Chemical Physics · Cross-listed from cond-mat.mtrl-sci
A finite-element Delta-Sternheimer approach for computing accurate all-electron RPA correlation energies of polyatomic molecules
Hao Peng, Haochen Liu, Chuhao Li et al. (+2 more)
Attaining a reliable complete basis set (CBS) limit remains a significant challenge in ab initio correlated electronic-structure calculations. Building on our previous work for atoms and diatomic molecules, we present a finite-element (FE) Delta Sternheimer approach for numerically accurate random phase approximation (RPA) calculations applicable to general molecules. This approach seamlessly integrates atomic orbital basis sets with FE grids, enabling an arbitrary precision representation of first order wavefunctions. As a result, the density response function and RPA correlation energies can be computed with fully controlled numerical precision. The Delta Sternheimer approach thus provides direct access to RPA correlation energies at the CBS limit, eliminating reliance on conventional extrapolation schemes. We apply this approach to two problems: The energy hierarchy of 20 water-dimer configurations and the atomization energies of 50 molecules from the G2 set. For the water dimer, we examine the basis set dependence of the isomer energy ordering. For the G2 set, we investigate the residual numerical uncertainty in the conventional extrapolated CBS limit, both with and without correction for basis-set superposition error (BSSE).
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All authors Hao Peng, Haochen Liu, Chuhao Li, Hehu Xie, Xinguo Ren
Medical Physics
Airway Mucus Rheology: Physical Insights for Navigating through Health to Pathology and Clinical Applications
Zhiwei Liu, Bo Che, Hailin Zhang et al. (+1 more)
Airway mucus is a complex gel with an anisotropic three-dimensional network structure. As a crucial component of the respiratory defense barrier, it plays a vital role in maintaining airway hydration and supporting the function of airway epithelial
cells. Through linear and nonlinear rheological mechanisms such as ciliary motion and coughing, airway mucus expels foreign pathogens and toxic nano- and microparticles while selectively allowing the passage of specific nutrients and
proteins. These protective and clearance functions depend on the proper rheological properties of mucus under normal physiological conditions. However, in respiratory disease such as CF, COPD, asthma, and COVID-19, excessive mucus secretion is often accompanied by abnormal rheological behaviors. This leads to impaired mucus flow, airway obstruction, and potentially life-threatening conditions. Therefore, this review examines the rheological behaviors of airway mucus in relation to health and disease, focusing on both macrorheology and microrheology. The review highlights those changes in the chemical composition and microstructure of airway mucus, especially under pathological conditions, that can significantly alter its rheological behavior. Rheological parameters can also serve as biological indicators to study the role of mucus in clearance functions and aid in developing pulmonary drug delivery systems. By integrating findings from both macro- and microrheological studies, this review aims to enhance our understanding of the complex behavior of airway mucus, supporting better diagnosis, treatment, and management of chronic respiratory diseases.
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All authors Zhiwei Liu, Bo Che, Hailin Zhang, Linhong Deng
Optics
Sub-ppb CO2 Detection based on Dissipative Whispering Gallery mode Microcavity Sensor
Shujing Ruan, Guangzhen Gao, Jianing Zhang et al. (+7 more)
Whispering gallery mode (WGM) microcavities feature ultrahigh Q-factors and small mode volumes, offering strong light-matter interactions for sensing applications. However, unmodified surfaces are weakly responsive togas-phase refractive index changes, limiting trace gas detection. In this work, we propose a novel dissipative sensing scheme based on a non-functionalized WGM microcavity and experimentally demonstrate its feasibility and performance through trace-level CO2 detection. Unlike conventional dispersive sensing that tracks resonance frequency shifts, our approach leverages enhanced local fields and thermal effects at the coupling region to convert weak absorption into measurable variations in resonance depth. A modulation-demodulation method suppresses low-frequency noise, with parameters optimized experimentally. The sensor achieves quantitative detection over 1.5-400 ppm (R2 > 0.99), ~1.13% accuracy, a minimum detection limit of 0.12 ppb at 424 s integration-five orders of magnitude better than dispersive WGM sensors. Long term CO2 monitoring further demonstrates stability and resistance to environmental perturbations. Compared with state-of-the-art cavity-enhanced and photoacoustic sensors, our system delivers at least an order of magnitude lower baseline detection while maintaining compact size, ambient pressure operation, and low cost, highlighting its potential for scalable, real-world deployment.
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All authors Shujing Ruan, Guangzhen Gao, Jianing Zhang, Haotian Wang, Dongxing Cheng, Jun Guo, Chuanyong Ren, Weidong Chen, Deyuan Shen, Tingdong Cai
Optics
Widely tunable cavity-enhanced backward difference-frequency generation
Ming-Yuan Gao, Yue-Wei Song, Ren-Hui Chen et al. (+3 more)
Difference-frequency generation (DFG) is a powerful technique for generating widely tunable infrared radiation. However, conventional phase-matching schemes may require tuning multiple parameters-such as the wavelengths,
crystal temperature,
crystal angle, and poling period-to achieve wide tunability, which increases the complexity of practical operation. In this work, we employ a backward quasi-phase-matching scheme with distinctive tuning characteristics and demonstrate pump-enhanced continuous-wave DFG output tunable from 1751 nm to 2451 nm (700 nm range) in a bulk
crystal. The tuning is achieved solely by varying the pump wavelength and the signal wavelength (less than 5 nm), enabling continuous, rapid, and room-temperature operation. The tuning characteristics, power-scaling behavior, and output stability are experimentally verified with the idler wavelength set at 2000 nm. The approach offers a new paradigm for widely tunable infrared radiation generation and holds promise for applications in spectroscopy and biomedical sensing.
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All authors Ming-Yuan Gao, Yue-Wei Song, Ren-Hui Chen, Yin-Hai Li, Zhi-Yuan Zhou, Bao-Sen Shi
Plasma Physics
Modelling-driven requirements for Error Field Control Coil application to initial JT-60SA plasmas
L. Pigatto, G. Frello, Y.Q. Liu et al. (+4 more)
JT-60SA is a large superconducting tokamak built in Naka, Japan. After the successful achievement of its first MA-class
plasma, the installation of several additional sub-systems, including a set of non-axisymmetric Error Field Correction Coils (EFCC), is ongoing. Optimization of future JT-60SA
plasma scenarios will critically depend on the correct use of EFCC, including careful fulfillment of system specifications. In addition to that, preparation and risk mitigation of early ITER operations will greatly benefit from the experience gained by early EFCC application to JT-60SA experiments, in particular to optimize error field detection and control strategies. In this work, EFCC application in JT-60SA Initial Research Phase I perspective scenarios is modeled including
plasma response. Impact of (Resonant)
Magnetic Perturbations on the different
plasma scenarios is assessed for both core and pedestal regions by the linear resistive MHD code MARS-F. The dominant core response to EFs is discussed case by case and compared to mode locking thresholds from literature. Typical current/voltage amplitudes and wave-forms are then compared to EFCC specifications in order to assess a safe operational space.
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All authors L. Pigatto, G. Frello, Y.Q. Liu, L. Novello, M. Takechi, E. Tomasina, T. Bolzonella
Atmospheric and Oceanic Physics · Cross-listed from astro-ph.EP
Eruption column modelling of explosive volcanism on Venus
Maxence Lefèvre, Matteo Cerminara and Antonio Costa
Volcanism on Venus has never been directly observed, but several measurements indicate present-day activity. Volcanism could potentially play a role in climatic processes on Venus, especially in the sulfur cycle like on Earth. Observation of volcanic activity is the primary objective of future Venus spacecraft. However, there are many unknowns regarding its Venusian characteristics, like the condition at the vent, the volatile content and composition. Past modelling efforts have only studied explosive volcanic plume propagation over a limited range of flow parameters at the vent and in an idealised Venus atmospheric configuration. We propose to use the 1D FPLUME volcanic plume model in a realistic Venusian environment. In similar Venusian conditions, the height of the plume is consistent with past modelling. The present study shows that explosive volcanism would preferably reach 15 km of altitude. Under certain conditions, plumes are able to reach the VenSpec-H tropospheric altitude range of observations and even the 45 km cloud floor. For the first time, the impact of wind was quantified, and the super-rotating winds have a substantial impact by plume-bending of reducing the height of plumes. Contrary to the Earth, the atmospheric heat capacity depends greatly on temperature, and will disadvantage lower plumes and allow larger plumes to propagate at higher altitudes. The high latitude atmospheric environment, due to the thermal profile and weaker winds, is favorable to plumes reaching higher altitudes.
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Biological Physics
Sparing of DNA irradiated with Ultra-High Dose-Rates under Physiological Oxygen and Salt conditions
Marc Benjamin Hahn, Sepideh Aminzadeh-Gohari, Anna Grebinyk et al. (+9 more)
Cancer treatment with radiotherapy aims to kill tumor
cells and spare healthy tissue.Thus,the experimentally observed sparing of healthy tissue by the FLASH effect during irradiations with ultra-high dose rates (UHDR) enables clinicians to extend the therapeutic window.However, the underlying radiobiological and chemical mechanisms are far from being understood.DNA is one of the main molecular targets for radiotherapy.Ionizing radiation damage to DNA in water depends strongly on salt,pH,buffer and oxygen content of the solvent.Here we present a study of plasmid DNA pUC19,irradiated with 18MeV electrons at low dose rates (LDR) and UHDR under tightly controlled ambient and physiological oxygen conditions in PBS at pH 7.4.For the first time a sparing effect of DNA strand-break induction between UHDR(>10MGy/s) and LDR(<0.1Gy/s) irradiated plasmid DNA under physiological oxygen, salt and pH is observed for total doses above 10Gy.Under physiological oxygen (physoxia,5%O2,40mmHg),more single (SSB) and double strand-breaks (DSB) are observed when exposed to LDR, than to UHDR.This behaviour is absent for ambient oxygen (normoxia,21%O2,150-160mmHg).The experiments are accompanied by TOPAS-nBio based particle-scattering and chemical MCS to obtain information about the yields of reactive oxygen species (ROS).Hereby,an extended set of chemical reactions was considered, which improved upon the discrepancy between experiment and simulations of previous works, and allowed to predict DR dependent g-values of hydrogen peroxide (H2O2).To explain the observed DNA sparing effect under FLASH conditions at physoxia,the following model was proposed:The interplay of O2 with OH induced H-abstraction at the phosphate backbone,and the conversion of DNA base-damage to SSB,under consideration of the dose-rate dependent H3O+ yield via beta elimination processes is accounted for, to explain the observed behavior.
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All authors Marc Benjamin Hahn, Sepideh Aminzadeh-Gohari, Anna Grebinyk, Matthias Gross, Andreas Hoffmann, Xiangkun Li, Anne Oppelt, Chris Richard, Felix Riemer, Frank Stephan, Elif Tarakci, Daniel Villani
Atmospheric and Oceanic Physics
Evaluating the Prediction of Wind Power Ramping Events in the Belgian Offshore Zone
Ruoke Meng, Geert Smet, Dieter Van den Bleeken et al. (+5 more)
Evaluations are presented for the prediction of wind power ramping events in the Belgian Offshore Zone. Two models from the Royal Meteorological Institute of Belgium are verified: the operational ALARO-4km and its version with Wind Farm Parameterization (WFP). Power predictions are produced using power curves and machine learning (ML). As standard metrics such as MAE are insufficient for evaluating ramps, the proposed framework incorporates time and power buffers, enabling a flexible assessment that tolerates minor errors. Results indicate that WFP models enhance ramping prediction skill, while ML provides more balanced forecasts by reducing both misses and false alarms. A Ramp
Alignment Score is also introduced to quantify temporal errors by forecast lead time, confirming that WFP models yield smaller average timing errors. Moreover, the framework reveals that severe precipitation is a strong indicator of large, predictable ramps, whereas lighter precipitation is associated with greater forecast errors.
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All authors Ruoke Meng, Geert Smet, Dieter Van den Bleeken, Aaron Van Poecke, Hossein Tabari, Peter Hellinckx, Piet Termonia, Joris Van den Bergh
Optics
Photothermal Fourier-plane Phase Synchronization for Interferometric Scattering Microscopy
Shupei Lin, Nanfang Jiao, Yevhenii Shaidiuk et al. (+7 more)
We introduce and experimentally implement
Fourier-plane phase synchronization for optical microscopy, and demonstrate its performance with interferometric scattering microscopy. By combining a photothermal phase plate and
laser beam scanning, we realize a synchronized phase for all scattering components on the
Fourier plane of high numerical-aperture microscopes, where the evanescent waves and optical aberration normally produce highly inhomogeneous phase distributions. We achieve an almost perfect point spread function, exhibiting a tighter focus with 50% enhancement of the signal and ideal circular
symmetry. Particularly, by synchronizing the phase to $\pi/2$, we demonstrate the background speckles exhibit an anti-symmetric dependence on axial defocus, enabling the effective suppression of the speckles via defocus integration and thus the detection of 10 nm particles immobilized on the substrate. The concept and technique of seamless dynamic phase control on the
Fourier plane constitute a key asset for modern optical microscopy.
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All authors Shupei Lin, Nanfang Jiao, Yevhenii Shaidiuk, Delong Feng, Jingwei Luo, Yihao Yu, Lukasz Bujak, Jianwei Tang, Marek Piliarik, Xue-Wen Chen
Optics
Wideband Low-Scattering Dual-Polarized Phased Array with Stepped Ground
Yu Luo, Shi-Gang Fang, Peng-Fa Li et al. (+3 more)
This paper proposes a wideband dual-polarized phased array with ultra-wideband scattering cross section (SCS) reduction. The
antenna elements are loaded on a bilateral stepped ground. This ground is carefully designed in terms of height difference, step number, and length to achieve phase cancellation near the normal direction. Wideband dipoles with vertical electric coupling are designed. The radiation frequency band covers the X-band (40%) under VSWR < 2.8. Array patterns are synthesized with the two subarrays, covering the scanning range from -45 to +45 degrees. The monostatic SCSs of the proposed 17 x 8 array prototype have been reduced within 3.6 - 30 GHz, with an averaged reduction of over 19.4/18.9 dB and an averaged in-band reduction of over 15.4/16.6 dB, under the normal x/y polarized incident waves respectively.
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All authors Yu Luo, Shi-Gang Fang, Peng-Fa Li, Yuhao Feng, Shi-Wei Qu, Shiwen Yang
Geophysics
Experimental and numerical study on the influence of extra-depth on cut blasting post-blast damage
Changda Zheng, Renshu Yang, Chenxi Ding et al. (+3 more)
Cutting is a key factor affecting the speed of blasting excavation. With the continuous advancement of deep-hole blasting technology, determining the optimal extra-depth of the cut relative to the non-cut blast hole is of paramount importance. By combining model experiments and numerical simulations, this study systematically investigates the effect of extra-depth on post-blast damage morphology, damage width, depth, area, and fractal damage. Furthermore, numerical simulations were employed to validate the experimental results from the perspectives of damage
evolution and peak pressure at monitoring points. The main findings are as follows: The extra-depth of the cut has a significant nonlinear effect on the blasting outcome. As the extra-depth increases, blasting damage initially increases but decreases beyond a critical depth due to marginal effects, leading to the appearance of residual blast hole features. Under experimental conditions, when the extra-depth was 15 mm, the damage depth, width, and area reached their maximum values of 43.5 mm, 109.9 mm, and 6055.2 mm, respectively, indicating optimal blasting performance. The fractal damage of the specimen also exhibited a significant trend of initially increasing and then decreasing. The maximum fractal damage, obtained by deriving the fitting curve, was 0.75, corresponding to an extra-depth of 13.7 mm. The numerical simulation results are in good agreement with the experimental findings, showing a significant downward shift in the peak pressure points in the damage zone with increasing extra-depth. In summary, an appropriate extra-depth can achieve optimal borehole utilization, while an excessive extra-depth can lead to residual blast hole formation at the bottom of the cut, reducing effectiveness. This study provides theoretical guidance for optimizing the design parameters of extra-depth in deep-hole blasting.
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All authors Changda Zheng, Renshu Yang, Chenxi Ding, Songlin He, Bo Wang, Yongzhong Yuan
Computational Physics
Towards In-Situ Failure Assessment: Deep Learning on DIC Results for Laminated Composites
Amir Mohammad Mirzaei
Predicting fracture load in laminated composites with stress raisers is challenging due to complex failure mechanisms such as delamination, fibre breakage, and
matrix cracking, which are heavily influenced by fibre orientation, layup sequence, and notch geometry. This study aims to address this by developing a novel deep learning framework that leverages solely experimental strain field data from Digital Image Correlation (DIC) for accurate, in-situ predictions–bypassing the need for finite element simulations or empirical calibrations. Two complementary architectures are explored: a multi-layer perceptron (MLP) that processes numerical values of maximum principal strain from a targeted rectangular region ahead of the notch, enhanced by advanced feature selection (mutual information, Lasso, and SHAP) to focus on critical data points; and a convolutional neural network (CNN) trained on full-field strain images, bolstered by data augmentation to handle variability and prevent overfitting. Validated across 116 quasi-static tests encompassing 31 distinct configurations–including six layups (quasi-isotropic to highly anisotropic) with four off-axis angles for open-hole specimens, and one cross-ply layup with four off-axis and four on-axis notch orientations for U-notched specimens–the MLP and CNN achieve coefficients of determination (R^2) of 0.86 and 0.82, respectively. This framework captures a broad spectrum of damage modes and responses, from brittle fibre-dominated fracture to ductile delamination-driven failure, and due to its computational efficiency and reliance only on DIC measurements, the approach enables practical in-situ fracture load estimation.
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Accelerator Physics
Landau Damping
Xavier Buffat
Landau damping is a key mechanism to preserve the stability of particle beams under the influence of various collective forces that would otherwise spoil its quality through beam instabilities. We describe its root cause as well as ways to control it in order to design and operate particle accelerators.
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Classical Physics
Enhancement of mechanical properties of high modulus polypropylene grade for multilayer sewage pipes applications
Helena Khoury Moussa, Georges Challita, Houssem Badreddine et al. (+6 more)
Advances in technology have provided fresh generations of stiff polypropylene block copolymers for
gravity sewerage applications. The aim of this study is to further enhance the stiffness of these materials through the incorporation of inorganic fillers. In this study, three talc filled PP and one glass fiber filled PP composites were characterized in order to be used as a middle layer in a three-layer sewage pipe. The obtained results showed an increase of approximately more than 100% and 250% in tensile and flexural
moduli by the use of 30%–50___wt% talc-filled PP and 30___wt.% glass fiber-filled PP, respectively. This high increase in the
rigidity of the material would allow manufacturing pipes with improving ring stiffness. Composites filled with 30___wt% talc or glass fiber showed good filler-
matrix interaction and good filler distribution and dispersion. However, reduced filler-
matrix interaction was observed in the case of the composite filled with 50___wt% talc. In addition, the use of Differential Scanning Calorimetry analysis revealed that the addition of fillers enhanced the crystallization temperature of the polypropylene
matrix. Furthermore, Thermogravimetric Analysis showed that the high modulus PP grade retained its thermal stability in the various composites.
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All authors Helena Khoury Moussa, Georges Challita, Houssem Badreddine, Guillaume Montay, Bruno Guelorget, Thomas Vallon, Wadih Yared, Marwan Abi Rizk, Akram Alhussein
Medical Physics · Cross-listed from cs.CV
Robust High-Resolution Multi-Organ Diffusion MRI Using Synthetic-Data-Tuned Prompt Learning
Chen Qian, Haoyu Zhang, Junnan Ma et al. (+26 more)
Clinical adoption of multi-shot
diffusion-weighted
magnetic resonance imaging (multi-shot DWI) for body-wide tumor diagnostics is limited by severe motion-induced phase artifacts from respiration, peristalsis, and so on, compounded by multi-organ, multi-slice, multi-direction and multi-b-value complexities. Here, we introduce a reconstruction framework, LoSP-
Prompt, that overcomes these challenges through physics-informed modeling and synthetic-data-driven
prompt learning. We model inter-shot phase variations as a high-order Locally Smooth Phase (LoSP), integrated into a low-rank Hankel
matrix reconstruction. Crucially, the
algorithm's rank parameter is automatically set via
prompt learning trained exclusively on synthetic abdominal DWI data emulating physiological motion. Validated across 10,000+ clinical images (43 subjects, 4 scanner models, 5 centers), LoSP-
Prompt: (1) Achieved twice the spatial resolution of clinical single-shot DWI, enhancing liver lesion conspicuity; (2) Generalized to seven diverse anatomical regions (liver, kidney, sacroiliac, pelvis, knee, spinal cord,
brain) with a single model; (3) Outperformed state-of-the-art methods in image quality, artifact suppression, and noise reduction (11 radiologists' evaluations on a 5-point scale, $p<0.05$), achieving 4-5 points (excellent) on kidney DWI, 4 points (good to excellent) on liver, sacroiliac and spinal cord DWI, and 3-4 points (good) on knee and tumor
brain. The approach eliminates navigator signals and realistic data supervision, providing an interpretable, robust solution for high-resolution multi-organ multi-shot DWI. Its scanner-agnostic performance signifies transformative potential for precision oncology.
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All authors Chen Qian, Haoyu Zhang, Junnan Ma, Liuhong Zhu, Qingrui Cai, Yu Wang, Ruibo Song, Lv Li, Lin Mei, Xianwang Jiang, Qin Xu, Boyu Jiang, Ran Tao, Chunmiao Chen, Shufang Chen, Dongyun Liang, Qiu Guo, Jianzhong Lin, Taishan Kang, Mengtian Lu, Liyuan Fu, Ruibin Huang, Huijuan Wan, Xu Huang, Jianhua Wang, Di Guo, Hai Zhong, Jianjun Zhou, Xiaobo Qu
Optics
Polarization Multiplexed Metalens Array Optical Chip for High-Performance LWIR Polarimetric Camera
Shichuan Wang, Tie Hu, Zihan Mei et al. (+5 more)
Compared with traditional infrared thermal imaging, polarimetric imaging provides additional polarization information, which effectively enhances object contours and image contrast, with broad application in both military and civilian domains. However, the traditional long-wave infrared polarimetric camera suffers from severe thermal noise, low sensitivity and limited detection accuracy. To address the aforementioned problems, a novel cooled LWIR polarimetric camera based on an achromatic polarization multiplexed germanium-based metalens array optical chip is reported in this paper, enabling high-precision division of focal plane linearly polarimetric imaging. The proposed system demonstrates high-precision linearly polarimetric imaging, with the metalens array achieving an average transmittance of 84.7% across the 8.4~11.6{\mu}m band and a polarization extinction ratio exceeding 10. The metasurface-based camera attains an average polarization reconstruction error below 0.981%, markedly surpassing state-of-the-art commercial LWIR polarimetric systems. Additionally, the new camera presents excellent polarimetric imaging capability for complex scenes. To the best of our knowledge, this represents the world's first LWIR polarimetric camera utilizing the metasurface optical chip with performance superior to commercial cameras, promoting the practical development of metasurface-integrated devices.
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All authors Shichuan Wang, Tie Hu, Zihan Mei, Xuancheng Peng, Bing Yan, Wenhong Zhou, Ming Zhao, Zhenyu Yang
Chemical Physics
Thermodynamically Consistent Incorporation of the Langmuir Adsorption Model into Compressible Fluctuating Hydrodynamics
Hyun Tae Jung, Hyungjun Kim, Alejandro L. Garcia et al. (+4 more)
For a gas-solid interfacial system where chemical species undergo reversible adsorption, we develop a mesoscopic
stochastic modeling method that simulates both gas-phase hydrodynamics and surface coverage dynamics by coupling the Langmuir adsorption model with compressible fluctuating hydrodynamics. To this end, we derive a thermodynamically consistent mass-energy update scheme that accounts for how the mass and energy variables in the gas and surface subsystems should be updated according to the changes in the number of molecules of each species in each subsystem due to adsorption and desorption events. By performing a
stochastic analysis for the ideal Langmuir model and the full hydrodynamic system, we analytically confirm that our mass-energy update scheme captures thermodynamic
equilibrium predicted by
equilibrium statistical mechanics. We find that an internal energy correction term is needed, which is attributed to the difference in the mean kinetic energy of gas molecules colliding with the surface from that computed from the Maxwell-Boltzmann distribution. By performing an
equilibrium simulation study for an ideal gas mixture of CO and Ar with CO undergoing reversible adsorption, we validate our overall simulation method and implementation.
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All authors Hyun Tae Jung, Hyungjun Kim, Alejandro L. Garcia, Andrew J. Nonaka, John B. Bell, Ishan Srivastava, Changho Kim
Applied Physics · Cross-listed from quant-ph
Investigating the performance of RPM JTWPAs by optimizing LC-resonator elements
M.A. Gali Labarias, T. Yamada, Y. Nakashima et al. (+2 more)
Resonant phase-matched Josephson traveling-wave parametric amplifiers (RPM JTWPAs) play a key role in
quantum computing and
quantum information applications due to their low-noise, broadband amplification, and quadrature squeezing capabilities. This research focuses on optimizing RPM JTWPAs through numerical optimization of parametrized resonator elements to maximize gain, bandwidth and quadrature squeezing. Our results show that optimized resonators can increase the maximum gain and squeezing by more than 5 dB in the ideal noiseless case. However, introducing the effects of loss through a lumped-element model reveals that gain saturates with increasing loss, while squeezing modes degrade rapidly, regardless of resonator optimization. These results highlight the potential of resonator design to significantly improve amplifier performance, as well as the challenges posed by current fabrication technologies and inherent losses.
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All authors M.A. Gali Labarias, T. Yamada, Y. Nakashima, Y. Urade, K. Inomata
Computational Physics
Constrained bilinear optimal control of reactive evolution equations
Zhexian Li, Felipe de Barros and Ketan Savla
We consider constrained bilinear optimal control of second-order linear
evolution partial differential equations (PDEs) with a reaction term on the half line, where control arises as a time-dependent reaction coefficient and constraints are imposed on the state and control variables. These PDEs represent a wide range of physical phenomena in fluid flow, heat, and mass transfer. Existing computational methods for this type of control problems only consider constraints on the control variable and lack global convergence guarantee. In this paper, we propose a novel optimize-then-discretize framework for computing constrained bilinear optimal control with both state and control constraints. Unlike existing methods that derive optimality conditions directly from the PDE constraint, this framework first replaces the PDE constraint with an equivalent integral representation of the PDE solution and then derives optimality conditions for the reformulated problem. The integral representation, derived from the unified transform method, does not involve differential operators, and thus explicit expressions for necessary conditions of optimality can be derived using the Karush-Kuhn-Tucker conditions for infinite-dimensional optimization. Discretizing the optimality conditions results in a system of finite-dimensional smooth nonlinear equations, which can be efficiently solved using existing
algorithms with guaranteed global convergence at a quadratic rate. This is in contrast with discretize-then-optimize methods that discretize the PDE first and then solve the optimality conditions of the approximated finite-dimensional problem. Computational results for two applications, namely
nuclear reactivity control and water quality treatment in a reactor, are presented to illustrate the effectiveness of the proposed framework.
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Computational Physics
Random walk models of anisotropic diffusion on rectangular and hexagonal lattices
Luke P. Filippini, Adrianne L. Jenner and Elliot J. Carr
The diffusive transport of particles in anisotropic media is a fundamental phenomenon in computational, medical and biological disciplines. While deterministic models (partial differential equations) of such processes are well established, their inability to capture inherent randomness, and the assumption of a large number of particles, hinders their applicability. To address these issues, we present several equivalent (discrete-space discrete-time) random walk models of
diffusion described by a spatially-invariant tensor on a two-dimensional domain with no-flux boundary conditions. Our approach involves discretising the deterministic model in space and time to give a homogeneous Markov chain governing particle movement between (spatial)
lattice sites over time. The spatial discretisation is carried out using a vertex-centred element-based finite volume method on rectangular and hexagonal
lattices, and a forward Euler discretisation in time yields a nearest-neighbour random walk model with simple analytical expressions for the transition probabilities. For each
lattice configuration, analysis of these expressions yields constraints on the time step duration, spatial steps and
diffusion tensor to ensure the probabilities are between zero and one. We find that model implementation on a rectangular
lattice can be achieved with a constraint on the
diffusion tensor, whereas a hexagonal
lattice overcomes this limitation (no restrictions on the
diffusion tensor). Overall, the results demonstrate good visual and quantitative (mean-squared error) agreement between the deterministic model and random walk simulations for several test cases. All results are obtained using MATLAB code available on GitHub (https://github.com/lukefilippini/Filippini2025).
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Geophysics
Evaluating Multi-station Phase Picking Algorithm Phase Neural Operator (PhaseNO) on Local Seismic Networks
Qingkai Kong, Avigyan Chatterjee, Chengping Chai et al. (+6 more)
Reliable automatic phase picking is important for many seismic applications. With the development of machine learning approaches, many
algorithms are proposed, evaluated and applied to different areas. Many of these
algorithms are single station based, while recent proposed methods start to combine surrounding stations into consideration in the problem of phase picking. Among these
algorithms, the Phase Neural Operator (PhaseNO) shows promising results on regional
datasets comparing to existing
algorithms. But there are many use cases for the local seismic networks in our community, therefore in this paper we evaluate the performance of PhaseNO on 4 different local
datasets and compared the results to PhaseNet and EQTransformer. We used both individual phase picking metrics as well as association metrics to illustrate the performance of PhaseNO. With manually reviewing the newly detected events, we find the PhaseNO model outperformed the single station-based approaches in the local-scale use cases due to its consideration of coherent signals from multiple stations. We also explored PhaseNO's behaviors when only using one station, as well as gradually increase the number of stations in the seismic network to understand it better. Overall, using the off-the-shelf machine learning based phase pickers, PhaseNO demonstrated its good performance on local-scale seismic networks.
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All authors Qingkai Kong, Avigyan Chatterjee, Chengping Chai, Alex Dzubay, Kayla A. Kroll, Josh C. Stachnik, Scott Fertig, Jeffrey Liefer, Paul Friberg
Medical Physics
Mechanics-guided parametric modeling of intranasal spray devices and formulations for targeted drug delivery to the nasopharynx
Md Tariqul Hossain, Abir Malakar, Mohammad Yeasin et al. (+10 more)
Improving the efficacy of nasal sprays by enhancing targeted drug delivery to intra-airway tissue sites prone to infection onset is hypothesized to be achievable through an optimization of key device and formulation parameters, such as the sprayed droplet sizes, spray cone angle, and formulation density. This study focuses on the nasopharynx, a primary locus of early viral entry, as the optimal target for intranasal drug delivery. 3D anatomical upper airway geometries reconstructed from high-resolution computed
tomography scans were used to numerically evaluate a cone injection approach, with inert particles mimicking the motion of sprayed droplets within an underlying inhaled airflow field. We have considered monodisperse sprayed particles sized between 10 to 50 microns, six densities ranging from 1.0 to 1.5 g/ml for the constituent formulation, and twelve plume angles spanning 1 to 70 degrees subtended by the spray jet at the nozzle position. Large Eddy Simulation-based modeling of the inhaled airflow physics within the anatomical domains was coupled with a Lagrangian particle-tracking framework to derive the drug deposition trend at the nasopharynx. The resulting globally averaged deposition contour map, obtained by interpolating the outcomes for the discrete test parameters, revealed that nasopharyngeal deposition peaked for droplet sizes 25 to 45 microns and plume angles equal to or less than 30 degrees. In addition, the formulation density of 1.0 g/ml yielded the highest mean deposition rate, over the tested range of sprayed particle sizes and plume angles. The findings were experimentally validated through representative physical spray tests conducted in a 3D-printed replica of one of the test geometries and collectively demonstrate that rational optimization of the intranasal spray design is attainable, with substantial enhancement of targeted drug delivery to the nasopharynx.
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All authors Md Tariqul Hossain, Abir Malakar, Mohammad Yeasin, William O'Connell, Mohammad Mehedi Hasan Akash, Azadeh Borojeni, Devranjan Samanta, Gerallt Williams, Joshua Reineke, Goncalo Farias, Sunghwan Jung, Julie Suman, Saikat Basu
Applied Physics
Unveiling Retention Loss Mechanism in FeFETs with Gate-side Interlayer by Decoupling Trapped Charges and Ferroelectric Polarization
Runhao Han, Tao Hu, Jia Yang et al. (+10 more)
We propose a direct experimental extraction technique for trapped charges and quantitative energy band diagrams in the FeFETs with metal-
insulator-ferroelectric-
insulator-
semiconductor (MIFIS) structure, derived from the physical relationship between Vth and gate-side interlayer (G.IL) thickness. By decoupling trapped charges and ferroelectric polarization, we reveal that: (i) The gateinjected charges and channel-injected charges are excessive and maintain consistent ratios to ferroelectric polarization (~170% and ~130%, respectively). (ii) Retention loss originates from the detrapping of gate-injected charges rather than ferroelectric depolarization. (iii) As the G.IL thickens, the gate-injected charge de-trapping path transforms from gate-side to channel-side. To address the retention loss, careful material design, optimization, and bandgap engineering in the MIFIS structure are crucial. This work advances the understanding of high retention strategies for MIFIS-FeFETs in 3D FE NAND.
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All authors Runhao Han, Tao Hu, Jia Yang, Saifei Dai, Yajing Ding, Mingkai Bai, Xianzhou Shao, Junshuai Chai, Hao Xu, Qing Luo, Wenwu Wang, Tianchun Ye, Xiaolei Wang
Instrumentation and Detectors
Performance of FBK VUV-HD3 and HPK VUV4 SiPMs in the Light-only Liquid Xenon (LoLX) Detector
Xiang Li, David Gallacher, Stephanie Bron et al. (+10 more)
Silicon Photomultipliers (SiPMs) are a critical technology for the next generation of rare-event search experiments using
liquid xenon (LXe). LXe scintillation is emitted primarily in the
vacuum-ultraviolet (VUV), requiring photosensors with high VUV detection efficiency under cryogenic operating conditions. Direct characterization of VUV-sensitive SiPMs in LXe is therefore important for informing detector design and comparing device response. This work presents a direct comparison of Fondazione Bruno Kessler (FBK) VUV-HD3 and Hamamatsu (HPK) VUV4 SiPMs operated simultaneously within the Light-only
Liquid Xenon (LoLX) detector. Using data collected with gamma sources placed outside the detector, we characterized the relative performance of these photosensors. Our analysis reveals that under these operating conditions, the HPK SiPMs observe 33-38% less light than the FBK devices, a larger difference than predicted by standard PDE models in
vacuum measurements. We show that this discrepancy is resolved by an angular and wavelength dependent PDE model incorporating surface shadowing effects into our optical simulation, which then accurately reproduces the experimental data. This finding has significant implications for the selection and implementation of photosensors in future large-scale LXe detectors.
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All authors Xiang Li, David Gallacher, Stephanie Bron, Thomas Brunner, Austin de St Croix, Frédéric Girard, Colin Hempel, Mouftahou Bakary Latif, Simon Lavoie, Chloé Malbrunot, Fabrice Retière, Marc-André Tétrault, Lei Wang
Chemical Physics
High-entropy perovskites as new photocatalysts for cocatalyst-free water splitting
Ho Truong Nam Hai, Makoto Arita and Kaveh Edalati
The photocatalytic water-splitting process is thermodynamically challenging and requires catalysts with suitable band structures, as well as the presence of supporting cocatalysts. By considering the unique charge carrier mobility in perovskites, this study introduces three new ABO3-type high-
entropy perovskites (Ba1/2Sr1/2)(Ti1/3Zr1/3Hf1/3)O3, (Ba1/2Sr1/2)(Ga1/3In1/3Sn1/3)O3 and (Ba1/2Sr1/2)(Ti1/3Zr1/3Sn1/3)O3 for cocatalyst-free photocatalysis. The three catalysts, having a single-phase cubic structure, are designed by considering configurational
entropy, tolerance factor, octahedral factor, ionic radius deviation and valence deviation of >1.5R (R: gas constant), 0.9-1.0, 0.4-0.8, >0.3 and >0.3, respectively. The perovskites exhibit similar valence band tops, while their bandgaps vary slightly depending on the composition at the B-site (slightly lower bandgap by including d10 cations). Additionally, all three materials demonstrate effective hydrogen generation without the need for added cocatalysts. This investigation confirms that high-
entropy oxide perovskites can offer significant potential for cocatalyst-free photocatalytic reactions.
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Optics
Solid-State High-Order Harmonic Generation: Emerging Frontiers in Ultrafast and Quantum Light Science
Marcelo F. Ciappina
High-order harmonic generation (HHG) in solids has emerged as a versatile platform for exploring ultrafast and
quantum-coherent phenomena in condensed matter. Recent advances reveal Berry-phase and topological effects in harmonic emission, strong-field control of
excitons and
lattice motion, the generation of nonclassical light states driven by
quantum and squeezed fields, and the emergence of orbital-angular-momentum transfer in solid-state high-harmonic generation. Nanostructured and hybrid plasmonic-
semiconductor platforms enable enhanced and spectrally tunable HHG, while interferometric and cryogenic setups allow attosecond-resolved phase measurements. On the theoretical side, multiband and topological models incorporating dephasing, propagation, and electron-hole coherence effects have deepened our understanding of the interplay between interband and intraband dynamics. These developments establish solid-state HHG as a bridge between ultrafast spectroscopy,
quantum optics, and material science, paving the way toward
quantum-engineered attosecond sources and coherent control of light-matter interactions in solids.
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Chemical Physics
Numerical Integration of Slater Basis Functions Over Prolate Spheroidal Grids
Alexander Stark, Nathan Meier, Jeffrey Hatch et al. (+3 more)
Slater basis functions have desirable properties that can improve electronic structure simulations, but improved numerical integration methods are needed. This work builds upon the SlaterGPU library for evaluation of
Hamiltonian matrix elements in the resolution-of-the-identity approximation. In particular, a Prolate Spheroidal grid will provide sufficient integral accuracy to employ larger basis sets (quadruple-
zeta and greater) in practical computations involving polyatomics. To integrate 3-center Coulomb and
nuclear attraction terms, an improved grid representation around the 3rd center is introduced. The RMSEs of the integral quantities are evaluated and compared to the previous numerical integration method used in SlaterGPU (Becke Partitioning), resulting in a ~3 order of magnitude reduction in the error for 2-center integral quantities. The procedure is generally applicable to polyatomic systems, GPU accelerated for high performance computing, and tested on self-consistent field and full configuration interaction wavefunctions. Results for a number of 3-atom models as well as propanediyl (C3H6) demonstrate the reliability of the new integration scheme.
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All authors Alexander Stark, Nathan Meier, Jeffrey Hatch, Joshua Kammeraad, Duy-Khoi Dang, Paul Zimmerman
Applied Physics · Cross-listed from cs.LG
Automotive Crash Dynamics Modeling Accelerated with Machine Learning
Mohammad Amin Nabian, Sudeep Chavare, Deepak Akhare et al. (+3 more)
Crashworthiness assessment is a critical aspect of automotive design, traditionally relying on high-fidelity finite element (FE) simulations that are computationally expensive and time-consuming. This work presents an exploratory comparative study on developing machine learning-based surrogate models for efficient prediction of structural deformation in crash scenarios using the NVIDIA PhysicsNeMo framework. Given the limited prior work applying machine learning to structural crash dynamics, the primary contribution lies in demonstrating the feasibility and engineering utility of the various modeling approaches explored in this work. We investigate two state-of-the-art neural network architectures for modeling crash dynamics: MeshGraphNet, and Transolver. Additionally, we examine three strategies for modeling transient dynamics: time-conditional, the standard Autoregressive approach, and a stability-enhanced Autoregressive scheme incorporating rollout-based training. The models are evaluated on a comprehensive Body-in-White (BIW) crash
dataset comprising 150 detailed FE simulations using LS-DYNA. The
dataset represents a structurally rich vehicle assembly with over 200 components, including 38 key components featuring variable thickness distributions to capture realistic manufacturing variability. Each model utilizes the undeformed mesh geometry and component characteristics as inputs to predict the spatiotemporal
evolution of the deformed mesh during the crash sequence. Evaluation results show that the models capture the overall deformation trends with reasonable fidelity, demonstrating the feasibility of applying machine learning to structural crash dynamics. Although not yet matching full FE accuracy, the models achieve orders-of-magnitude reductions in computational cost, enabling rapid design exploration and early-stage optimization in crashworthiness evaluation.
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All authors Mohammad Amin Nabian, Sudeep Chavare, Deepak Akhare, Rishikesh Ranade, Ram Cherukuri, Srinivas Tadepalli