Peer Revenue Fully funded

Papers

Fresh from arXiv, typeset properly. Highlighted words trade on the market.

Physics Physics

physics Watch Physics

Monday 1 December

Optics · Cross-listed from quant-ph

Antibunching in locally driven dissipative Lieb lattices

Alex Ferrier, Michał Matuszewski, Piotr Deuar et al. (+1 more)

In Lieb lattices, geometric frustration and destructive interference of hopping cancels the occupation of certain sites, leading to flat-band physics. Here, we show numerically how, in the driven-dissipative Bose-Hubbard (DDBH) model arranged into Lieb lattices and related geometries, specific localised driving schemes can repurpose this interference to generate enhanced antibunching via a mechanism similar to the so-called unconventional photon blockade. Stochastic simulations using the positive-P method allow us to calculate occupations and second order correlations exactly for extended lattices. We use this to optimise the parameters for the possible observation of this effect in polariton micropillar experiments. This work demonstrates the possibility of using localised driving and interference effects to generate non-trivial quantum correlations in open quantum lattice systems. Specifically, producing antibunching in the dark sites of the flat band system rather than the usual and less useful bunching.
Read the full abstract

All authors Alex Ferrier, Michał Matuszewski, Piotr Deuar, Marzena H. Szymańska

Optics

Orbit-orbit photonics: Harnessing vortex-trajectory interplay for light manipulation

Raghvendra P. Chaudhary, Imon Kalyan and Nir Shitrit

Light can carry a spin angular momentum, an intrinsic and extrinsic orbital angular momentum, associated with a circular polarization, optical vortex beams, and varying beam trajectories, respectively. The interplay between these momenta yields the spin-orbit interaction of light, in which the spin (circular polarization) controls the spatial (orbital) degrees of freedom of light: either the extrinsic (trajectory) or the intrinsic orbital angular momentum (vortex). While the well-known spin-orbit interaction of light plays a crucial role in nano-optics by providing spin-controlled light manipulation, the interaction between the intrinsic and the extrinsic orbital angular momentum - the orbit-orbit interaction of light - has remained elusive. In this interplay, the helical phase fronts of optical vortices control the spatial trajectory of light, giving rise to vortex-dependent shifts of optical beams. We report the orbit-orbit interaction of light in a plasmonic ellipse cavity, whose unique geometry facilitates the interplay when a vortex is considered in one of the foci of the ellipse. In this configuration, the orbit-orbit interaction is achieved by the interplay between the vortex of the source and the ellipse-induced transverse shift of the source beam, positioned at one of the focal points - thus inducing transverse vortex-dependent shifts at the second focal point. Strikingly, the orbit-orbit interaction of light significantly enhances the toolbox available for controlling light by leveraging the manifold orbital angular momentum states for vortex-controlled light manipulation - in contrast to light manipulation based on the spin-orbit interaction, which exploits the binary polarization helicity.
Read the full abstract

Computational Physics · Cross-listed from cond-mat.mtrl-sci

First-principles screening of materials with extreme effective masses

Szymon Błazucki, Junfeng Qiao, Aleksandr Poliukhin et al. (+1 more)

The effective mass of charge carriers is a fundamental descriptor of the electronic structure of materials, and can be used to assess performance in electronics applications, or to screen for thermoelectrics and transparent conductors. Here, we perform a high-throughput computational screening of approximately 20,000 experimentally known three-dimensional stoichiometric inorganics obtained from the Materials Cloud 3D structure database. By combining density-functional theory calculations and maximally localized Wannier functions, we are able to compute the full conductivity effective mass tensor for electrons and holes from the Boltzmann transport equation in the constant relaxation-time approximation. This approach captures the effects of band non-parabolicity, anisotropy, and valley multiplicity that would be neglected by standard parabolic fittings. The screening identifies a curated set of candidates exhibiting extreme electronic properties, from ultra-low to ultra-large effective masses, these latter associated with flat-band physics. We validate the workflow by recovering established high-mobility semiconductors and highlight promising novel candidates. Furthermore, we classify materials by their mass anisotropy and discuss the physical limits of defining a conductivity effective mass in narrow-gap regimes at room temperature. Importantly, the resulting dataset provides a systematic roadmap to search for high-performance materials in novel chemical spaces.
Read the full abstract

All authors Szymon Błazucki, Junfeng Qiao, Aleksandr Poliukhin, Nicola Marzari

Chemical Physics

Accelerated Machine Learning Force Field for Predicting Thermal Conductivity of Organic Liquids

Wei Feng, Siyuan Liu, Hongyi Wang et al. (+11 more)

The thermal conductivity of organic liquids is a vital parameter influencing various industrial and environmental applications, including energy conversion, electronics cooling, and chemical processing. However, atomistic simulation of thermal conductivity of organic liquids has been hindered by the limited accuracy of classical force fields and the huge computational demand of ab initio methods. In this work, we present a machine learning force field (MLFF)-based molecular dynamics simulation workflow to predict the thermal conductivity of 20 organic liquids. Here, we introduce the concept of differential attention into the MLFF architecture for enhanced learning ability, and we use density of the liquids to align the MLFF with experiments. As a result, this workflow achieves a mean absolute percentage error of 14% for the thermal conductivity of various organic liquids, significantly lower than that of the current off-the-shelf classical force field (78%). Furthermore, the MLFF is rewritten using Triton language to maximize simulation speed, enabling rapid prediction of thermal conductivity.
Read the full abstract

All authors Wei Feng, Siyuan Liu, Hongyi Wang, Zhenliang Mu, Zhichen Pu, Xu Han, Tianze Zheng, Zhenze Yang, Zhi Wang, Weihao Gao, Yidan Cao, Kuang Yu, Sheng Gong, Wen Yan

Chemical Physics

Temperature Dependence of Charge and Exciton Transport in One-Dimensional Systems Subject to Static and Dynamic Disorder

William Barford

The temperature-dependence of dynamical properties (e.g., the asymptotic diffusion coefficient and the sub-diffusive exponent) are calculated for charges and excitons in one-dimensional systems subject to static and dynamic disorder. These properties are determined by three complementary methods. One approach is via the time-integration of the velocity autocorrelation function. The second is via the mean-squared-displacement of thermal wavepackets subject to stochastic collapse via Lindblad jump operators. These two methods are applicable in the high-temperature regime, where the noise is temporally uncorrelated. In this regime the noise causes particle localization and the transport is diffusive. The third approach – applicable in the low-temperature regime – is weak-coupling Redfield theory. Here, static disorder causes particle localization. When the dynamics is diffusive, the diffusion coefficient is a non-monotonic function of temperature, increasing with temperature in the low-temperature Environment Assisted Quantum Transport regime and decreasing with temperature in the high-temperature quantum-Zeno regime. For any temperature, static and dynamic disorder decreases the diffusion coefficient. The dynamics is non-diffusive for thermal energies deep within the manifold of local-ground-states, where the sub-diffusive exponent decreases with increasing disorder and decreasing temperature.
Read the full abstract

Atomic Physics

A Platform for Evanescently Trapping Rb-87 Using Silicon Nitride Strip Waveguides Buried in Silica

Sam J. Harding and Carrie Weidner

Cold-atom systems have emerged as a highly promising avenue for quantum-enhanced position, navigation, and timing applications. However, their wider adoption is currently hampered in part by the large footprint of the systems. In leveraging the miniaturisation possible through photonic integrated circuits, cold-atom sensors would be able to reach much wider commercial adoption. In this paper, we introduce a platform for evanescently trapping 87Rb using strip silicon nitride waveguides buried in silica using red- and blue-detuned fundamental and higher-order modes, providing a three-dimensional adjustable trap for BEC-based, chip-scale work in quantum science and technologies.
Read the full abstract

Instrumentation and Detectors

An Open-Path Eddy-Covariance Laser Spectrometer for Simultaneous Monitoring of CO$_2$, CH$_4$, and H$_2$O

Viacheslav Meshcherinov, Iskander Gazizov, Bogdan Pravuk et al. (+9 more)

We present E-CAHORS $-$ a compact mid-infrared open-path diode-laser spectrometer designed for the simultaneous measurement of carbon dioxide, methane, and water vapor concentrations in the near-surface atmospheric layer. These measurements, combined with simultaneous data from a three-dimensional anemometer, can be used to determine fluxes using the eddy-covariance method. The instrument utilizes two interband cascade lasers operating at 2.78 $\mu$m and 3.24 $\mu$m within a novel four-pass M-shaped optical cell, which provides high signal power and long-term field operation without requiring active air sampling. Two detection techniques $-$ tunable diode laser absorption spectroscopy (TDLAS) and a simplified wavelength modulation spectroscopy (sWMS) $-$ were implemented and evaluated. Laboratory calibration demonstrated linear responses for all gases (R$^2$ $\approx$ 0.999) and detection precisions at 10 Hz of 311 ppb for CO$_2$, 8.87 ppb for CH$_4$, and 788 ppb for H$_2$O. Field tests conducted at a grassland site near Moscow showed strong correlations (R = 0.91 for CO$_2$ and H$_2$O, R = 0.67 for CH$_4$) with commercial LI-COR LI-7200 and LI-7700 analyzers. The TDLAS mode demonstrated lower noise and greater stability under outdoor conditions, while sWMS provided baseline-free spectra but was more sensitive to power fluctuations. E-CAHORS combines high precision, multi-species sensing capability with low power consumption (10 W) and a compact design (4.2 kg).
Read the full abstract

All authors Viacheslav Meshcherinov, Iskander Gazizov, Bogdan Pravuk, Viktor Kazakov, Sergei Zenevich, Maxim Spiridonov, Shamil Gazizov, Gennady Suvorov, Olga Kuricheva, Yuri Lebedev, Imant Vinogradov, Alexander Rodin

Plasma Physics

Effects of Turbulent Energy Exchange between Electrons and Ions on Global Temperature Profiles

T. Kato, H. Sugama and M. Honda

Microscale turbulence drives not only particle and heat transport but also energy exchange between different particle species. Previous local gyrokinetic studies have shown that turbulent energy exchange can exceed collisional exchange in weakly collisional plasmas, and that ion temperature gradient (ITG) turbulence may hinder ion heating by alpha-heated electrons. In addition, it has been clarified that trapped electron mode (TEM) turbulence transfers energy from electrons to ions, thereby enhancing ion heating. In this work, we extend these studies by examining the impact of turbulent energy exchange on the global temperature profiles at a steady state using the one-dimensional transport solver GOTRESS. For the case of DIII-D discharge 128913 [A. E. White et al., Phys. Plasmas 15, 056116 (2008)], turbulent energy exchange has minimal influence on temperature profiles. However, in the case of enhanced electron heating in a DIIID like tokamak plasma, energy transfer from hot electrons to cold ions driven by TEM turbulence becomes comparable to, or even exceeds, the collisional contribution, leading to a significant increase in the ion temperature profile. For ITER Baseline and SPARC standard H-mode scenarios [N.T. Howard et al., Nucl. Fusion 65, 016002(2024), P. Rodriguez Fernandez et al., J. Plasma Phys. 86, 865860503(2020)], the turbulent energy exchange is largely compensated by the collisional one, producing only small effects. These results indicate that the impact of turbulent energy exchange on the global temperature profiles in steady state conditions of future fusion reactor scenarios is expected to be negligibly small, although it can become significant in situations such as plasma start up phases, where the heating power is strongly unbalanced between electrons and ions.
Read the full abstract

Instrumentation and Detectors

Neural network-based deconvolution for GeV-Scale Gamma-Ray Spectroscopy

Zhuofan Zhang, Mingxuan Wei, Kyle Fleck et al. (+4 more)

High-energy gamma-ray spectroscopy is crucial for studying and advancing the application of high-energy photons in areas like strong-field physics, high-energy-density science, and laboratory astrophysics. However, high-energy gamma-ray spectroscopy in the multi-MeV to GeV range faces significant challenges in precise spectral reconstruction. This study presents a machine learning-based inversion approach that combines a spectrometer design with advanced deconvolution algorithms. We develop a gamma-ray spectrometer optimized through Monte Carlo simulations for maximum positron yield and minimal noise. A two-stage neural network framework is proposed based on the structure of the spectrometer: a denoising autoencoder suppresses statistical noise in measured positron spectra, while a U-Net architecture solves the ill-posed inverse problem to reconstruct incident gamma spectra. This approach establishes a new methodology for gamma-ray diagnostics in strong-field QED experiments and high-energy photon sources.
Read the full abstract

All authors Zhuofan Zhang, Mingxuan Wei, Kyle Fleck, Jun Liu, Xinjian Tan, Gianluca Sarri, Wenchao Yan

Geophysics · Cross-listed from cs.CV

Depth Matching Method Based on ShapeDTW for Oil-Based Mud Imager

Fengfeng Li, Zhou Feng, Hongliang Wu et al. (+4 more)

In well logging operations using the oil-based mud (OBM) microresistivity imager, which employs an interleaved design with upper and lower pad sets, depth misalignment issues persist between the pad images even after velocity correction. This paper presents a depth matching method for borehole images based on the Shape Dynamic Time Warping (ShapeDTW) algorithm. The method extracts local shape features to construct a morphologically sensitive distance matrix, better preserving structural similarity between sequences during alignment. We implement this by employing a combined feature set of the one-dimensional Histogram of Oriented Gradients (HOG1D) and the original signal as the shape descriptor. Field test examples demonstrate that our method achieves precise alignment for images with complex textures, depth shifts, or local scaling. Furthermore, it provides a flexible framework for feature extension, allowing the integration of other descriptors tailored to specific geological features.
Read the full abstract

All authors Fengfeng Li, Zhou Feng, Hongliang Wu, Hao Zhang, Han Tian, Peng Liu, Lixin Yuan

Optics

Unsupervised and Supervised Algorithms for Identification of Sample Pixels in FTIR Images

Xiangyu Zhao, Yudong Tian, Jingzhu Shao et al. (+1 more)

Mid-InfraRed spectroscopy is a promising label-free technique that can offer insights into morphological and pathological alterations in biological tissues at the molecular level. Owing to the development of the Fourier Transform InfraRed (FTIR) spectrometer, combined with scanning devices, FTIR images can be produced by simultaneously acquiring spectral data from multiple spatial points, generating comprehensive chemical maps. In the data pre-processing, the identification of the sample pixels, with the background pixels excluded, is important for further effective feature extraction in FTIR images. Here, we present three algorithms realized in unsupervised and supervised approaches for the identification of the sample pixels. The algorithms demonstrate accurate prediction results of the sample and background pixels, and the supervised method further enables the automatic detection. These findings highlight thorough and robust solutions to the sample pixels detection problem in FTIR images, contributing to the FTIR signal processing and future research with FTIR images.
Read the full abstract

All authors Xiangyu Zhao, Yudong Tian, Jingzhu Shao, Chongzhao Wu

Chemical Physics

The Spin-MInt Algorithm: an Accurate and Symplectic Propagator for the Spin-Mapping Representation of Nonadiabatic Dynamics

Lauren E. Cook, James R. Rampton and Timothy J. H. Hele

Mapping methods, including the Meyer-Miller-Stock-Thoss (MMST) mapping and spin-mapping, are commonly utilised to simulate nonadiabatic dynamics by propagating classical mapping variable trajectories. Recent work confirmed the Momentum Integral (MInt) algorithm is the only known symplectic algorithm for the MMST Hamiltonian. To our knowledge, no symplectic algorithm has been published for the spin-mapping representation without obtaining Cartesian variables and utilising the MInt algorithm. Here, we present the Spin-MInt algorithm which directly propagates the spin-mapping variables. First, we consider a two-level system which maps onto a spin-vector on a Bloch sphere. Despite the spin-variables being non-canonical, we rigorously prove the Spin-MInt is symplectic using a canonical variable transformation. We determine that the Spin-MInt is a symmetrical, second-order, time-reversible, angle invariant and geometric structure preserving algorithm. Computationally, for a one-dimensional spin-boson model, the Spin-MInt and MInt algorithms are symplectic, satisfy Liouville's theorem, provide second-order energy conservation and are more accurate than a previously-published angle-based algorithm. We additionally present accurate correlation functions for a multi-dimensional spin-boson model. We also extend this methodology to a general number of electronic states and present accurate population results for a three-state Morse potential. The Spin-MInt is faster than the MInt algorithm for all tested models, particularly so for large nuclear degrees of freedom. We believe this to be the first known symplectic algorithm for propagating the nonadiabatic spin-mapping Hamiltonian and one of the first rigorously symplectic algorithms in the case of non-trivial coupling between canonical and spin systems. These results should guide and improve future simulations.
Read the full abstract

Instrumentation and Detectors

Combined Effects of Transient Ionizing and Electromagnetic Pulse on Vertical NPN Bipolar Transistor

Meiqing Zhong, Cui Meng, Yinong Liu et al. (+4 more)

Combined effects of transient ionizing and electromagnetic pulse on vertical NPN bipolar transistor were experimentally investigated under pulsed X-ray irradiation. Technology computer-aided design (TCAD) simulation method was also employed to explore the underlying physical mechanisms. The results demonstrate that the combined effect of a positive pulse injected into the collector (CEMP) and pulsed X-ray irradiation exceeds the linear superposition of their individual effects. Conversely, the combined effect of a positive pulse injected into the base (BEMP) and pulsed X-ray irradiation aligns closely with the results observed under BEMP acting alone. Mechanism analysis reveals that when CEMP and pulsed X-ray irradiation act simultaneously, there is a significant increase in both the drift photocurrent at the collector junction and the diffusion photocurrent near the collector junction. However, when BEMP and pulsed X-ray irradiation act simultaneously, these photocurrent components remain small, leading to a combined effect similar to the results observed when BEMP acts alone. These findings provide critical insights for the radiation-hardening design of bipolar circuits in harsh radiation environments.
Read the full abstract

All authors Meiqing Zhong, Cui Meng, Yinong Liu, Lanfeng Yuan, Chicheng Liu, Bolun Feng, Maoxing Zhang

Applied Physics · Cross-listed from cs.LG

Reconstructing Multi-Scale Physical Fields from Extremely Sparse Measurements with an Autoencoder-Diffusion Cascade

Letian Yi, Tingpeng Zhang, Mingyuan Zhou et al. (+3 more)

Extreme sensor sparsity makes full-field reconstruction a fundamentally ill-posed problem in scientific sensing,where the goal is to infer physical fields from sparse measurements.In this regime,the posterior is severely underconstrained and inherently multimodal,making its approximation highly ill-conditioned.Specifically,deterministic mappings collapse uncertainty,direct conditional learning cannot cover the space of possible observation-conditioned solutions,and likelihood-guided sampling becomes highly sensitive to noise and sensor configurations.These limitations result in unstable posterior estimates and highlight the need for modeling uncertainty in a structural manner.To this end,we propose Cascaded Sensing,a hierarchical framework that restructures posterior inference across scales.Rather than modeling the full-field posterior directly,Cas-Sensing first resolves global structural ambiguity through a deterministic coarse-stage estimator.A neural-operator-based functional autoencoder,trained with masked inputs,maps sparse observations to a coarse-scale structural field,acting analogously to a maximum a posteriori estimator that selects the dominant global configuration.This structural anchor fixes the principal degrees of freedom of the posterior and transforms the problem into a better-conditioned residual inference task.A conditional diffusion model then learns only the refined-scale residual distribution,confining sampling to a stable neighborhood of plausible solutions and suppressing competition among observation-consistent modes.To enhance robustness under varying sensing conditions,we introduce mask-cascade training,which exposes the model to diverse sparse observation patterns through intermediate coarse reconstructions.During inference,manifold-constrained guidance enforces observation consistency as a refinement mechanism rather than a global mode-selection process.
Read the full abstract

All authors Letian Yi, Tingpeng Zhang, Mingyuan Zhou, Guannan Wang, Quanke Su, Zhilu Lai

Computational Physics

Neural Network Perturbation Theory (NNPT): Learning Residual Corrections from Exact Solutions

Zhenhao Chen, Mutian Shen, Boris Fain et al. (+1 more)

Many complex physical systems naturally decompose into an exactly solvable component augmented by a perturbative correction. Rather than directly employing neural networks to analyze complex physical systems, we introduce Neural Network Perturbation Theory (NNPT)–a correction learning approach that predicts residual perturbations after analytically subtracting known exact solutions. Using the gravitational three-body problem as testbed, we vary Jovian mass from f=0.05 to 30 times its physical value while holding network architecture fixed. An equalized-accuracy protocol with 1% tolerance reveals an unexpected non-monotonic capacity profile: capacity peaks at f=5 in the late integrable regime (3x32, 2242 parameters), remains elevated through the transition region (f~15-17), then decreases in the fully chaotic regime (f>=17, requiring only 2x32 with 1186 parameters)–a 47% reduction from peak. With symplectic integrator energy conservation below 2x10^{-4}, this counterintuitive phenomenon reflects genuine physical structure rather than numerical artifacts. Sequential correction experiments show negligible refinement (||y2||/||y1||~0.997), confirming single-stage networks capture dominant perturbative features without hierarchical decomposition. The capacity transition at f_c=16.6+-2.8 aligns with Chirikov's resonance-overlap criterion. Intermediate-complexity regimes impose maximal capacity requirements, while fully chaotic dynamics undergo ergodic smoothing–trajectory-specific fluctuations become irreducible noise, leaving only statistically smooth corrections requiring fewer parameters.
Read the full abstract

All authors Zhenhao Chen, Mutian Shen, Boris Fain, Zohar Nussinov

Chemical Physics

Quantized plasmon modes for metallic nanoparticles of arbitrary shape with a generic dielectric function

Marco Romanelli, Gabriel Gil and Stefano Corni

In this work we introduce an effective approach to quantize the electromagnetic response of plasmonic metallic nanostructures. Their shape is arbitrary and they feature a realistic description of the frequency-dependent metal dielectric function that is based on experimental data. The derived quantum modes correctly reproduce the linear response macroscopic polarization of the nanoparticle upon external drive according to classical macroscopic Maxwell equations in the quasistatic limit. We further investigate the coupling of these modes to a quantum-chemical molecular description. The presented methodology paves the way for accurate modeling of plexcitonic system, where strong plasmon-molecule coupling and/or strong-driving fields call for a quantized description of the plasmonic response.
Read the full abstract

Optics

Near-infrared polarimetric imaging with nonlinear flat-optics

Evgenii Menshikov, Marco A. López Sánchez, Paolo Franceschini et al. (+4 more)

A compact and broadband polarimetric imaging platform is presented, based on second-harmonic generation (SHG) in nonlinear flat-optics. The system employs periodic all-dielectric AlGaAs gratings to induce polarization-dependent SH emission, enabling pixel by pixel direct retrieval of the full Stokes vector from an input intensity distribution in the near-infrared range. By engineering the geometry and orientation of the polarimetric units, sensitivity to linear and circular polarization components is achieved. A superpixel design comprising four polarimetric structures allows accurate reconstruction of the polarization state without moving parts or sequential measurements. This approach offers a scalable, passive, and cost-effective solution for polarimetric imaging, particularly suited for near-infrared applications.
Read the full abstract

All authors Evgenii Menshikov, Marco A. López Sánchez, Paolo Franceschini, Andrea Tognazzi, Domenico de Ceglia, Kristina Frizyuk, Costantino De Angelis

Classical Physics

Investigation of Al-Si-Cu alloys as phase change materials for high temperature thermal energy storage

Laura Teodorescu, Ángel Serrano, Kyran Williamson et al. (+4 more)

The present work explores the suitability of Al-Cu-Si ternary alloys as hightemperature metallic phase change materials (PCMs) for thermal energy storage systems (TESS) operating between 550__C and 850__C. While prior research has primarily focused on thermodynamic modeling or thermal property characterization below 600{\textdegree}C, this work provides a comprehensive experimental assessment of selected invariant compositions within the Al-Cu-Si system. CALPHAD calculations were performed using FactSage 8.2 software (SGTE and FTLite database) to guide alloy selection, followed by synthesis and detailed characterization of melting point, latent heat, specific heat, thermal diffusivity, and thermal conductivity. Critically, the thermal reliability of these materials was evaluated under repeated solid-liquid cycling up to 100 cycles in oxidizing atmospheres, revealing their stability and degradation profiles. Additionally, dilatometry and density analysis were conducted to provide an in-depth understanding of the alloys and practical properties for end users. Among the tested alloys, several demonstrated high volumetric energy densities (over 500 kWh/m${}^3$ for a temperature difference of 300{\textdegree}C) and good thermal durability, establishing Al-Cu-Si alloys as promising PCM candidates for industrial-scale high-temperature energy storage applications. This study fills a notable gap in the literature by combining phase selection, comprehensive thermophysical property evaluation, and long-term cycling analysis above 600{\textdegree}C.
Read the full abstract

All authors Laura Teodorescu, Ángel Serrano, Kyran Williamson, Cristina Luengo, Artem Nikulin, Elena Palomo del Barrio, Grégory Largiller

Fluid Dynamics

Neural Networks for Predicting Permeability Tensors of 2D Porous Media: Comparison of Convolution- and Transformer-based Architectures

Sigurd Vargdal, Paula Reis, Henrik Andersen Sveinsson et al. (+1 more)

Permeability is a central concept in the macroscopic description of flow through porous media, with applications spanning from oil recovery to hydrology. Traditional methods for determining the permeability tensor involving flow simulations or experiments can be time consuming and resource-intensive, while analytical methods, e.g., based on the Kozeny-Carman equation, may be too simplistic for accurate prediction based on pore-scale features. In this work, we explore deep learning as a more efficient alternative for predicting the permeability tensor based on two-dimensional binary images of porous media, segmented into solid ($1$) and void ($0$) regions. We generate a dataset of 24,000 synthetic random periodic porous media samples with specified porosity and characteristic length scale. Using Lattice-Boltzmann simulations, we compute the permeability tensor for flow through these samples with values spanning three orders of magnitude. We evaluate three families of image-based deep learning models: ResNet (ResNet-$50$ and ResNet-$101$), Vision Transformers (ViT-T$16$ and ViT-S$16$) and ConvNeXt (Tiny and Small). To improve model generalisation, we employ techniques such as weight decay, learning rate scheduling, and data augmentation. The effect of data augmentation and dataset size on model performance is studied, and we find that they generally increase the accuracy of permeability predictions. We also show that ConvNeXt and ResNet converge faster than ViT and degrade in performance if trained for too long. ConvNeXt-Small achieved the highest $R^2$ score of $0.99460$ on $4,000$ unseen test samples. These findings underscore the potential to use image-based neural networks to predict permeability tensors accurately.
Read the full abstract

All authors Sigurd Vargdal, Paula Reis, Henrik Andersen Sveinsson, Gaute Linga

Atomic Physics

Rci-Q: an improved QED correction model for the GRASP2018 package

Karol Kozioł

The Rci-Q package is an extension to the GRASP2018 suite, improving the model of estimating the quantum-electrodynamics corrections to the energy levels. The Flambaum-Ginges radiative potential method is used to estimate the leading self-energy correction to electron energy in many electron atoms. The new fitting prefactors to parameterize radiative potential are presented. The correction to self-energy originating from finite nucleus size is included. The Wichmann-Kroll part of the vacuum polarization potential is also implemented.
Read the full abstract

Atmospheric and Oceanic Physics

Snow cover over the Iberian mountains in km-scale global climate simulations: evaluation and projected changes

Diego García-Maroto, Elsa Mohino, Luis Durán et al. (+2 more)

Mountains represent a crucial natural resource for many regions worldwide, particularly in Mediterranean areas such as the Iberian Peninsula, where climate change can have profound consequences. However, due to their coarse resolution, traditional climate models cannot properly represent the complexity of orographic processes. Storm-resolving models, with horizontal resolutions finer than 10 km, offer a path forward to provide high-quality information for local adaptation even in complex terrain. In this work, we assess the performance of one such model, the IFS-FESOM developed within the EU-NextGEMS project, in simulating the historical seasonal snow cover in the main Iberian mountain ranges and projecting climate change effects. It is evaluated against four high-resolution reanalysis-based products, satellite, and in situ observations. Despite a positive bias in the length of the snow cover season and the number of snowfall days, the model can represent features of the snow climatology, such as its elevation dependency, seasonal cycle, and mean covered area, successfully within the range of uncertainty of the reference data. In addition, the projection under the SSP3-7.0 scenario shows a marked reduction of the snow season across almost all of Iberia, with low- to mid-elevations and the southernmost ranges showing the greatest reductions. These changes are linked to reduced snowfall days, caused primarily by rising temperatures but also by decreasing precipitation, particularly in southern and Mediterranean sectors. Our results suggest that storm-resolving simulations can provide local climate projections of snow information in complex terrain without regional downscaling.
Read the full abstract

All authors Diego García-Maroto, Elsa Mohino, Luis Durán, Álvaro González-Cervera, Xabier Pedruzo-Bagazgoitia

Space Physics · Cross-listed from astro-ph.SR

Velocity-space turbulent cascade in the near-Sun solar wind: first insights from the Parker Solar Probe mission

A. Larosa, O. Pezzi, T. Bowen et al. (+11 more)

In space plasmas, the rarity of collisions leads to complex structures in the velocity space where a turbulent cascade of the velocity distribution function fluctuations is thought to occur. Previous studies have explored this phenomenon using the Hermite decomposition of the ion velocity distribution function (VDF) in both magnetosheath data and numerical simulations. In this work, we investigate the Hermite spectrum of the ion VDFs measured by Parker Solar Probe in the inner heliosphere. We analyze a superalfvénic stream at a radial distances of $R \approx 28 R_{sun}$ and a subalfvénic at $R \approx 11 R_{sun}$, the former characterized by a prevalence of VDFs with suprathermal beams (also known as hammerhead). The Hermite analysis is also compared with various proxies of energization and dissipation, in order to establish a connection between turbulent cascades in real space and those in the velocity space. A qualitative agreement between the energization proxies and the Hermite analysis is observed. The results are suggestive of the presence of a dual cascade in real and velocity space.
Read the full abstract

All authors A. Larosa, O. Pezzi, T. Bowen, L. Sorriso-Valvo, N. Sioulas, F. Pucci, D. Trotta, J. L. Verniero, R. Livi, S. Bharati Das, A. Chasapis, D. Perrone, F. Valentini, S. Servidio

Optics

All-optical directional switching of non-thermal photocurrents in plasmonic nanocircuits

Roméo Zapata, Diana Singh, Obren Markovic et al. (+9 more)

Controlling the flow of electricity in metallic circuits with light is a key goal for future optoelectronics. In this work, we demonstrate all-optical generation and directional control of non-thermal drift photocurrents in a plasmonic gold wire. We attribute this phenomenon to the Inverse Faraday Effect and show that the current's direction can be precisely reversed at a subwavelength scale by tailoring the incident light's polarization or laser beam position. A bespoke polarization modulation technique is employed to unambiguously separate ultrafast drift currents from the ubiquitous photothermal background. We further reveal a collaborative mechanism where macroscopic thermal gradients, acting as a driving force, are used to extract and remotely detect the locally-generated nanoscale photocurrents. This robust control and detection scheme paves the way for reconfigurable, all-optical nanocircuitry capable of ultrafast on-chip processing.
Read the full abstract

All authors Roméo Zapata, Diana Singh, Obren Markovic, Chantal Hareau, Xingyu Yang, Ye Mou, Catherine Schwob, Bruno Gallas, Maria Sanz-Paz, Gérard Colas-des-Francs, Alexandre Bouhelier, Mathieu Mivelle

Medical Physics

Sulfate phantoms to mimic NIR photoacoustic response of whole blood at selected oxygen saturations

Léa Davenet, Arthur Billon, Jacques Battaglia et al. (+2 more)

This paper presents a practical guide to prepare inorganic solutions that mimic the photoacoustic properties of whole blood at specific levels of oxygenation in the range of 700 nm to 850 nm, both in terms of optical absorption properties and the Grüneisen coefficient. The goal is to provide aqueous solutions that can be injected in tissue-mimicking imaging phantoms to test the sensitivity and spectral capabilities of photoacoustic imaging systems operating at multiple wavelengths to estimate blood oxygenation.
Read the full abstract

All authors Léa Davenet, Arthur Billon, Jacques Battaglia, S Lori Bridal, Jérôme Gateau

Instrumentation and Detectors

A new dual RF sensor in gas detection and humidity influence

Julien George, Hamida Hallil, Corinne Dejous et al. (+4 more)

In this paper, we propose to study the influence of moisture on passive radiofrequency (RF) sensors associated with poly (3,4-ethylenedioxythiophene) polystyrene sulfonate - multi walled carbon nanotubes (PEDOT:PSS-MWCNT) as a sensitive material. The designed sensor consists of a copper ring resonator and two PEDOT:PSS-MWCNT stubs located in the maximum electric fields of the fundamental resonant mode operating at 3.94 GHz. For performing a differential analysis, experiments are conducted by considering the resonant structure with and without a sensitive layer. Our experiments show the high interference of humidity on the sensor's performances and highlight the importance of differential study to decorrelate physical and chemical surrounding parameters. This differential innovative structure paves the way for a very high sensitive passive RF sensor dedicated for gas sensing.
Read the full abstract

All authors Julien George, Hamida Hallil, Corinne Dejous, Eric Cloutet, Aurélien Perigaud, Stéphane Bila, Dominique Baillargeat

Classical Physics · Cross-listed from math.AP

Mathematical and numerical study of a model for navigation in stratified waters

Zeina Rammal, Matthieu Brachet, Germain Rousseaux et al. (+1 more)

We derive a linear model of navigation in a two-layer fluid with a variable velocity of the ship. A spectral version of the model including a Rayleigh damping term is analyzed. We prove that the Cauchy problem has a unique solution if the velocity and if the initial data are sufficiently regular. The case of a constant speed is thoroughly investigated and the importance of a critical speed which separates two types of regimes is pointed out. We propose a numerical scheme based on the discrete Fourier transform for the space discretization and on an exponential integrator for the time discretization. We prove an error estimate for the exponential integrator. Numerical experiments in one and two space dimensions complete the theoretical results.
Read the full abstract

All authors Zeina Rammal, Matthieu Brachet, Germain Rousseaux, Morgan Pierre

Optics

Effects of thermal annealing and film thickness on the structural and optical properties of indium-tin-oxide thin films

Ding Xu, Wen Zhou, Yuxin Du et al. (+3 more)

Indium-tin oxide (ITO) is a crucial functional layer for the optoelectronic applications, such as non-volatile color display thin films based on the ITO/phase-change material (PCM)/ITO/reflective metal multilayer structures on a silicon substrate. In addition to non-volatile color tuning by PCMs, thermally induced crystallization may alter the optical properties of ITO layers as well. But the potential change in color of the ITO layers is not considered so far. In this work, we investigate the structural and optical properties of ITO thin films via X-ray diffraction, spectroscopic ellipsometry and ultraviolet-visible spectrophotometry measurements. After thermal annealing at 250 {\deg}C, the ITO thin films of 15-100 nm get crystallized with strong changes in refractive index n and extinction coefficient k in the visible light range. However, for the 5-nm ITO thin film, crystallization is only observed after thermal annealing at 350 {\deg}C and the change in color is limited upon phase transition. We provide a colormap of the ITO/platinum/silicon structure in terms of the annealing temperature (150-350 {\deg}C) and ITO film thickness (5-100 nm). Our work suggests that the intrinsic change in colors of ITO layers should also be considered for the PCM-based reconfigurable display application.
Read the full abstract

All authors Ding Xu, Wen Zhou, Yuxin Du, Junying Zhang, Wei Zhang, Jiangjing Wang

Computational Physics · Cross-listed from cond-mat.mtrl-sci

An Investigation of Thermal Properties of Cu-Au Janus Nanoparticles

Mehmet Akif Cebeci, Hatice Zor Oguz, Sevgi Ozdemir Kart et al. (+1 more)

In this paper, the thermal and structural properties of Cu-Au (Copper-Gold) Janus nanoparticles with a diameter of 5 nm are investigated by using molecular dynamics (MD) simulations within the interactions defined by the many-body embedded atom model (EAM). A set of nanoparticle models has been constructed, with varying Cu and Au ratios. MD method is carried out to calculate the melting temperature, heat capacity, radial distribution function (RDF), Lindemann index, mean square displacement (MSD), and diffusion coefficients of these models. The findings demonstrate that nanoparticles rich in Cu exhibit a higher melting temperature and more defined phase transitions. In contrast, structures rich in gold exhibited reduced melting temperatures and showed surface-initiated melting behaviours. MD study highlights that the thermal stability and atomic mobility of Cu-Au Janus nanoparticles depend on the composition ratio and the dispersion of materials.
Read the full abstract

All authors Mehmet Akif Cebeci, Hatice Zor Oguz, Sevgi Ozdemir Kart, Hasan Huseyin Kart

Biological Physics

Active Force Dynamics in Red Blood Cells Under Non-Invasive Optical Tweezers

Arnau Dorn, Clara Luque-Rioja, Macarena Calero et al. (+3 more)

Red blood cells (RBCs) sustain mechanical stresses associated with microcirculatory flow through ATP-driven plasma membrane flickering. This is an active phenomenon driven by motor proteins that regulate interactions between the spectrin cytoskeleton and the lipid bilayer; it is manifested in RBC shape fluctuations reflecting the cell's mechanical and metabolic state. Yet, direct quantification of the forces and energetic costs underlying this non-equilibrium behavior remains challenging due to the invasiveness of existing techniques. Here, a minimally invasive method that combines bead-free, low-power optical tweezers with high-speed video microscopy was employed to track local membrane forces and displacements in single RBCs during the same time window. This independent dual-channel measurement enabled the construction of a mechano-dynamic phase space for RBCs under different chemical treatments, that allowed for differentiating between metabolic and structural states based on their fluctuation-force signatures. Quantification of mechanical work during flickering demonstrated that membrane softening enhanced fluctuations while elevating energy dissipation. The proposed optical tweezers methodology provides a robust framework for mapping the active mechanics of living cells, enabling precise probing of cellular physiology and detection of biomechanical dysfunction in diseases.
Read the full abstract

All authors Arnau Dorn, Clara Luque-Rioja, Macarena Calero, Diego Herráez-Aguilar, Francisco Monroy, Niccolò Caselli

Applied Physics

Acoustic helical dichroism enhanced by chiral quasi-bound states in the continuum

Qing Tong, Tong Fu, Yuqiong Cheng et al. (+1 more)

Acoustic helical dichroism (HD) arises from the interaction between vortex beams carrying orbital angular momentum (OAM) and chiral media, yet such chiral sound-matter interactions are typically weak. Here, we employ quasi-bound states in the continuum (QBICs) in acoustic meta-cavities composed of coupled Helmholtz resonators to enhance acoustic HD. We design both achiral and chiral meta-cavities that support QBICs in the form of vortex states with high Q-factors. Using full-wave numerical simulations, we show that the QBICs in the achiral meta-cavities cannot enhance acoustic HD due to the absence of a chiral wavefront. In contrast, the chiral meta-cavity exhibits a pronounced HD enhancement through the QBICs with a 3D helical wavefront, which can be excited by incident waves either with or without OAM. Our work identifies two essential requirements for enhancing acoustic HD effect via QBICs: a high Q-factor of the states and 3D chirality of the state fields, which usually compromise each other in conventional acoustic resonators. The findings open new avenues for achieving strong chiral sound-matter interactions, with potential applications in acoustic chiral sensing and acoustic OAM manipulation.
Read the full abstract

All authors Qing Tong, Tong Fu, Yuqiong Cheng, Shubo Wang