Chemical Physics · Cross-listed from cond-mat.mtrl-sci
DeepH-pack: A general-purpose neural network package for deep-learning electronic structure calculations
Yang Li, Yanzhen Wang, Boheng Zhao et al. (+15 more)
In computational physics and materials science, first-principles methods, particularly density functional theory, have become central tools for electronic structure prediction and materials design. Recently, rapid advances in artificial intelligence (AI) have begun to reshape the research landscape, giving rise to the emerging field of deep-learning electronic structure calculations. Despite numerous pioneering studies, the field remains in its early stages; existing software implementations are often fragmented, lacking unified frameworks and standardized interfaces required for broad community adoption. Here we present DeepH-pack, a comprehensive and unified software package that integrates first-principles calculations with deep learning. By incorporating fundamental physical principles into neural-network design, such as the nearsightedness principle and the equivariance principle, DeepH-pack achieves robust cross-scale and cross-material generalizability. This allows models trained on small-scale structures to generalize to large-scale and previously unseen materials. The toolkit preserves first-principles accuracy while accelerating electronic structure calculations by several orders of magnitude, establishing an efficient and intelligent computational paradigm for large-scale materials simulation, high-throughput materials database construction, and AI-driven materials discovery.
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All authors Yang Li, Yanzhen Wang, Boheng Zhao, Xiaoxun Gong, Yuxiang Wang, Zechen Tang, Zixu Wang, Zilong Yuan, Jialin Li, Minghui Sun, Zezhou Chen, Honggeng Tao, Baochun Wu, Yuhang Yu, He Li, Felipe H. da Jornada, Wenhui Duan, Yong Xu
Plasma Physics
A First-Principles Closure for Nonlocal Magnetized Transport
Nicholas Mitchell, David Chapman and Grigory Kagan
A reduced kinetic method (RKM) for describing nonlocal transport in magnetized
plasmas is derived from first principles and considered in a 1D3V geometry. Unlike standard nonlocal closures, this RKM uses the Fokker-Planck collision operator, therefore local transport results are naturally reproduced for small Knudsen number. An inhibited peak heat flux and preheat of the conductive heat flux are observed, which are expected from physical arguments and previous kinetic studies. Nonlocal behavior of other transport fluxes, namely the Righi-Leduc, Peltier, Ettingshausen, Nernst, thermal force, friction, cross friction, viscous stress, and gyroviscous stress terms are also demonstrated. Neglecting the nonlinear component of the Fokker-Planck collision operator is justified a posteriori. An especially computationally efficient and analytically simpler version of the RKM is presented.
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Physics Education
What happens if you put your head in the Geneva water jet? An inquiry-based physics activity exploring fluid dynamics
Maria Alice Gasparini
We describe a physics education activity for third-year Bachelor students, inspired by a humorous question about the Geneva water jet. The exercise engages students in key scientific practices: reformulating everyday questions in scientific terms, constructing simplified models, performing semi-quantitative estimations, and comparing alternative solution methods. Students explore approaches based on Bernoulli principle and a power analysis, revealing consistent results when assumptions are carefully considered. The activity emphasizes critical
reasoning, including identifying relevant data, making approximations, and applying energy and mass conservation to incompressible fluids. It also fosters metacognitive skills and higher-order thinking (HOT), illustrating the universality of fundamental physical principles across diverse phenomena. By situating the task in a relatable, real-world context, the activity motivates students while exposing them to problem-solving challenges rarely encountered in traditional instruction, such as
Fermi-type estimation and cross-context knowledge transfer.
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Computational Physics · Cross-listed from cond-mat.stat-mech
Data-driven Reduction of Transfer Operators for Particle Clustering Dynamics
Nathalie Wehlitz, Grigorios A. Pavliotis, Christof Schütte et al. (+1 more)
We develop an operator-based framework to coarse-grain interacting particle systems that exhibit clustering dynamics. Starting from the particle-based transfer operator, we first construct a sequence of reduced representations: the operator is projected onto concentrations and then further reduced by representing the concentration dynamics on a
geometric low-dimensional
manifold and an adapted finite-state discretization. The resulting coarse-grained transfer operator is finally estimated from dynamical simulation data by inferring the transition probabilities between the Markov states. Applied to systems with multichromatic and Morse interaction potentials, the reduced model reproduces key features of the clustering process, including transitions between cluster configurations and the emergence of metastable states. Spectral analysis and transition-path analysis of the estimated operator reveal implied time scales and dominant transition pathways, providing an interpretable and efficient description of particle-clustering dynamics.
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All authors Nathalie Wehlitz, Grigorios A. Pavliotis, Christof Schütte, Stefanie Winkelmann
Geophysics
Enhanced 3D Gravity Inversion Using ResU-Net with Density Logging Constraints: A Dual-Phase Training Approach
Siyuan Dong, Jinghuai Gao, Shuai Zhou et al. (+2 more)
Gravity exploration has become an important geophysical method due to its low cost and high efficiency. With the rise of artificial intelligence, data-driven
gravity inversion methods based on deep learning (DL) possess physical property recovery capabilities that conventional regularization methods lack. However, existing DL methods suffer from insufficient prior information constraints, which leads to inversion models with large data fitting errors and unreliable results. Moreover, the inversion results lack constraints and matching from other exploration methods, leading to results that may contradict known geological conditions. In this study, we propose a novel approach that integrates prior density well logging information to address the above issues. First, we introduce a depth weighting function to the neural network (NN) and train it in the weighted density parameter domain. The NN, under the constraint of the weighted forward operator, demonstrates improved inversion performance, with the resulting inversion model exhibiting smaller data fitting errors. Next, we divide the entire network training into two phases: first training a large pre-trained network Net-I, and then using the density logging information as the constraint to get the optimized fine-tuning network Net-II. Through testing and comparison in synthetic models and Bishop Model, the inversion quality of our method has significantly improved compared to the unconstrained data-driven DL inversion method. Additionally, we also conduct a comparison and discussion between our method and both the conventional focusing inversion (FI) method and its well logging constrained variant. Finally, we apply this method to the measured data from the San Nicolas mining area in Mexico, comparing and analyzing it with two recent
gravity inversion methods based on DL.
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All authors Siyuan Dong, Jinghuai Gao, Shuai Zhou, Baohai Wu, Hongfa Jia
Atmospheric and Oceanic Physics
STIPP: Space-time in situ postprocessing over the French Alps using proper scoring rules
David Landry, Isabelle Gouttevin, Hugo Merizen et al. (+2 more)
We propose Space-time in situ postprocessing (STIPP), a machine learning model that generates spatio-temporally consistent weather forecasts for a network of station locations. Gridded forecasts from classical numerical weather prediction or data-driven models often lack the necessary precision due to unresolved local effects. Typical statistical postprocessing methods correct these biases, but often degrade spatio-temporal correlation structures in doing so. Recent works based on generative modeling successfully improve spatial correlation structures but have to forecast every lead time independently. In contrast, STIPP makes joint spatio-temporal forecasts which have increased accuracy for surface temperature, wind, relative humidity and precipitation when compared to baseline methods. It makes hourly ensemble predictions given only a six-hourly deterministic forecast, blending the boundaries of postprocessing and temporal interpolation. By leveraging a multivariate proper scoring rule for training, STIPP contributes to ongoing work data-driven atmospheric models supervised only with distribution marginals.
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All authors David Landry, Isabelle Gouttevin, Hugo Merizen, Claire Monteleoni, Anastase Charantonis
Chemical Physics · Cross-listed from cond-mat.soft
Interplay of Structure and Dynamics in Solid Polymer Electrolytes: a Molecular Dynamics Study of LiPF6/polypropylene carbonate
Amaury Coste, Thomas Meyer, Claire Villevielle et al. (+3 more)
Solid-state batteries (SSB) are emerging as next-generation electrochemical energy storage devices. Achieving high energy density in SSB relies on solid
polymer electrolytes (SPE) that are electrochemically stable against both lithium metal and high-potential positive electrodes, two conditions that are difficult to satisfy without chemical degradation. In this work, molecular dynamics simulations are employed to investigate the relationship between structure and dynamics in carbonate-based SPE composed of polypropylene carbonate and lithium hexafluorophosphate (LiPF$_6$), at salt concentrations ranging from 0.32 to 1.21 mol$/$kg. Structural properties are analyzed under ambient pressure at the experimentally relevant temperature $T = 353$ K. Since the slow dynamical processes governing ion transport in these systems are inaccessible to direct molecular dynamics, transport properties are simulated at elevated temperatures up to 900 K and extrapolated to $T = 353$ K using Arrhenius behavior. The results reveal strong ionic correlations, a limited fraction of free ions, and a predominance of negatively charged clusters, especially at high salt concentration. At high temperature, the self-
diffusion coefficient of Li$^+$ exceeds that of PF$_6^-$ due to weaker Li$^+$-carbonate and ion-ion interactions. However, at $T = 353$ K, Li$^+$ mobility becomes lower than that of the anion, consistent with typical experimental observations in SPE. As expected, the ionic conductivity $\sigma$ increases with temperature, while at $T = 353$ K it exhibits a maximum for salt concentrations between 1.0 and 1.1 mol$/$kg. Overall, the estimated physico-chemical parameters highlight the key role of ion correlations in SPE and suggest strategies to optimize electrolyte performance. The Arrhenius extrapolation approach used here provides valuable insight into ion transport mechanisms in solid
polymer electrolytes.
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All authors Amaury Coste, Thomas Meyer, Claire Villevielle, Fannie Alloin, Stefano Mossa, Benoit Coasne
Fluid Dynamics
ML enhanced measurement of the electrostatic charge distribution of powder conveyed through a duct
Christoph Wilms, Wenchao Xu, Gizem Ozler et al. (+3 more)
The electrostatic charge acquired by powders during transport through ducts can cause devastating
dust explosions. Our recently developed
laser-optical measurement technique can resolve the powder charge along a one-dimensional (1D) path. However, the charge across the duct's complete two-dimensional (2D) cross-section, which is the critical parameter for process safety, is generally unavailable due to limited optical access. To estimate the complete powder charge distribution in a conveying duct, we propose a machine learning (ML) approach using a shallow neural network (SNN). The ML
algorithm is trained with cross-sectional data extracted from four different three-dimensional direct numerical simulations of a turbulent duct flow with varying particle size. Through this training with simulation data, the ML
algorithm can estimate the powder charge distribution in the duct's cross-section based on only 1D measurements. The results reveal an average $L^1$-error of the reconstructed 2D cross-section of 1.63 %.
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All authors Christoph Wilms, Wenchao Xu, Gizem Ozler, Simon Jantač, Sonja Schmelter, Holger Grosshans
Optics
A Vehicle-portable Ultra-stable Laser for Operating on Highways
Dongdong Jiao, Qing Li, Jing Gao et al. (+6 more)
Portable ultra-stable
lasers are essential for high-precision measurements. This study presents a 1550 nm vehicle-portable ultra-stable
laser designed for continuous real-time operation on highways. We implement several measures to mitigate environmental impacts, including active temperature control with a standard deviation of mK/day to reduce frequency drift of the optical reference cavity, all-polarization-maintaining fiber devices to enhance the robustness of the optical path, and highly integrated electronic units to diminish thermal effects. The performance of the ultra-stable
laser is evaluated through real-time beat frequency measurements with another similar ultra-stable
laser over a transport distance of approximately 100 km, encompassing rural roads, national roads, urban roads, and expressways. The results indicate frequency stability of approximately 10-12/(0.01s-100 s) during transport, about 5E-14/s while the vehicle is stationary with the engine running, and around 3E-15/s with the engine off, all without active vibration isolation. This work marks the first recorded instance of a portable ultra-stable
laser achieving continuous real-time operation on highways and lays a crucial foundation for non-laboratory applications, such as mobile
laser communication and dynamic free-space time-frequency comparison.
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All authors Dongdong Jiao, Qing Li, Jing Gao, Linbo Zhang, Mengfan Wu, Qi Zang, Jianing Wang, Guanjun Xu, Tao Liu
Optics
High-Q AlN microresonators for nonlinear near-infrared and near-visible photonics
Yulei Ding, Yuming Huang, Zhongdong Yin et al. (+9 more)
High Q-factors of microresonators are crucial for nonlinear integrated photonics, as many nonlinear dynamics have quadratic or even cubic dependence on Q-factors. The unique material properties make AlN microresonators invaluable for microcomb generation, Raman lasing and visible integrated photonics. However, the loss level of AlN falls behind other integrated platforms. By optimizing the fabrication, we demonstrate record Q-factors of 5.4$\times$10$^6$ and 2.2$\times$10$^6$ for AlN microresonators in the near-infrared and near-visible, respectively. Polarized-mode-interaction was used to create anomalous dispersion to support bright AlN Dirac
solitons. Measurement of polarization-dependent spectra reveals the polarization hybridization of the Dirac
soliton. In a microresonator with normal dispersion, Raman assisted four-wave-mixing (RFWM) was observed to initiate platicon formation, adding an approach to generate normal dispersion microcombs. A design of width-varying waveguides was used to ensure both efficient coupling and high Q-factor for racetrack microresonators at 780 nm. The microresonator was pumped to generate near-visble Raman
laser at 820 nm with a fundamental linewidth narrower than 220 Hz. Our work unlocks new opportunities for integrated AlN photonics by improving Q-factors and uncovering nonlinear dynamics in AlN microresonators.
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All authors Yulei Ding, Yuming Huang, Zhongdong Yin, Yifei Wang, Kewei Liu, Yanan Guo, Liang Zhang, Qi Zhang, Jianchang Yan, Junxi Wang, Changxi Yang, Chengying Bao
Instrumentation and Detectors
Development of CMOS LGAD sensors for the ALICE~3 Time of Flight detector
Umberto Follo
The next-generation ALICE 3 experiment at the High-Luminosity LHC (HL-LHC) requires detector technologies that combine fine spatial resolution, fast timing, and an extremely low material budget. This paper presents the design, characterization, and beam-test performance of MadPix, a monolithic CMOS sensor featuring an internal avalanche gain layer. The sensor is implemented in a 110 nm CMOS imaging process and demonstrates the portability of the Low Gain Avalanche Diode concept to a standard CMOS technology. The results showed an intrinsic gain between 10 and 13 and a time resolution of 75 ps.
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Computational Physics · Cross-listed from cond-mat.mtrl-sci
Transformation Journey of Zr-based MOFs: Study on Mechanics and Hydrogen Storage under Doping Regulation
Yanhuai Ding, Dan Qian and Zhipeng Liu
This study delves into the transformation journey of Zr-based Metal-Organic Frameworks (MOFs), focusing on enhancing their mechanical properties and hydrogen storage capacities through doping regulation. MOFs, a versatile class of crystalline porous materials, have garnered significant
attention due to their unique properties and broad potential applications in gas storage, separation, catalysis, and sensing. Among them, Zr-based MOFs stand out for their exceptional stability and high surface area. This research systematically investigates six key Zr-based MOFs (UIO-66, UIO-67, UIO-68, MOF-801, MOF-802, and MOF-841) using multiscale computational methods, including molecular dynamics (MD) simulations, grand canonical Monte Carlo (GCMC) simulations, and density functional theory (DFT). The study explores the impact of metal ion substitution (Fe, Co, Ni, Cu, Zn) on the mechanical and hydrogen storage properties of these MOFs. Our findings reveal that metal ion substitution significantly influences the mechanical stability and hydrogen adsorption capacity of Zr-based MOFs, providing valuable insights for the design and optimization of high-performance MOF materials.
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Instrumentation and Detectors · Cross-listed from astro-ph.IM
SpaceWire-based Data Acquisition Network for the Solar Flare Sounding Rocket Experiment FOXSI-4 and FOXSI-5
Shunsaku Nagasawa, Athanasios Pantazides, Kristopher Cooper et al. (+10 more)
We developed a SpaceWire-based data acquisition (DAQ) system for the FOXSI-4 and FOXSI-5 sounding rocket experiments, which aim to observe
solar flares with high sensitivity and dynamic range using direct X-ray focusing optics. The FOXSI-4 mission, launched on April 17, 2024, achieved the first direct focusing observation of a GOES M1.6 class
solar flare with imaging spectroscopy capabilities in the soft and hard X-ray energy ranges, using a suite of advanced detectors, including two CMOS sensors, four CdTe double-sided strip detectors (CdTe-DSDs), and a Quad-Timepix3 detector. To accommodate the high
photon flux from a
solar flare and these diverse detector types, a modular DAQ network architecture was implemented based on SpaceWire and the Remote
Memory Access Protocol (RMAP). This modular architecture enabled fast, reliable, and scalable communication among various onboard components, including detectors, readout boards, onboard computers, and telemetry systems. In addition, by standardizing the communication interface and modularizing each detector unit and its associated electronics, the architecture also supported distributed development among collaborating institutions, simplifying integration and reducing overall complexity. To realize this architecture, we developed FPGA-based readout boards (SPMU-001 and SPMU-002) that support SpaceWire communication for high-speed data transfer and flexible instrument control. In addition, a real-time ground support system was developed to handle telemetry and command operations during flight, enabling live monitoring and adaptive configuration of onboard instruments in response to the properties of the observed
solar flare. The same architecture is being adopted for the upcoming FOXSI-5 mission, scheduled for launch in 2026.
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All authors Shunsaku Nagasawa, Athanasios Pantazides, Kristopher Cooper, Riko Shimizu, Savannah Perez-Piel, Takahiro Minami, Yixian Zhang, Hunter Kanniainen, Shin Watanabe, Tadayuki Takahashi, Noriyuki Narukage, Juan Camilo Buitrago Casas, Lindsay Glesener
Optics
Thermally adaptive textile inspired by morpho butterfly for all-season comfort and visible aesthetics
Yan Wang, Zhuowen Xie, Wangkai Jiang et al. (+8 more)
The longstanding challenge of transitioning from static, appearance-limited passive daytime radiative cooling (PDRC) materials to systems that are both dynamically adaptive and aesthetically versatile in personal thermal management stems from the inherent compromise between color saturation and cooling power. Inspired by the Morpho butterfly, which decouples structural color from thermal function, we introduce a modular adaptive textile that dissolves this conflict. A dynamic thermochromic membrane autonomously switches
solar reflectance from 0.6 (heating state) to 0.9 (cooling state), while an APC layer supplies angle-independent, high-saturation structural color independent of the thermal state and fully replaceable, enabling on-demand color rewriting. Consequently, outdoor tests show 5-7 {\deg}C surface-temperature reduction versus commercial colored fabrics under hot conditions, with no compromise in
solar-heating performance under cold conditions. Complemented by hydrophobicity and breathability, this color-reconfigurable, energy-free approach offers a general pathway toward aesthetic and adaptive thermal comfort.
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All authors Yan Wang, Zhuowen Xie, Wangkai Jiang, Ting-Ting Li, Honglei Cai, Jun Zhang, Hui Wang, Shuo Shi, Jintu Fan, Jianchen Hu, Ke-Qin Zhang
Optics
Photonic Waveguide Circuit Integrated with Carbon Nanotube Single-Photon Source Operating at Room Temperature
Clement Deleau, Chee Fai Fong, Finn L. Sebastian et al. (+2 more)
Photonic integrated
circuits require robust room-temperature single-
photon sources to enable scalable
quantum technologies. Single-walled carbon nanotubes (CNTs), with their unique excitonic properties and chemical tunability, are attractive candidates, but their integration into photonic
circuits remains challenging. In this work, we demonstrate the integration of functionalized CNTs as room-temperature single-
photon emitters into photonic cavities and waveguide
circuits. (6,5) CNTs with aryl sp$^3$ defects are either stochastically deposited via drop-casting or deterministically positioned on photonic cavities using an anthracene-assisted transfer method guided by real-time photoluminescence monitoring. Photoluminescence spectra reveal cavity-enhanced emission, while second-order autocorrelation measurements confirm single-
photon propagation through the photonic integrated
circuit, highlighting the potential of CNTs for scalable, room-temperature
quantum photonic applications.
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All authors Clement Deleau, Chee Fai Fong, Finn L. Sebastian, Jana Zaumseil, Yuichiro K. Kato
Accelerator Physics · Cross-listed from hep-ph
The Future of Higgs Boson Physics
Michael E. Peskin
In this lecture, I discuss measurements of the properties of the
Higgs boson and related observables in the era of
Higgs factories. This highly motivated experimental program is the challenge for the next generation of particle physicists.
Optics
Uncooled low-noise thin-film optomechanical resonator for thermal sensing on lithium niobate
Yue Yu, Ran Yin, Ian Anderson et al. (+9 more)
Optomechanical transduction harnesses the interaction between optical fields and mechanical motion to achieve sensitive measurement of weak mechanical quantities with inherently low noise. Lithium niobate combines low optical loss, strong piezoelectricity, high intrinsic fQ_m factor, and low thermal conductivity, making it promising for exploring optomechanical platforms targeting thermal sensing applications. Here, we developed an integrated optomechanical platform on thin-film lithium niobate with precisely engineered optical, mechanical, and thermal fields within a compact 40 {\mu}m by 40 {\mu}m footprint. The platform integrates suspended microring resonators with ultrathin central membranes, reducing mechanical stiffness and effective mass while maintaining a high optical factor Q_o of 1e6 and mechanical quality factor Q_m of 1117, which increases to 5.1e4 after oscillation. The design suppresses thermal dissipation into the silicon substrate and enhances thermal sensitivity, achieving a temperature coefficient of frequency of -124 ppm/K and a noise-equivalent power of 6.2 nW/sqrt(Hz) at 10 kHz at room temperature. This compact and scalable platform opens up new opportunities for high-sensitivity thermal sensing, supports heterogeneous integration with infrared absorbers for uncooled infrared detection, and enables fully integrated, all-optical on-chip readout, paving the way toward large-format, low-noise infrared sensing arrays.
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All authors Yue Yu, Ran Yin, Ian Anderson, Yinan Wang, Jack Kramer, Chun-Ho Lee, Xinyi Ren, Zaijun Chen, Michelle Povinelli, Dan Wasserman, Ruochen Lu, Mengjie Yu
Fluid Dynamics
Data-Driven Flow Initialization Framework for CFD Acceleration of Underwater Vehicle in Vertical-Plane Oblique Motion
Tianli Hu, Chengsheng Wu, Jun Ding et al. (+3 more)
Accurate prediction of flow fields around
underwater vehicles undergoing vertical-plane oblique motions is critical for hydrodynamic analysis, but it often requires computationally expensive CFD simulations. This study proposes a Data-Driven Flow Initialization (DDFI) framework that accelerates CFD simulation by integrating deep neural network (DNN) to predict full-domain flow fields. Using the suboff hull under various inlet velocities and angles of attack as an example, a DNN is trained to predict velocity, pressure, and turbulent quantities based on mesh geometry, operating conditions, and hybrid vectors. The DNN can provide reasonably accurate predictions with a relative error about 3.3%. To enhance numerical accuracy while maintaining physical consistency, the DNN-predicted flow fields are utilized as initial solutions for the CFD solver, achieving up to 3.5-fold and 2.0-fold speedup at residual thresholds of 5*10^(-6)and 5*10^(-8), respectively. This method maintains physical consistency by refining neural network outputs via traditional CFD solvers, balancing computational efficiency and accuracy. Notably, reducing the size of training set does not exert an essential impact on acceleration performance. Besides, this method exhibits cross-mesh generalization capability. In general, this proposed hybrid approach offers a new pathway for high-fidelity and efficient full-domain flow field predictions around complex
underwater vehicles.
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All authors Tianli Hu, Chengsheng Wu, Jun Ding, Xing Wang, Yu Yang, Jianchun Wang
Plasma Physics
RONS Generation in Plasma-Activated Saline for Wound Healing
Punit Kumar and Priti Saxena
This study explores the physicochemical modifications and antimicrobial potential of
plasma activated saline generated by exposing Sodium Chloride and Ringer solution to atmospheric pressure dielectric barrier discharge
plasma.
Plasma activation produced reactive oxygen and nitrogen species leading to changes in pH, redox potential, conductivity and concentrations of Hydrogen Peroxide, Nitrogen and Nitrous
Oxides. Effects of activation time, voltage, and gas composition were analyzed. Antimicrobial activity against Staphylococcus aureus, Pseudomonas aeruginosa and E coli was assessed via MIC, CFU reduction and biofilm inhibition tests. Optimal
plasma exposure achieved strong microbial inactivation with good biocompatibility. SEM and FTIR confirmed membrane damage, supporting PAS as a safe, nonantibiotic wound irrigation and disinfectant solution.
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Optics
Guangfeng Wang, Juan Feng, Tong Zheng et al. (+3 more)
We introduce quasi-
symmetry groups in optics emerging from the commutation between mirror operation and the
spin-orbit interaction (SOI) of light. Contrary to the principle of
symmetry inheritance in free-space optics, where the
symmetry of any structured field is strictly constrained by that of its source, we show that strong SOI enables quasi-
symmetry-protected formation of meron
lattices even when the underlying optical sources violate the nominal rotational
symmetry. By analyzing the Hermiticity of the electric-dipole radiation amplitude in a circular polarization basis, we derive an effective mirror operator acting only on a subset of C3 polarized dipole emitters, forming a quasi-
symmetry group that commutes with SOI. This quasi-
symmetry guarantees exact C3 merons and gives rise to a robust polarization zone within which continuously varying input polarizations generate identical topological textures. Our work establishes quasi-
symmetry as a new fundamental principle in optical physics and opens pathways to engineered topological structures of light beyond conventional
symmetry constraints.
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All authors Guangfeng Wang, Juan Feng, Tong Zheng, Yijie Shen, Xianfeng Chen, Bo Wang
Optics · Cross-listed from cond-mat.mes-hall
Stable boundary modes for fragile topology from spontaneous PT-symmetry breaking
Kang Yang, Fei Song and Piet W. Brouwer
Two-dimensional topological
insulators protected by nonlocal
symmetries or with fragile
topology usually do not admit robust in-gap edge modes due to the incompatibility between the
symmetry and the boundary. Here, we show that in a parity-time (PT) symmetric system robust in-gap topological edge modes can be stably induced by non-Hermitian couplings that spontaneously break the PT
symmetry of the eigenstates. The topological edge modes traverse the imaginary spectral gap between a pair of fragile topological bands, which is opened by the presence of the non-Hermitian perturbation. We demonstrate that the net number of resulting in-gap modes is protected by an operator version of anomaly cancellation that extends beyond the Hermitian limit. The results imply that loss and gain can in principle drive fragile topological phenomena to stable topological phenomena.
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Optics
Modulating anomalous thermal quenching behavior of stimulation luminescence via high-orbit electronic satellite-stabilized Trap state in germanate-based phosphors for 5D optical data storage
Wenqian Xu, Dangli Gao, Xiangyu Zhang et al. (+3 more)
Persistent luminescence (PersL) materials, widely used in emergency lighting and information storage, are primarily employed at room temperature. However, their luminescent performance deteriorates sharply at high temperatures. Herein, a serials of Mg2GeO4:Ti4+,Ln3+ (Ln = Tb, Eu) phosphors demonstrated anomalous thermal quenching PersL due to the temperature-dependent
Fermi-Dirac distribution of bound charge carriers of Ti4+Mg2+ as remote electron traps and VMg2+ as hole traps. The high carrier retention rate of phosphors is attributed to the ability of Ti4+Mg2+ positive charge center to strongly trap non-bonding electrons over a long range (about 20 angstroms) as the electronic satellite for its stable operation. Under external optical/thermal stimulation, the released electrons and holes recombine at the different luminescent levels of Tb3+, resulting in the emission of different PersL branching ratios. Using these phosphors, we have developed 5D optical data storage (2D plane + trap depth + temperature + time) and the encrypted engine program for high-temperature aerospace engines. This study reveals the energy storage process of long-range trapping and releasing electrons by Ti4+ electron traps, and provides a new design concept for the design of PersL materials.
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All authors Wenqian Xu, Dangli Gao, Xiangyu Zhang, Wenna Gao, Dingjun Jia, Yuhua Wang
Biological Physics · Cross-listed from cond-mat.soft
Cellular wrapping of elastic particles by a supported lipid membrane
Amir Khosravanizadeh, Pierre Sens and Farshid Mohammad-Rafiee
Constancy of life vitally depends on the internalization of particles through biomembranes. Of particular interest, cellular uptake, including phagocytosis, receptor-mediated endocytosis, and membrane fusion, critically depends on the elasticity of particles. Cellular membranes are strongly linked to a supporting cytoskeleton. However, in most previous studies, the effect of this cortical network somehow is overlooked. In this paper, we study the cellular wrapping of a membrane around a 2D elastic particle in the presence of a substrate mimicking cytoskeleton. Our simulations show that the impact of particle flexibility on the wrapping process depends on the magnitude of the membrane particle adhesion. In contrast, the extent of membrane protrusions formed around the target always increases with target stiffness. Since the extension of membrane protrusions is an essential step in the phagocytosis process, this result may indicate a selective behavior of macrophages in the phagocytosis of aged red blood
cells.
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Chemical Physics
Musical Molecules: Sonifying the IR Spectra and Modeling Intramolecular Vibrational Energy Redistribution of Small Molecules
Sophia H. Kim and Eric J. Heller
This work explores how small molecules sound. Infrared (IR) spectra of HCl, H2O, NH3, and acetone are mapped into the audible range using a simple anharmonic oscillator model and NIST vibrational data. Comparing harmonic and anharmonic sonifications reveals systematic pitch flattening, beating, and the emergence of combination bands, which are analyzed with spectrograms and autocorrelation functions. A time-dependent model of intramolecular vibrational energy redistribution (IVR) in acetone, implemented by "plucking" a single mode, produces evolving sound textures that mirror energy flow through the molecule. These results suggest that sonified IR spectra can provide an intuitive, pedagogical window into anharmonicity, mode coupling, and IVR.
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Optics
Freestanding Resist Metasurface Supporting Higher-Order BICs for Efficient Field Enhancement in TMD Monolayers
Chih-Zong Deng, Sunhao Shi, Chun-Hao Chiang et al. (+4 more)
Enhancing light-matter coupling in two-dimensional (2D)
semiconductors such as transition metal dichalcogenide monolayers remains a central challenge in nanophotonics due to their atomic thickness, which limits their interaction volume with light. Here, we demonstrate that first-order quasi-bound states in the continuum (quasi-BICs) supported by a freestanding metasurface provide exceptionally strong surface field enhancement, enabling efficient coupling with a tungsten disulfide (WS2) monolayer. Triangular-
lattice polymer patterns on silicon nitride membranes are fabricated to realize these higher-order modes. Simulations reveal that first-order quasi-BICs exhibit much stronger field enhancement than zeroth-order modes at the top surface where the WS2 monolayer is placed. Photoluminescence (PL) measurements confirm a remarkable PL enhancement factor of 127 for first-order quasi-BICs, over six times larger than that of zeroth-order quasi-BICs. These results establish higher-order BICs in freestanding metasurfaces as a powerful route to engineer light-matter interactions in 2D
semiconductors for advanced nanophotonic and
quantum photonic applications.
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All authors Chih-Zong Deng, Sunhao Shi, Chun-Hao Chiang, Mu-Hsin Chen, Jui-Han Fu, Vincent Tung, Ya-Lun Ho
Applied Physics
Acoustic Analogy of Quantum Baldin Sum Rule for Optimal Causal Scattering
Sichao Qu, Zixiong Yu, Erqian Dong et al. (+2 more)
The mass law is a cornerstone in predicting sound transmission loss, yet it neglects the constraints of causal dispersion. Current causality-based theories, such as the Rozanov limit, are applicable only to one-port reflective absorbers. Here, we derive a universal sum rule governing causal scattering in acoustic systems, establishing a rigorous analogy to the Baldin sum rule in
quantum field theory. This relation reveals that the integral of the extinction cross-section is fundamentally locked by the scatterer's static effective mass and stiffness, which is validated numerically using seminal examples of
underwater metamaterials. Furthermore, the proposed sum rule predicts an optimal condition for an anomalously broadened transmission loss bandwidth, as experimentally observed through the spectral shaping effect of an acoustic Fano resonator. Our findings open up an unexplored avenue for enhancing the scattering bandwidth of passive metamaterials.
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All authors Sichao Qu, Zixiong Yu, Erqian Dong, Min Yang, Nicholas X. Fang
Applied Physics · Cross-listed from cond-mat.mtrl-sci
Contact resistance and interfacial engineering: Advances in high-performance 2D-TMD based devices
Xiongfang Liu, Kaijian Xing, Chi Sin Tang et al. (+7 more)
The development of advanced electronic devices is contingent upon sustainable material development and pioneering research breakthroughs. Traditional
semiconductor-based electronic technology faces constraints in material thickness scaling and energy efficiency. Atomically thin two-dimensional (2D) transition metal dichalcogenides (TMDs) have emerged as promising candidates for next-generation nanoelectronics and optoelectronic applications, boasting high electron mobility, mechanical strength, and a customizable band gap. Despite these merits, the
Fermi level pinning effect introduces uncontrollable Schottky barriers at metal-2D-TMD contacts, challenging prediction through the Schottky-Mott rule. These barriers fundamentally lead to elevated contact resistance and limited current-delivery capability, impeding the enhancement of 2D-TMD transistor and integrated
circuit properties. In this review, we succinctly outline the
Fermi pinning effect mechanism and peculiar contact resistance behavior at metal/2D-TMD interfaces. Subsequently, highlights on the recent advances in overcoming contact resistance in 2D-TMDs devices, encompassing interface interaction and hybridization, van der Waals (vdW) contacts, prefabricated metal transfer and charge-transfer doping will be addressed. Finally, the discussion extends to challenges and offers insights into future developmental prospects.
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All authors Xiongfang Liu, Kaijian Xing, Chi Sin Tang, Shuo Sun, Pan Chen, Dong-Chen Qi, Mark B. H. Breese, Michael S. Fuhrer, Andrew T. S. Wee, Xinmao Yin
Applied Physics
Implementation of Reservoir Computing Using Coupled Microelectromechanical Drum Resonators via Sideband-Pumped Phonon-Cavity Dynamics
Theresa Farah, Loïc Flis, Pierre Laly et al. (+6 more)
Reservoir computing is a bio-inspired machine learning paradigm that exploits the intrinsic dynamics of nonlinear systems with fading
memory for efficient temporal information processing. Microelectromechanical resonators offer a promising platform for reservoir computing as they inherently possess the requisite nonlinear and temporal properties while also facilitating the integration of sensing and computing within a single platform. In this work, we experimentally demonstrate a physical reservoir computing platform based on two capacitively coupled drum resonators, operating in the MHz frequency regime. Taking advantage of the concept of
phonon-cavity electromechanics, a pump tone is applied at the sideband of the
phonon cavity while probing one of the coupled modes, analogous to optomechanical systems, thereby creating nonlinear dynamics in energy transfer between the two resonators. Physical reservoir computing is implemented by exploiting the nonlinear response induced through pump amplitude modulation in combination with a time-delay feedback loop, and the performance is evaluated using both parity and Normalized Auto-Regressive Moving Average
benchmarks. This work demonstrates a compact microelectromechanical platform for the integration of sensing and reservoir computing. Moreover, the sideband pumping scheme can further extend conventional single resonator reservoir computing to a multimode architecture.
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All authors Theresa Farah, Loïc Flis, Pierre Laly, Guo-En Chang, Jun-Yu Ou, Yoshishige Tsuchiya, Yan Pennec, Bahram Djafari-Rouhani, Xin Zhou
Plasma Physics
GKFieldFlow: A Spatio-Temporal Neural Surrogate for Nonlinear Gyrokinetic Turbulence
Arash Ashourvan
We present GKFieldFlow, a novel three-dimensional autoregressive deep learning surrogate model for nonlinear gyrokinetic
turbulence. Based on the architecture FieldFlow-Net, this model combines a multi-resolution 3D U-Net encoder-decoder that operates on evolving
plasma potential fields. A dilated temporal convolutional network (TCN) learns the nonlinear time
evolution of latent
turbulence features. GKFieldFlow simultaneously (i) predicts ion and electron energy fluxes, and particle flux directly from CGYRO
turbulence, and (ii) predicts future potential fields autoregressively with desired spatial resolution. This enables the model to replicate both instantaneous transport and the underlying spatio-temporal dynamics that generate it. The architecture is physics-informed in its design: 3D convolutions preserve the anisotropic geometry and phase structure of gyrokinetic fluctuations, while dilated temporal convolutions capture multiscale dynamical couplings such as
turbulence and zonal-flow interactions,
turbulence decorrelation, and intermittent bursty transport. We provide a complete technical description of the data structure, model components, and rationale behind each architectural choice. The model achieves high accuracy across all three transport channels, with multi-horizon inference maintaining robustness. Autoregressive field rollouts preserve the spectral content, phase coherence, and energy distribution of the CGYRO nonlinear state with strong fidelity, and flux predictions remain consistent with CGYRO within a small fractional error. This work presents GKFieldFlow as a data-driven reduced model that can jointly learn
turbulence dynamics and transport.
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Biological Physics
Holotomography in 2025: From Morphometric Imaging to AI-Driven Multimodal Phenotyping
YongKeun Park
By 2025, holotomography (HT) has matured from a niche optical modality into a versatile platform for quantitative, label-free imaging in biomedicine. By reconstructing the three-dimensional refractive-index (RI) distribution of
cells and tissues, HT enables high-resolution volumetric imaging with low phototoxicity and minimal sample perturbation. This Review surveys recent advances in the field and highlights three emerging directions: (i) the incorporation of deep-learning approaches for virtual staining, phenotypic classification, and automated analysis; (ii) the extension of HT to structurally complex biological systems, including organoids and thick tissue specimens; and (iii) the integration of HT with complementary modalities, such as Raman and polarization-sensitive microscopy, to enhance molecular and biophysical specificity. We summarize current HT applications spanning subcellular phenotyping, metabolic and mechanical profiling, and early-stage clinical studies in areas such as infectious disease and pathology. Finally, we discuss remaining technical and translational challenges and outline a roadmap for the prospective integration of HT into digital pathology and high-throughput screening workflows.
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