The prediction for $P_{\rm des}$ at zero delay in this case, increases by an order of magnitude, reducing its discrepancy with the experimental value. Altogether, our results hint to the importance of accounting for the temperature dependence of the electronic structure and properties in describing the extreme conditions generated in 2PC experiments.
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All authors Raúl Bombín, Alberto S. Muzas, Alfredo Serrano Jiménez, J. Iñaki Juaristi, Maite Alducin
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Low-to-mid Al content ($x\sim$ 0-0.56) Al$_x$In$_{1-x}$N layers deposited on Si(100) by radio-frequency sputtering
R. Blasco, S. Valdueza-Felip, D. Montero et al. (+3 more)
Radio-frequency (RF) sputtering is a low-cost technique for the deposition of large-area single-phase AlInN on silicon layers with application in photovoltaic devices. Here, the effect of the Al mole fraction x from 0 to 0.56 on the structural, morphological, electrical, and optical properties of $n$ Al$_x$In$_{1-x}$N layers deposited at 550 $^\circ$C on p-Si(100) by RF sputtering is studied. X-ray diffraction data show a wurtzite structure oriented along the c-axis in all samples, where the full width at half maximum of the rocking curve around the InN (0002) diffraction peak decreases from $\sim 9^\circ$ to $\sim 3^\circ$ while incorporating Al to the AlInN layer. The rootmean-square surface roughness, estimated from atomic force microscopy, evolves from 20 nm for InN to 1.5 nm for Al$_{0.56}$In$_{0.44}$N. Low-temperature photoluminescence spectra show a blueshift of the emission energy from 1.59 eV (779 nm) for InN to 1.82 eV (681 nm) for Al$_{0.35}$In$_{0.65}$N according to the Al content rise.
Hall effect measurements of Al$_x$In$_{1-x}$N ($0<x <0.35$) on sapphire samples grown simultaneously point to a residual n-type carrier concentration in the 1021 cm$^{-3}$ range. The developed n-AlInN/p-Si junctions present promising material properties to explore their performance operating as
solar cell devices.
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All authors R. Blasco, S. Valdueza-Felip, D. Montero, M. Sun, J. Olea, F. B. Naranjo
Applied Physics
InN nanowire solar cells on Si with amorphous Si interlayer deposited by sputtering
M. Sun, R. Gómez, B. Damilano et al. (+3 more)
Here, we report the first experimental demonstration of InN nanowire
solar cells deposited by RF sputtering with a bandgap energy of 1.78 eV. By adding an amorphous Si (a-Si) buffer to the n-InN/p-Si structure, we have improved the photovoltaic performance of the resulting devices while maintaining their material quality. We have firstly optimized the deposition of Si on Si(100) by DC sputtering, obtaining an amorphous material with bandgap energy of 1.39 eV. Then we have studied the influence of the thickness of the a-Si buffer layer (0-25 nm) on the structural, morphological, electrical, and optical properties of InN nanowires on Si (100) substrates. With the use of a 15-nm buffer, n-InN/a-Si/p-Si nanowire heterojunction
solar cells exhibit a promising short-
circuit current density of 17 mA/cm2, open
circuit voltage of 0.37 V and fill factor of 35.5%, pointing to a power-conversion efficiency of 2.3% under 1-sun (AM 1.5G) illumination. These work demonstrated that the combination of in-situ sputtered a-Si, which could serve as potential passivation layer, and the light trapping enhancement by the nanostructured active layer leads to an improvement of the photovoltaic efficiency of sputtered III-nitride devices.
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All authors M. Sun, R. Gómez, B. Damilano, J. M. Asensi, F. B. Naranjo, S. Valdueza-Felip
Optics
Etendue and Radiance Conservation in Transformation Optics: Strict Analytical Bounds on Field Enhancement
Mohammad Mehdi Sadeghi and Mustafa Sarisaman
We establish an intuitive connection between transformation optics (TO) and the classical invariants of etendue and radiance that hasn't been made before. Through explicit application of the optical metric formulation of TO, we demonstrate that any smooth, passive, impedance-matched transformation performs as a canonical (
symplectic) mapping on optical phase space. In combination with
Hamiltonian ray dynamics, this indicates that Liouville
theorem is as well applicable to TO media: the phase-space volume element is preserved, radiance remains constant along rays, and etendue won't decrease under any passive TO mapping. Based on this novel notion, which has never been mentioned in TO literature before, we develop a radiance-invariant phase-space measure specific to TO media. We then utilize it to find severe analytical limitations on field enhancement. We demonstrate that the maximum attainable average intensity in any passive TO concentrator is exclusively constrained by the area-compression ratio of the coordinate mapping, irrespective of material implementation. Using the same paradigm on zero-index media, optical-null media, and illusion devices reveals the same rules. Consequently, our findings demonstrate that transformation optics (TO) can redistribute intensity but cannot increase radiance, and they give a Liouville-type
theorem within this field. This provides a consistent, metric-based elucidation for the intrinsic limitations of concentration in passive metamaterials and extreme-index platforms.
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Instrumentation and Detectors
Improvements of the GPU Processing Framework for ALICE
David Rohr
ALICE is the dedicated heavy ion experiment at the LHC at CERN and records lead-lead collisions at a rate of up to 50 kHz. The detector with the highest data rate of up to 3.4 TB/s is the TPC. ALICE performs the full online TPC processing corresponding to more than 95% of the total workload on GPUs, and when there is no beam in the LHC, the online computing farm's GPUs are used to speed up the offline processing. After the deployment of the first version of the online TPC processing needed for data taking, ALICE has implemented many improvements to its GPU processing framework. These include a run time compilation mode applying on the fly optimizations, improvements to parallelize / speed up the GPU compilation, debugging modes to guarantee reproducible and deterministic results in concurrent reconstruction, and framework features to leverage common components in the code of different detectors. The proceedings give an overview of the ALICE experience with GPUs in online and offline processing and present the latest GPU processing framework features.
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Fluid Dynamics
A Priori Assessment of Rotational Invariance in Multiscale Convolutional Neural Network-Based Subgrid-Scale Model for Wall-Bounded Turbulent Flows
Bahrul Jalaali and Kie Okabayashi
This study proposes a rotationally invariant data-driven subgrid-scale (SGS) model for large-eddy simulation (LES) of wall-bounded turbulent flows. Building upon the multiscale convolutional neural network subgrid-scale model, which outputs SGS stress tensors ($\tau_{ij}$) as the baseline, the deep neural network (DNN) architecture is modified to satisfy the principle of material objectivity by removing the bias terms and batch normalization layers while incorporating a spatial
transformer network (STN)
algorithm. The model was trained on a turbulent channel flow at $\mathrm{Re}_\tau = 180$ and evaluated using both non-rotated and rotated inputs. The results show that the model performs well in predicting $\tau_{ij}$ and key
turbulence statistics, including dissipation, backscatter, and SGS transport. These quantities reflect the ability of the model to reproduce the energy transfer between the resolved scale and SGS. Moreover, it effectively generalizes to unseen rotated inputs, accurately predicting $\tau_{ij}$ despite the input configurations not being encountered during the training. These findings highlight that modifying the DNN architecture and integrating the STN-based
algorithm improves the ability to recognize and correctly respond to rotated inputs. The proposed data-driven SGS model addresses the key limitations of common data-driven SGS approaches, particularly their sensitivity to rotated input conditions. It also marks an important advancement in data-driven SGS modeling for LES, particularly in flow configurations where rotational effects are non-negligible.
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Chemical Physics
Quantum Jump Approach for Photosynthetic Energy Transfer with Chemical Reaction and Fluorescence Loss
Rui Li, Yi Li, Kai-Ya Zhang et al. (+1 more)
Recently, the coherent modified Redfield theory (CMRT) has been widely used to simulate the excitation-energy-transfer (EET) processes in photosynthetic systems. However, the numerical simulation of the CMRT is computationally expensive when dealing with large-scale systems, e.g. photosystem I (PSI) and II (PSII). On the other hand, the chemical reaction and fluorescence loss traditionally treated by the non-Hermitian
Hamiltonian approach may result in significantly error in a wide range of parameters. To address these issues, we introduce a
quantum jump approach (QJA) based on the CMRT to simulate the
evolution of photosynthetic complexes including both the chemical reaction and fluorescence loss. The QJA shows higher accuracy and efficiency in simulating the EET processes. The QJA-CMRT approach may provide a powerful tool to design and optimize artificial photosynthetic systems, which benefits future innovation in the field of energy.
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All authors Rui Li, Yi Li, Kai-Ya Zhang, Qing Ai
Physics and Society
Empirical Validation of Continuum Traffic Flow Model of Capacity Drop at Sag and Tunnel Bottlenecks
Shin-ichiro Kai, Ryota Horiguchi, Jian Xing et al. (+1 more)
This study validates the continuum traffic flow model of capacity drop at sag and tunnel bottlenecks, as proposed by Jin (2018) and Wada et al. (2020), through empirical analysis. Specifically, after addressing the limitations in the existing studies, we calibrate the model using data from multiple congestion events at several expressway bottlenecks. We then demonstrate that the model can reproduce the observed speed recovery near the head of the queue, and assess whether both estimated bottleneck capacities and locations are consistent with observed traffic conditions. Finally, as an application of the calibration results, we examine the relationship between the spatial changes in the estimated traffic capacity and longitudinal gradients.
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All authors Shin-ichiro Kai, Ryota Horiguchi, Jian Xing, Kentaro Wada
Optics
Single-Axis Ptychographic Coherent Diffractive Imaging for Spectroscopic and Wavefront Retrieval
Qijun You, Lingshuo Meng, Fangrui Quan et al. (+1 more)
We present a novel axial ptychographic coherent diffractive imaging (AP-CDI) technique designed to overcome the critical throughput bottleneck of conventional methods. By replacing the 2D raster scan with a simple 1D axial scan, our approach reduces the number of required diffraction patterns by approximately an order of magnitude while maintaining high-fidelity reconstruction. We have experimentally validated this concept, successfully performing simultaneous spectroscopic imaging of a sample and quantitative wavefront characterization of the illumination, thereby accurately quantifying the chromatic aberration of a broadband field. This capability establishes AP-CDI as a highly efficient and versatile tool for real-time, multi-modal imaging, with immediate potential in ultrafast science, material characterization, and live-
cell bio-imaging.
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All authors Qijun You, Lingshuo Meng, Fangrui Quan, Wei Cao