Showing 1–50 of 221 results for author: Wang, N

Booster-based beam recycling for swap-out injection at the High Energy Photon Source

Authors: Zhe Duan, Jinhui Chen, Yaoyao Du, Yuanyuan Guo, Jun He, Xiyang Huang, Daheng Jia, Jingyi Li, Fang Liu, Peng Liu, Zhi Liu, Xiaohan Lu, Yanhua Lu, Cai Meng, Yuemei Peng, Saike Tian, Guanwen Wang, Jiuqing Wang, Na Wang, Yuanyuan Wei, Gang Xu, Haisheng Xu, Yaliang Zhao, Ying Zhao, Yi Jiao , et al. (1 additional authors not shown)

Abstract: Fourth-generation synchrotron light sources employ ultralow-emittance storage rings with stringent injection requirements. On-axis swap-out injection alleviates the dependence on storage-ring dynamic aperture, but high-charge operation requires an efficient injector architecture capable of producing high-charge replacement bunches. This paper presents the accelerator physics design and performance… ▽ More Fourth-generation synchrotron light sources employ ultralow-emittance storage rings with stringent injection requirements. On-axis swap-out injection alleviates the dependence on storage-ring dynamic aperture, but high-charge operation requires an efficient injector architecture capable of producing high-charge replacement bunches. This paper presents the accelerator physics design and performance analysis of a booster-based beam-recycling swap-out injection scheme implemented at the High Energy Photon Source (HEPS). In this approach, the full-energy booster serves as both an injector and a high-energy accumulator. An extracted storage-ring bunch is returned to the booster, merged with a low-charge bunch previously injected from the linac and accelerated to full energy. Following high-energy damping, the merged bunch is reinjected into the original storage-ring bucket. The scheme avoids the need for a dedicated accumulator ring while enabling high-charge bunch replacement. The recycling scheme was commissioned through staged machine studies. Full recycling-chain simulations, commissioning studies, and measured performance analysis are presented. The measured results characterize the recycling operation and quantify the transmission efficiency and performance limitations of the complete recycling loop. These results demonstrate the feasibility of the booster-based beam-recycling architecture and establish its operational basis for high-charge swap-out injection in future fourth-generation synchrotron light sources. △ Less

Submitted 17 August, 2026; originally announced August 2026.

Comments: Submitted to Accelerator: Science, Technology & Applications; 14 pages, 8 figures, 3 tables

3D Molecular Representation Learning for Organic Mixtures: Viscosity and Density Prediction

Authors: Haicheng Qu, Yanyi Su, Ning Wang, Shangqian Chen, Zhifeng Gao, Jun Cheng, Qi Ou

Abstract: The viscosity and density of organic mixtures are essential properties for designing lubricants, solvents, and heat transfer fluids. In engineering practice, formulating a functional fluid requires understanding how these properties change with composition and temperature. However, exhaustive experimental characterization across the full parameter space is impractical due to the vast number of pos… ▽ More The viscosity and density of organic mixtures are essential properties for designing lubricants, solvents, and heat transfer fluids. In engineering practice, formulating a functional fluid requires understanding how these properties change with composition and temperature. However, exhaustive experimental characterization across the full parameter space is impractical due to the vast number of possible species and combinations. Here we introduce a mixture-aware 3D molecular representation learning strategy, built upon a pre-trained molecular encoder, that jointly encodes component structures, mole fractions, and temperature to achieve accurate predictions for organic mixtures. Fine-tuning on publicly available datasets covering a wide range of binary organic mixtures yields test-set R2 values of 0.973 for dynamic viscosity and 0.996 for density, significantly outperforming traditional machine learning baselines. Beyond this overall accuracy, the model captures non-monotonic viscosity changes upon mixing, surpassing simple linear or logarithmic mixing rules. The architecture is extendable to ternary and multicomponent mixtures, as verified via preliminary experiments. Using this model, we quantitatively analyze how molecular structure-branching, cycloalkane, and aromatic rings-affects viscosity-temperature behavior, which benefits the design of lubricants with superior viscosity-temperature performance. Altogether, this work provides a practical, data-driven tool for mixture property prediction, accelerating the rational formulation of functional fluids in chemical engineering. △ Less

Submitted 6 August, 2026; originally announced August 2026.

Coherent seeding and control of dynamical ferroelectricity by phonon anharmonicity

Authors: Junhan Huang, Yongkang Ju, Xinbo Wang, Li Yue, Hao Wang, Qiaomei Liu, Tianchen Hu, Yuchen Cui, Liyu Shi, Shangfei Wu, Sijie Zhang, Dong Wu, Peizhe Tang, Tao Dong, Nan-Lin Wang

Abstract: Optical control of quantum materials has progressed along two separate directions: creating non-equilibrium states inaccessible at equilibrium, and coherently controlling ultrafast dynamics with multi-pulse protocols. Ferroelectricity is especially attractive in this context because its order parameter, macroscopic polarization, directly links inversion-symmetry breaking to functional response. Ye… ▽ More Optical control of quantum materials has progressed along two separate directions: creating non-equilibrium states inaccessible at equilibrium, and coherently controlling ultrafast dynamics with multi-pulse protocols. Ferroelectricity is especially attractive in this context because its order parameter, macroscopic polarization, directly links inversion-symmetry breaking to functional response. Yet light-induced ferroelectricity has so far been confined to quantum paraelectrics near the ferroelectric instability, where critical fluctuations obscure the formation of a homogeneous ferroelectric state and complicate its deterministic coherent control. Unifying these capabilities -- preparing a symmetry-broken state and then coherently steering its functionality -- remains a central challenge. Here we show that intense terahertz excitation of a soft phonon mode induces a ferroelectric state in centrosymmetric PbTe, a thermoelectric material with strong lattice anharmonicity but no ferroelectric transition at finite temperature. The light-induced symmetry-broken state can be realized up to about 100 K, without relying on local dipolar fluctuations. Experiment and theory together reveal that terahertz-driven anharmonic coupling between degenerate transverse optical phonons underlies this ferroelectric induction. Furthermore, we demonstrate coherent amplification and suppression of the induced polarization via a double-pulse-excitation protocol. These results establish terahertz-driven anharmonic mode coupling as a general strategy for controlling mode-mediated functionalities in quantum materials, opening a route to ultrafast information processing. △ Less

Submitted 22 June, 2026; originally announced June 2026.

High-speed electrically driven liquid-crystal compact optical skyrmion encoder

Authors: Yu-Ping Tang, Zhenyu Guo, Ze-Yu Wang, Le Yu, Long-Yang Wang, Nilo Mata-Cervera, Yu Wang, Ning Wang, Yijie Shen, Ling-Ling Ma, Yan-Qing Lu

Abstract: Optical skyrmions possess topological polarization textures that can maintain topological robustness under external perturbations, making them promising carriers for disturbance-resistant optical information transmission. However, existing optical skyrmion generation schemes mostly rely on static optical elements or fixed nanostructures, making high-speed dynamic switching of the topological state… ▽ More Optical skyrmions possess topological polarization textures that can maintain topological robustness under external perturbations, making them promising carriers for disturbance-resistant optical information transmission. However, existing optical skyrmion generation schemes mostly rely on static optical elements or fixed nanostructures, making high-speed dynamic switching of the topological state difficult. Here, we propose a high-speed switchable optical skyrmion generator based on a patterned liquid-crystal spin-orbit device. The device employs the in-plane orientation of liquid crystals to imprint a fixed Pancharatnam-Berry geometric phase, while an applied voltage rapidly tunes the liquid-crystal retardance, enabling reversible switching between skyrmion and non-skyrmion states. Experimental results show that the device exhibits millisecond electrical response, with bidirectional response times of 1.76 ms and 0.72 ms, corresponding to an ideal cycling rate of approximately 403 Hz, making it the fastest switchable optical skyrmion generator to date. Furthermore, by exploiting this rapid topological refreshing capability, we demonstrate image encoding and decoding, providing a new liquid-crystal device platform for high-speed, refreshable, and disturbance-resistant topological optical information transmission. △ Less

Submitted 17 June, 2026; originally announced June 2026.

Comments: 15 pages, 5 figures

Temporal Faraday effect enabled by Floquet-induced chirality

Authors: Neng Wang, Guo Ping Wang

Abstract: The Faraday effect is a hallmark of nonreciprocal light-matter interactions and traditionally requires magnetic bias or intrinsically chiral media. Here we introduce a temporal chiral metamaterial in which an effective chiral response is generated entirely by Floquet modulation, without magnetic fields or structurally chiral constituents. The medium is realized by periodically rotating the princip… ▽ More The Faraday effect is a hallmark of nonreciprocal light-matter interactions and traditionally requires magnetic bias or intrinsically chiral media. Here we introduce a temporal chiral metamaterial in which an effective chiral response is generated entirely by Floquet modulation, without magnetic fields or structurally chiral constituents. The medium is realized by periodically rotating the principal axes of the permittivity and permeability tensors in time. Using a nonlocal temporal effective medium theory derived from Hamiltonian homogenization, we show that the resulting chiral parameter is an odd function of the wavevector, giving rise to intrinsic nonreciprocity despite Onsager-symmetric constitutive relations. This Floquet-induced chirality produces a temporal Faraday effect, in which the polarization plane of a linearly polarized wave rotates continuously in time. The direction and magnitude of the rotation are programmable through the modulation sequence and remain invariant under both spatial and temporal reversal. Our work establishes Floquet-induced chirality as a fundamentally new mechanism for nonreciprocal light control and opens a route to reconfigurable polarization manipulation in time-modulated photonic systems. △ Less

Submitted 15 June, 2026; originally announced June 2026.

Comments: 20 pages, 5 figures

Ultrafast Terahertz and Optical Spectroscopy under Synergetic Extreme Conditions

Authors: Xinbo Wang, Tao Dong, Jianlin Luo, Nanlin Wang

Abstract: Elucidating and manipulating emergent phases in complex materials requires direct access to their low-energy collective modes. Terahertz (THz) time-domain and ultrafast optical spectroscopies have emerged as indispensable experimental tools, enabling the probing of intrinsic electrodynamics and the coherent control of non-equilibrium states. At the Synergetic Extreme Condition User Facility (SECUF… ▽ More Elucidating and manipulating emergent phases in complex materials requires direct access to their low-energy collective modes. Terahertz (THz) time-domain and ultrafast optical spectroscopies have emerged as indispensable experimental tools, enabling the probing of intrinsic electrodynamics and the coherent control of non-equilibrium states. At the Synergetic Extreme Condition User Facility (SECUF), we have developed a suite of intense ultrashort light sources covering the near-infrared, mid-infrared, and THz spectral ranges. By integrating these strong-field pulses with extreme sample environments, such as low temperatures, strong magnetic fields, and high pressures, we have established several state-of-the-art spectroscopy platforms. In this article, we outline the technical specifications of each setup and highlight representative user experiments. The presented results underscore the exceptional capability of the THz experimental unit (A4-2) to explore ultrafast dynamics across a multi-parameter thermodynamic phase space. △ Less

Submitted 1 June, 2026; originally announced June 2026.

Comments: 10 pages, 6 figure, Accepted in Chinese Physics B

Tunable Mid-Infrared Chiral Selective Absorber Based on Asymmetric V-Shaped Metasurfaces Driven by Chiral quasi-Bound State in the Continum

Authors: Yuqing Liu, Yi Wang, Ruihan Ma, Nianzhao Wang, Mengtao Sun, Yuqing Cheng

Abstract: Efficient discrimination of circularly polarized light (CPL) is of great significance in photonics. In this study, we propose a nanophotonic device based on asymmetric V-shaped metasurfaces that exhibits strong discrimination between left- and right-circularly polarized (LCP and RCP) light at the resonant wavelengths of the absorption spectra. The chiral-selective response originates from a quasi-… ▽ More Efficient discrimination of circularly polarized light (CPL) is of great significance in photonics. In this study, we propose a nanophotonic device based on asymmetric V-shaped metasurfaces that exhibits strong discrimination between left- and right-circularly polarized (LCP and RCP) light at the resonant wavelengths of the absorption spectra. The chiral-selective response originates from a quasi-bound state in the continuum (qBIC) mode enabled by controlled symmetry breaking in the V-shaped structure, which exhibits high absorption under LCP illumination while the resonance is strongly suppressed under RCP incidence, leading to a large absorption contrast between the two circular polarizations. This significant absorption difference enables highly efficient chiral discrimination. Furthermore, the resonant wavelength can be broadly tuned from 5200 to 6200 nm by scaling the structural dimensions without compromising the high absorption contrast between LCP and RCP. This work not only enables high-performance chiral detection and separation, but also offers valuable guidance for reconfigurable chiral nanodevices, with promising applications in areas such as enantiomer sensing, chiral imaging, and CPL spectroscopy. △ Less

Submitted 4 June, 2026; originally announced June 2026.

Comments: 15 pages, 8 figures

Analog photonic simulator for large-scale transport

Authors: Mengyu Zhao, Xuezhi Zhu, Nikita Guseynov, Yewei Yuan, Na Wang, Meihong Wang, Yunyun Cao, Shi Jin, Nana Liu, Changde Xie, Kunchi Peng, Xiaolong Su

Abstract: Transport equations describe how physical quantities -- such as mass, energy, momentum, concentration, probability, or fields -- are carried, propagated, or redistributed through space and time, forming a foundational class of partial differential equations across science and engineering. However, high-dimensional partial differential equations are difficult to represent on digital grids because t… ▽ More Transport equations describe how physical quantities -- such as mass, energy, momentum, concentration, probability, or fields -- are carried, propagated, or redistributed through space and time, forming a foundational class of partial differential equations across science and engineering. However, high-dimensional partial differential equations are difficult to represent on digital grids because the number of degrees of freedom grows exponentially with dimension. Continuous-variable quantum photonics on the other hand can represent and evolve these large-scale fields without first discretizing space into a discrete grid. We demonstrate a large-scale analog photonic simulator for the constant-coefficient advection equation, a transport equation that is a fundamental benchmark for scientific computing. The solution of a $d$-variable advection equation is encoded into $d$ optical modes, so that the partial differential equation evolution maps directly to programmable phase-space displacements generated by optical quadrature momenta. Using a time-domain continuous-variable quantum photonic platform, we validate programmable control with $20,000$ single-mode squeezed states and $20,000$ two-mode squeezed states, and implement transport dynamics on a $20,000$-mode cluster-state resource. Homodyne measurements then verifies mode-resolved displacement control, which can provide first and second-order moment information of the solution to the advection equation, with final achievable relative error as low as $0.8\%$ and $0.92\%$ for first and second-order moment observables respectively. Our results establish continuous-variable photonics as a suitable programmable analog platform for large-scale advection equations. △ Less

Submitted 30 May, 2026; originally announced June 2026.

Comments: 47 pages, 22 figures, 14 tables

Improved n=1 Empirical Error Field Penetration Threshold Scaling with Ohmic and L-Mode Conventional Tokamak Plasma Discharges

Authors: E. M. Bursch, J. K. Park, N. C. Logan, F. Mao, N. Wang, C. F. B. Zimmermann, R. J. Buttery, C. Paz-Soldan, M. Pharr, L. Piron, G. Szepesi, H. Wang, S. M. Yang, JET Contributors, EUROfusion Tokamak Exploitation Team

Abstract: This paper presents an updated n=1 error field penetration threshold scaling, which increases fit quality compared to previous error field scaling laws, is produced from an expanded database, and exhibits reduced uncertainty in projections to future conventional tokamaks. It improves confidence in tokamak engineering tolerances, which are a significant driver of cost and time constraints on device… ▽ More This paper presents an updated n=1 error field penetration threshold scaling, which increases fit quality compared to previous error field scaling laws, is produced from an expanded database, and exhibits reduced uncertainty in projections to future conventional tokamaks. It improves confidence in tokamak engineering tolerances, which are a significant driver of cost and time constraints on device construction. We add J-TEXT data, new JET data, and create the scaling using only conventional tokamak Ohmic and L-mode experiments. Since H-mode plasmas are more resilient to error field penetration, this scaling predicts what is likely the most dangerous regime of error field penetration for new tokamak designs. These decisions improve confidence in the error field penetration threshold scaling and its application in the construction and design decisions of any future conventional tokamak or FPP. △ Less

Submitted 29 April, 2026; originally announced April 2026.

Long lasting plasma density structures utilizing tailored density profiles

Authors: Mufei Luo, Caterina Riconda, Anna Grassi, Ning Wang, Jonathan Wurtele, Istvan Pusztai, Tünde Fülöp

Abstract: Using fully kinetic Particle In Cell simulations, we investigate the stability and performance of autoresonant plasma beat wave excitation in plasmas with tailored density profiles. We show that a prescribed spatial variation of the background density sustains continuous phase locking between the driving laser beat and the excited plasma mode, thereby enabling precise control of the plasma wave pa… ▽ More Using fully kinetic Particle In Cell simulations, we investigate the stability and performance of autoresonant plasma beat wave excitation in plasmas with tailored density profiles. We show that a prescribed spatial variation of the background density sustains continuous phase locking between the driving laser beat and the excited plasma mode, thereby enabling precise control of the plasma wave packet shape and group velocity and providing an alternative to frequency chirping of the drive lasers. The density gradient scale is found to govern the nonlinear autoresonant growth, and the attainable saturation amplitude can exceed the classical Rosenbluth Liu prediction and, for appropriate laser intensities, approach the nonrelativistic wave breaking limit. We show that a four laser configuration in a steep parabolic density profile can generate a specially confined two phase quasiperiodic plasma lattice. The generation of such structures may lead to novel applications in plasma photonics. △ Less

Submitted 15 March, 2026; originally announced March 2026.

Coupled Cluster con MōLe: Molecular Orbital Learning for Neural Wavefunctions

Authors: Luca Thiede, Abdulrahman Aldossary, Andreas Burger, Jorge Arturo Campos-Gonzalez-Angulo, Ning Wang, Alexander Zook, Melisa Alkan, Kouhei Nakaji, Taylor Lee Patti, Jérôme Florian Gonthier, Mohammad Ghazi Vakili, Alán Aspuru-Guzik

Abstract: Density functional theory (DFT) is the most widely used method for calculating molecular properties; however, its accuracy is often insufficient for quantitative predictions. Coupled-cluster (CC) theory is the most successful method for achieving accuracy beyond DFT and for predicting properties that closely align with experiment. It is known as the ''gold standard'' of quantum chemistry. Unfortun… ▽ More Density functional theory (DFT) is the most widely used method for calculating molecular properties; however, its accuracy is often insufficient for quantitative predictions. Coupled-cluster (CC) theory is the most successful method for achieving accuracy beyond DFT and for predicting properties that closely align with experiment. It is known as the ''gold standard'' of quantum chemistry. Unfortunately, the high computational cost of CC limits its widespread applicability. In this work, we present the Molecular Orbital Learning (MōLe) architecture, an equivariant machine learning model that directly predicts CC's core mathematical objects, the excitation amplitudes, from the mean-field Hartree-Fock molecular orbitals as inputs. We test various aspects of our model and demonstrate its remarkable data efficiency and out-of-distribution generalization to larger molecules and off-equilibrium geometries, despite being trained only on small equilibrium geometries. Finally, we also examine its ability to reduce the number of cycles required to converge CC calculations. MōLe can set the foundations for high-accuracy wavefunction-based ML architectures to accelerate molecular design and complement force-field approaches. △ Less

Submitted 23 February, 2026; originally announced February 2026.

Wavefront Control and Intensity Modulation of Third Harmonic Generation in Nonlocal Metasurfaces

Authors: Yu Tian, Nuo Wang, Qi Liu, Shuyuan Xiao, Tingting Liu, Olivier J. F. Martin, Ying Gu

Abstract: Metasurfaces have emerged as a promising platform for integrated nonlinear optics. Nonlocal metasurfaces enable high nonlinear conversion efficiency, while the local ones can offer versatile wavefront control, yet achieving both within a single metasurface remains challenging. Here, using a nonlocal phase gradient metasurface, we firstly demonstrate efficient third harmonic generation (THG) with p… ▽ More Metasurfaces have emerged as a promising platform for integrated nonlinear optics. Nonlocal metasurfaces enable high nonlinear conversion efficiency, while the local ones can offer versatile wavefront control, yet achieving both within a single metasurface remains challenging. Here, using a nonlocal phase gradient metasurface, we firstly demonstrate efficient third harmonic generation (THG) with polarization-dependent wavefront control. Leveraging nonlocal nonlinear geometric phase existing at resonance, the third harmonic light with distinct polarizations is deflected into $\pm$ 2nd and $\pm$ 4th diffraction orders, simultaneously achieving a conversion efficiency up to $1.45\times 10^{-4}$ under a pump intensity of $1 GW/cm^{2}$. Then, by introducing a secondary fundamental beam, whose generated third harmonic light overlaps with that of the first beam, the intensity modulation of THG is obtained. The THG efficiency can be tuned from $3.9 \times 10^{-9}$ to $5.5 \times 10^{-3}$ by varying the relative phase, polarization and intensity of two fundamental beams. Through utilizing the advantages of both local and nonlocal metasurfaces, our results effectively pave the way to on-chip nonlinear photonic devices and signal processing. △ Less

Submitted 2 February, 2026; originally announced February 2026.

Comments: 5 figures

Efficient high-harmonic generation in van der Waals ferroelectric NbOI$_2$ crystals

Authors: Tianchen Hu, Feng Li, Junhan Huang, Chen Qian, Ruoxuan Ding, Hao Wang, Qiaomei Liu, Qiong Wu, Ruifeng Lu, Chunmei Zhang, Nanlin Wang

Abstract: Layered NbOX$_2$ ($X=\mathrm{Cl,\,Br,\,I}$), a member of the van der Waals ferroelectric family, exhibits intrinsic ferroelectricity and pronounced nonlinear optical responses, making it a promising candidate for integrated nanophotonics applications. While previous studies have emphasized the material's strong second-order nonlinear responses, higher-order nonlinear responses are still mostly une… ▽ More Layered NbOX$_2$ ($X=\mathrm{Cl,\,Br,\,I}$), a member of the van der Waals ferroelectric family, exhibits intrinsic ferroelectricity and pronounced nonlinear optical responses, making it a promising candidate for integrated nanophotonics applications. While previous studies have emphasized the material's strong second-order nonlinear responses, higher-order nonlinear responses are still mostly unexplored. This work systematically investigates NbOI$_2$ using high harmonic generation (HHG) spectroscopy. Driven by an intense mid-infrared laser field centered at $\sim4~μ\mathrm{m}$ wavelength, highly anisotropic odd- and even-order harmonics up to the 16th order are generated at a low peak intensity of $0.4~\mathrm{TW\,cm^{-2}}$, extending beyond the material's bandgap. Both bulk and flake forms of NbOI$_2$ display pronounced harmonic emission from the near-infrared to the deep-ultraviolet spectral region, with a notably high overall conversion efficiency compared to other known materials. Polarization-resolved measurements reveal that even-order harmonics remain aligned with the crystal polar axis regardless of the driving-field orientation, whereas odd-order harmonics are dynamically affected. First-principles calculations suggest that the flat valence band associated with Peierls dimerization enhances HHG efficiency through electron correlation. These findings provide fresh perspectives on HHG in van der Waals ferroelectric materials and facilitate the development of compact and tunable quantum light sources. △ Less

Submitted 29 January, 2026; originally announced January 2026.

Bayesian Full-waveform Monitoring of CO2 Storage with Fluid-flow Priors via Generative Modeling

Authors: Haipeng Li, Nanzhe Wang, Louis J. Durlofsky, Biondo L. Biondi

Abstract: Quantitative monitoring of subsurface changes is essential for ensuring the safety of geological CO2 sequestration. Full-waveform monitoring (FWM) can resolve these changes at high spatial resolution, but conventional deterministic inversion lacks uncertainty quantification and incorporates only limited prior information. Deterministic approaches can also yield unreliable results with sparse and n… ▽ More Quantitative monitoring of subsurface changes is essential for ensuring the safety of geological CO2 sequestration. Full-waveform monitoring (FWM) can resolve these changes at high spatial resolution, but conventional deterministic inversion lacks uncertainty quantification and incorporates only limited prior information. Deterministic approaches can also yield unreliable results with sparse and noisy seismic data. To address these limitations, we develop a Bayesian FWM framework that combines reservoir flow physics with generative prior modeling. Prior CO2 saturation realizations are constructed by performing multiphase flow simulations on prior geological realizations. Seismic velocity is related to saturation through rock physics modeling. A variational autoencoder (VAE) trained on the priors maps high-dimensional CO2 saturation fields onto a low-dimensional, approximately Gaussian latent space, enabling efficient Bayesian inference while retaining the key geometrical structure of the CO2 plume. Hamiltonian Monte Carlo (HMC) is used to infer CO2 saturation changes from time-lapse seismic data and to quantify associated uncertainties. Numerical results show that this approach improves inversion stability and accuracy under extremely sparse and noisy acquisition, whereas deterministic methods become unreliable. Statistical seismic monitoring provides posterior uncertainty estimates that identify where additional measurements would most reduce ambiguity and mitigate errors arising from biased rock physics parameters. The framework combines reservoir physics, generative priors, and Bayesian inference to provide uncertainty quantification for time-lapse monitoring of CO2 storage and other subsurface processes. △ Less

Submitted 13 December, 2025; originally announced December 2025.

A power-in-bucket model enabled designs of nanostructure-enhanced waveguides for highly efficient wide-angle light couplings

Authors: Wenbo Luo, Yitong Gu, Jianwei Wang, Fei Yu, Chunlei Yu, Lili Hu, Zhichao Ruan, Ning Wang

Abstract: Well-designed nanostructures on fiber facets can boost wide-angle light coupling and thus gain considerable attention because of the potential for intensive applications. However, previous theories commonly concentrate on the configurations of the bare waveguide, lacking full consideration of structure-assisted couplings. Here, a power-in-bucket (PIB) model is introduced to explore the coupling be… ▽ More Well-designed nanostructures on fiber facets can boost wide-angle light coupling and thus gain considerable attention because of the potential for intensive applications. However, previous theories commonly concentrate on the configurations of the bare waveguide, lacking full consideration of structure-assisted couplings. Here, a power-in-bucket (PIB) model is introduced to explore the coupling behavior of structure-modified waveguides. The analytical model investigates two representative coupling scenarios,including Gaussian beam and plane wave excitation. The PIB-computed coefficient η enhancements agree well with the experimental values, especially for the multiple-mode fibers under large-angle illuminations. Using PIB to optimize the beam-fiber parameters, we show that at the incidence angle of 37 degree, η could increase from 0.3320 to 0.5102 by the identical ring gratings. Overall, the proposed model provides a useful account of the mechanism of grating-aided light couplings. These findings would be of great help in designing structure-enhanced probes for wide-angle broadband light collection applications. △ Less

Submitted 10 December, 2025; originally announced December 2025.

Comments: 18 pages, 5 figures

A dynamic shim approach for correcting eddy current effects in diffusion-prepared MRI acquisition using a multi-coil AC/DC shim-array

Authors: Congyu Liao, Jason P. Stockmann, Zhitao Li, Zhixing Wang, Mengze Gao, Lincoln Craven-Brightman, Monika Sliwiak, Charles Biggs, Jack Glad, Jiazheng Zhou, Yurui Qian, Zheng Zhong, Nan Wang, Hua Wu, Thomas Grafendorfer, Fraser Robb, Bernhard Gruber, Azma Mareyam, Adam B. Kerr, Xiaozhi Cao, Kawin Setsompop

Abstract: Purpose: We developed a dynamic B0 shimming approach using a 46-channel AC/DC shim array to correct phase errors caused by eddy currents from diffusion-encoding gradients in diffusion-prepared MRI, enabling high b-value imaging without the SNR loss from the use of magnitude stabilizer. Methods: A 46-channel AC/DC shim array and corresponding amplifier system were built. Spin echo prescans with and… ▽ More Purpose: We developed a dynamic B0 shimming approach using a 46-channel AC/DC shim array to correct phase errors caused by eddy currents from diffusion-encoding gradients in diffusion-prepared MRI, enabling high b-value imaging without the SNR loss from the use of magnitude stabilizer. Methods: A 46-channel AC/DC shim array and corresponding amplifier system were built. Spin echo prescans with and without diffusion preparation were then used to rapidly measure eddy current induced phase differences. These phase maps were used as targets in an optimization framework to compute compensatory shim currents for multi-shot 3D diffusion-prepared acquisitions. Results: The proposed method allows flexible use of the AC/DC shim array to correct undesirable eddy current effects in diffusion-prepared MRI. Phantom and in vivo experiments demonstrate whole-brain, cardiac-gated, multi-shot 3D diffusion-prepared imaging without the use of magnitude stabilizers. The approach enables preservation of full SNR while achieving reliable diffusion encoding at b-values up to 2000 s/mm2. Conclusions: This work demonstrates a new strategy for applying an AC/DC shim array to compensate for eddy current induced phase errors in diffusion-prepared MRI. By eliminating the need for magnitude stabilizer, it enables efficient high-quality diffusion imaging with full signal sensitivity retained. △ Less

Submitted 8 November, 2025; originally announced November 2025.

Comments: 35 pages, 10 figures

Journal ref: Magnetic Resonance in Medicine, 2026

Temporal Transfer Matrix Method for Exceptional-Point Media via Canonical Basis Expansion

Authors: Neng Wang, Guo Ping Wang

Abstract: We present a generalized temporal transfer matrix method (TTMM) for time-varying media that accurately captures wave dynamics in media operating at exceptional points (EPs). The method expands wave fields in the canonical basis of each temporal layer and derives the complete time evolution of all basis vectors. Temporal matching and phase-delay matrices are constructed from the generalized modal m… ▽ More We present a generalized temporal transfer matrix method (TTMM) for time-varying media that accurately captures wave dynamics in media operating at exceptional points (EPs). The method expands wave fields in the canonical basis of each temporal layer and derives the complete time evolution of all basis vectors. Temporal matching and phase-delay matrices are constructed from the generalized modal matrices and their corresponding eigenvalues. Additionally, an amplitude-boosting matrix is introduced to account for the power-law amplification of field amplitudes associated with EP dynamics. This matrix depends only on the order of the EP and naturally reduces to the identity matrix in its absence. The proposed TTMM is validated through two representative EP media, demonstrating its accuracy and broad applicability. △ Less

Submitted 3 November, 2025; originally announced November 2025.

Comments: 21 pages, 4 figures

Temporal Scattering at Irremovable Exceptional Points in Lossless Drude Media

Authors: Neng Wang, Shuyong Chen, Guo Ping Wang

Abstract: We investigate temporal scattering in lossless Drude media and reveal an overlooked role of the zero-frequency flat band associated with static polarization charge. This flat band forms an exceptional line spanning all wavenumbers and can be directly excited during temporal scattering at photonic time interfaces, generating non-propagating static fields alongside the usual reflected and transmitte… ▽ More We investigate temporal scattering in lossless Drude media and reveal an overlooked role of the zero-frequency flat band associated with static polarization charge. This flat band forms an exceptional line spanning all wavenumbers and can be directly excited during temporal scattering at photonic time interfaces, generating non-propagating static fields alongside the usual reflected and transmitted waves. Eigenvector coalescence at the corresponding exceptional points leads to two distinctive features absent in previously studied systems: a static mode whose amplitude increases linearly with time, and an additional static component arising from the system's generalized eigenvector. Remarkably, these effects occur without violating total energy conservation, underscoring the Hermitian nature of the dynamics. Our findings present a new physical picture of temporal scattering, sharply distinct from that in dispersionless and Lorentz-dispersive media. △ Less

Submitted 30 October, 2025; originally announced October 2025.

Comments: 18 pages, 4 figures

MC BTS: simultaneously resolving magnetization transfer effect and relaxation for multiple components

Authors: Albert Jang, Hyungseok Jang, Nian Wang, Alexey Samsonov, Fang Liu

Abstract: We propose a signal acquisition and modeling framework for multi-component tissue quantification that encompasses transmit field inhomogeneity, multi-component relaxation and magnetization transfer (MT) effects. By applying off-resonance irradiation between excitation and acquisition within an RF-spoiled gradient-echo scheme, in combination with multiple echo-time acquisitions, both Bloch-Siegert… ▽ More We propose a signal acquisition and modeling framework for multi-component tissue quantification that encompasses transmit field inhomogeneity, multi-component relaxation and magnetization transfer (MT) effects. By applying off-resonance irradiation between excitation and acquisition within an RF-spoiled gradient-echo scheme, in combination with multiple echo-time acquisitions, both Bloch-Siegert shift and magnetization transfer effects are simultaneously induced while relaxation and spin exchange processes occur concurrently. Simulation results showed excellent agreement with the derived analytical signal equation across a wide range of flip angles and echo times. Monte Carlo analyses further validated that the three-pool parameter estimation pipeline performed robustly over various signal-to-noise ratio conditions. Multi-parameter fitting results from in vivo brain and knee studies yielded values consistent with previously reported literature. Collectively, these findings confirm that the proposed method can reliably characterize multi-component tissue parameters in macromolecule-rich environments while effectively compensating for $B_1^+$ inhomogeneity. △ Less

Submitted 7 October, 2025; originally announced October 2025.

A Deep-Learning-Driven Optimization-Based Inverse Solver for Accelerating the Marchenko Method

Authors: Ning Wang, Tariq Alkhalifah

Abstract: The Marchenko method is a powerful tool for reconstructing full-wavefield Green's functions using surface-recorded seismic data. These Green's functions can then be utilized to produce subsurface images that are not affected by artifacts caused by internal multiples. Despite its advantages, the method is computationally demanding, primarily due to the iterative nature of estimating the focusing fu… ▽ More The Marchenko method is a powerful tool for reconstructing full-wavefield Green's functions using surface-recorded seismic data. These Green's functions can then be utilized to produce subsurface images that are not affected by artifacts caused by internal multiples. Despite its advantages, the method is computationally demanding, primarily due to the iterative nature of estimating the focusing functions, which links the Green's functions to the surface reflection response. To address this limitation, an optimization-based solver is proposed to estimate focusing functions in an efficient way. This is achieved by training a network to approximate the forward modeling problem on a small subset of pre-computed focusing function pairs, mapping final up-going focusing functions obtained via the conventional iterative scheme to their initial estimates. Once trained, the network is fixed and used as the forward operator within the Marchenko framework. For a given target location, an input is initialized and iteratively updated through backpropagation to minimize the mismatch between the output of the fixed network and the known initial up-going focusing function. The resulting estimate is then used to compute the corresponding down-going focusing function and the full Green's functions based on the Marchenko equations. This strategy significantly reduces the computational cost compared to the traditional Marchenko method based on conventional iterative scheme. Tests on a synthetic model, using only 0.8% of the total imaging points for training, show that the proposed approach accelerates the imaging process while maintaining relatively good imaging results, which is better than reverse time migration. Application to the Volve field data further demonstrates the method's robustness and practicality, highlighting its potential for efficient, large scale seismic imaging. △ Less

Submitted 20 September, 2025; originally announced September 2025.

Comments: 15 pages, 7 figures

Blowup of solutions for compressible viscoelastic fluid

Authors: Sébastien Boyaval, Na Wang, Yuxi Hu

Abstract: We prove finite-time blowup of classical solutions for the compressible Upper Convective Maxwell (UCM) viscoelastic fluid system. By establishing a key energy identity and adapting Sideris' method for compressible flows, we derive a Riccati-type inequality for a momentum functional. For initial data with compactly supported perturbations satisfying a sufficiently large condition, all classical sol… ▽ More We prove finite-time blowup of classical solutions for the compressible Upper Convective Maxwell (UCM) viscoelastic fluid system. By establishing a key energy identity and adapting Sideris' method for compressible flows, we derive a Riccati-type inequality for a momentum functional. For initial data with compactly supported perturbations satisfying a sufficiently large condition, all classical solutions lose regularity in finite time. This constitutes the first rigorous blowup result for multidimensional compressible viscoelastic fluids. △ Less

Submitted 5 September, 2025; originally announced September 2025.

Binary Decision Process in Pre-Evacuation Behavior

Authors: Peng N. Wang, Peter B. Luh, Xuesong Lu, Peter Sincak, Laura Pitukova

Abstract: In crowd evacuation the time interval before decisive movement towards a safe place is defined as the pre-evacuation phase, and it has crucial impact on the total time required for safe egress. This process mainly refers to situation awareness and response to an external stressors, e.g., fire alarms. Due to the complexity of human cognitive process, simulation is used to study this important time… ▽ More In crowd evacuation the time interval before decisive movement towards a safe place is defined as the pre-evacuation phase, and it has crucial impact on the total time required for safe egress. This process mainly refers to situation awareness and response to an external stressors, e.g., fire alarms. Due to the complexity of human cognitive process, simulation is used to study this important time interval. In this paper a binary decision process is formulated to simulate pre-evacuation time of many evacuees in a given social context. The model combines the classic opinion dynamics (the French-DeGroot model) with binary phase transition to describe how group pre-evacuation time emerges from individual interaction. The model parameters are quantitatively meaningful to human factors research within socio-psychological background, e.g., whether an individual is stubborn or open-minded, or what kind of the social topology exists among the individuals and how it matters in aggregating individuals into social groups. The modeling framework also describes collective motion of many evacuee agents in a planar space, and the resulting multi-agent system is partly similar to the Vicsek flocking model, and it is meaningful to explore complex social behavior during phase transition of a non-equilibrium process. △ Less

Submitted 19 November, 2025; v1 submitted 1 August, 2025; originally announced August 2025.

Comments: 6 pages

FluidFormer: Transformer with Continuous Convolution for Particle-based Fluid Simulation

Authors: Nianyi Wang, Yu Chen, Shuai Zheng

Abstract: Learning-based fluid simulation networks have been proven as viable alternatives to traditional numerical solvers for the Navier-Stokes equations. Existing neural methods follow Smoothed Particle Hydrodynamics (SPH) frameworks, which inherently rely only on local inter-particle interactions. However, we emphasize that global context integration is also essential for learning-based methods to stabi… ▽ More Learning-based fluid simulation networks have been proven as viable alternatives to traditional numerical solvers for the Navier-Stokes equations. Existing neural methods follow Smoothed Particle Hydrodynamics (SPH) frameworks, which inherently rely only on local inter-particle interactions. However, we emphasize that global context integration is also essential for learning-based methods to stabilize complex fluid simulations. We propose the first Fluid Attention Block (FAB) with a local-global hierarchy, where continuous convolutions extract local features while self-attention captures global dependencies. This fusion suppresses the error accumulation and models long-range physical phenomena. Furthermore, we pioneer the first Transformer architecture specifically designed for continuous fluid simulation, seamlessly integrated within a dual-pipeline architecture. Our method establishes a new paradigm for neural fluid simulation by unifying convolution-based local features with attention-based global context modeling. FluidFormer demonstrates state-of-the-art performance, with stronger stability in complex fluid scenarios. △ Less

Submitted 2 August, 2025; originally announced August 2025.

Efficient Upside-Down Rayleigh-Marchenko Imaging through Self-Supervised Focusing Function Estimation

Authors: Ning Wang, Matteo Ravasi, Tariq Alkhalifah

Abstract: The Upside-Down Rayleigh-Marchenko (UD-RM) method has recently emerged as a powerful tool for retrieving subsurface wavefields and images free from artifacts caused by both internal and surface-related multiples. Its ability to handle acquisition setups with large cable spacing or sparse node geometries makes it particularly suitable for ocean-bottom seismic data processing. However, the widesprea… ▽ More The Upside-Down Rayleigh-Marchenko (UD-RM) method has recently emerged as a powerful tool for retrieving subsurface wavefields and images free from artifacts caused by both internal and surface-related multiples. Its ability to handle acquisition setups with large cable spacing or sparse node geometries makes it particularly suitable for ocean-bottom seismic data processing. However, the widespread application of the method is limited by the high computational cost required to estimate the focusing functions, especially when dealing with large imaging domains. To address this limitation, a self-supervised learning approach is proposed to accelerate the estimation of the focusing functions. Specifically, a U-Net network is trained on a small subset of image points from within the target area of interest, whose focusing functions are pre-computed using the conventional iterative scheme. The network is tasked to predict both the up- and down-going focusing functions from an initial estimate of the subsurface wavefields. Once trained, the network generalizes to remaining unseen imaging locations, enabling direct prediction of the focusing functions. Validation on a synthetic dataset with both dense and sparse receiver sampling using progressively fewer training points demonstrates the method's effectiveness. In both cases, the resulting images closely match those obtained from the UD-RM method with focusing functions retrieved by the conventional iterative approach at a much lower cost and significantly outperform mirror migration (when the same input dataset is used). Finally, an application to the Volve field data confirms the method's robustness in practical scenarios. The proposed approach enables seismic imaging at a fraction of the computational cost of the conventional UD-RM approach while maintaining imaging quality, underscoring its potential for large-scale seismic applications. △ Less

Submitted 29 July, 2025; originally announced July 2025.

Comments: 38 pages, 15 figures

Quantitative U/Th deposition and cleanliness control strategies in the JUNO site air

Authors: Jie Zhao, Chenyang Cui, Yongpeng Zhang, Gaosong Li, Nan Wang, Monica Sisti

Abstract: The Jiangmen Underground Neutrino Observatory (JUNO) employs a 20 kt liquid scintillator (LS) detector located 700 m underground. To meet its physics objectives, the LS must achieve an ultra-low $^{238}$U/$^{232}$Th content of 10$^{-17}$ g/g. Given that airborne dust exhibits radioactivity about 12 orders of magnitude higher, exceptional cleanliness is essential during on-site installation. The to… ▽ More The Jiangmen Underground Neutrino Observatory (JUNO) employs a 20 kt liquid scintillator (LS) detector located 700 m underground. To meet its physics objectives, the LS must achieve an ultra-low $^{238}$U/$^{232}$Th content of 10$^{-17}$ g/g. Given that airborne dust exhibits radioactivity about 12 orders of magnitude higher, exceptional cleanliness is essential during on-site installation. The total permissible dust mass in the 20 kt LS is only about 8 mg. To attain this, the acrylic vessel interior must comply with class 1,000 cleanliness. Pre-filling water spray cleaning improves cleanliness by roughly two orders of magnitude, requiring the overall environment to be maintained between class 10,000 and 100,000. At JUNO, a cleanroom management system has been implemented across the 120,000 m$^3$ underground experimental hall. Since May 2022, continuous laser particle monitoring has consistently achieved an average cleanliness class of 74,000. Furthermore, we developed a method to directly measure $^{238}$U/$^{232}$Th deposition rates on detector surfaces. Using ICP-MS, sensitivity reaches sub-ppt levels ($<$10$^{-12}$ g/g), enabling effective cleanliness control and assessment of external contamination during detector construction. △ Less

Submitted 3 March, 2026; v1 submitted 8 July, 2025; originally announced July 2025.

Nanoscale Ultrafast Lattice Modulation with Hard X-ray Free Electron Laser

Authors: Haoyuan Li, Nan Wang, Leon Zhang, Sanghoon Song, Yanwen Sun, May-Ling Ng, Takahiro Sato, Dillon Hanlon, Sajal Dahal, Mario D. Balcazar, Vincent Esposito, Selene She, Chance Caleb Ornelas-Skarin, Joan Vila-Comamala, Christian David, Nadia Berndt, Peter Richard Miedaner, Zhuquan Zhang, Matthias Ihme, Mariano Trigo, Keith A. Nelson, Jerome B. Hastings, Alexei A. Maznev, Laura Foglia, Samuel Teitelbaum , et al. (2 additional authors not shown)

Abstract: Understanding and controlling microscopic dynamics across spatial and temporal scales has driven major progress in science and technology over the past several decades. While ultrafast laser-based techniques have enabled probing nanoscale dynamics at their intrinsic temporal scales down to femto- and attoseconds, the long wavelengths of optical lasers have prevented the interrogation and manipulat… ▽ More Understanding and controlling microscopic dynamics across spatial and temporal scales has driven major progress in science and technology over the past several decades. While ultrafast laser-based techniques have enabled probing nanoscale dynamics at their intrinsic temporal scales down to femto- and attoseconds, the long wavelengths of optical lasers have prevented the interrogation and manipulation of such dynamics with nanoscale spatial specificity. With advances in hard X-ray free electron lasers (FELs), significant progress has been made developing X-ray transient grating (XTG) spectroscopy, aiming at the coherent control of elementary excitations with nanoscale X-ray standing waves. So far, XTGs have been probed only at optical wavelengths, thus intrinsically limiting the achievable periodicities to several hundreds of nm. By achieving sub-femtosecond synchronization of two hard X-ray pulses at a controlled crossing angle, we demonstrate the generation of an XTG with spatial periods of 10 nm. The XTG excitation drives a thermal grating that drives coherent monochromatic longitudinal acoustic phonons in the cubic perovskite, SrTiO3 (STO). With a third X-ray pulse with the same photon energy, time-and-momentum resolved measurement of the XTG-induced scattering intensity modulation provides evidence of ballistic thermal transport at nanometer scale in STO. These results highlight the great potential of XTG for studying high-wave-vector excitations and nanoscale transport in condensed matter, and establish XTG as a powerful platform for the coherent control and study of nanoscale dynamics. △ Less

Submitted 3 June, 2025; originally announced June 2025.

Comments: 35 pages, 3 figures, 9 extended data figures

EvidenceMoE: A Physics-Guided Mixture-of-Experts with Evidential Critics for Advancing Fluorescence Light Detection and Ranging in Scattering Media

Authors: Ismail Erbas, Ferhat Demirkiran, Karthik Swaminathan, Naigang Wang, Navid Ibtehaj Nizam, Stefan T. Radev, Kaoutar El Maghraoui, Xavier Intes, Vikas Pandey

Abstract: Fluorescence LiDAR (FLiDAR), a Light Detection and Ranging (LiDAR) technology employed for distance and depth estimation across medical, automotive, and other fields, encounters significant computational challenges in scattering media. The complex nature of the acquired FLiDAR signal, particularly in such environments, makes isolating photon time-of-flight (related to target depth) and intrinsic f… ▽ More Fluorescence LiDAR (FLiDAR), a Light Detection and Ranging (LiDAR) technology employed for distance and depth estimation across medical, automotive, and other fields, encounters significant computational challenges in scattering media. The complex nature of the acquired FLiDAR signal, particularly in such environments, makes isolating photon time-of-flight (related to target depth) and intrinsic fluorescence lifetime exceptionally difficult, thus limiting the effectiveness of current analytical and computational methodologies. To overcome this limitation, we present a Physics-Guided Mixture-of-Experts (MoE) framework tailored for specialized modeling of diverse temporal components. In contrast to the conventional MoE approaches our expert models are informed by underlying physics, such as the radiative transport equation governing photon propagation in scattering media. Central to our approach is EvidenceMoE, which integrates Evidence-Based Dirichlet Critics (EDCs). These critic models assess the reliability of each expert's output by providing per-expert quality scores and corrective feedback. A Decider Network then leverages this information to fuse expert predictions into a robust final estimate adaptively. We validate our method using realistically simulated Fluorescence LiDAR (FLiDAR) data for non-invasive cancer cell depth detection generated from photon transport models in tissue. Our framework demonstrates strong performance, achieving a normalized root mean squared error (NRMSE) of 0.030 for depth estimation and 0.074 for fluorescence lifetime. △ Less

Submitted 23 May, 2025; originally announced May 2025.

Comments: 18 pages, 4 figures

Self-supervised surface-related multiple suppression with multidimensional convolution

Authors: Shijun Cheng, Ning Wang, Tariq Alkhalifah

Abstract: Surface-related multiples pose significant challenges in seismic data processing, often obscuring primary reflections and reducing imaging quality. Traditional methods rely on computationally expensive algorithms, the prior knowledge of subsurface model, or accurate wavelet estimation, while supervised learning approaches require clean labels, which are impractical for real data. Thus, we propose… ▽ More Surface-related multiples pose significant challenges in seismic data processing, often obscuring primary reflections and reducing imaging quality. Traditional methods rely on computationally expensive algorithms, the prior knowledge of subsurface model, or accurate wavelet estimation, while supervised learning approaches require clean labels, which are impractical for real data. Thus, we propose a self-supervised learning framework for surface-related multiple suppression, leveraging multi-dimensional convolution to generate multiples from the observed data and a two-stage training strategy comprising a warm-up and an iterative data refinement stage, so the network learns to remove the multiples. The framework eliminates the need for labeled data by iteratively refining predictions using multiples augmented inputs and pseudo-labels. Numerical examples demonstrate that the proposed method effectively suppresses surface-related multiples while preserving primary reflections. Migration results confirm its ability to reduce artifacts and improve imaging quality. △ Less

Submitted 1 May, 2025; originally announced May 2025.

Flexible Perovskite/Silicon Monolithic Tandem Solar Cells Approaching 30% Efficiency

Authors: Yinqing Sun, Faming Li, Hao Zhang, Wenzhu Liu, Zenghui Wang, Lin Mao, Qian Li, Youlin He, Tian Yang, Xianggang Sun, Yicheng Qian, Yinyi Ma, Liping Zhang, Junlin Du, Jianhua Shi, Guangyuan Wang, Anjun Han, Na Wang, Fanying Meng, Zhengxin Liu, Mingzhen Liu

Abstract: Thanks to their excellent properties of low cost, lightweight, portability, and conformity, flexible perovskite-based tandem solar cells show great potentials for energy harvesting applications, with flexible perovskite/c-silicon tandem solar cells particularly promising for achieving high efficiency. However, performance of flexible perovskite/c-silicon monolithic tandem solar cells still greatly… ▽ More Thanks to their excellent properties of low cost, lightweight, portability, and conformity, flexible perovskite-based tandem solar cells show great potentials for energy harvesting applications, with flexible perovskite/c-silicon tandem solar cells particularly promising for achieving high efficiency. However, performance of flexible perovskite/c-silicon monolithic tandem solar cells still greatly lags, due to challenges in simultaneously achieving both efficient photocarrier transport and reliable mitigation of residual stress. Here, we reveal the critical role of perovskite phase homogeneity, for achieving high-efficient and mechanical-stable flexible perovskite/c-silicon heterojunction monolithic tandem solar cells (PSTs) with textured surface. Through ensuring high phase homogeneity, which promotes charge transfer across all facets of the pyramid on the textured substrates and releases the residual stress at the perovskite/c-silicon interface, we demonstrate flexible PSTs with a bending curvature of 0.44 cm-1, and a certified power conversion efficiency of 29.88% (1.04 cm2 aperture area), surpassing all other types of flexible perovskite-based photovoltaic devices. Our results can lead to broad applications and commercialization of flexible perovskite/c-silicon tandem photovoltaics. △ Less

Submitted 29 April, 2025; originally announced April 2025.

Journal ref: Nature Communications 2025

Engineering Graphene Nanoribbons via Periodically Embedding Oxygen Atoms

Authors: Yan Zhao, Li-Xia Kang, Yi-Jun Wang, Yi Wu, Guang-Yan Xing, Shi-Wen Li, Jinliang Pan, Nie-Wei Wang, Yin-Ti Ren, Ying Wang, Ya-Cheng Zhu, Xing-Qiang Shi, Mengxi Liu, Xiaohui Qiu, Pei-Nian Liu, Deng-Yuan Li

Abstract: Heteroatom doping is an important method for engineering graphene nanoribbons (GNRs) because of its ability to modify electronic properties by introducing extra electrons or vacancies. However, precisely integrating oxygen atoms into the lattice of GNRs is unexplored, and the resulting electronic properties remain elusive. Here, we achieve the precise embedding of oxygen atoms into the lattice of… ▽ More Heteroatom doping is an important method for engineering graphene nanoribbons (GNRs) because of its ability to modify electronic properties by introducing extra electrons or vacancies. However, precisely integrating oxygen atoms into the lattice of GNRs is unexplored, and the resulting electronic properties remain elusive. Here, we achieve the precise embedding of oxygen atoms into the lattice of GNRs via in situ formation of pyrans, synthesizing two types of oxygen-doped GNRs (O-doped chevron-GNR and O-doped chiral (2,1)-GNR). Using scanning tunneling microscopy, non-contact atomic force microscopy, and density functional theory calculations, the atomic structures and electronic properties of O-doped GNRs are determined, demonstrating that both GNRs are direct bandgap semiconductors with different sensitivities to oxygen dopants. Oxygen dopants have a minor impact on the bandgap of chevron-GNR but a significant effect on the bandgap of chiral (2,1)-GNR, which is attributed to the difference in density of states near the Fermi level between substituted intrinsic carbon atoms and their pristine counterparts. Compared with the pristine chiral (2,1)-GNR, the band structure of O-doped chiral (2,1)-GNR exhibits unexpected band edges transition, which is ascribed to sp2-hybridized oxygen atoms which introduces additional electrons to the conduction band of chiral (2,1)-GNR, leading to the upward shift of Fermi surface. △ Less

Submitted 25 April, 2025; originally announced April 2025.

Copper-impurity-free photonic integrated circuits enable deterministic soliton microcombs

Authors: Xinru Ji, Xurong Li, Zheru Qiu, Rui Ning Wang, Marta Divall, Andrey Gelash, Grigory Lihachev, Tobias J. Kippenberg

Abstract: Chip-scale optical frequency combs based on microresonators (microcombs) enable GHz-THz repetition rates, broad bandwidth, compactness, and compatibility with wafer-scale manufacturing. Silicon nitride photonic integrated circuits have become a leading platform due to their low loss, broad transparency, lithographic dispersion control, and commercial 200-mm-wafer foundry access. They have enabled… ▽ More Chip-scale optical frequency combs based on microresonators (microcombs) enable GHz-THz repetition rates, broad bandwidth, compactness, and compatibility with wafer-scale manufacturing. Silicon nitride photonic integrated circuits have become a leading platform due to their low loss, broad transparency, lithographic dispersion control, and commercial 200-mm-wafer foundry access. They have enabled system-level applications in optical communications, LiDAR, frequency synthesis, low-noise microwave generation, and convolutional processing. However, real-world deployment is hindered by the challenge of deterministic soliton microcomb generation, primarily due to thermal instabilities. Although techniques like pulsed pumping, fast scanning, and auxiliary lasers help mitigate these effects, they often add complexity or reduce soliton stability. In this work, we overcome thermal limitations and demonstrate deterministic soliton generation in silicon nitride photonic circuits. We trace the thermal effects to copper impurities within waveguides, originating from residual contaminants in CMOS-grade silicon wafers that are gettered into silicon nitride during fabrication. By developing effective copper removal techniques, we significantly reduce thermal instabilities. This enables soliton generation with arbitrary or slow laser scanning, removing a key barrier to microcomb deployment. Our approach is compatible with front-end-of-line foundry processing, paving the way for broader adoption of soliton microcomb technologies. △ Less

Submitted 25 April, 2025; originally announced April 2025.

Improving Significant Wave Height Prediction Using Chronos Models

Authors: Yilin Zhai, Hongyuan Shi, Chao Zhan, Qing Wang, Zaijin You, Nan Wang

Abstract: Accurate wave height prediction is critical for maritime safety and coastal resilience, yet conventional physics-based models and traditional machine learning methods face challenges in computational efficiency and nonlinear dynamics modeling. This study introduces Chronos, the first implementation of a large language model (LLM)-powered temporal architecture (Chronos) optimized for wave forecasti… ▽ More Accurate wave height prediction is critical for maritime safety and coastal resilience, yet conventional physics-based models and traditional machine learning methods face challenges in computational efficiency and nonlinear dynamics modeling. This study introduces Chronos, the first implementation of a large language model (LLM)-powered temporal architecture (Chronos) optimized for wave forecasting. Through advanced temporal pattern recognition applied to historical wave data from three strategically chosen marine zones in the Northwest Pacific basin, our framework achieves multimodal improvements: (1) 14.3% reduction in training time with 2.5x faster inference speed compared to PatchTST baselines, achieving 0.575 mean absolute scaled error (MASE) units; (2) superior short-term forecasting (1-24h) across comprehensive metrics; (3) sustained predictive leadership in extended-range forecasts (1-120h); and (4) demonstrated zero-shot capability maintaining median performance (rank 4/12) against specialized operational models. This LLM-enhanced temporal modeling paradigm establishes a new standard in wave prediction, offering both computationally efficient solutions and a transferable framework for complex geophysical systems modeling. △ Less

Submitted 25 April, 2025; v1 submitted 23 April, 2025; originally announced April 2025.

Comments: arXiv admin note: text overlap with arXiv:2403.07815 by other authors

Journal ref: Ocean Engineering, Volume 341, Part 2, 1 December 2025, Article 122502

Nonreciprocal quantum photon-pair source with chiral ferroelectric nematics

Authors: Jin-Tao Pan, Yun-Kun Wu, Ling-Ling Ma, Ning Wang, Xin-Yu Tao, Bo-Han Zhu, Shu Wang, Fang-Wen Sun, Guang-Can Guo, Hui Jing, Xi-Feng Ren, Yan-Qing Lu

Abstract: Quantum nonreciprocity-a fundamental phenomenon enabling directional control of quantum states and photon correlations-has long been recognized as pivotal for quantum technologies. However, the experimental realization of nonreciprocal quantum photon-pair generation, as a critical prerequisite for advancing quantum systems, continues to be an outstanding challenge that remains unaddressed in pract… ▽ More Quantum nonreciprocity-a fundamental phenomenon enabling directional control of quantum states and photon correlations-has long been recognized as pivotal for quantum technologies. However, the experimental realization of nonreciprocal quantum photon-pair generation, as a critical prerequisite for advancing quantum systems, continues to be an outstanding challenge that remains unaddressed in practice. Here, we experimentally implement a highly-efficient nonreciprocal quantum photon source in a micro/nano-scale helical structured nonlinear optical fluid. Intriguing helical quasi-phase matching is achieved by deliberately engineering the pitch of the chiral ferroelectric structure, thus enabling spontaneous parametric down-conversion with record-high brightness (5,801.6 Hz*mW-1, 10,071% enhancement over phase-mismatched systems) and high coincidence-to-accidental ratio, rivaling state-of-the-art centimeter-scale nonlinear crystals. In particular, by tailoring the ferroelectric helix structure with orthogonally aligned head and tail polarization vectors, we demonstrate up to 22.6 dB isolation in biphoton generation coupled with nonreciprocal quantum polarization states, while maintaining classical optical reciprocity. This quantum liquid-crystal-based platform, combining flexible tunability and superior performance of purely quantum nonreciprocity at micro/nano scales, builds a bridge between a wide range of soft-matter systems, nonreciprocal physics, and emerging quantum photonic technologies. △ Less

Submitted 14 March, 2025; originally announced March 2025.

Comments: 13 pages,4 figures

Optical Convolutional Spectrometer

Authors: Chunhui Yao, Jie Ma, Ningning Wang, Peng Bao, Wei Zhuo, Tao Zhang, Wanlu Zhang, Kangning Xu, Ting Yan, Liang Ming, Yuxiao Ye, Tawfique Hasan, Ian White, Richard Penty, Qixiang Cheng

Abstract: Optical spectrometers are fundamental across numerous disciplines in science and technology. However, miniaturized versions, while essential for in situ measurements, are often restricted to coarse identification of signature peaks and inadequate for metrological purposes. Here, we introduce a new class of spectrometer, leveraging the convolution theorem as its mathematical foundation. Our convolu… ▽ More Optical spectrometers are fundamental across numerous disciplines in science and technology. However, miniaturized versions, while essential for in situ measurements, are often restricted to coarse identification of signature peaks and inadequate for metrological purposes. Here, we introduce a new class of spectrometer, leveraging the convolution theorem as its mathematical foundation. Our convolutional spectrometer offers unmatched performance for miniaturized systems and distinct structural and computational simplicity, featuring a centimeter-scale footprint for the fully packaged unit, low cost (~$10) and a 2400 cm-1 (approximately 500 nm) bandwidth. We achieve excellent precision in resolving complex spectra with sub-second sampling and processing time, demonstrating a wide range of applications from industrial and agricultural analysis to healthcare monitoring. Specifically, our spectrometer system classifies diverse solid samples, including plastics, pharmaceuticals, coffee, flour and tea, with 100% success rate, and quantifies concentrations of aqueous and organic solutions with detection accuracy surpassing commercial benchtop spectrometers. We also realize the non-invasive sensing of human biomarkers, such as skin moisture (mean absolute error; MAE = 2.49%), blood alcohol (1.70 mg/dL), blood lactate (0.81 mmol/L), and blood glucose (0.36 mmol/L), highlighting the potential of this new class of spectrometers for low-cost, high-precision, portable/wearable spectral metrology. △ Less

Submitted 12 February, 2025; originally announced February 2025.

First experimental proof of PET imaging based on multi-anode MCP-PMTs with Cherenkov radiator-integrated window

Authors: Weiyan Pan, Lingyue Chen, Guorui Huang, Jun Hu, Wei Hou, Xianchao Huang, Xiaorou Han, Xiaoshan Jiang, Zhen Jin, Daowu Li, Jingwen Li, Shulin Liu, Zehong Liang, Lishuang Ma, Zhe Ning, Sen Qian, Ling Ren, Jianning Sun, Shuguang Si, Yunhua Sun, Long Wei, Ning Wang, Qing Wei, Qi Wu, Tianyi Wang , et al. (11 additional authors not shown)

Abstract: Improving the coincidence time resolution (CTR) of time-of-flight positron emission tomography (TOF-PET) systems to achieve a higher signal-to-noise ratio (SNR) gain or even direct positron emission imaging (dPEI) is of paramount importance for many advanced new clinical applications of PET imaging. This places higher demands on the timing performance of all aspects of PET systems. One effective a… ▽ More Improving the coincidence time resolution (CTR) of time-of-flight positron emission tomography (TOF-PET) systems to achieve a higher signal-to-noise ratio (SNR) gain or even direct positron emission imaging (dPEI) is of paramount importance for many advanced new clinical applications of PET imaging. This places higher demands on the timing performance of all aspects of PET systems. One effective approach is to use microchannel plate photomultiplier tubes (MCP-PMTs) for prompt Cherenkov photon detection. In this study, we developed a dual-module Cherenkov PET imaging experimental platform, utilising our proprietary 8 * 8-anode Cherenkov radiator-integrated window MCP-PMTs in combination with custom-designed multi-channel electronics, and designed a specific calibration and correction method for the platform. Using this platform, a CTR of 103 ps FWHM was achieved. We overcame the limitations of single-anode detectors in previous experiments, significantly enhanced imaging efficiency and achieved module-level Cherenkov PET imaging for the first time. Imaging experiments involving radioactive sources and phantoms of various shapes and types were conducted, which preliminarily validated the feasibility and advancement of this imaging method. In addition, the effects of normalisation correction and the interaction probability between the gamma rays and the MCP on the images and experimental results were analysed and verified. △ Less

Submitted 14 October, 2025; v1 submitted 10 February, 2025; originally announced February 2025.

Comments: 12 pages, 12 figures, manuscript has been submitted to Physics in Medicine & Biology and is under review

High-Accuracy Physical Property Prediction for Organics via Molecular Representation Learning: Bridging Data to Discovery

Authors: Qi Ou, Hongshuai Wang, Minyang Zhuang, Shangqian Chen, Lele Liu, Ning Wang, Zhifeng Gao

Abstract: The ongoing energy crisis has underscored the urgent need for energy-efficient materials with high energy utilization efficiency, prompting a surge in research into organic compounds due to their environmental compatibility, cost-effective processing, and versatile modifiability. To address the high experimental costs and time-consuming nature of traditional trial-and-error methods in the discover… ▽ More The ongoing energy crisis has underscored the urgent need for energy-efficient materials with high energy utilization efficiency, prompting a surge in research into organic compounds due to their environmental compatibility, cost-effective processing, and versatile modifiability. To address the high experimental costs and time-consuming nature of traditional trial-and-error methods in the discovery of highly functional organic compounds, we apply the 3D transformer-based molecular representation learning algorithm to construct a pre-trained model using 60 million semi-empirically optimized structures of small organic molecules, namely, Org-Mol, which is then fine-tuned with public experimental data to obtain prediction models for various physical properties. Despite the pre-training process relying solely on single molecular coordinates, the fine-tuned models achieves high accuracy (with $R^2$ values for the test set exceeding 0.95). These fine-tuned models are applied in a high-throughput screening process to identify novel immersion coolants among millions of automatically constructed ester molecules, resulting in the experimental validation of two promising candidates. This work not only demonstrates the potential of Org-Mol in predicting bulk properties for organic compounds but also paves the way for the rational and efficient development of ideal candidates for energy-saving materials. △ Less

Submitted 16 January, 2025; originally announced January 2025.

Journal ref: npj Computational Materials volume 11, Article number: 224 (2025)

Healing of the edge magnetic island in the island divertor configuration on J-TEXT

Authors: Zhangrong Hou, Song Zhou, Nengchao Wang, Yonghua Ding, Zhonghe Jiang, Yunfeng Liang, Zhengkang Ren, Feiyue Mao, Qinghu Yang, Jiaming Wang, Xin Xu, Yutong Yang, Jiankun Hua, Zijian Xuan, Chuanxu Zhao, Yangbo Li, Lei Yu, Donghui Xia, Zhipeng Chen, Zhoujun Yang, the J-TEXT team

Abstract: The phenomena of island healing and configuration transition induced by high-power electron cyclotron resonance heating (ECRH) have been investigated in the island divertor configuration on the J-TEXT tokamak. Experimental results reveal that the size of the edge open magnetic island with mode number m/n = 3/1 decreases substantially under specific ECRH conditions. This process, referred to as isl… ▽ More The phenomena of island healing and configuration transition induced by high-power electron cyclotron resonance heating (ECRH) have been investigated in the island divertor configuration on the J-TEXT tokamak. Experimental results reveal that the size of the edge open magnetic island with mode number m/n = 3/1 decreases substantially under specific ECRH conditions. This process, referred to as island healing, occurs when ECRH with a power of 500~600 kW is deposited in the plasma core or when 250 kW of ECRH is deposited at r = 0.5 a, where a is the minor radius. The reduction of the island width makes the island divertor ineffective and transition into the limiter configuration. A model incorporating the influence of ECRH on the scrape-off layer (SOL) thermoelectric current is proposed to explain the observed changes in the edge magnetic topology of the island divertor configuration. These findings suggest that ECRH should be deposited at the plasma core with carefully controlled power to ensure the stable and compatible operation of ECRH and the island divertor configuration in tokamaks. The results can provide insights into achieving robust operation of an island divertor in tokamaks. △ Less

Submitted 14 January, 2025; originally announced January 2025.

A Pioneering Neural Network Method for Efficient and Robust Fluid Simulation

Authors: Yu Chen, Shuai Zheng, Nianyi Wang, Menglong Jin, Yan Chang

Abstract: Fluid simulation is an important research topic in computer graphics (CG) and animation in video games. Traditional methods based on Navier-Stokes equations are computationally expensive. In this paper, we treat fluid motion as point cloud transformation and propose the first neural network method specifically designed for efficient and robust fluid simulation in complex environments. This model i… ▽ More Fluid simulation is an important research topic in computer graphics (CG) and animation in video games. Traditional methods based on Navier-Stokes equations are computationally expensive. In this paper, we treat fluid motion as point cloud transformation and propose the first neural network method specifically designed for efficient and robust fluid simulation in complex environments. This model is also the deep learning model that is the first to be capable of stably modeling fluid particle dynamics in such complex scenarios. Our triangle feature fusion design achieves an optimal balance among fluid dynamics modeling, momentum conservation constraints, and global stability control. We conducted comprehensive experiments on datasets. Compared to existing neural network-based fluid simulation algorithms, we significantly enhanced accuracy while maintaining high computational speed. Compared to traditional SPH methods, our speed improved approximately 10 times. Furthermore, compared to traditional fluid simulation software such as Flow3D, our computation speed increased by more than 300 times. △ Less

Submitted 3 January, 2025; v1 submitted 14 December, 2024; originally announced December 2024.

Comments: This paper has been accepted by AAAI Conference on Artificial Intelligence (AAAI-25)

Monolithic piezoelectrically tunable hybrid integrated laser with sub-fiber laser coherence

Authors: Andrey Voloshin, Anat Siddharth, Simone Bianconi, Alaina Attanasio, Andrea Bancora, Vladimir Shadymov, Sebastien Leni, Rui Ning Wang, Johann Riemensberger, Sunil A. Bhave, Tobias J. Kippenberg

Abstract: Ultra-low noise lasers are essential tools in a wide variety of applications, including data communication, light detection and ranging (LiDAR), quantum computing and sensing, and optical metrology. Recent advances in integrated photonics, specifically the development of ultra-low loss silicon nitride (Si$_3$N$_4$) platform, have allowed attaining performance that exceeds conventional legacy laser… ▽ More Ultra-low noise lasers are essential tools in a wide variety of applications, including data communication, light detection and ranging (LiDAR), quantum computing and sensing, and optical metrology. Recent advances in integrated photonics, specifically the development of ultra-low loss silicon nitride (Si$_3$N$_4$) platform, have allowed attaining performance that exceeds conventional legacy laser systems, including the phase noise of fiber lasers. This platform can moreover be combined with monolithic integration of piezoelectrical materials, enabling frequency agile low noise lasers. However, this approach has to date not surpassed the trade-off between ultra-low frequency noise and frequency agility. Here we overcome this challenge and demonstrate a fully integrated laser based on the Si$_3$N$_4$ platform with frequency noise lower than that of a fiber laser, while maintaining the capability for high-speed modulation of the laser frequency. The laser achieves an output power of 30 mW with an integrated linewidth of 4.3 kHz and an intrinsic linewidth of 3 Hz, demonstrating phase noise performance that is on par with or lower than commercial fiber lasers. Frequency agility is accomplished via a monolithically integrated piezoelectric aluminum nitride (AlN) micro-electro-mechanical system (MEMS) actuator, which enables a flat frequency actuation bandwidth extending up to 400 kHz. This combination of ultra-low noise and frequency agility is a useful feature enabling tight laser locking for frequency metrology, fiber sensing, and coherent sensing applications. Our results demonstrate the ability of 'next generation' integrated photonic circuits (beyond silicon) to exceed the performance of legacy laser systems in terms of coherence and frequency actuation. △ Less

Submitted 28 November, 2024; originally announced November 2024.

ACE-Net: AutofoCus-Enhanced Convolutional Network for Field Imperfection Estimation with application to high b-value spiral Diffusion MRI

Authors: Mengze Gao, Zachary Shah, Xiaozhi Cao, Nan Wang, Daniel Abraham, Kawin Setsompop

Abstract: Spatiotemporal magnetic field variations from B0-inhomogeneity and diffusion-encoding-induced eddy-currents can be detrimental to rapid image-encoding schemes such as spiral, EPI and 3D-cones, resulting in undesirable image artifacts. In this work, a data driven approach for automatic estimation of these field imperfections is developed by combining autofocus metrics with deep learning, and by lev… ▽ More Spatiotemporal magnetic field variations from B0-inhomogeneity and diffusion-encoding-induced eddy-currents can be detrimental to rapid image-encoding schemes such as spiral, EPI and 3D-cones, resulting in undesirable image artifacts. In this work, a data driven approach for automatic estimation of these field imperfections is developed by combining autofocus metrics with deep learning, and by leveraging a compact basis representation of the expected field imperfections. The method was applied to single-shot spiral diffusion MRI at high b-values where accurate estimation of B0 and eddy were obtained, resulting in high quality image reconstruction without need for additional external calibrations. △ Less

Submitted 21 November, 2024; originally announced November 2024.

Comments: 8 pages, 5 figures, submitted to International Society for Magnetic Resonance in Medicine 32th Scientific Meeting, 2025

Considerations and recommendations from the ISMRM Diffusion Study Group for preclinical diffusion MRI: Part 3 -- Ex vivo imaging: data processing, comparisons with microscopy, and tractography

Authors: Kurt G Schilling, Amy FD Howard, Francesco Grussu, Andrada Ianus, Brian Hansen, Rachel L C Barrett, Manisha Aggarwal, Stijn Michielse, Fatima Nasrallah, Warda Syeda, Nian Wang, Jelle Veraart, Alard Roebroeck, Andrew F Bagdasarian, Cornelius Eichner, Farshid Sepehrband, Jan Zimmermann, Lucas Soustelle, Christien Bowman, Benjamin C Tendler, Andreea Hertanu, Ben Jeurissen, Marleen Verhoye, Lucio Frydman, Yohan van de Looij , et al. (33 additional authors not shown)

Abstract: Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitat… ▽ More Preclinical diffusion MRI (dMRI) has proven value in methods development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. While dMRI enables in vivo non-invasive characterization of tissue, ex vivo dMRI is increasingly being used to probe tissue microstructure and brain connectivity. Ex vivo dMRI has several experimental advantages that facilitate high spatial resolution and high signal-to-noise ratio (SNR) images, cutting-edge diffusion contrasts, and direct comparison with histological data as a methodological validation. However, there are a number of considerations that must be made when performing ex vivo experiments. The steps from tissue preparation, image acquisition and processing, and interpretation of results are complex, with many decisions that not only differ dramatically from in vivo imaging of small animals, but ultimately affect what questions can be answered using the data. This work concludes a 3-part series of recommendations and considerations for preclinical dMRI. Herein, we describe best practices for dMRI of ex vivo tissue, with a focus on image pre-processing, data processing and model fitting, and tractography. In each section, we attempt to provide guidelines and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should lie. We end by providing guidelines on code sharing and data sharing, and point towards open-source software and databases specific to small animal and ex vivo imaging. △ Less

Submitted 24 October, 2024; originally announced November 2024.

Comments: Part 3 of 3 in "Considerations and recommendations for preclinical diffusion MRI"

Seven-octave ultrabroadband metamaterial absorbers via Q-weighted mode density modulation

Authors: Nengyin Wang, Sibo Huang, Zhiling Zhou, Din Ping Tsai, Jie Zhu, Yong Li

Abstract: Absorption is a crucial parameter in shaping wave propagation dynamics, yet achieving ultra-broadband absorption remains highly challenging, particularly in balancing low-frequency and broad bandwidth. Here, we present a metamaterial absorber (MMA) capable of achieving simultaneous spectral coverage across a seven-octave range of near-perfect absorption from 100 Hz to 12,800 Hz by engineering the… ▽ More Absorption is a crucial parameter in shaping wave propagation dynamics, yet achieving ultra-broadband absorption remains highly challenging, particularly in balancing low-frequency and broad bandwidth. Here, we present a metamaterial absorber (MMA) capable of achieving simultaneous spectral coverage across a seven-octave range of near-perfect absorption from 100 Hz to 12,800 Hz by engineering the quality-factor-weighted (Q-weighted) mode density. The Q-weighted mode density considers mode density, resonant frequencies, radiative loss, and intrinsic loss of multiple resonant modes, providing a comprehensive approach to govern broadband absorption properties. By optimizing the number of resonant modes and managing intrinsic losses, our approach achieves an intensive Q-weighted mode density across an ultra-wide bandwidth, enabling ultra-broadband absorption with high efficiency. These findings significantly advance the bandwidth capabilities of state-of-the-art MMAs and pave the way for the development of ultra-broadband metamaterial devices across various wave systems. △ Less

Submitted 31 October, 2024; originally announced November 2024.

Heat transfer enhancement of N-Ga-Al semiconductors heterogeneous interfaces

Authors: Wenzhu Luo, Ershuai Yin, Lei Wang, Wenlei Lian, Neng Wang, Qiang Li

Abstract: Heat transfer enhancement of N-Ga-Al semiconductor heterostructure interfaces is critical for the heat dissipation in GaN-based electronic devices, while the effect of the AlxGa(1-x)N transition layer component concentration and thickness on the heat transfer mechanism at the GaN-AlN interface is unclear. In this paper, using molecular dynamics simulations based on machine learning potentials, the… ▽ More Heat transfer enhancement of N-Ga-Al semiconductor heterostructure interfaces is critical for the heat dissipation in GaN-based electronic devices, while the effect of the AlxGa(1-x)N transition layer component concentration and thickness on the heat transfer mechanism at the GaN-AlN interface is unclear. In this paper, using molecular dynamics simulations based on machine learning potentials, the interfacial thermal conductance (ITC) between GaN-AlxGa(1-x)N, AlN-AlxGa(1-x)N and GaN-AlxGa(1-x)N-AlN heterostructure interfaces are calculated for different transition layer thicknesses with different concentrations of Al fractions, and the reasons for the change of ITC and its heat transfer mechanism were explained by the phonon density of states and the spectral heat current. GaN-AlN heterostructure ITC at 300 K is calculated to be 557 MW/(m2K), and the ITCs of GaN-Al0.5Ga0.5N and AlN-Al0.5Ga0.5N are improved by 128% and 229% compared to GaN-AlN, whereas the ITCs of GaN-Al0.7Ga0.3N-AlN containing a 0.5 nm transition layer improved by 27.6%. This is because elemental doping enhances phonon scattering near the interface thereby promoting phonon energy redistribution, but the bulk thermal resistance of the AlxGa(1-x)N layer also increases rapidly with increasing doping ratio, and ITC is affected by a combination of these two factors. This work aims to understand the mechanism of transition layer component concentration and thickness on the heat transfer at the GaN-AlN contact interface, which provides a useful guide for better thermal design of the GaN-AlN heterostructure interface. △ Less

Submitted 10 October, 2024; originally announced October 2024.

Comments: 18 pages, 10 figures

ACM Class: J.2.7

Unlocking Real-Time Fluorescence Lifetime Imaging: Multi-Pixel Parallelism for FPGA-Accelerated Processing

Authors: Ismail Erbas, Aporva Amarnath, Vikas Pandey, Karthik Swaminathan, Naigang Wang, Xavier Intes

Abstract: Fluorescence lifetime imaging (FLI) is a widely used technique in the biomedical field for measuring the decay times of fluorescent molecules, providing insights into metabolic states, protein interactions, and ligand-receptor bindings. However, its broader application in fast biological processes, such as dynamic activity monitoring, and clinical use, such as in guided surgery, is limited by long… ▽ More Fluorescence lifetime imaging (FLI) is a widely used technique in the biomedical field for measuring the decay times of fluorescent molecules, providing insights into metabolic states, protein interactions, and ligand-receptor bindings. However, its broader application in fast biological processes, such as dynamic activity monitoring, and clinical use, such as in guided surgery, is limited by long data acquisition times and computationally demanding data processing. While deep learning has reduced post-processing times, time-resolved data acquisition remains a bottleneck for real-time applications. To address this, we propose a method to achieve real-time FLI using an FPGA-based hardware accelerator. Specifically, we implemented a GRU-based sequence-to-sequence (Seq2Seq) model on an FPGA board compatible with time-resolved cameras. The GRU model balances accurate processing with the resource constraints of FPGAs, which have limited DSP units and BRAM. The limited memory and computational resources on the FPGA require efficient scheduling of operations and memory allocation to deploy deep learning models for low-latency applications. We address these challenges by using STOMP, a queue-based discrete-event simulator that automates and optimizes task scheduling and memory management on hardware. By integrating a GRU-based Seq2Seq model and its compressed version, called Seq2SeqLite, generated through knowledge distillation, we were able to process multiple pixels in parallel, reducing latency compared to sequential processing. We explore various levels of parallelism to achieve an optimal balance between performance and resource utilization. Our results indicate that the proposed techniques achieved a 17.7x and 52.0x speedup over manual scheduling for the Seq2Seq model and the Seq2SeqLite model, respectively. △ Less

Submitted 15 November, 2024; v1 submitted 9 October, 2024; originally announced October 2024.

Comments: 7 pages, 6 figures

Bulk-spatiotemporal vortex correspondence in gyromagnetic double-zero-index media

Authors: Ruo-Yang Zhang, Xiaohan Cui, Yuan-Song Zeng, Jin Chen, Wenzhe Liu, Mudi Wang, Dongyang Wang, Zhao-Qing Zhang, Neng Wang, Geng-Bo Wu, C. T. Chan

Abstract: Photonic double-zero-index media, distinguished by concurrently zero-valued permittivity and permeability, exhibit extraordinary properties not found in nature. Remarkably, the notion of zero-index can be substantially expanded by generalizing the constitutive parameters from null scalars to nonreciprocal tensors with nonzero matrix elements but zero determinants. Here, we experimentally realize s… ▽ More Photonic double-zero-index media, distinguished by concurrently zero-valued permittivity and permeability, exhibit extraordinary properties not found in nature. Remarkably, the notion of zero-index can be substantially expanded by generalizing the constitutive parameters from null scalars to nonreciprocal tensors with nonzero matrix elements but zero determinants. Here, we experimentally realize such a new class of gyromagnetic double-zero-index metamaterials possessing both double-zero-index features and nonreciprocal hallmarks. As an intrinsic property, this metamaterial always emerges at a spin-1/2 Dirac point of a topological phase transition. We discover and rigorously prove that a spatiotemporal reflection vortex singularity is always anchored to the metamaterial's Dirac point, with the vortex charge being determined by the topological invariant leap across the phase transition. This establishes a unique bulk-spatiotemporal vortex correspondence that extends the protected boundary effects into the time domain and exclusively characterizes topological phase transition points, setting it apart from any pre-existing bulk-boundary correspondence. Based on this correspondence, we propose and experimentally demonstrate a mechanism to deterministically generate optical spatiotemporal vortex pulses with firmly fixed central frequency and momentum, hence showing unparalleled robustness. Our findings uncover deep connections between zero-refractive-index photonics, topological photonics, and singular optics, opening the avenue for the manipulation of space-time topological light fields via the inherent topology of extreme-parameter metamaterials. △ Less

Submitted 12 August, 2024; originally announced August 2024.

Comments: 9 pages, 5 figures

Ultrafast tunable photonic integrated Pockels extended-DBR laser

Authors: Anat Siddharth, Simone Bianconi, Rui Ning Wang, Zheru Qiu, Andrey S. Voloshin, Mohammad J. Bereyhi, Johann Riemensberger, Tobias J. Kippenberg

Abstract: Frequency-agile lasers that can simultaneously feature low noise characteristics as well as fast mode hop-free frequency tuning are keystone components for applications ranging from frequency modulated continuous wave (FMCW) LiDAR, to coherent optical communication and gas sensing. The hybrid integration of III-V gain media with low-loss photonic integrated circuits (PICs) has recently enabled int… ▽ More Frequency-agile lasers that can simultaneously feature low noise characteristics as well as fast mode hop-free frequency tuning are keystone components for applications ranging from frequency modulated continuous wave (FMCW) LiDAR, to coherent optical communication and gas sensing. The hybrid integration of III-V gain media with low-loss photonic integrated circuits (PICs) has recently enabled integrated lasers with faster tuning and lower phase noise than the best legacy systems, including fiber lasers. In addition, lithium niobate on insulator (LNOI) PICs have enabled to exploit the Pockels effect to demonstrate self-injection locked hybrid lasers with tuning rates reaching peta-hertz per second. However, Pockels-tunable laser archetypes relying on high-Q optical microresonators have thus far only achieved limited output powers, are difficult to operate and stabilize due to the dynamics of self-injection locking, and require many analog control parameters. Here, we overcome this challenge by leveraging an extended distributed Bragg reflector (E-DBR) architecture to demonstrate a simple and turn-key operable frequency-agile Pockels laser that can be controlled with single analog operation and modulation inputs. Our laser supports a continuous mode hop-free tuning range of over 10 GHz with good linearity and flat actuation bandwidth up to 10 MHz, while achieving over 15 mW in-fiber output power at 1545 nm and kHz-level intrinsic linewidth, a combination unmet by legacy bulk lasers. This hybrid laser design combines an inexpensive reflective semiconductor optical amplifier (RSOA) with an electro-optic DBR PIC manufactured at wafer-scale on a LNOI platform. We showcase the performance and flexibility of this laser in proof-of-concept coherent optical ranging (FMCW LiDAR) demonstration, achieving a 4 cm distance resolution and in a hydrogen cyanide spectroscopy experiment. △ Less

Submitted 3 August, 2024; originally announced August 2024.

Comments: 10 pages, 5 figures

Deep Learning Framework for History Matching CO2 Storage with 4D Seismic and Monitoring Well Data

Authors: Nanzhe Wang, Louis J. Durlofsky

Abstract: Geological carbon storage entails the injection of megatonnes of supercritical CO2 into subsurface formations. The properties of these formations are usually highly uncertain, which makes design and optimization of large-scale storage operations challenging. In this paper we introduce a history matching strategy that enables the calibration of formation properties based on early-time observations.… ▽ More Geological carbon storage entails the injection of megatonnes of supercritical CO2 into subsurface formations. The properties of these formations are usually highly uncertain, which makes design and optimization of large-scale storage operations challenging. In this paper we introduce a history matching strategy that enables the calibration of formation properties based on early-time observations. Early-time assessments are essential to assure the operation is performing as planned. Our framework involves two fit-for-purpose deep learning surrogate models that provide predictions for in-situ monitoring well data and interpreted time-lapse (4D) seismic saturation data. These two types of data are at very different scales of resolution, so it is appropriate to construct separate, specialized deep learning networks for their prediction. This approach results in a workflow that is more straightforward to design and more efficient to train than a single surrogate that provides global high-fidelity predictions. The deep learning models are integrated into a hierarchical Markov chain Monte Carlo (MCMC) history matching procedure. History matching is performed on a synthetic case with and without 4D seismic data, which allows us to quantify the impact of 4D seismic on uncertainty reduction. The use of both data types is shown to provide substantial uncertainty reduction in key geomodel parameters and to enable accurate predictions of CO2 plume dynamics. The overall history matching framework developed in this study represents an efficient way to integrate multiple data types and to assess the impact of each on uncertainty reduction and performance predictions. △ Less

Submitted 21 January, 2025; v1 submitted 2 August, 2024; originally announced August 2024.

Comments: 52 pages, 21 figures

Numerical study of cavitation bubble dynamics in a flowing tube

Authors: Nian Wang, Odumuyiwa A. Odumosu, Tianyou Wang, Zhizhao Che

Abstract: Cavitation in tubes is a common occurrence in nature and engineering applications. Previous studies of cavitation bubble dynamics mainly consider bubbles in stagnant-water tubes, but the dynamics of cavitation bubbles in tubes with flow is not clear. This study investigates the dynamics of cavitation bubbles in tubes with flow by numerical simulations. The results show that, unlike bubbles in stag… ▽ More Cavitation in tubes is a common occurrence in nature and engineering applications. Previous studies of cavitation bubble dynamics mainly consider bubbles in stagnant-water tubes, but the dynamics of cavitation bubbles in tubes with flow is not clear. This study investigates the dynamics of cavitation bubbles in tubes with flow by numerical simulations. The results show that, unlike bubbles in stagnant-water tubes, bubbles under the combined effects of water inflow and tube wall confinement exhibit asymmetric behavior along the axis of the tube. The inflow suppresses the development of the bubble interface near the tube inlet, causing that side of the interface to move with the inflow. In contrast, the expansion and contraction of the bubble and the generation of liquid jets occur on the side near the outlet. This feature results in significant asymmetry in the bubble interface, therefore we introduce a skewness parameter to characterize the difference in length between the left and right parts of the bubble during the bubble evolution. The evolution of the bubble significantly affects the mass flow rate at the outlet of the tube, and even leads to backflow during the bubble contraction process. △ Less

Submitted 2 August, 2024; originally announced August 2024.

Comments: 18 pages, 15 figures

Study of the decay and production properties of $D_{s1}(2536)$ and $D_{s2}^*(2573)$

Authors: M. Ablikim, M. N. Achasov, P. Adlarson, O. Afedulidis, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, H. -R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann , et al. (645 additional authors not shown)

Abstract: The $e^+e^-\rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ processes are studied using data samples collected with the BESIII detector at center-of-mass energies from 4.530 to 4.946~GeV. The absolute branching fractions of $D_{s1}(2536)^- \rightarrow \bar{D}^{*0}K^-$ and $D_{s2}^*(2573)^- \rightarrow \bar{D}^0K^-$ are measured for the first time to be… ▽ More The $e^+e^-\rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ processes are studied using data samples collected with the BESIII detector at center-of-mass energies from 4.530 to 4.946~GeV. The absolute branching fractions of $D_{s1}(2536)^- \rightarrow \bar{D}^{*0}K^-$ and $D_{s2}^*(2573)^- \rightarrow \bar{D}^0K^-$ are measured for the first time to be $(35.9\pm 4.8\pm 3.5)\%$ and $(37.4\pm 3.1\pm 4.6)\%$, respectively. The measurements are in tension with predictions based on the assumption that the $D_{s1}(2536)$ and $D_{s2}^*(2573)$ are dominated by a bare $c\bar{s}$ component. The $e^+e^-\rightarrow D_s^+D_{s1}(2536)^-$ and $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ cross sections are measured, and a resonant structure at around 4.6~GeV with a width of 50~MeV is observed for the first time with a statistical significance of $15σ$ in the $e^+e^-\rightarrow D_s^+D^*_{s2}(2573)^-$ process. It could be the $Y(4626)$ found by the Belle collaboration in the $D_s^+D_{s1}(2536)^{-}$ final state, since they have similar masses and widths. There is also evidence for a structure at around 4.75~GeV in both processes. △ Less

Submitted 10 July, 2024; originally announced July 2024.

Enhancing interfacial thermal transport by nanostructures: Monte Carlo simulations with ab initio phonon properties

Authors: Wenzhu Luo, Neng Wang, Wenlei Lian, Ershuai Yin, Qiang Li

Abstract: Recent experiments have indicated that employing nanostructures can enhance interfacial heat transport, but the mechanism by which different structural morphologies and dimensions contribute to the full-spectrum phonon interfacial transport remains unclear. In this paper, a multiscale method to study the thermal transfer at nanostructured interfaces is developed by combining density functional cal… ▽ More Recent experiments have indicated that employing nanostructures can enhance interfacial heat transport, but the mechanism by which different structural morphologies and dimensions contribute to the full-spectrum phonon interfacial transport remains unclear. In this paper, a multiscale method to study the thermal transfer at nanostructured interfaces is developed by combining density functional calculation, Monte Carlo simulation, and diffuse mismatch method. The changes in the transport paths and contributions to thermal conductance of different frequency phonons caused by changes in nanostructure morphology and size are investigated. The results show that, compared to the triangular and trapezoidal nanostructures, the rectangular nanostructures are more beneficial in enhancing the probability of the reflected phonons encountering the interface, and thus the phonon interfacial transmittance. The nanostructure makes the interfacial heat flow extremely heterogeneous, with significant transverse heat flow occurring at the sidewalls, resulting in a new thermal conduction pathway. The phenomena of multiple reflections and double transmission together lead to the existence of the optimal dimension that maximizes the nanostructures enhancement effect on interfacial heat transfer. The optimal nanostructure width is 100 nm when the height is 100 nm and the maximum interfacial thermal conductance enhancement ratio is 1.31. These results can guide the design of heat transfer enhancement structures at the interface of the actual high-power chips. △ Less

Submitted 27 June, 2024; originally announced June 2024.

Comments: 18 pages, 9 figures

ACM Class: J.2.7