Showing 1–50 of 102 results for author: He, Q

Edge-Enhanced Diffractive Neural Networks Based on Spin-Multiplexed Nonlocal Metasurfaces

Authors: Qianqian He, Kenan Guo, Jumin Qiu, Shuyuan Xiao, Tingting Liu

Abstract: Single-layer diffractive neural networks often face classification accuracy bottlenecks due to limited wavefront modulation capabilities. Edge detection, as an optical image processing technique, extracts image contours and offers a promising way to simplify classification tasks. However, integrating edge detection and DNN-based classification on a single chip remains a challenge. Here, we propose… ▽ More Single-layer diffractive neural networks often face classification accuracy bottlenecks due to limited wavefront modulation capabilities. Edge detection, as an optical image processing technique, extracts image contours and offers a promising way to simplify classification tasks. However, integrating edge detection and DNN-based classification on a single chip remains a challenge. Here, we propose an integrated nonlocal meta-platform that achieves all-optical edge detection and DNN-based classification via spin-multiplexing. By exploiting the dispersion properties of the nonlocal Huygens' metasurface, the co-polarized component in the output light performs momentum-space filtering for real-time edge detection. The cross-polarized component undergoes geometric phase modulation to execute image classification within the DNN. We couple quasi-bound states in the continuum and magnetic dipole resonances in crescent-shaped nanopillars, achieving a high polarization conversion efficiency of approximately $55\%$. This edge-enhanced DNN architecture significantly reduces data redundancy, elevating the classification accuracy of the single-layer network on the MNIST dataset from $64.2\%$ to $80.7\%$. Our work provides a compact, high-efficiency solution for integrated all-optical machine vision and intelligent photonic computing. △ Less

Submitted 15 June, 2026; originally announced June 2026.

A nuclear clock based on $^{229}$Th

Authors: Beichen Huang, Gaowei Yan, Qi Xiao, Wenhao Bu, Zhen Zhang, Chengchun Zhao, Chao Yan, Zhi-Ang Chen, Peixiong Zhang, Gleb Penyazkov, Zhenhai Zhan, Lingfeng Yan, Yuefei Wang, Lin Li, Shanming Li, Xiaobo Qian, Xuegang Liu, Qiange He, Taoxiang Sun, Haochen Tian, Binkun Lu, Ningyuan Ma, Juxian Li, Yanzhang Wu, Qiaorui Gong , et al. (13 additional authors not shown)

Abstract: Atomic clocks have made time and frequency the most precisely measured quantities in physics, progressing from microwave standards that realize the SI second to optical clocks that now reach unprecedented levels of precision. A nuclear clock would shift the frequency reference from an electronic transition to the uniquely low-lying, laser-accessible isomeric transition in the $^{229}$Th nucleus, o… ▽ More Atomic clocks have made time and frequency the most precisely measured quantities in physics, progressing from microwave standards that realize the SI second to optical clocks that now reach unprecedented levels of precision. A nuclear clock would shift the frequency reference from an electronic transition to the uniquely low-lying, laser-accessible isomeric transition in the $^{229}$Th nucleus, offering a route to compact, robust timekeeping and sensitive tests of fundamental physics. However, turning recent advances in spectroscopy of the $^{229}$Th nuclear resonance into clock operation requires the nuclear transition to serve as a stable discriminator for steering a traceable oscillator. Here we demonstrate the operation of a $^{229}$Th nuclear clock by stabilizing a continuous-wave narrow-linewidth 148.4 nm vacuum-ultraviolet (VUV) laser to a resolved nuclear transition in a solid-state host. This clock operation is enabled by fast frequency discrimination based on phototube photocurrent readout of the transmitted VUV power. The 10 $μ$W VUV laser, generated by four-wave mixing in cadmium vapour, provides a high-signal-to-noise absorption signal from a home-grown $^{229}$Th:CaF$_2$ crystal, allowing the laser to be locked to a weakly temperature-sensitive nuclear transition. The clock reaches a fractional frequency instability of $2\times10^{-12}/\sqrt{τ/s} $, where $τ$ is the averaging time. Remarkably, nuclear-clock frequencies measured with two distinct crystals agree at the $10^{-13}$ level, demonstrating the reproducibility of solid-state nuclear frequency references. By making a laser-addressed atomic nucleus an operational clock reference, this work extends quantum metrology from electronic to nuclear transitions, and opens a new platform for compact clocks, solid-state nuclear quantum sensors and precision tests of fundamental physics. △ Less

Submitted 7 June, 2026; originally announced June 2026.

Programmable cavity-enhanced telecom quantum memory in thin-film lithium niobate

Authors: Chengdong Yang, Hanwen Guo, Yu-Yang An, Qian He, Chi Lu, Ziheng Jiang, Yan-Qing Lu, Shining Zhu, Xiao-Song Ma

Abstract: Spectrally multiplexed telecom quantum networks require quantum memories combining efficient storage with programmable frequency addressing. An integrated implementation should therefore unite a native telecom transition, efficient storage, and fast on-chip spectral control. Here we demonstrate a cavity-enhanced memory in an isotopically purified $^{167}\mathrm{Er}^{3+}$-doped thin-film lithium ni… ▽ More Spectrally multiplexed telecom quantum networks require quantum memories combining efficient storage with programmable frequency addressing. An integrated implementation should therefore unite a native telecom transition, efficient storage, and fast on-chip spectral control. Here we demonstrate a cavity-enhanced memory in an isotopically purified $^{167}\mathrm{Er}^{3+}$-doped thin-film lithium niobate microring. Long-lived hyperfine shelving states enable persistent, high-contrast atomic frequency comb preparation with a single-component lifetime of $277.6(52.6)$~s, while cavity impedance matching yields $23.3(5)\%$ on-chip efficiency for 100-ns storage. The intrinsic electro-optic response enables frequency-selective storage and routing at rates up to 20~MHz. We further store and retrieve time-energy-entangled telecom photons, violating an entanglement-witness bound by more than 11 standard deviations. Our results establish erbium-doped thin-film lithium niobate as a programmable light--matter interface for spectrally multiplexed quantum networks. △ Less

Submitted 15 July, 2026; v1 submitted 14 May, 2026; originally announced May 2026.

Comments: 29 pages, 24 figures

Gel-Chemistry-Dependent Heavy-Metal Ion Transport and Immobilization in Cementitious Nanopores: A Molecular Dynamics Study

Authors: Weiqiang Chen, Qiyao He, Kai Gong

Abstract: Cementitious materials are widely used for hazardous-waste encapsulation, yet the molecular mechanisms governing heavy-metal ion retention across different gel chemistries remain insufficiently resolved. Here, classical molecular dynamics simulations were employed to investigate the adsorption-controlled mobility of representative heavy-metal ions (Pb2+, Ba2+, and Cs+) within nanopores of C-S-H, C… ▽ More Cementitious materials are widely used for hazardous-waste encapsulation, yet the molecular mechanisms governing heavy-metal ion retention across different gel chemistries remain insufficiently resolved. Here, classical molecular dynamics simulations were employed to investigate the adsorption-controlled mobility of representative heavy-metal ions (Pb2+, Ba2+, and Cs+) within nanopores of C-S-H, C-(N)-A-S-H, and N-A-S-H gels. By combining pore-averaged diffusivity, spatially resolved diffusivity and residence-time analysis, ion-density profiles, two-dimensional adsorption maps, radial distribution functions, coordination analysis, and interfacial binding-strength descriptors, this study establishes a comparative atomistic framework linking gel surface chemistry to ion mobility suppression under nanoconfinement. Ion mobility is substantially reduced in all gel nanopores relative to bulk solutions, but the extent and mechanism of suppression vary strongly with gel chemistry. C-(N)-A-S-H with higher Al/Si ratios exhibits the strongest retention, driven by ion accumulation around Al-linked oxygen species via an ion-exchange-like mechanism with charge-balancing Na+. C-S-H immobilizes ions primarily through surface hydroxyl oxygens and Ca-mediated linkages, whereas N-A-S-H exhibits more distributed binding environments. Pb2+ and Ba2+ exhibit broadly similar immobilization mechanisms, whereas Cs+ shows more distinct, gel-dependent interactions with silicate and aluminosilicate oxygen sites. A relative total binding strength (rTBS) descriptor is introduced, showing a strong positive correlation with the extent of ion immobilization across gel types, ion species, and pore sizes examined. These results clarify gel-specific and ion-specific mechanisms controlling heavy-metal retention in idealized cementitious nanopores. △ Less

Submitted 12 May, 2026; originally announced May 2026.

Comments: 130 pages

Fully Analog Resonant Recurrent Neural Network via Metacircuit

Authors: Zixin Zhou, Tianxi Jiang, Menglong Yang, Zhihua Feng, Qingbo He, Shiwu Zhang

Abstract: Physical neural networks offer a transformative route to edge intelligence, providing superior inference speed and energy efficiency compared to conventional digital architectures. However, realizing scalable, end-to-end, fully analog recurrent neural networks for temporal information processing remains challenging due to the difficulty of faithfully mapping trained network models onto physical ha… ▽ More Physical neural networks offer a transformative route to edge intelligence, providing superior inference speed and energy efficiency compared to conventional digital architectures. However, realizing scalable, end-to-end, fully analog recurrent neural networks for temporal information processing remains challenging due to the difficulty of faithfully mapping trained network models onto physical hardware. Here we present a fully analog resonant recurrent neural network (R$^2$NN) implemented via a metacircuit architecture composed of coupled electrical local resonators. A reformulated mechanical-electrical analogy establishes a direct mapping between the R$^2$NN model and metacircuit elements, enabling accurate physical implementation of trained neural network parameters. By integrating jointly trainable global resistive coupling and local resonances, which generate effective frequency-dependent negative resistances, the architecture shapes an impedance landscape that steers currents along frequency-selective pathways. This mechanism enables direct extraction of discriminative spectral features, facilitating real-time temporal classification of raw analog inputs while bypassing analog-to-digital conversion. We demonstrate the cross-domain versatility of this framework using integrated hardware for tactile perception, speech recognition, and condition monitoring. This work establishes a scalable, fully analog paradigm for intelligent temporal processing and paves the way for low-latency, resource-efficient physical neural hardware for edge intelligence. △ Less

Submitted 19 April, 2026; originally announced April 2026.

Comments: 23 pages, 6 figures

Attosecond quantum spectroscopy with entangled photon pairs

Authors: Zijian Lyu, Fengxiao Sun, Sili Yi, Jingze Li, Haodong Liu, Qiongyi He, Qihuang Gong, Misha Ivanov, Yunquan Liu

Abstract: Bright squeezed light from parametric down-conversion in the infrared (IR) frequency range has triggered the emergence of attosecond quantum optics -- a new research field at the interface of quantum optics, strong-field physics, and attosecond technology. Two challenges arise at this interface: transferring quantum features of the IR light sources to the ultraviolet (UV) and extreme ultraviolet (… ▽ More Bright squeezed light from parametric down-conversion in the infrared (IR) frequency range has triggered the emergence of attosecond quantum optics -- a new research field at the interface of quantum optics, strong-field physics, and attosecond technology. Two challenges arise at this interface: transferring quantum features of the IR light sources to the ultraviolet (UV) and extreme ultraviolet (XUV) frequency range via strong-field nonlinearities, and exploiting quantum optical properties of the nonlinear optical response as a new probe in ultrafast dynamics. Here, we address both by driving high-harmonic generation (HHG) in solids with entangled photon pairs either in degenerate or non-degenerate frequency modes. In the degenerate mode, single-shot measurements of harmonics up to the 10th order reveal strong photon bunching whose $g^{(2)}$ first grows and then decreases with the harmonic order. We show that this behavior tracks different microscopic mechanisms responsible for harmonic emission, demonstrating the potential of attosecond quantum optical spectroscopy. In the non-degenerate case, the harmonics retain quantum-induced correlations, verified by wavelength-resolved second-order cross-correlation maps. Our findings demonstrate transfer of quantum photon correlations into the XUV domain and open a pathway toward quantum-enhanced attosecond spectroscopy and control of ultrafast dynamics in solids. △ Less

Submitted 8 April, 2026; originally announced April 2026.

Mid-wave infrared photothermal microscopy for molecular and metabolic imaging in deep tissues and spheroids

Authors: Mingsheng Li, Yuhao Yuan, Guangrui Ding, Hongli Ni, Biwen Gao, Dashan Dong, Qinshu He, Hongjian He, Xinyan Teng, Yuwei Sun, Dingcheng Sun, Qing Xia, Thao Pham, Ji-Xin Cheng

Abstract: High-resolution chemical imaging within deep tissues and intact spheroids remains a grand challenge. Here, we introduce mid-wave infrared photothermal (MWIP) microscopy operating in the underexplored 2000-2500 nm spectral window for submicron-resolution molecular and metabolic imaging in intact tumor spheroids and deep tissues. A dark-field photothermal detection scheme significantly suppresses wa… ▽ More High-resolution chemical imaging within deep tissues and intact spheroids remains a grand challenge. Here, we introduce mid-wave infrared photothermal (MWIP) microscopy operating in the underexplored 2000-2500 nm spectral window for submicron-resolution molecular and metabolic imaging in intact tumor spheroids and deep tissues. A dark-field photothermal detection scheme significantly suppresses water background and enhances contrast. By accessing strong carbon-hydrogen combination absorptions, a detection limit of 0.12% for dimethyl sulfoxide is achieved, comparable to stimulated Raman scattering microscopy. Depth-resolved imaging of endogenous biomolecules up to 500 micrometers in excised mouse skin and brain tissues is demonstrated. MWIP further enables depth-resolved tracking of transdermal drug transport via carbon-deuterium overtone absorption. Using deuterium metabolic probes, fatty-acid metabolism is imaged at 200 micrometers deep within intact tumor spheroids through carbon-deuterium overtone and combination bands. Collectively, MWIP offers a platform for functional imaging of 3D biological systems in their native environments. △ Less

Submitted 5 March, 2026; originally announced March 2026.

Differentiable Inverse Modeling with Physics-Constrained Latent Diffusion for Heterogeneous Subsurface Parameter Fields

Authors: Zihan Lin, QiZhi He

Abstract: We present a latent diffusion-based differentiable inversion method (LD-DIM) for PDE-constrained inverse problems involving high-dimensional spatially distributed coefficients. LD-DIM couples a pretrained latent diffusion prior with an end-to-end differentiable numerical solver to reconstruct unknown heterogeneous parameter fields in a low-dimensional nonlinear manifold, improving numerical condit… ▽ More We present a latent diffusion-based differentiable inversion method (LD-DIM) for PDE-constrained inverse problems involving high-dimensional spatially distributed coefficients. LD-DIM couples a pretrained latent diffusion prior with an end-to-end differentiable numerical solver to reconstruct unknown heterogeneous parameter fields in a low-dimensional nonlinear manifold, improving numerical conditioning and enabling stable gradient-based optimization under sparse observations. The proposed framework integrates a latent diffusion model (LDM), trained in a compact latent space, with a differentiable finite-volume discretization of the forward PDE. Sensitivities are propagated through the discretization using adjoint-based gradients combined with reverse-mode automatic differentiation. Inversion is performed directly in latent space, which implicitly suppresses ill-conditioned degrees of freedom while preserving dominant structural modes, including sharp material interfaces. The effectiveness of LD-DIM is demonstrated using a representative inverse problem for flow in porous media, where heterogeneous conductivity fields are reconstructed from spatially sparse hydraulic head measurements. Numerical experiments assess convergence behavior and reconstruction quality for both Gaussian random fields and bimaterial coefficient distributions. The results show that LD-DIM achieves consistently improved numerical stability and reconstruction accuracy of both parameter fields and corresponding PDE solutions compared with physics-informed neural networks (PINNs) and physics-embedded variational autoencoder (VAE) baselines, while maintaining sharp discontinuities and reducing sensitivity to initialization. △ Less

Submitted 26 December, 2025; originally announced December 2025.

Comments: 33 pages, 16 figures

Checkerboard-type Zhang-Rice States in Overdoped Cuprate Superconductors

Authors: Xiongfang Liu, Kun Han, Yan Peng, Yuanjie Ning, Jing Wu, Zhaoyang Luo, Difan Zhou, Zhigang Zeng, Qian He, Chuanbing Cai, Mark. B. H. Breese, Ariando Ariando, Chi Sin Tang, George A. Sawatzky, Mi Jiang, Xinmao Yin

Abstract: Cuprate superconductors remain central to condensed matter physics due to their technological relevance and unconventional, incompletely understood electronic behavior. While the canonical phase diagram and low-energy models have been shaped largely by studies of underdoped and moderately doped cuprates, the overdoped regime has received comparatively limited attention.Here, we track the evolution… ▽ More Cuprate superconductors remain central to condensed matter physics due to their technological relevance and unconventional, incompletely understood electronic behavior. While the canonical phase diagram and low-energy models have been shaped largely by studies of underdoped and moderately doped cuprates, the overdoped regime has received comparatively limited attention.Here, we track the evolution of the electronic structure from optimal to heavy overdoping in La2-xSrxCuO4(LSCO) using broadband optical spectroscopy across x=0.15-0.60. The measured spectral changes--including the redistribution of Zhang-Rice-related spectral weigh--are in qualitative agreement with determinant quantum Monte Carlo simulations of the three-orbital Emery model, which together indicate a pronounced reconstruction of the electronic structure beyond hole concentrations x>0.2. Guided by these observations, we propose a spontaneous checkerboard-type Zhang-Rice electronic configuration that captures the coexistence of itinerant and localized carriers characteristic of the heavily overdoped state. Our results refine the doping-dependent Zhang-Rice-based framework for cuprates, illuminate how correlations persist deep into the overdoped regime, and provide new constraints on microscopic mechanisms of high-temperature superconductivity, with broader implications for correlated transition-metal oxides. △ Less

Submitted 10 December, 2025; originally announced December 2025.

Mamyshev oscillator based on gain-managed nonlinearity and chirped pulse amplification

Authors: Baofu Zhang, Sihua Lu, Qiurun He, Ya Lin, Zhongxing Jiao, Biao Wang

Abstract: We experimentally demonstrate a Mamyshev oscillator based on gain-managed nonlinearity and chirped pulse amplification. Different from other Mamyshev oscillators, the gain-managed nonlinear regime serves as a seed provider instead of a power amplifier in one arm of this laser. The output pulse energy over 300 nJ and pulse width of 739 fs has been achieved from the chirped pulsed amplification in a… ▽ More We experimentally demonstrate a Mamyshev oscillator based on gain-managed nonlinearity and chirped pulse amplification. Different from other Mamyshev oscillators, the gain-managed nonlinear regime serves as a seed provider instead of a power amplifier in one arm of this laser. The output pulse energy over 300 nJ and pulse width of 739 fs has been achieved from the chirped pulsed amplification in another arm. This configuration provided a new approach to design a high-energy ultrafast laser. △ Less

Submitted 31 March, 2026; v1 submitted 1 December, 2025; originally announced December 2025.

Comments: 13 pages, 6 figures

Low-Energy Free-Electron Nonclassical Lasing

Authors: Mai Zhang, Yu Wang, Chang-Ling Zou, Lei Ying, Qiongyi He, Guang-Can Guo, Chun-Hua Dong

Abstract: Harnessing a beam of slow free electrons in artificial photonic structures offers a powerful, tunable platform for generating nonclassical light without the need for heavy physical equipment. Here we present a theory of nonclassical lasing, demonstrating how incoherent electrons in photonic crystal cavities can coherently emit photons through collective dynamics. When photon emission rate exceeds… ▽ More Harnessing a beam of slow free electrons in artificial photonic structures offers a powerful, tunable platform for generating nonclassical light without the need for heavy physical equipment. Here we present a theory of nonclassical lasing, demonstrating how incoherent electrons in photonic crystal cavities can coherently emit photons through collective dynamics. When photon emission rate exceeds cavity losses, nonclassical lasing with sub-Poissonian photon statistics emerges, driven by multi-photon Rabi oscillations. At specific coupling strengths, quantum state trapping effect emerges, producing high-fidelity Fock states at room temperature (e.g. nearly 90%-fidelity of four photon Fock state). Notably, the frequency of the emitted photons can be readily tuned via the velocity of the injected electrons to match cavity modes. This approach supports photonic integration and offers a scalable, energy-efficient platform for room-temperature quantum light sources and advanced studies in quantum electrodynamics. △ Less

Submitted 13 November, 2025; originally announced November 2025.

Thermodynamically consistent modeling and simulation of two-fluid magnetohydrodynamic equations

Authors: Ting Xiao, Qiaolin He

Abstract: Based on a rigorous thermodynamic framework, this work develops a two-fluid magnetohydrodynamic model grounded in the Helmholtz free energy formalism. The model maintains full thermodynamic consistency by simultaneously satisfying energy conservation and entropy production laws in two-fluid systems. By analyzing the convex-concave structure of the Helmholtz free energy density, we systematically d… ▽ More Based on a rigorous thermodynamic framework, this work develops a two-fluid magnetohydrodynamic model grounded in the Helmholtz free energy formalism. The model maintains full thermodynamic consistency by simultaneously satisfying energy conservation and entropy production laws in two-fluid systems. By analyzing the convex-concave structure of the Helmholtz free energy density, we systematically derive key thermodynamic variables-chemical potential, entropy density, and internal energy-in a self-consistent manner. Building on this foundation, we construct a temporally discrete numerical scheme that inherits the thermodynamic consistency of the continuous model. The scheme is proven to adhere rigorously to both the first and second laws of thermodynamics. For the implemented two-dimensional degenerate system, we establish comprehensive a priori error estimates in space and time. Numerical simulations validate the model's effectiveness in capturing essential plasma phenomena, demonstrating its applicability to complex physical scenarios. △ Less

Submitted 5 November, 2025; v1 submitted 17 August, 2025; originally announced September 2025.

Thermodynamically consistent modelling and simulation of the moving contact line problem in non-isothermal compressible two-phase flows

Authors: Junkai Wang, Qiaolin He

Abstract: According to the dynamic van der Waals theory, we propose a thermodynamically consistent model for non-isothermal compressible two-phase flows with contact line motion. In this model, fluid temperature is treated as a primary variable, characterized by the proposed temperature equation instead of being obtained from intermediate variables such as total energy density, internal energy density and e… ▽ More According to the dynamic van der Waals theory, we propose a thermodynamically consistent model for non-isothermal compressible two-phase flows with contact line motion. In this model, fluid temperature is treated as a primary variable, characterized by the proposed temperature equation instead of being obtained from intermediate variables such as total energy density, internal energy density and entropy density. The hydrodynamic boundary conditions, which represent a generalization of the generalized Navier slip boundary condition in non-isothermal flows, are imposed on the proposed model. We then develop the dimensionless form of the model and prove that it rigorously satisfies the first and second laws of thermodynamics. Two numerical schemes based on the dimensionless system are constructed: one is fully coupled and thermodynamically consistent, namely strictly satisfying the temporally discrete first and second laws of thermodynamics; the other, designed by extending the multiple scalar auxiliary variable approach to entropy production, is decoupled, linear, and unconditionally entropy-stable. Several numerical results are presented to validate the effectiveness and stability of the proposed method. △ Less

Submitted 7 August, 2025; originally announced August 2025.

MSC Class: 76T10; 76N20; 80M99; 65Z05

MXene triggers high toughness, high strength and low hysteresis hydrogels for printed artificial tissue

Authors: Chendong Zhao, Yaxing Li, Qinglong He, Shangpeng Qin, Huiqi Xie, Chuanfang Zhang

Abstract: Substituting load-bearing tissues requires hydrogels with rapid processability, excellent mechanical strength and fatigue resistance. Conventional homogeneously polymerized hydrogels with short-chains/excessive branching exhibit low strength/toughness, being inadequate for artificial tissues. Here we introduce the heterogeneous polymerization-accelerated reaction kinetics on the Ti3C2Tx MXene micr… ▽ More Substituting load-bearing tissues requires hydrogels with rapid processability, excellent mechanical strength and fatigue resistance. Conventional homogeneously polymerized hydrogels with short-chains/excessive branching exhibit low strength/toughness, being inadequate for artificial tissues. Here we introduce the heterogeneous polymerization-accelerated reaction kinetics on the Ti3C2Tx MXene microreactor and sluggish kinetics beyond-to rapidly produce hydrogels within minutes. This allows the hyperbranched domains embedded within a highly entangled matrix, leading to excellent strength (2.4 MPa)/toughness (75.2 kJ m-2) and low hysteresis (2.9%) in hydrogels superior to the rest ones. The rapid liquid-to-solid transition triggered by MXene suggests the great possibility of 3D printed robust hydrogels toward artificial tissue. Importantly, these printed hydrogels-based artificial ligaments have demonstrated impressive load-bearing capacity, wear resistance, and suturability compared to commercial analogs. △ Less

Submitted 15 June, 2025; originally announced June 2025.

Modality-AGnostic Image Cascade (MAGIC) for Multi-Modality Cardiac Substructure Segmentation

Authors: Nicholas Summerfield, Qisheng He, Alex Kuo, Ahmed I. Ghanem, Simeng Zhu, Chase Ruff, Joshua Pan, Anudeep Kumar, Prashant Nagpal, Jiwei Zhao, Ming Dong, Carri K. Glide-Hurst

Abstract: Cardiac substructure delineation is emerging in treatment planning to minimize the risk of radiation-induced heart disease. Deep learning offers efficient methods to reduce contouring burden but currently lacks generalizability across different modalities and overlapping structures. This work introduces and validates a Modality-AGnostic Image Cascade (MAGIC) deep-learning pipeline for comprehensiv… ▽ More Cardiac substructure delineation is emerging in treatment planning to minimize the risk of radiation-induced heart disease. Deep learning offers efficient methods to reduce contouring burden but currently lacks generalizability across different modalities and overlapping structures. This work introduces and validates a Modality-AGnostic Image Cascade (MAGIC) deep-learning pipeline for comprehensive and multi-modal cardiac substructure segmentation. MAGIC is implemented through replicated encoding and decoding branches of an nnU-Net backbone to handle multi-modality inputs and overlapping labels. First benchmarked on the multi-modality whole-heart segmentation (MMWHS) dataset including cardiac CT-angiography (CCTA) and MR modalities, twenty cardiac substructures (heart, chambers, great vessels (GVs), valves, coronary arteries (CAs), and conduction nodes) from clinical simulation CT (Sim-CT), low-field MR-Linac, and cardiac CT-angiography (CCTA) modalities were delineated to train semi-supervised (n=151), validate (n=15), and test (n=30) MAGIC. For comparison, fourteen single-modality comparison models (two MMWHS modalities and four subgroups across three clinical modalities) were trained. Methods were evaluated for efficiency and against reference contours through the Dice similarity coefficient (DSC) and two-tailed Wilcoxon Signed-Rank test (p<0.05). Average MMWHS DSC scores across CCTA and MR inputs were 0.88(0.08) and 0.87(0.04) respectively with significant improvement over unimodal baselines. Average 20-structure DSC scores were 0.75(0.16) for Sim-CT, 0.68(0.21) for MR-Linac, and 0.80(0.16) for CCTA. Furthermore, >80% and >70% reductions in training time and parameters were achieved, respectively. MAGIC offers an efficient, lightweight solution capable of segmenting multiple image modalities and overlapping structures in a single model without compromising segmentation accuracy. △ Less

Submitted 2 November, 2025; v1 submitted 12 June, 2025; originally announced June 2025.

Application of machine learning algorithm in temperature field reconstruction

Authors: Qianyu He, Huaiwei Sun, Yubo Li, Zhiwen You, Qiming Zheng, Yinghan Huang, Sipeng Zhu, Fengyu Wang

Abstract: This study focuses on the stratification patterns and dynamic evolution of reservoir water temperatures, aiming to estimate and reconstruct the temperature field using limited and noisy local measurement data. Due to complex measurement environments and technical limitations, obtaining complete temperature information for reservoirs is highly challenging. Therefore, accurately reconstructing the t… ▽ More This study focuses on the stratification patterns and dynamic evolution of reservoir water temperatures, aiming to estimate and reconstruct the temperature field using limited and noisy local measurement data. Due to complex measurement environments and technical limitations, obtaining complete temperature information for reservoirs is highly challenging. Therefore, accurately reconstructing the temperature field from a small number of local data points has become a critical scientific issue. To address this, the study employs Proper Orthogonal Decomposition (POD) and sparse representation methods to reconstruct the temperature field based on temperature data from a limited number of local measurement points. The results indicate that satisfactory reconstruction can be achieved when the number of POD basis functions is set to 2 and the number of measurement points is 10. Under different water intake depths, the reconstruction errors of both POD and sparse representation methods remain stable at around 0.15, fully validating the effectiveness of these methods in reconstructing the temperature field based on limited local temperature data. Additionally, the study further explores the distribution characteristics of reconstruction errors for POD and sparse representation methods under different water level intervals, analyzing the optimal measurement point layout scheme and potential limitations of the reconstruction methods in this case. This research not only effectively reduces measurement costs and computational resource consumption but also provides a new technical approach for reservoir temperature analysis, holding significant theoretical and practical importance. △ Less

Submitted 18 February, 2025; originally announced February 2025.

Exceptional-Point-Induced Nonequilibrium Entanglement Dynamics in Bosonic Networks

Authors: Chenghe Yu, Mingsheng Tian, Ningxin Kong, Matteo Fadel, Xinyao Huang, Qiongyi He

Abstract: Exceptional points (EPs), arising in non-Hermitian systems, have garnered significant attention in recent years, enabling advancements in sensing, wave manipulation, and mode selectivity. However, their role in quantum systems, particularly in influencing quantum correlations, remains underexplored. In this work, we investigate how EPs control multimode entanglement in bosonic chains. Using a Bogo… ▽ More Exceptional points (EPs), arising in non-Hermitian systems, have garnered significant attention in recent years, enabling advancements in sensing, wave manipulation, and mode selectivity. However, their role in quantum systems, particularly in influencing quantum correlations, remains underexplored. In this work, we investigate how EPs control multimode entanglement in bosonic chains. Using a Bogoliubov-de Gennes (BdG) framework to describe the Heisenberg equations, we identify EPs of varying orders and uncover spectral transitions between purely real, purely imaginary, and mixed eigenvalue spectra. These spectral regions, divided by EPs, correspond to three distinct entanglement dynamics: oscillatory, exponential, and hybrid. Remarkably, we demonstrate that higher-order EPs, realized by non-integer-pi hopping phases or nonuniform interaction strengths, significantly enhance the degree of multimode entanglement compared to second-order EPs. Our findings provide a pathway to leveraging EPs for entanglement control and exhibit the potential of non-Hermitian physics in advancing quantum technologies. △ Less

Submitted 19 January, 2026; v1 submitted 6 February, 2025; originally announced February 2025.

Comments: 9 pages, 4 figures

A Metal-Insulator Transition of the Buried MnO2 Monolayer in Complex Oxide Heterostructure

Authors: Heng-Jui Liu, Jheng-Cyuan Lin, Yue-Wen Fang, Jing-Ching Wang, Bo-Chao Huang, Xiang Gao, Rong Huang, Philip R. Dean, Peter D. Hatton, Yi-Ying Chin, Hong-Ji Lin, Chien-Te Chen, Yuichi Ikuhara, Ya-Ping Chiu, Chia-Seng Chang, Chun-Gang Duan, Qing He, Ying-Hao Chu

Abstract: Functionalities in crystalline materials are determined by 3-dimensional collective interactions of atoms. The confinement of dimensionality in condensed matter provides an exotic research direction to understand the interaction of atoms, thus can be used to tailor or create new functionalities in material systems. In this study, a 2-dimensional transition metal oxide monolayer is constructed insi… ▽ More Functionalities in crystalline materials are determined by 3-dimensional collective interactions of atoms. The confinement of dimensionality in condensed matter provides an exotic research direction to understand the interaction of atoms, thus can be used to tailor or create new functionalities in material systems. In this study, a 2-dimensional transition metal oxide monolayer is constructed inside complex oxide heterostructures based on the theoretical predictions. The electrostatic boundary conditions of oxide monolayer in the heterostructure is carefully designed to tune the chemical, electronic, and magnetic states of oxide monolayer. The challenge of characterizing such an oxide monolayer is overcome by a combination of transmission electron microscopy, x-ray absorption spectroscopy, cross-sectional scanning tunneling microscopy, and electrical transport measurements. An intriguing metal-insulator transition associated with a magnetic transition is discovered in the MnO2 monolayer. This study paves a new route to understand the confinement of dimensionality and explore new intriguing phenomena in condensed matters. △ Less

Submitted 31 January, 2025; originally announced January 2025.

Comments: 23 pages, 6 figures

Journal ref: Adv. Mater., 28: 9142-9151(2016)

AI-Driven Reinvention of Hydrological Modeling for Accurate Predictions and Interpretation to Transform Earth System Modeling

Authors: Cuihui Xia, Lei Yue, Deliang Chen, Yuyang Li, Hongqiang Yang, Ancheng Xue, Zhiqiang Li, Qing He, Guoqing Zhang, Dambaru Ballab Kattel, Lei Lei, Ming Zhou

Abstract: Traditional equation-driven hydrological models often struggle to accurately predict streamflow in challenging regional Earth systems like the Tibetan Plateau, while hybrid and existing algorithm-driven models face difficulties in interpreting hydrological behaviors. This work introduces HydroTrace, an algorithm-driven, data-agnostic model that substantially outperforms these approaches, achieving… ▽ More Traditional equation-driven hydrological models often struggle to accurately predict streamflow in challenging regional Earth systems like the Tibetan Plateau, while hybrid and existing algorithm-driven models face difficulties in interpreting hydrological behaviors. This work introduces HydroTrace, an algorithm-driven, data-agnostic model that substantially outperforms these approaches, achieving a Nash-Sutcliffe Efficiency of 98% and demonstrating strong generalization on unseen data. Moreover, HydroTrace leverages advanced attention mechanisms to capture spatial-temporal variations and feature-specific impacts, enabling the quantification and spatial resolution of streamflow partitioning as well as the interpretation of hydrological behaviors such as glacier-snow-streamflow interactions and monsoon dynamics. Additionally, a large language model (LLM)-based application allows users to easily understand and apply HydroTrace's insights for practical purposes. These advancements position HydroTrace as a transformative tool in hydrological and broader Earth system modeling, offering enhanced prediction accuracy and interpretability. △ Less

Submitted 7 January, 2025; originally announced January 2025.

Quantum dynamics of photophysical aggregates in conjugated polymers

Authors: Henry J. Kantrow, Elizabeth Gutiérrez-Meza, Hongmo Li, Qiao He, Martin Heeney, Natalie Stingelin, Eric R. Bittner, Carlos Silva-Acuña, Hao Li, Félix Thouin

Abstract: Photophysical aggregates are ubiquitous in many solid-state microstructures adopted by conjugated polymers, in which $π$ electrons interact with those in other polymer chains or those in other chromophores along the chain. These interactions fundamentally define the electronic and optical properties of the polymer film. While valuable insight can be gained from linear excitation and photoluminesce… ▽ More Photophysical aggregates are ubiquitous in many solid-state microstructures adopted by conjugated polymers, in which $π$ electrons interact with those in other polymer chains or those in other chromophores along the chain. These interactions fundamentally define the electronic and optical properties of the polymer film. While valuable insight can be gained from linear excitation and photoluminescence spectra, nonlinear coherent excitation spectral lineshapes provide intricate understanding on the electronic couplings that define the aggregate and their fluctuations. Here, we discuss the coherent two-dimensional excitation lineshape of a model hairy-rod conjugated polymer. At zero population waiting time, we find a $π/2$ phase shift between the 0-0 and 0-1 vibronic peaks in the real and imaginary components of the complex coherent spectrum, as well as a dynamic phase rotation with population waiting time over timescales that are longer than the optical dephasing time. We conjecture that these are markers of relaxation of the photophysical aggregate down the tight manifold of the exciton band. These results highlight the potential for coherent spectroscopy via analysis of the complex spectral lineshape to become a key tool to develop structure-property relationships in complex functional materials. △ Less

Submitted 2 December, 2024; v1 submitted 21 November, 2024; originally announced November 2024.

Comments: 9 manuscript pages plus supplementary material document. Submitted to the Journal of Chemical Physics

One-Sided Device-Independent Random Number Generation Through Fiber Channels

Authors: Jinfang Zhang, Yi Li, Mengyu Zhao, Dongmei Han, Jun Liu, Meihong Wang, Qihuang Gong, Yu Xiang, Qiongyi He, Xiaolong Su

Abstract: Randomness is an essential resource and plays important roles in various applications ranging from cryptography to simulation of complex systems. Certified randomness from quantum process is ensured to have the element of privacy but usually relies on the device's behavior. To certify randomness without the characterization for device, it is crucial to realize the one-sided device-independent rand… ▽ More Randomness is an essential resource and plays important roles in various applications ranging from cryptography to simulation of complex systems. Certified randomness from quantum process is ensured to have the element of privacy but usually relies on the device's behavior. To certify randomness without the characterization for device, it is crucial to realize the one-sided device-independent random number generation based on quantum steering, which guarantees security of randomness and relaxes the demands of one party's device. Here, we distribute quantum steering between two distant users through a 2 km fiber channel and generate quantum random numbers at the remote station with untrustworthy device. We certify the steering-based randomness by reconstructing covariance matrix of the Gaussian entangled state shared between distant parties. Then, the quantum random numbers with a generation rate of 7.06 Mbits/s are extracted from the measured amplitude quadrature fluctuation of the state owned by the remote party. Our results demonstrate the first realization of steering-based random numbers extraction in a practical fiber channel, which paves the way to the quantum random numbers generation in asymmetric networks. △ Less

Submitted 13 November, 2024; originally announced November 2024.

Harnessing Zn-Volatility for Compositional Tuning in PtZn Nanoalloy Catalysts

Authors: Bingqing Yao, Chaokai Xu, Yaxin Tang, Yankun Du, Shengdong Tan, Sheng Dai, Guangfu Luo, Qian He

Abstract: Bimetallic nanoalloys have gained extensive attention due to their tunable properties and wide range of catalytic applications. However, achieving good compositional control in nanoalloy catalysts remains a formidable challenge. In this work, we demonstrate that heat treatment can be used to tune the composition of Pt-Zn nanoalloy catalysts, leveraging the volatile nature of zinc to enhance their… ▽ More Bimetallic nanoalloys have gained extensive attention due to their tunable properties and wide range of catalytic applications. However, achieving good compositional control in nanoalloy catalysts remains a formidable challenge. In this work, we demonstrate that heat treatment can be used to tune the composition of Pt-Zn nanoalloy catalysts, leveraging the volatile nature of zinc to enhance their performance in propane dehydrogenation. Through identical location (scanning) transmission electron microscopy (IL-(S)TEM) using an in-situ EM gas cell, as well as other complementary techniques, we observed that the zinc content of the Pt-Zn nanoalloy particles decreased over time of the heat treatment under hydrogen. The rate of change depends on the original composition of the particles, as well as the heat treatment conditions such as temperature and flow rate. Our experimental results and theoretical calculations suggest that Zn in the intermetallic phase might be more stable, providing an opportunity for precise tuning the nanoparticle compositions. This approach presents a viable strategy for developing better Pt-Zn catalysts for propane dehydrogenation. △ Less

Submitted 19 July, 2024; originally announced July 2024.

Deep Neural Networks with Symplectic Preservation Properties

Authors: Qing He, Wei Cai

Abstract: We propose a deep neural network architecture designed such that its output forms an invertible symplectomorphism of the input. This design draws an analogy to the real-valued non-volume-preserving (real NVP) method used in normalizing flow techniques. Utilizing this neural network type allows for learning tasks on unknown Hamiltonian systems without breaking the inherent symplectic structure of t… ▽ More We propose a deep neural network architecture designed such that its output forms an invertible symplectomorphism of the input. This design draws an analogy to the real-valued non-volume-preserving (real NVP) method used in normalizing flow techniques. Utilizing this neural network type allows for learning tasks on unknown Hamiltonian systems without breaking the inherent symplectic structure of the phase space. △ Less

Submitted 28 June, 2024; originally announced July 2024.

MSC Class: 37J11; 70H15; 68T07

Robust Ptychographic Reconstruction with an Out-of-Focus Electron Probe

Authors: Shoucong Ning, Wenhui Xu, Pengju Sheng, Leyi Loh, Stephen Pennycook, Fucai Zhang, Michel Bosman, Qian He

Abstract: As a burgeoning technique, out-of-focus electron ptychography offers the potential for rapidly imaging atomic-scale large fields of view (FoV) using a single diffraction dataset. However, achieving robust out-of-focus ptychographic reconstruction poses a significant challenge due to the inherent scan instabilities of electron microscopes, compounded by the presence of unknown aberrations in the pr… ▽ More As a burgeoning technique, out-of-focus electron ptychography offers the potential for rapidly imaging atomic-scale large fields of view (FoV) using a single diffraction dataset. However, achieving robust out-of-focus ptychographic reconstruction poses a significant challenge due to the inherent scan instabilities of electron microscopes, compounded by the presence of unknown aberrations in the probe-forming lens. In this study, we substantially enhance the robustness of out-of-focus ptychographic reconstruction by extending our previous calibration method (the Fourier method), which was originally developed for the in-focus scenario. This extended Fourier method surpasses existing calibration techniques by providing more reliable and accurate initialization of scan positions and electron probes. Additionally, we comprehensively explore and recommend optimized experimental parameters for robust out-of-focus ptychography, includingaperture size and defocus, through extensive simulations. Lastly, we conduct a comprehensive comparison between ptychographic reconstructions obtained with focused and defocused electron probes, particularly in the context of low-dose and precise phase imaging, utilizing our calibration method as the basis for evaluation. △ Less

Submitted 22 June, 2024; originally announced June 2024.

Comments: 22 pages, 6 figures

Manipulating Spectral Windings and Skin Modes through Nonconservative Couplings

Authors: Ningxin Kong, Chenghe Yu, Yilun Xu, Matteo Fadel, Xinyao Huang, Qiongyi He

Abstract: The discovery of the non-Hermitian skin effect (NHSE) has revolutionized our understanding of wave propagation in non-Hermitian systems, highlighting unexpected localization effects beyond conventional theories. Here, we discover that NHSE, accompanied by multitype spectral phases, can be induced by manipulating nonconservative couplings. By characterizing the spectra through the windings of the e… ▽ More The discovery of the non-Hermitian skin effect (NHSE) has revolutionized our understanding of wave propagation in non-Hermitian systems, highlighting unexpected localization effects beyond conventional theories. Here, we discover that NHSE, accompanied by multitype spectral phases, can be induced by manipulating nonconservative couplings. By characterizing the spectra through the windings of the energy bands, we demonstrate that band structures with identical, opposite, and even twisted windings can be achieved. These inequivalent types of spectra originate from the multichannel interference resulting from the interplay between conservative and nonconservative couplings. Associated with the multitype spectra, unipolar and bipolar NHSE with different eigenmode localizations can be observed. Additionally, our findings link the nonreciprocal transmission properties of the system to multiple spectral phases, indicating a connection with the skin modes. This paper paves new pathways for investigating non-Hermitian topological effects and manipulating nonreciprocal energy flow. △ Less

Submitted 13 November, 2024; v1 submitted 21 June, 2024; originally announced June 2024.

Comments: 12 pages, 6 figures

Journal ref: Physical Review A 110, 053507 (2024)

Nanometer-resolution 3D Tomographic and Vectorial Near-field Imaging in Dielectric Optical Resonators

Authors: Bingbing Zhu, Qingnan Cai, Yaxin Liu, Sheng Zhang, Weifeng Liu, Qiong He, Lei Zhou, Zhensheng Tao

Abstract: All-dielectric optical nano-resonators, exhibiting exotic near-field distributions upon excitations, have emerged as low-loss, versatile and highly adaptable components in nanophotonic structures for manipulating electromagnetic waves and enhancing light-matter interactions. However, achieving experimental full three-dimensional characterization of near-fields within dielectric nano-resonators pos… ▽ More All-dielectric optical nano-resonators, exhibiting exotic near-field distributions upon excitations, have emerged as low-loss, versatile and highly adaptable components in nanophotonic structures for manipulating electromagnetic waves and enhancing light-matter interactions. However, achieving experimental full three-dimensional characterization of near-fields within dielectric nano-resonators poses significant challenges. Here, we develop a novel technique using high-order sideband generation to image near-field wave patterns inside dielectric optical nano-resonators. By exploiting the phase-sensitivity of various harmonic orders that enables the detection of near-field distributions at distinct depths, we achieve three-dimensional tomographic and near-field imaging with nanometer resolution inside a micrometer-thick silicon anapole resonator. Furthermore, our method offers high-contrast polarization sensitivity and phase-resolving capability, providing comprehensive vectorial near-field information. Our approach can potentially be applied to diverse dielectric metamaterials, and becomes a valuable tool for comprehensive characterization of near-field wave phenomena within dielectric materials. △ Less

Submitted 26 February, 2025; v1 submitted 18 June, 2024; originally announced June 2024.

Large-scale cluster quantum microcombs

Authors: Ze Wang, Kangkang Li, Yue Wang, Xin Zhou, Yinke Cheng, Boxuan Jing, Fengxiao Sun, Jincheng Li, Zhilin Li, Bingyan Wu, Qihuang Gong, Qiongyi He, Bei-Bei Li, Qi-Fan Yang

Abstract: An optical frequency comb comprises a cluster of equally spaced, phase-locked spectral lines. Replacing these classical components with correlated quantum light gives rise to cluster quantum frequency combs, providing abundant quantum resources for measurement-based quantum computation and multi-user quantum networks. We propose and generate cluster quantum microcombs within an on-chip optical mic… ▽ More An optical frequency comb comprises a cluster of equally spaced, phase-locked spectral lines. Replacing these classical components with correlated quantum light gives rise to cluster quantum frequency combs, providing abundant quantum resources for measurement-based quantum computation and multi-user quantum networks. We propose and generate cluster quantum microcombs within an on-chip optical microresonator driven by multi-frequency lasers. Through resonantly enhanced four-wave mixing processes, continuous-variable cluster states with 60 qumodes are deterministically created. The graph structures can be programmed into one- and two-dimensional lattices by adjusting the configurations of the pump lines, which are confirmed inseparable based on the measured covariance matrices. Our work demonstrates the largest-scale cluster states with unprecedented raw squeezing levels from a photonic chip, offering a compact and scalable platform for computational and communicational tasks with quantum advantages. △ Less

Submitted 16 December, 2024; v1 submitted 15 June, 2024; originally announced June 2024.

Estimation of railway vehicle response for track geometry evaluation using branch Fourier neural operator

Authors: Qingjing Wang, Wenhao Ding, Qing He, Ping Wang

Abstract: In railway transportation, the evaluation of track geometry is an indispensable requirement to ensure the safety and comfort of railway vehicles. A promising approach is to directly use vehicle dynamic responses to assess the impact of track geometry defects. However, the computational cost of obtaining the dynamic response of the vehicle body using dynamics simulation methods is large. Thus, it i… ▽ More In railway transportation, the evaluation of track geometry is an indispensable requirement to ensure the safety and comfort of railway vehicles. A promising approach is to directly use vehicle dynamic responses to assess the impact of track geometry defects. However, the computational cost of obtaining the dynamic response of the vehicle body using dynamics simulation methods is large. Thus, it is important to obtain the dynamic response of the vehicle-track coupled system efficiently and accurately. In this work, a branch Fourier neural operator (BFNO) model is proposed to obtain the dynamic response of the vehicle-track coupled system. The model takes into account the nonlinear relationship of the vehicle-track coupled system and realizes the fast and accurate estimation of the system dynamic response. The relative loss (rLSE) of BFNO model is 2.04%, which is reduced by 64%, compared with the traditional neural network (CNN-GRU). In the frequency domain, BFNO model achieves the effective estimation of the dynamic response of the system within the primary frequency range. Compared with the existing methods, our proposed model can make predictions at unseen time steps, enabling predictions from low to high time resolutions. Meanwhile, our proposed model is superior to commercial software in terms of efficiency. In the evaluation of track geometry, users can use pre-trained BFNO to obtain the dynamic response with almost no computational cost. △ Less

Submitted 28 February, 2024; originally announced February 2024.

Ultra-short lifetime isomer studies from photonuclear reactions using laser-driven ultra-intense γ-ray

Authors: Di Wu, Haoyang Lan, Jiaxing Liu, Huangang Lu, Jianyao Zhang, Jianfeng Lv, Xuezhi Wu, Hui Zhang, Yadong Xia, Qiangyou He, Jie Cai, Qianyi Ma, Yuhui Xia, Zhenan Wang, Meizhi Wang, Zhiyan Yang, Xinlu Xu, Yixing Geng, Chen Lin, Wenjun Ma, Yanying Zhao, Haoran Wang, Fulong Liu, Chuangye He, Jinqing Yu , et al. (7 additional authors not shown)

Abstract: Isomers, ubiquitous populations of relatively long-lived nuclear excited states, play a crucial role in nuclear physics. However, isomers with half-life times of several seconds or less barely had experimental cross section data due to the lack of a suitable measuring method. We report a method of online γ spectroscopy for ultra-short-lived isomers from photonuclear reactions using laser-driven ul… ▽ More Isomers, ubiquitous populations of relatively long-lived nuclear excited states, play a crucial role in nuclear physics. However, isomers with half-life times of several seconds or less barely had experimental cross section data due to the lack of a suitable measuring method. We report a method of online γ spectroscopy for ultra-short-lived isomers from photonuclear reactions using laser-driven ultra-intense γ-rays. The fastest time resolution can reach sub-ps level with γ-ray intensities >10^{19}/s ({\geqslant} 8 MeV). The ^{115}In(γ, n)^{114m2}In reaction (T_{1/2} = 43.1 ms) was first measured in the high-energy region which shed light on the nuclear structure studies of In element. Simulations showed it would be an efficient way to study ^{229m}Th (T_{1/2} = 7 μs), which is believed to be the next generation of nuclear clock. This work offered a unique way of gaining insight into ultra-short lifetimes and promised an effective way to fill the gap in relevant experimental data. △ Less

Submitted 23 February, 2024; originally announced February 2024.

Choroidal thinning assessment through facial video analysis

Authors: Qinghua He, Yi Zhang, Mengxi Shen, Giovanni Gregori, Philip J. Rosenfeld, Ruikang K. Wang

Abstract: Different features of skin are associated with various medical conditions and provide opportunities to evaluate and monitor body health. This study created a strategy to assess choroidal thinning through the video analysis of facial skin. Videos capturing the entire facial skin were collected from 48 participants with age-related macular degeneration (AMD) and 12 healthy individuals. These facial… ▽ More Different features of skin are associated with various medical conditions and provide opportunities to evaluate and monitor body health. This study created a strategy to assess choroidal thinning through the video analysis of facial skin. Videos capturing the entire facial skin were collected from 48 participants with age-related macular degeneration (AMD) and 12 healthy individuals. These facial videos were analyzed using video-based trans-angiosomes imaging photoplethysmography (TaiPPG) to generate facial imaging biomarkers that were correlated with choroidal thickness (CT) measurements. The CT of all patients was determined using swept-source optical coherence tomography (SS-OCT). The results revealed the relationship between relative blood pulsation amplitude (BPA) in three typical facial angiosomes (cheek, side-forehead and mid-forehead) and the average macular CT (r = 0.48, p < 0.001; r = -0.56, p < 0.001; r = -0.40, p < 0.01). When considering a diagnostic threshold of 200μm, the newly developed facial video analysis tool effectively distinguished between cases of choroidal thinning and normal cases, yielding areas under the curve of 0.75, 0.79 and 0.69. These findings shed light on the connection between choroidal blood flow and facial skin hemodynamics, which suggests the potential for predicting vascular diseases through widely accessible skin imaging data. △ Less

Submitted 29 January, 2024; originally announced January 2024.

Comments: 8 pages, 4 figures

Deep FBSDE Neural Networks for Solving Incompressible Navier-Stokes Equation and Cahn-Hilliard Equation

Authors: Yangtao Deng, Qiaolin He

Abstract: Efficient algorithms for solving high-dimensional partial differential equations (PDEs) has been an exceedingly difficult task for a long time, due to the curse of dimensionality. We extend the forward-backward stochastic neural networks (FBSNNs) which depends on forward-backward stochastic differential equation (FBSDE) to solve incompressible Navier-Stokes equation. For Cahn-Hilliard equation, we… ▽ More Efficient algorithms for solving high-dimensional partial differential equations (PDEs) has been an exceedingly difficult task for a long time, due to the curse of dimensionality. We extend the forward-backward stochastic neural networks (FBSNNs) which depends on forward-backward stochastic differential equation (FBSDE) to solve incompressible Navier-Stokes equation. For Cahn-Hilliard equation, we derive a modified Cahn-Hilliard equation from a widely used stabilized scheme for original Cahn-Hilliard equation. This equation can be written as a continuous parabolic system, where FBSDE can be applied and the unknown solution is approximated by neural network. Also our method is successfully developed to Cahn-Hilliard-Navier-Stokes (CHNS) equation. The accuracy and stability of our methods are shown in many numerical experiments, specially in high dimension. △ Less

Submitted 19 June, 2024; v1 submitted 7 January, 2024; originally announced January 2024.

Design of a full-filed transmission X-ray microscope with 30nm resolution

Authors: Keliang Liao, Qili He, Panyun Li, Maohua Song, Peiping Zhu

Abstract: A full-field transmission hard X-ray microscope (TXM) with 30nm resolution was designed and its prototype was constructed. The TXM relies on a compact, high stiffness, low heat dissipation and low vibration design philosophy and utilizes Fresnel Zone plate (FZP) as imaging optics. The design of the TXM was introduced in detail, including the optical layout, the parameters of the FZP, the mechanica… ▽ More A full-field transmission hard X-ray microscope (TXM) with 30nm resolution was designed and its prototype was constructed. The TXM relies on a compact, high stiffness, low heat dissipation and low vibration design philosophy and utilizes Fresnel Zone plate (FZP) as imaging optics. The design of the TXM was introduced in detail, including the optical layout, the parameters of the FZP, the mechanical design of the TXM instrument. Preliminary imaging result with 52nm spatial resolution was achieved. △ Less

Submitted 15 November, 2023; originally announced January 2024.

Correlation-pattern-based Continuous-variable Entanglement Detection through Neural Networks

Authors: Xiaoting Gao, Mathieu Isoard, Fengxiao Sun, Carlos E. Lopetegui, Yu Xiang, Valentina Parigi, Qiongyi He, Mattia Walschaers

Abstract: Entanglement in continuous-variable non-Gaussian states provides irreplaceable advantages in many quantum information tasks. However, the sheer amount of information in such states grows exponentially and makes a full characterization impossible. Here, we develop a neural network that allows us to use correlation patterns to effectively detect continuous-variable entanglement through homodyne dete… ▽ More Entanglement in continuous-variable non-Gaussian states provides irreplaceable advantages in many quantum information tasks. However, the sheer amount of information in such states grows exponentially and makes a full characterization impossible. Here, we develop a neural network that allows us to use correlation patterns to effectively detect continuous-variable entanglement through homodyne detection. Using a recently defined stellar hierarchy to rank the states used for training, our algorithm works not only on any kind of Gaussian state but also on a whole class of experimentally achievable non-Gaussian states, including photon-subtracted states. With the same limited amount of data, our method provides higher accuracy than usual methods to detect entanglement based on maximum-likelihood tomography. Moreover, in order to visualize the effect of the neural network, we employ a dimension reduction algorithm on the patterns. This shows that a clear boundary appears between the entangled states and others after the neural network processing. In addition, these techniques allow us to compare different entanglement witnesses and understand their working. Our findings provide a new approach for experimental detection of continuous-variable quantum correlations without resorting to a full tomography of the state and confirm the exciting potential of neural networks in quantum information processing. △ Less

Submitted 31 October, 2023; originally announced October 2023.

Comments: 9 pages (incl. appendix), 6 figures, comments welcome!

Journal ref: Phys. Rev. Lett. 132, 220202 (2024)

How enlightened self-interest guided global vaccine sharing benefits all: a modelling study

Authors: Zhenyu Han, Qianyue Hao, Qiwei He, Katherine Budeski, Depeng Jin, Fengli Xu, Kun Tang

Abstract: Background: Despite the consensus that vaccines play an important role in combating the global spread of infectious diseases, vaccine inequity is still rampant with deep-seated mentality of self-priority. This study aims to evaluate the existence and possible outcomes of a more equitable global vaccine distribution and explore a concrete incentive mechanism that promotes vaccine equity. Methods: W… ▽ More Background: Despite the consensus that vaccines play an important role in combating the global spread of infectious diseases, vaccine inequity is still rampant with deep-seated mentality of self-priority. This study aims to evaluate the existence and possible outcomes of a more equitable global vaccine distribution and explore a concrete incentive mechanism that promotes vaccine equity. Methods: We design a metapopulation epidemiological model that simultaneously considers global vaccine distribution and human mobility, which is then calibrated by the number of infections and real-world vaccination records during COVID-19 pandemic from March 2020 to July 2021. We explore the possibility of the enlightened self-interest incentive mechanism, i.e., improving one's own epidemic outcomes by sharing vaccines with other countries, by evaluating the number of infections and deaths under various vaccine sharing strategies using the proposed model. To understand how these strategies affect the national interests, we distinguish the imported and local cases for further cost-benefit analyses that rationalize the enlightened self-interest incentive mechanism behind vaccine sharing. ... △ Less

Submitted 13 October, 2023; originally announced October 2023.

Comments: Accepted by Journal of Global Health

X-ray computed tomography reconstruction algorithm for refractive index gradient

Authors: Keliang Liao, Qili He, Panyun Li, Liang Luo, Peiping Zhu

Abstract: The aim of this research is to reconstruct the 3D X-ray refractive index gradient maps by the proposed vector Radon transform and its inverse, assuming that the small-angle deviation condition is met. Theoretical analyses show that the X-ray beam can be modeled as a streamline with continuous change of direction in a row when measured in one grating period, which allows the extraction of the refra… ▽ More The aim of this research is to reconstruct the 3D X-ray refractive index gradient maps by the proposed vector Radon transform and its inverse, assuming that the small-angle deviation condition is met. Theoretical analyses show that the X-ray beam can be modeled as a streamline with continuous change of direction in a row when measured in one grating period, which allows the extraction of the refraction angle signals. Experimental results show that all the 2D refraction signals of different directions can be acquired by a standard circular scanning procedure, which is typically used in the X-ray differential phase-contrast computed tomography. Furthermore, the 3D refractive index gradient maps that contain the directional density changes, can also be accurately reconstructed. △ Less

Submitted 4 September, 2023; originally announced September 2023.

Comments: accepted by IGTA2023

Physics-data-driven intelligent optimization for large-scale meta-devices

Authors: Yingli Ha, Yu Luo, Mingbo Pu, Fei Zhang, Qiong He, Jinjin Jin, Mingfeng Xu, Yinghui Guo, Xiaogang Li, Xiong Li, Xiaoliang Ma, Xiangang Luo

Abstract: Meta-devices have gained significant attention and have been widely utilized in optical systems for focusing and imaging, owing to their lightweight, high-integration, and exceptional-flexibility capabilities. However, based on the assumption of local phase approximation, traditional design method neglect the local lattice coupling effect between adjacent meta-atoms, thus harming the practical per… ▽ More Meta-devices have gained significant attention and have been widely utilized in optical systems for focusing and imaging, owing to their lightweight, high-integration, and exceptional-flexibility capabilities. However, based on the assumption of local phase approximation, traditional design method neglect the local lattice coupling effect between adjacent meta-atoms, thus harming the practical performance of meta-devices. Using physics-driven or data-driven optimization algorithms can effectively solve the aforementioned problems. Nevertheless, both of the methods either involve considerable time costs or require a substantial amount of data sets. Here, we propose a physics-data-driven approach based "intelligent optimizer" that enables us to adaptively modify the sizes of the studied meta-atom according to the sizes of its surrounding ones. Such a scheme allows to mitigate the undesired local lattice coupling effect, and the proposed network model works well on thousands of datasets with a validation loss of 3*10-3. Experimental results show that the 1-mm-diameter metalens designed with the "intelligent optimizer" possesses a relative focusing efficiency of 93.4% (as compared to ideal focusing) and a Strehl ratio of 0.94. In contrast to the previous inverse design method, our method significantly boosts designing efficiency with five orders of magnitude reduction in time. Our design approach may sets a new paradigm for devising large-scale meta-devices. △ Less

Submitted 2 June, 2023; originally announced June 2023.

Comments: manuscripts:19 pages, 4 figures; Supplementary Information: 11 pages, 12 figures

HL-nets: Physics-informed neural networks for hydrodynamic lubrication with cavitation

Authors: Yiqian Cheng, Qiang He, Weifeng Huang, Ying Liu, Yanwen Li, Decai Li

Abstract: Recently, physics-informed neural networks (PINNs) have emerged as a promising method for solving partial differential equations (PDEs). In this study, we establish a deep learning computational framework, HL-nets, for computing the flow field of hydrodynamic lubrication involving cavitation effects. Two classical cavitation conditions, i.e., the Swift-Stieber (SS) condition and the Jakobsson-Flob… ▽ More Recently, physics-informed neural networks (PINNs) have emerged as a promising method for solving partial differential equations (PDEs). In this study, we establish a deep learning computational framework, HL-nets, for computing the flow field of hydrodynamic lubrication involving cavitation effects. Two classical cavitation conditions, i.e., the Swift-Stieber (SS) condition and the Jakobsson-Floberg-Olsson (JFO) condition, are implemented in the PINNs to solve the Reynolds equation. For the non-negativity constraint of the SS cavitation condition, a penalizing scheme with a residual of the non-negativity and an imposing scheme with a continuous differentiable non-negative function are proposed. For the complementarity constraint of the JFO cavitation condition, the pressure and cavitation fraction are taken as the neural network outputs, and the residual of the Fischer-Burmeister (FB) equation constrains their complementary relationships. Multi-task learning (MTL) methods are applied to balance the newly introduced loss terms described above. To estimate the accuracy of HL-nets, we present a numerical solution of the Reynolds equation for oil-lubricated bearings involving cavitation. The results indicate that the proposed HL-nets can highly accurately simulate hydrodynamic lubrication involving cavitation phenomena. The imposing scheme can effectively improve the accuracy of the training results of PINNs, and it is expected to have great potential to be applied to different fields where the non-negativity constraint is needed. △ Less

Submitted 18 December, 2022; originally announced April 2023.

STCF Conceptual Design Report: Volume 1 -- Physics & Detector

Authors: M. Achasov, X. C. Ai, R. Aliberti, L. P. An, Q. An, X. Z. Bai, Y. Bai, O. Bakina, A. Barnyakov, V. Blinov, V. Bobrovnikov, D. Bodrov, A. Bogomyagkov, A. Bondar, I. Boyko, Z. H. Bu, F. M. Cai, H. Cai, J. J. Cao, Q. H. Cao, Z. Cao, Q. Chang, K. T. Chao, D. Y. Chen, H. Chen , et al. (413 additional authors not shown)

Abstract: The Super $τ$-Charm facility (STCF) is an electron-positron collider proposed by the Chinese particle physics community. It is designed to operate in a center-of-mass energy range from 2 to 7 GeV with a peak luminosity of $0.5\times 10^{35}{\rm cm}^{-2}{\rm s}^{-1}$ or higher. The STCF will produce a data sample about a factor of 100 larger than that by the present $τ$-Charm factory -- the BEPCII,… ▽ More The Super $τ$-Charm facility (STCF) is an electron-positron collider proposed by the Chinese particle physics community. It is designed to operate in a center-of-mass energy range from 2 to 7 GeV with a peak luminosity of $0.5\times 10^{35}{\rm cm}^{-2}{\rm s}^{-1}$ or higher. The STCF will produce a data sample about a factor of 100 larger than that by the present $τ$-Charm factory -- the BEPCII, providing a unique platform for exploring the asymmetry of matter-antimatter (charge-parity violation), in-depth studies of the internal structure of hadrons and the nature of non-perturbative strong interactions, as well as searching for exotic hadrons and physics beyond the Standard Model. The STCF project in China is under development with an extensive R\&D program. This document presents the physics opportunities at the STCF, describes conceptual designs of the STCF detector system, and discusses future plans for detector R\&D and physics case studies. △ Less

Submitted 5 October, 2023; v1 submitted 28 March, 2023; originally announced March 2023.

Journal ref: Front. Phys. 19(1), 14701 (2024)

On the uncertainty analysis of the data-enabled physics-informed neural network for solving neutron diffusion eigenvalue problem

Authors: Yu Yang, Helin Gong, Qihong Yang, Yangtao Deng, Qiaolin He, Shiquan Zhang

Abstract: In practical engineering experiments, the data obtained through detectors are inevitably noisy. For the already proposed data-enabled physics-informed neural network (DEPINN) \citep{DEPINN}, we investigate the performance of DEPINN in calculating the neutron diffusion eigenvalue problem from several perspectives when the prior data contain different scales of noise. Further, in order to reduce the… ▽ More In practical engineering experiments, the data obtained through detectors are inevitably noisy. For the already proposed data-enabled physics-informed neural network (DEPINN) \citep{DEPINN}, we investigate the performance of DEPINN in calculating the neutron diffusion eigenvalue problem from several perspectives when the prior data contain different scales of noise. Further, in order to reduce the effect of noise and improve the utilization of the noisy prior data, we propose innovative interval loss functions and give some rigorous mathematical proofs. The robustness of DEPINN is examined on two typical benchmark problems through a large number of numerical results, and the effectiveness of the proposed interval loss function is demonstrated by comparison. This paper confirms the feasibility of the improved DEPINN for practical engineering applications in nuclear reactor physics. △ Less

Submitted 17 March, 2023; v1 submitted 15 March, 2023; originally announced March 2023.

Comments: The experiments in Figures 6 and 10 in the article have errors that need to be corrected. Moreover, we intend to make massive changes to the content of the article, and therefore need to withdraw the article

Infrared ellipsometry study of the charge dynamics in K3p-terphenyl

Authors: Qi He, P. Marsik, F. Le Mardelé, B. Xu, Meenakshi Sharma, N. Pinto, A. Perali, C. Di Nicola, C. Pettinari, D. Baeriswyl, C. Bernhard

Abstract: We report an infrared ellipsometry study of the charge carrier dynamics in polycrystalline Kxp-terphenyl samples with nominal $x=3$, for which signatures of high-temperature superconductivity were previously reported. The infrared spectra are dominated by two Lorentzian bands with maxima around 4 000 cm$^{-1}$ and 12 000 cm$^{-1}$ which, from a comparison with calculations based on a Hückel model… ▽ More We report an infrared ellipsometry study of the charge carrier dynamics in polycrystalline Kxp-terphenyl samples with nominal $x=3$, for which signatures of high-temperature superconductivity were previously reported. The infrared spectra are dominated by two Lorentzian bands with maxima around 4 000 cm$^{-1}$ and 12 000 cm$^{-1}$ which, from a comparison with calculations based on a Hückel model are assigned to intra-molecular excitations of $π$ electrons of the anionic p-terphenyl molecules. The inter-molecular electronic excitations are much weaker and give rise to a Drude peak and a similarly weak Lorentzian band around 220 cm$^{-1}$. A dc resistivity of about 0.3 $Ω$ cm at 300 K is deduced from the IR data, comparable to values measured by electrical resistivity on a twin sample. The analysis of the temperature dependence of the low-frequency response reveals a gradual decrease of the plasma frequency and the scattering rate of the Drude peak below 300 K that gets anomalously enhanced below 90 K. The corresponding missing spectral weight of the Drude peak appears blue-shifted towards the Lorentz-band at 220 cm$^{-1}$. This characteristic blue-shift signifies an enhanced localization of the charge carriers at low temperatures and contrasts the behavior expected for a bulk superconducting state for which the missing spectral weight would be redshifted to a delta-function at zero frequency that accounts for the loss-free response of the superconducting condensate. Our data might still be compatible with a filamentary superconducting state with a volume fraction well below the percolation limit for which the spatial confinement of the condensate can result in a plasmonic resonance at finite frequency. △ Less

Submitted 24 February, 2023; v1 submitted 30 January, 2023; originally announced February 2023.

Comments: 12 pages, 7 figures

MSC Class: 14J60 ACM Class: J.2.9

A Hybrid Deep Neural Operator/Finite Element Method for Ice-Sheet Modeling

Authors: QiZhi He, Mauro Perego, Amanda A. Howard, George Em Karniadakis, Panos Stinis

Abstract: One of the most challenging and consequential problems in climate modeling is to provide probabilistic projections of sea level rise. A large part of the uncertainty of sea level projections is due to uncertainty in ice sheet dynamics. At the moment, accurate quantification of the uncertainty is hindered by the cost of ice sheet computational models. In this work, we develop a hybrid approach to a… ▽ More One of the most challenging and consequential problems in climate modeling is to provide probabilistic projections of sea level rise. A large part of the uncertainty of sea level projections is due to uncertainty in ice sheet dynamics. At the moment, accurate quantification of the uncertainty is hindered by the cost of ice sheet computational models. In this work, we develop a hybrid approach to approximate existing ice sheet computational models at a fraction of their cost. Our approach consists of replacing the finite element model for the momentum equations for the ice velocity, the most expensive part of an ice sheet model, with a Deep Operator Network, while retaining a classic finite element discretization for the evolution of the ice thickness. We show that the resulting hybrid model is very accurate and it is an order of magnitude faster than the traditional finite element model. Further, a distinctive feature of the proposed model compared to other neural network approaches, is that it can handle high-dimensional parameter spaces (parameter fields) such as the basal friction at the bed of the glacier, and can therefore be used for generating samples for uncertainty quantification. We study the impact of hyper-parameters, number of unknowns and correlation length of the parameter distribution on the training and accuracy of the Deep Operator Network on a synthetic ice sheet model. We then target the evolution of the Humboldt glacier in Greenland and show that our hybrid model can provide accurate statistics of the glacier mass loss and can be effectively used to accelerate the quantification of uncertainty. △ Less

Submitted 26 January, 2023; originally announced January 2023.

Dynamics of flexible fibers in confined shear flows at finite Reynolds numbers

Authors: Jian Su, Kun Ma, Zhongyu Yan, Qiaolin He, Xinpeng Xu

Abstract: We carry out a numerical study on the dynamics of a single non-Brownian flexible fiber in two-dimensional Couette flows at finite Reynolds numbers. We employ the bead-spring model of flexible fibers to extend the fluid particle dynamics (FPD) method that is originally developed for rigid particles in viscous liquids. We implement the extended FPD method using a multiple-relaxation-time (MRT) schem… ▽ More We carry out a numerical study on the dynamics of a single non-Brownian flexible fiber in two-dimensional Couette flows at finite Reynolds numbers. We employ the bead-spring model of flexible fibers to extend the fluid particle dynamics (FPD) method that is originally developed for rigid particles in viscous liquids. We implement the extended FPD method using a multiple-relaxation-time (MRT) scheme of the lattice Boltzmann method (LBM). The numerical scheme is validated firstly by a series of benchmark simulations that involve liquid-solid coupling. The method is then used to study the dynamics of flexible fibers in Couette flows. We only consider the highly symmetric case where the fibers are placed on the symmetry center of Couette flows and we focus on the effects of the fiber stiffness, the confinement strength, and the finite Reynolds number (from 1 to 10). A diagram of the fiber shape is obtained. For fibers under weak confinement and a small Reynolds number, three distinct tumbling orbits have been identified. (1) Jeffery orbits of rigid fibers. The fibers behave like rigid rods and tumble periodically without any visible deformation. (2) S-turn orbits of slightly flexible fibers. The fiber is bent to an S-shape and is straightened again when it orients to an angle of around 45 degrees relative to the positive x direction. (3) S-coiled orbits of fairly flexible fibers. The fiber is folded to an S-shape and tumbles periodically and steadily without being straightened anymore during its rotation. Moreover, the fiber tumbling is found to be hindered by increasing either the Reynolds number or the confinement strength, or both. △ Less

Submitted 22 February, 2023; v1 submitted 28 December, 2022; originally announced December 2022.

Comments: 16 pages, 10 figures

Journal ref: Physics of Fluids 35, 043308 (2023)

Quantum storage of entangled photons at telecom wavelengths in a crystal

Authors: Ming-Hao Jiang, Wenyi Xue, Qian He, Yu-Yang An, Xiaodong Zheng, Wen-Jie Xu, Yu-Bo Xie, Yanqing Lu, Shining Zhu, Xiao-Song Ma

Abstract: The quantum internet -- in synergy with the internet that we use today -- promises an enabling platform for next-generation information processing, including exponentially speed-up distributed computation, secure communication, and high-precision metrology. The key ingredients for realizing such a global network are the distribution and storage of quantum entanglement. As ground-based quantum netw… ▽ More The quantum internet -- in synergy with the internet that we use today -- promises an enabling platform for next-generation information processing, including exponentially speed-up distributed computation, secure communication, and high-precision metrology. The key ingredients for realizing such a global network are the distribution and storage of quantum entanglement. As ground-based quantum networks are likely to be based on existing fiber networks, telecom-wavelength entangled photons and corresponding quantum memories are of central interest. Recently, $\rm^{167}Er^{3+}$ ions have been identified as a promising candidate for an efficient, broadband quantum memory at telecom wavelength. However, to date, no storage of entangled photons, the crucial step of quantum memory using these promising ions, $\rm^{167}Er^{3+}$, has been reported. Here, we demonstrate the storage and recall of the entangled state of two telecom photons generated from an integrated photonic chip based on a silicon nitride micro-ring resonator. Combining the natural narrow linewidth of the entangled photons and long storage time of $\rm^{167}Er^{3+}$ ions, we achieve storage time of 1.936 $μ$s, more than 387 times longer than in previous works. Successful storage of entanglement in the crystal is certified by a violation of an entanglement witness with more than 23 standard deviations (-0.234 $\pm$ 0.010) at 1.936 $μ$s storage time. These results pave the way for realizing quantum networks based on solid-state devices. △ Less

Submitted 26 August, 2023; v1 submitted 25 December, 2022; originally announced December 2022.

Comments: We have improved the storage time of quantum entanglement to over 1.9 $μ$s with a efficiency of about 2%, using partial nuclear spin polarization as well as better frequency locking for the laser system. We have also enhanced the coherence of signal photons, which are generated from an entangled photon-pair source based on an integrated microring resonator with a quality factor exceeding 10^6

Journal ref: Nat Commun 14, 6995 (2023)

Intrinsic ion migration dynamics in a one-dimensional organic metal halide hybrid

Authors: Zhenqi Hua, Azza Ben-Akacha, Qingquan He, Tianhan Liu, Gillian Boyce, Margaret van Deventer, Xinsong Lin, Hanwei Gao, Biwu Ma, Peng Xiong

Abstract: Metal halide perovskites possess many physical properties amenable to optoelectronic applications, whereas the realization of these potentials has been hampered by their environmental and electronic instabilities. The morphological and molecular low dimensional perovskites and perovskite related materials have shown much promise in enhancing the chemical stability due to their unique molecular str… ▽ More Metal halide perovskites possess many physical properties amenable to optoelectronic applications, whereas the realization of these potentials has been hampered by their environmental and electronic instabilities. The morphological and molecular low dimensional perovskites and perovskite related materials have shown much promise in enhancing the chemical stability due to their unique molecular structures. Here we report on robust and reproducible four-terminal (4T) electrical measurements in a one-dimensional (1D) organic metal halide hybrid, (R)-α-methylbenzylammonium lead triiodide ((R-α-MBA)PbI3) made possible by its chemical stability. The results reveal a distinct intrinsic ion migration dynamic of single exponential, which underlies the unique 4T I-V characteristics. The dynamic is directly verified by real-time measurements of the transient ionic current. Our observations are consistent with photo-activation and field-assisted ion migration. The elucidated intrinsic ion dynamics may provide the physical basis for understanding and modelling the ubiquitous hysteresis in metal halides based electronic devices and new insights into the dynamics of ion migration in metal halide perovskites and hybrids in general. △ Less

Submitted 18 October, 2022; originally announced October 2022.

Journal ref: ACS Energy Lett. 2022, 7, XXX, 3753_3760

Physics-informed neural networks of the Saint-Venant equations for downscaling a large-scale river model

Authors: Dongyu Feng, Zeli Tan, QiZhi He

Abstract: Large-scale river models are being refined over coastal regions to improve the scientific understanding of coastal processes, hazards and responses to climate change. However, coarse mesh resolutions and approximations in physical representations of tidal rivers limit the performance of such models at resolving the complex flow dynamics especially near the river-ocean interface, resulting in inacc… ▽ More Large-scale river models are being refined over coastal regions to improve the scientific understanding of coastal processes, hazards and responses to climate change. However, coarse mesh resolutions and approximations in physical representations of tidal rivers limit the performance of such models at resolving the complex flow dynamics especially near the river-ocean interface, resulting in inaccurate simulations of flood inundation. In this research, we propose a machine learning (ML) framework based on the state-of-the-art physics-informed neural network (PINN) to simulate the downscaled flow at the subgrid scale. First, we demonstrate that PINN is able to assimilate observations of various types and solve the one-dimensional (1-D) Saint-Venant equations (SVE) directly. We perform the flow simulations over a floodplain and along an open channel in several synthetic case studies. The PINN performance is evaluated against analytical solutions and numerical models. Our results indicate that the PINN solutions of water depth have satisfactory accuracy with limited observations assimilated. In the case of flood wave propagation induced by storm surge and tide, a new neural network architecture is proposed based on Fourier feature embeddings that seamlessly encodes the periodic tidal boundary condition in the PINN's formulation. Furthermore, we show that the PINN-based downscaling can produce more reasonable subgrid solutions of the along-channel water depth by assimilating observational data. The PINN solution outperforms the simple linear interpolation in resolving the topography and dynamic flow regimes at the subgrid scale. This study provides a promising path towards improving emulation capabilities in large-scale models to characterize fine-scale coastal processes. △ Less

Submitted 6 October, 2022; originally announced October 2022.

Phase-controlled asymmetric optomechanical entanglement against optical backscattering

Authors: Jing-Xue Liu, Ya-Feng Jiao, Ying Li, Xun-Wei Xu, Qiong-Yi He, Hui Jing

Abstract: Quantum entanglement plays a key role in both understanding the fundamental aspects of quantum physics and realizing various quantum devices for practical applications. Here we propose how to achieve coherent switch of optomechanical entanglement in an optical whispering-gallery-mode resonator, by tuning the phase difference of the driving lasers. We find that the optomechanical entanglement and t… ▽ More Quantum entanglement plays a key role in both understanding the fundamental aspects of quantum physics and realizing various quantum devices for practical applications. Here we propose how to achieve coherent switch of optomechanical entanglement in an optical whispering-gallery-mode resonator, by tuning the phase difference of the driving lasers. We find that the optomechanical entanglement and the associated two-mode quantum squeezing can be well tuned in a highly asymmetric way, providing an efficient way to protect and enhance quantum entanglement against optical backscattering, in comparison with conventional symmetric devices. Our findings shed a new light on improving the performance of various quantum devices in practical noisy environment, which is crucial in such a wide range of applications as noise-tolerant quantum processing and the backscattering-immune quantum metrology. △ Less

Submitted 13 February, 2023; v1 submitted 26 September, 2022; originally announced September 2022.

Comments: 11 pages, 6 figures

Journal ref: SCIENCE CHINA Physics, Mechanics & Astronomy 66, 230312 (2023)

Caught in the Crossfire: Fears of Chinese-American Scientists

Authors: Yu Xie, Xihong Lin, Ju Li, Qian He, Junming Huang

Abstract: The US leadership in science and technology has greatly benefitted from immigrants from other countries, most notably from China in the recent decades. However, feeling the pressure of potential federal investigation since the 2018 launch of the China Initiative under the Trump administration, Chinese-origin scientists in the US now face higher incentives to leave the US and lower incentives to ap… ▽ More The US leadership in science and technology has greatly benefitted from immigrants from other countries, most notably from China in the recent decades. However, feeling the pressure of potential federal investigation since the 2018 launch of the China Initiative under the Trump administration, Chinese-origin scientists in the US now face higher incentives to leave the US and lower incentives to apply for federal grants. Analyzing data pertaining to institutional affiliations of more than 2.3 million scientific papers, we find a steady increase in the return migration of Chinese-origin scientists from the US back to China. We also conducted a survey of Chinese-origin scientists employed by US universities in tenure or tenure-track positions (n=1300), with results revealing general feelings of fear and anxiety that lead them to consider leaving the US and/or stop applying for federal grants. △ Less

Submitted 23 September, 2022; v1 submitted 21 September, 2022; originally announced September 2022.

Comments: 16 pages, 2 figures

ACM Class: J.4

Regulating effect of biaxial strain on electronic, optical and photocatalytic properties in promising X2PAs (X = Si, Ge and Sn) monolayers

Authors: Qi-Wen He, Yang Wu, Chun-Hua Yang, He-Na Zhang, Dai-Song Tang, Cailong Liu, Xiao-Chun Wang

Abstract: Photocatalytic water splitting is an effective way to obtain renewable clean energy. The challenge is to design tunable photocatalyst to meet the needs in different environments. At the same time, the oxygen and hydrogen evolution reactions (OER and HER) on the photocatalyst should be separated, which will be conducive to the separation of products. The electronic, optical and photocatalytic prope… ▽ More Photocatalytic water splitting is an effective way to obtain renewable clean energy. The challenge is to design tunable photocatalyst to meet the needs in different environments. At the same time, the oxygen and hydrogen evolution reactions (OER and HER) on the photocatalyst should be separated, which will be conducive to the separation of products. The electronic, optical and photocatalytic properties of Janus X2PAs (X = Si, Ge and Sn) monolayers are explored by first-principles calculation. All the strain-free X2PAs monolayers exhibit excellent photocatalytic properties with suitable band edge positions straddling the standard redox potential of water and large visible light absorption coefficients (up to 105 cm-1). Interestingly, the intrinsic internal electric field is favorable for separating photogenerated carriers to different surfaces of the monolayer. It contributes to realize the OER and HER separated on different sides of the monolayer. In particular, the energy band edge positions of X2PAs monolayers can be well adjusted by biaxial strain. Then it can effectively modulate photocatalytic reactions, suggesting X2PAs monolayers can be a piezo-photocatalytic switch between the OER, HER and full-reaction of redox for water splitting. This investigation not only highlights that the photocatalyst X2PAs monolayers with the separated OER and HER can be effectively tuned by the mechanical strain, but also provides a new strategy for designing highly adaptable and tunable piezo-photocatalysts. △ Less

Submitted 8 September, 2022; originally announced September 2022.

Comments: 22 pages, 5 figures

A data-enabled physics-informed neural network with comprehensive numerical study on solving neutron diffusion eigenvalue problems

Authors: Yu Yang, Helin Gong, Shiquan Zhang, Qihong Yang, Zhang Chen, Qiaolin He, Qing Li

Abstract: We present a data-enabled physics-informed neural network (DEPINN) with comprehensive numerical study for solving industrial scale neutron diffusion eigenvalue problems (NDEPs). In order to achieve an engineering acceptable accuracy for complex engineering problems, a very small amount of prior data from physical experiments are suggested to be used, to improve the accuracy and efficiency of train… ▽ More We present a data-enabled physics-informed neural network (DEPINN) with comprehensive numerical study for solving industrial scale neutron diffusion eigenvalue problems (NDEPs). In order to achieve an engineering acceptable accuracy for complex engineering problems, a very small amount of prior data from physical experiments are suggested to be used, to improve the accuracy and efficiency of training. We design an adaptive optimization procedure with Adam and LBFGS to accelerate the convergence in the training stage. We discuss the effect of different physical parameters, sampling techniques, loss function allocation and the generalization performance of the proposed DEPINN model for solving complex problem. The feasibility of proposed DEPINN model is tested on three typical benchmark problems, from simple geometry to complex geometry, and from mono-energetic equation to two-group equations. Numerous numerical results show that DEPINN can efficiently solve NDEPs with an appropriate optimization procedure. The proposed DEPINN can be generalized for other input parameter settings once its structure been trained. This work confirms the possibility of DEPINN for practical engineering applications in nuclear reactor physics. △ Less

Submitted 13 November, 2022; v1 submitted 29 August, 2022; originally announced August 2022.

Nonreciprocal Amplification Transition in a Driven-Dissipative Quantum Network

Authors: Mingsheng Tian, Fengxiao Sun, Kaiye Shi, Haitan Xu, Qiongyi He, Wei Zhang

Abstract: We study the transport properties of a driven-dissipative quantum network, where multiple bosonic cavities such as photonic microcavities are coupled through a nonreciprocal bus with unidirectional transmission. For short-range coupling between the cavities, the occurrence of nonreciprocal amplification can be linked to a topological phase transition of the underlying dynamic Hamiltonian. However,… ▽ More We study the transport properties of a driven-dissipative quantum network, where multiple bosonic cavities such as photonic microcavities are coupled through a nonreciprocal bus with unidirectional transmission. For short-range coupling between the cavities, the occurrence of nonreciprocal amplification can be linked to a topological phase transition of the underlying dynamic Hamiltonian. However, for long-range coupling, we find that the nonreciprocal amplification transition deviates drastically from the topological phase transition. Nonetheless, we show that the nonreciprocal amplification transition can be connected to the emergence of zero-energy edge states of an auxiliary Hamiltonian with chiral symmetry even in the long-range coupling limit. We also investigate the stability, the crossover from short to long-range coupling, and the bandwidth of the nonreciprocal amplification. Our work has potential application in signal transmission and amplification, and also opens a window to non-Hermitian systems with long-range coupling and nontrivial boundary effects. △ Less

Submitted 14 July, 2022; originally announced July 2022.

Comments: 5 pages, 4 figures

Journal ref: Photonics Research 11, 852 (2023)