Showing 1–50 of 343 results for author: Chen, R

Superconducting Hydride Mg2RhH6 Experimentally Achieved at Lower Pressure

Authors: Linjing Wu, Zelong Wang, Guiqi Liu, Jun Zhang, Yanfeng Ge, Yuanhao Su, Runteng Chen, Hongyu Liu, Wenmin Li, Sijia Zhang, Jingcheng Zhu, Jianfa Zhao, Zheng Deng, Shaomin Feng, Jing Song, Qingqing Liu, Xiang Li, Haozhe Liu, Panpan Kong, Xiancheng Wang, Changqing Jin

Abstract: Although tremendous progress has been made in recent years in the field of polyhydride superconductors, the realization of high critical temperature superconductivity still relies on formidable high pressures. Searching for superconducting hydrides at lower pressures is of particular importance. Here we report the first experimental synthesis of the Mg2RhH6, which achieves superconductivity under… ▽ More Although tremendous progress has been made in recent years in the field of polyhydride superconductors, the realization of high critical temperature superconductivity still relies on formidable high pressures. Searching for superconducting hydrides at lower pressures is of particular importance. Here we report the first experimental synthesis of the Mg2RhH6, which achieves superconductivity under a significantly reduced pressure of 30 GPa. The synthesis of Mg2RhH6 proceeds via a two step process (1) preparation of the Mg2RhH5 precursor containing hydrogen atoms stabilized by covalent bonds, followed by (2) hydrogen supplementation resulting in the filling of electrons into anti bonding orbitals above 30 GPa, which was accompanied by the structural transition from RhH5 square pyramid to RhH6 octahedron. Superconductivity is achieved at 30 GPa with a Tc of 24 K, which is further enhanced to 29 K at 53 GPa, evidenced by a sharp drop of resistivity to zero and characteristic suppression of Tc under applied magnetic fields. Our experiments prove the Mg2RhH6 superconductor to be thermodynamically stable above 30 GPa, making it the first case exhibiting a Tc of approximately 30 K at a readily accessible pressure. This study pioneers a highly promising pathway for the rational design and discovery of high temperature superconductors within phonon mediated BCS framework. △ Less

Submitted 19 August, 2026; v1 submitted 16 August, 2026; originally announced August 2026.

Comments: 25 pages, 4 figures

MSC Class: 82C27; 82D40; 82D55

Degradation of Proton Exchange Membrane Water Electrolyzers in Accelerated Stress Tests of Dynamic Load Cycling

Authors: Yunyi Zhang, Qingbo Gao, Jiawei Yang, Zhen Zeng, Rui Chen, Tianyou Wang, Zhizhao Che

Abstract: Proton exchange membrane water electrolysis (PEMWE) is a promising technology for harnessing intermittent renewable energy. This study experimentally investigates the degradation of PEMWE under fluctuating power supply, characterized by dynamic load cycling under accelerated stress test (AST). We focus on the effects of key parameters of dynamic loading, including the peak voltage and cycling freq… ▽ More Proton exchange membrane water electrolysis (PEMWE) is a promising technology for harnessing intermittent renewable energy. This study experimentally investigates the degradation of PEMWE under fluctuating power supply, characterized by dynamic load cycling under accelerated stress test (AST). We focus on the effects of key parameters of dynamic loading, including the peak voltage and cycling frequency, in a set of simplified AST protocols designed to represent selected features of dynamic load fluctuations associated with variable renewable-energy operation. The results unveil the intricate relationship between the structural characteristics of the catalyst layer and the electrochemical performance. An elevated peak voltage accelerates the degradation in the initial phase of the AST. However, a low cycling frequency can mitigate the degradation by limiting the rise in various resistance forms, whereas a higher cycling frequency exacerbates the degradation primarily by increasing mass transport resistance, suggesting a frequency-sensitive deterioration of the system's components. △ Less

Submitted 3 August, 2026; originally announced August 2026.

Journal ref: International Journal of Hydrogen Energy, 259 (2026), 156699

Electro-Optic Active Metasurfaces for High-Speed Photonic Applications

Authors: Mingwei Tang, Ruochong Chen, Yue Cao, Chao Meng, Fei Ding, Yadong Deng, Yubing Han, Kai Wei, Sergey I. Bozhevolnyi

Abstract: Metasurfaces are artificially engineered ultrathin nanostructured surfaces, capable of flexibly manipulating light-matter interactions on compact platforms, and thereby of great significance for a wide range of applications within modern optics and photonics, including communications, computing, sensing, and quantum technologies. However, the inherently static nature of conventional metasurfaces s… ▽ More Metasurfaces are artificially engineered ultrathin nanostructured surfaces, capable of flexibly manipulating light-matter interactions on compact platforms, and thereby of great significance for a wide range of applications within modern optics and photonics, including communications, computing, sensing, and quantum technologies. However, the inherently static nature of conventional metasurfaces severely limits their functionalities and thus range of possible applications. Benefiting from integration of the metasurface platform for shaping optical wavefronts with ultrafast electro-optic (EO) materials, active EO metasurfaces have emerged as a frontier research direction targeting advanced photonic devices. This paper systematically reviews the latest progress in this field, featuring a comprehensive comparison of performances and application scenarios of mainstream EO materials such as lithium niobate, barium titanate and organic EO polymers. Modulation mechanisms based on the Pockels and Kerr effects along with the corresponding active metasurface implementations are summarized. Furthermore, improvements in modulation efficiency enabled by advantageously exploiting resonant structural designs and associated phenomena, including Fabry-Perot resonances, Mie resonances, surface plasmon polaritons, quasi-bound states in the continuum, surface lattice resonances, and guided-mode resonances, are presented and summerized in detail. Current challenges related to metasurface design, nanofabrication, performance and heterogeneous integration are also discussed. Finally, future research directions are outlined, highlighting interdisciplinary developments, novel material engineering, and AI-assisted design as key pathways to enable practical use of active EO metasurfaces in modern optics and photonics, including quantum information technologies. △ Less

Submitted 1 August, 2026; originally announced August 2026.

Comments: 61 pages, 9 figures

A Single-Trace Surface Integral Equation Solver for Simulation of Open Bianisotropic Metasurfaces Described by Generalized Sheet Transition Conditions

Authors: Sebastian Celis Sierra, Junze Shao, Ran Zhao, Rui Chen, Partha Mondal, Hakan Bagci

Abstract: A single-trace surface integral equation (SIE) solver incorporating generalized sheet transition conditions (GSTCs) is presented for the simulation of three-dimensional (3D) open bianisotropic metasurfaces. The metasurface is modeled as an infinitesimally thin, non-enclosing sheet across which the GSTCs enforce the electromagnetic field discontinuities through four surface susceptibility tensors.… ▽ More A single-trace surface integral equation (SIE) solver incorporating generalized sheet transition conditions (GSTCs) is presented for the simulation of three-dimensional (3D) open bianisotropic metasurfaces. The metasurface is modeled as an infinitesimally thin, non-enclosing sheet across which the GSTCs enforce the electromagnetic field discontinuities through four surface susceptibility tensors. The proposed solver uses a single set of equivalent surface currents on the sheet, in place of the two sets used by prior multi-trace formulations. The scattered fields on both faces of the sheet, expressed through SIE operators acting on these currents, are substituted into the GSTCs. The resulting system of equations is then discretized using Rao--Wilton--Glisson basis functions. This solver models an open metasurface directly, without an artificial closure, and applies to both planar and curved geometries. It is validated against analytical solutions for polarization rotation and perfect reflection, and is used to model a realistic broadband absorber whose susceptibility tensors are retrieved from full-wave simulation data. A direct comparison shows that the single-trace formulation attains lower error than a multi-trace formulation while using significantly fewer unknowns. △ Less

Submitted 22 July, 2026; originally announced July 2026.

High-accuracy ultrasonic positioning of calibration sources in the Jiangmen Underground Neutrino Observatory

Authors: Ziqian Xiang, Rongcheng Chen, Zhangmin Chen, Qian Chen, Diwash Ghimire, Jiaqi Hui, Junting Huang, Junjie Jiang, Daijin Li, Haojing Lai, Kai Luo, Rui Li, Yilin Liao, Jianglai Liu, Yue Meng, Yazhen Shi, Duo Teng, Linwei Tao, Qi Wang, Changsheng Ye, Guolei Zhu, Ping Zhang, Tao Zhang

Abstract: Precise source positioning is essential for detector calibration in large liquid scintillator detectors such as JUNO, particularly in regions where purely mechanical control is insufficient. An ultrasonic positioning system has been developed to reconstruct the three-dimensional coordinates of a calibration source without interfering with photon collection or contaminating the liquid scintillator.… ▽ More Precise source positioning is essential for detector calibration in large liquid scintillator detectors such as JUNO, particularly in regions where purely mechanical control is insufficient. An ultrasonic positioning system has been developed to reconstruct the three-dimensional coordinates of a calibration source without interfering with photon collection or contaminating the liquid scintillator. The method combines a sound-speed modeling based on dedicated laboratory measurements and in-detector temperature profiles, waveform-based arrival-time reconstruction, and an in-situ calibration of the effective receiver geometry using central-axis deployments. With six active receivers, central-axis positioning yields a mean error of 1.23 cm relative to the known deployment reference. For off-axis operation in the Cable Loop System calibration plane, a detector-realistic simulation that includes timing resolution, sound-speed variation, and receiver-coordinate smearing predicts a positioning uncertainty of 2.40 cm. These results demonstrate that ultrasonic positioning can provide centimetre-level source accuracy for large liquid scintillator detectors and can support off-axis calibration in JUNO-like experiments. △ Less

Submitted 22 July, 2026; originally announced July 2026.

Comments: 19 pages, 15 figures

A CubeSat Electronics System for Dual-Satellite Coordinated Soft X-Ray Polarimetry

Authors: Lirong Xie, Shiqiang Zhou, Kai Chen, Zuke Feng, Difan Yi, Ran Chen, Ruinan Fan, Cheng Lian, Ziyi Zhang, Siying Liu, Dong Wang, Xiangming Sun, Enwei Liang, Huanbo Feng, Hongbang Liu

Abstract: Addressing the unique requirements for a wide field of view and rapid response in soft X-ray polarization measurements of transient sources such as gamma-ray bursts, this paper proposes the design of a high-reliability CubeSat payload electronics system for dual-satellite cooperative observation. The system employs a Gas Microchannel Pixel Detector (GMPD) and a Topmetal-L sensor as its core compon… ▽ More Addressing the unique requirements for a wide field of view and rapid response in soft X-ray polarization measurements of transient sources such as gamma-ray bursts, this paper proposes the design of a high-reliability CubeSat payload electronics system for dual-satellite cooperative observation. The system employs a Gas Microchannel Pixel Detector (GMPD) and a Topmetal-L sensor as its core components, forming a highly integrated, low-noise payload hardware platform. It achieves a field of view of 90$^\circ$ $\times$ 90$^\circ$, a sensitive area of 3.69 cm$^{2}$, and a power consumption of less than 6 W, operating within an energy range of 2--10 keV. The system incorporates autonomous high-voltage (HV) ramp-up/ramp-down control and a dual-protection mechanism based on count rate and discharge events, enabling in-orbit responses to risks such as the South Atlantic Anomaly and solar particle events. It also supports single-event upset detection and recovery, as well as in-orbit firmware upgrades. The communication interface adopts a redundant primary/backup Controller Area Network (CAN) bus design, with measured channel switching times of less than 100 ms, meeting the demands for real-time command interaction in dual-satellite coordination. By utilizing a large-array pixel sensor with region-of-interest readout and integrating an in-orbit track compression algorithm, the system significantly reduces data storage and downlink transmission resource burdens. Ground tests demonstrate an equivalent noise charge of 22.35 e$^{-}$, HV monitoring linearity better than $\pm$0.5\%, and an output range extending to -5 kV. Thermal vacuum cycling tests show no performance degradation after five cycles between -5 $^\circ$C and 40 $^\circ$C. This work demonstrates the system's capability for autonomous observation, intelligent coordination, and reliable operation in complex space environments. △ Less

Submitted 6 July, 2026; originally announced July 2026.

An ultralow-loss integrated photonic platform for discrete-variable quantum information processing

Authors: Ruiyang Chen, Zeying Zhong, Sanli Huang, Sicheng Zeng, Zhenyuan Shang, Yue Hu, Zhen Chen, Yuan Chen, Shuyi Li, Xue Bai, Yi-Han Luo, Junqiu Liu

Abstract: Photonic integrated circuits offer a scalable and robust route toward quantum information technologies by consolidating photon sources and linear optical networks onto compact, wafer-manufacturable chips. Although silicon photonics has enabled diverse discrete-variable quantum breakthroughs -- spanning multiphoton entanglement, quantum networking, and photonic qubit fusion for quantum computing --… ▽ More Photonic integrated circuits offer a scalable and robust route toward quantum information technologies by consolidating photon sources and linear optical networks onto compact, wafer-manufacturable chips. Although silicon photonics has enabled diverse discrete-variable quantum breakthroughs -- spanning multiphoton entanglement, quantum networking, and photonic qubit fusion for quantum computing -- scaling these platforms beyond proof-of-principle demonstrations remains severely constrained by a critical system-level bottleneck. Optical loss compounds rapidly across photon generation, routing, and state analysis, causing multiphoton generation probabilities to plummet exponentially as circuit depth and complexity grow. Here we overcome this rate-loss barrier by demonstrating a monolithic, ultralow-loss silicon nitride (Si$_3$N$_4$) integrated photonic platform engineered for high-performance discrete-variable quantum information processing. Our architecture seamlessly integrates narrowband photon-pair sources with low-loss qubit-fusion circuits and reconfigurable state-analysis interferometers. The on-chip sources prepare Einstein-Podolsky-Rosen (EPR) states with a fidelity of 0.9875(3) and exhibit near-unity photon indistinguishability, yielding a heralded Hong-Ou-Mandel interference visibility of 0.990(6). By executing on-chip fusion of two EPR states, we synthesize and characterize four-photon Greenberger-Horne-Zeilinger states with a record fidelity of 0.943(8) and a fourfold count rate of 27 Hz -- more than two orders of magnitude higher than previous silicon-photonic implementations. Combined with standard CMOS-compatible fabrication on 150-mm-diameter wafers, these results establish ultralow-loss Si$_3$N$_4$ integrated photonics as a definitive, manufacturable platform for deployable, large-scale quantum information processors. △ Less

Submitted 20 July, 2026; v1 submitted 25 June, 2026; originally announced June 2026.

Calibration and Performance of Germanium High Voltage Detectors for SuperCDMS SNOLAB

Authors: M. F. Albakry, I. Alkhatib, D. Alonso-González, J. Anczarski, T. Aralis, T. Aramaki, A. Ashtari Esfahani, I. Ataee Langroudy, R. Bhattacharyya, A. J. Biffl, P. L. Brink, M. Buchanan, R. Bunker, B. Cabrera, R. Calkins, R. A. Cameron, P. Camus, C. Cartaro, D. G. Cerdeño, Y. -Y. Chang, M. Chaudhuri, J. -H. Chen, R. Chen, J. Cooley, J. Corbett , et al. (118 additional authors not shown)

Abstract: As SuperCDMS SNOLAB is getting ready to search for low mass dark matter particles, using cryogenic Ge and Si detectors, a set of six of the new SuperCDMS High Voltage (HV) detectors (four Ge and two Si) were tested in the Cryogenic Underground TEst facility (CUTE) at SNOLAB. This provided the first opportunity to gain experience with this new detector type and assess their performance thoroughly u… ▽ More As SuperCDMS SNOLAB is getting ready to search for low mass dark matter particles, using cryogenic Ge and Si detectors, a set of six of the new SuperCDMS High Voltage (HV) detectors (four Ge and two Si) were tested in the Cryogenic Underground TEst facility (CUTE) at SNOLAB. This provided the first opportunity to gain experience with this new detector type and assess their performance thoroughly under low background conditions. Here we describe the SuperCDMS HV detector concept and discuss some of the newly developed analysis methods and approaches. Focusing on the Ge detectors, we investigate the detector performance under voltage bias (up to 90 V), exercise the low energy (keV to sub-keV range) calibration based on the electron capture peaks generated by the decay of $^{71}$Ge, assess the detector resolution, and demonstrate the unexpected (and encouraging) ability of these detectors to also measure high energy interactions in the hundreds of keV range with good resolution (better than 3% at 356 keV). △ Less

Submitted 24 June, 2026; originally announced June 2026.

Comments: 16 pages, 12 figures, submitted to Astroparticle Physics Journal

Doppler-enhanced superheterodyne Rydberg microwave receiver

Authors: Yuwen Yin, Ruimin Chen, Shibing Ji, Jinlian Hu, Shaofeng Wang, Yunhui He, Jingxu Bai, Xiao-Qiang Shao, Yuechun Jiao, Jianming Zhao

Abstract: We report the enhanced sensitivity of the Rydberg microwave (MW) receiver by exploiting the Doppler effect in a vapor cell. A two-photon Rydberg ladder scheme is implemented via the co-propagation of probe and coupling lasers, which enhances the Doppler effect. When an MW field is applied, microwave dressing modifies the velocity-dependent resonance condition, enabling stronger contributions from… ▽ More We report the enhanced sensitivity of the Rydberg microwave (MW) receiver by exploiting the Doppler effect in a vapor cell. A two-photon Rydberg ladder scheme is implemented via the co-propagation of probe and coupling lasers, which enhances the Doppler effect. When an MW field is applied, microwave dressing modifies the velocity-dependent resonance condition, enabling stronger contributions from atoms with non-zero velocities and leading to an enhancement of the EIT transmission. Based on this mechanism, we achieve a sensitivity of $35.1\ \mathrm{nV\ cm^{-1}\ Hz^{-1/2}}$ using the heterodyne technique, which is 1.5 times better than that obtained in the counter-propagating configuration. Meanwhile, the required local oscillator (LO) field is reduced by a factor of 17.6 compared with the counter-propagating configuration, which is advantageous for applications requiring minimal radiation and low power consumption. Moreover, the co-propagating configuration is more amenable to integration or portable sensing platforms because multiple laser fields can be delivered through a single optical fiber. △ Less

Submitted 23 June, 2026; originally announced June 2026.

Comments: 6pages, 3 figures

Report number: Appl. Phys. Lett. 129, 014003 (2026)

Journal ref: Applied Physics Letters, 2026

PiMiX 2.0: AI-enhanced Data Fusion for Radiographic Imaging and Tomography

Authors: Zhehui Wang, Shanny Lin, Nicholas Amano, Susan S. Glenn, Ramya Gurunathan, Katie Liu, Nathan E. Peterson, Michelle A. Espy, Adam Thompson, Amy J. Clarke, Ray T. Chen

Abstract: Extending earlier work in Physics-informed Meta-instrument for eXperiments (PiMiX) [1], PiMiX~2.0 is an artificial-intelligence (AI)-enhanced data-fusion and analysis framework that integrates multi-experiment multi-modal radiographic imaging and tomography (RadIT) with physics-informed reasoning and agentic AI workflows. The framework supports automated data ingestion, multimodal image processing… ▽ More Extending earlier work in Physics-informed Meta-instrument for eXperiments (PiMiX) [1], PiMiX~2.0 is an artificial-intelligence (AI)-enhanced data-fusion and analysis framework that integrates multi-experiment multi-modal radiographic imaging and tomography (RadIT) with physics-informed reasoning and agentic AI workflows. The framework supports automated data ingestion, multimodal image processing from one or more experiments, three-dimensional (3D) and time-resolved three-dimensional (4D) reconstruction, and physics-aware interpretation of experimental observations. The PiMiX agents are designed for deployment on desktop and laptop systems commonly used in experimental workflows, while remaining scalable to high-performance computing environments for computationally intensive tasks. By coupling RadIT instrumentation and measurements with geometry, physics, computation, and statistical inference, PiMiX 2.0 aims to accelerate RadIT data processing, knowledge extraction, improve reproducibility, and enable more integrated analysis and workflows in high-temperature plasmas, nuclear fusion, advanced manufacturing, other static and dynamic experiments. △ Less

Submitted 20 June, 2026; v1 submitted 17 June, 2026; originally announced June 2026.

Comments: 9 pages, 4 figures, 1 table. Work presented in the 26th Topical Conference on High Temperature Plasma Diagnostics Conference, Cambridge, MA, USA (June 7 - 11, 2026)

Report number: Los Alamos National Laboratory Report LA-UR-26-25014

A cryogenic gas target for high-intensity radioactive ion beam production at HIRFL-RIBLL

Authors: Xiao Fang, Shiwei Xu, Longhui Ru, Ruiqi Chen, Bingshui Gao, Song Guo, Jun Hu, Huiming Jia, Xinyue Li, Chengjian Lin, Enqiang Liu, Chengui Lu, Junbing Ma, Jun Su, Xiaodong Tang, Jiansong Wang, Shengquan Yan, Lei Yang, Ruojun Yang, Yanyun Yang, Gaolong Zhang, Liyong Zhang, Ningtao Zhang, Zhichao Zhang

Abstract: A liquid-nitrogen-cooled cryogenic gas target system has been developed and installed for radioactive ion beam (RIB) production at the Radioactive Ion Beam Line in Lanzhou (RIBLL). Light-element gases ($\mathrm{H}_2$, $\mathrm{D}_2$, and $^4\mathrm{He}$) filled in the target cell were cooled to cryogenic temperatures, with the gas-cell outlet temperature typically monitored at 82--86 K during beam… ▽ More A liquid-nitrogen-cooled cryogenic gas target system has been developed and installed for radioactive ion beam (RIB) production at the Radioactive Ion Beam Line in Lanzhou (RIBLL). Light-element gases ($\mathrm{H}_2$, $\mathrm{D}_2$, and $^4\mathrm{He}$) filled in the target cell were cooled to cryogenic temperatures, with the gas-cell outlet temperature typically monitored at 82--86 K during beam irradiation and operating pressures up to 1000 mbar. The system was used to produce $^{7}\mathrm{Be}$, $^{16}\mathrm{N}$, and $^{15}\mathrm{O}$ RIBs via the $^{1}\mathrm{H}(^{7}\mathrm{Li}, ^{7}\mathrm{Be})n$, $^{2}\mathrm{H}(^{15}\mathrm{N}, ^{16}\mathrm{N})p$, and $^{1}\mathrm{H}(^{15}\mathrm{N}, ^{15}\mathrm{O})n$ inverse kinematics reactions, yielding purities of 85\%, 99\%, and 95\%, with intensities of $1.02\times10^{6}$, $2.7\times10^{5}$, and $1.0\times10^{5}$ pps, respectively. A $^{93m}\mathrm{Mo}$ isomer beam was also produced via the $\mathrm{^4He(^{94}Zr,} 5n)^{93m}\mathrm{Mo}$ reaction, achieving an intensity of $5.38\times10^{3}$ pps and a purity of 20\% (which can be further improved to $\sim$50\% with offline time-of-flight gating). By delivering a broader range of high-intensity secondary RIBs, this setup establishes a robust platform at RIBLL for low- and medium-energy nuclear astrophysics and reaction studies. △ Less

Submitted 28 June, 2026; v1 submitted 2 June, 2026; originally announced June 2026.

Transformer-based Neural Operators for 3D Wind Field Prediction over Complex Mountainous Terrain

Authors: Yujia Zhang, Jiaxi Qi, Ruiyan Chen, Yong Liu, Yuzhou Zhang, Lyulin Kuang, Rita Zhang, Shengze Cai

Abstract: Accurate prediction of three-dimensional (3D) wind fields over complex mountainous terrain is essential for renewable energy deployment and regional weather modeling. Traditional computational fluid dynamics (CFD) simulations face two fundamental bottlenecks: expert-intensive mesh generation around irregular topography, and iterative solvers that require hours to days even on high-performance clus… ▽ More Accurate prediction of three-dimensional (3D) wind fields over complex mountainous terrain is essential for renewable energy deployment and regional weather modeling. Traditional computational fluid dynamics (CFD) simulations face two fundamental bottlenecks: expert-intensive mesh generation around irregular topography, and iterative solvers that require hours to days even on high-performance clusters. Recent neural operator approaches accelerate inference, but typically fail to resolve the sharp, localized velocity gradients induced by complex terrain features. Here, we present a transformer-based dual-attention neural-operator framework for 3D wind field prediction over complex mountainous terrain, and validate its effectiveness through two instantiations on representative point-based (mesh-free) and graph-based neural-operator architectures, namely Patch-solver and Patch-GTO. Trained on a large CFD-generated dataset spanning diverse terrain geometries and inflow conditions, the framework enables rapid prediction of steady-state wind field while maintaining competitive accuracy. It also demonstrates robust zero-shot transfer to real-world mountainous sites across several diverse locations, outperforming existing neural operator baselines by 10% in relative error. We further verify that incorporating sparse observational data (1% spatial coverage) reduces prediction error by 16.89% relative to the corresponding model without sparse data input and by 32.75% relative to advanced neural operator baselines on unseen terrains. This framework establishes a generalizable computational paradigm across domains, promising to be a real-time tool for wind resource assessment over complex mountainous terrain and related atmosphere-surface interaction studies. △ Less

Submitted 25 May, 2026; originally announced May 2026.

Comments: 50 pages, 10 figures. Accepted for publication in Communications Physics-Nature

A PEMFC-based combined cooling heating and power (CCHP) system with flexible energy supply: Thermodynamic and economic analyses

Authors: Yuanming Wang, Shaowen Deng, Rui Chen, Zhen Zeng, Tianyou Wang, Zhizhao Che

Abstract: Proton exchange membrane fuel cell (PEMFC) systems offer a key approach to hydrogen utilization, and PEMFC-based combined cooling, heating, and power (CCHP) systems pave the way for an efficient and clean energy supply to buildings. In conventional PEMFC-CCHP systems, the heating/cooling capacity and electrical power output are strongly coupled, making it difficult to meet diverse energy demands.… ▽ More Proton exchange membrane fuel cell (PEMFC) systems offer a key approach to hydrogen utilization, and PEMFC-based combined cooling, heating, and power (CCHP) systems pave the way for an efficient and clean energy supply to buildings. In conventional PEMFC-CCHP systems, the heating/cooling capacity and electrical power output are strongly coupled, making it difficult to meet diverse energy demands. This paper presents a novel energy system that integrates an organic Rankine cycle and an absorption heat pump in a parallel configuration, which enables flexible regulation of electricity-cooling capacities in summer and electricity-heating capacities in winter by adjusting the splitting ratio of the waste heat. The impacts of the splitting ratio and key operating parameters on thermodynamic performance and economic performance are quantitatively evaluated. The results show that the ORC can improve electrical efficiency by 2.19 percentage points in summer and 2.78 percentage points in winter. When the current density is fixed at 0.4 A/cm2 and the splitting ratio increases from 0 to 0.5, the cooling capacity of the system varies from 1294 to 647 W, and the heating capacity varies from 2660 to 1330 W. The economic performance is more sensitive to electricity price and hydrogen price than to other parameters, confirmed by their high sensitivity coefficients for net present value (NPV) and internal rate of return (IRR). This system possesses excellent thermodynamic and economic properties, thereby offering significant potential for reducing building energy consumption and carbon emissions. △ Less

Submitted 23 May, 2026; originally announced May 2026.

Journal ref: Energy Conversion and Management, 351, (2026) 120989

Characterization of Aluminum Microwave SQUID Multiplexers for CE$ν$NS Detection

Authors: James Amidei, Antoine Armatol, Corinne Augier, Louis Bailly-Salins, Guillaume Baulieu, Laurent Bergé, Julien Billard, Juliette Blé, Gaby Brenot, Guillaume Bres, Jean-Louis Bret, Alexandre Broniatowski, Martino Calvo, Antonella Cavanna, Antoine Cazes, Emanuela Celi, David Chaize, Mohammed Chala, Maurice Chapellier, Luke Chaplinsky, Ran Chen, Ion Cojocari, Jules Colas, Laurent Couraud, Elspeth Cudmore , et al. (70 additional authors not shown)

Abstract: We present the design, fabrication, and characterization of an aluminum-based six-channel microwave SQUID multiplexer ($μ$MUX) prototype for transition-edge sensor (TES) readout in the RICOCHET experiment. The device consists of aluminum coplanar-waveguide resonators and RF SQUIDs with Dolan-style Al/AlO$_x$/Al Josephson junctions. By measuring the resonator scattering parameters at a range of pro… ▽ More We present the design, fabrication, and characterization of an aluminum-based six-channel microwave SQUID multiplexer ($μ$MUX) prototype for transition-edge sensor (TES) readout in the RICOCHET experiment. The device consists of aluminum coplanar-waveguide resonators and RF SQUIDs with Dolan-style Al/AlO$_x$/Al Josephson junctions. By measuring the resonator scattering parameters at a range of probe tone frequencies, powers, and flux bias points, we demonstrate agreement between the device response and existing multiplexer models. We also characterize the noise performance in both open-loop and flux-ramping modes. With a high electron mobility transistor (HEMT) amplifier, open-loop measurements yield a flux sensitivity of 1-1.5 $μΦ_0/\sqrt{Hz}$. With flux-ramp modulation, low-frequency 1/f noise is suppressed, and the flux sensitivity is around 3-4 $μΦ_0/\sqrt{Hz}$, corresponding to a current sensitivity of 24-33 $pA/\sqrt{Hz}$ at the input coil. We further demonstrate a reduction in readout noise by incorporating a Josephson traveling-wave parametric amplifier (JTWPA) between the $μ$MUX and the HEMT. This achieves an open-loop flux sensitivity of 0.3-0.6 $μΦ_0/\sqrt{Hz}$ and an effective system noise temperature below 1 K. These results establish aluminum $μ$MUX devices as a viable and extensible readout technology for low-noise cryogenic detector arrays. △ Less

Submitted 22 May, 2026; originally announced May 2026.

General-Purpose Photonic Computing Primitive for Contemporary Artificial Intelligence

Authors: Shupeng Ning, Chenghao Feng, Zhenxiang Xu, Hanqing Zhu, David Z. Pan, Jiaqi Gu, Ray T. Chen

Abstract: Photonic computing offers a promising route to accelerating artificial intelligence (AI) by providing high analog bandwidth, low latency, and low energy consumption. However, existing optical neural networks (ONNs) struggle with substantial hardware overhead and limited support for the dynamic, arbitrary matrix operations essential for modern AI architectures. Here we present the dynamic universal… ▽ More Photonic computing offers a promising route to accelerating artificial intelligence (AI) by providing high analog bandwidth, low latency, and low energy consumption. However, existing optical neural networks (ONNs) struggle with substantial hardware overhead and limited support for the dynamic, arbitrary matrix operations essential for modern AI architectures. Here we present the dynamic universal encoding tensorcore (DUET), a general-purpose photonic computing paradigm based on vectorized operand differential interferometric cells (VODICs). By exploiting inherent structural symmetry, this design provides a full-range linear encoding interface that directly accommodates signed operands. This approach eliminates the sign-based path splitting, nonlinear remapping, and auxiliary preprocessing typically required in conventional ONNs, thereby reducing latency and minimizing hardware and memory overhead. We further implement a hardware-aware training (HAT) strategy to alleviate the impact of on-chip non-idealities and ensure stable inference. DUET is experimentally validated across diverse architectures and application domains, ranging from image classification and medical segmentation to Transformer-based content generation, demonstrating competitive performance. By extending optical computing to universal, full-range operators across diverse model architectures, DUET provides a viable pathway toward general-purpose optical acceleration for contemporary AI workloads. △ Less

Submitted 21 May, 2026; originally announced May 2026.

A Residual-Subspace Constraint Framework for Fourier Ptychographic Microscopy

Authors: Sui-peng Wang, Si-yi Xie, Chang-tao Cai, Zhun Wei, Rui Chen

Abstract: The reconstruction fidelity of computational optical imaging is fundamentally constrained by the model-reality gap, i.e., the inevitable discrepancy between idealized forward models and the physical imaging process. Conventional paradigms attempt to bridge this gap through exhaustive system calibration or explicit parameter estimation, which are often computationally intensive and prone to severe… ▽ More The reconstruction fidelity of computational optical imaging is fundamentally constrained by the model-reality gap, i.e., the inevitable discrepancy between idealized forward models and the physical imaging process. Conventional paradigms attempt to bridge this gap through exhaustive system calibration or explicit parameter estimation, which are often computationally intensive and prone to severe non-convex stagnation. This paper introduces a Residual-Subspace Constraint Framework (RSCF) to achieve robust Fourier ptychographic microscopy. Instead of treating residuals as unstructured errors, RSCF leverages subspace decomposition to decouple low-rank, systematic mismatches from stochastic noise, thereby isolating stable information manifolds that remain invariant to forward-model inaccuracies. By embedding this subspace constraint into the iterative engine, the framework selectively suppresses error-amplifying components, enabling high-fidelity phase and amplitude recovery without explicit hardware calibration. Numerical simulations and experimental validations demonstrate that RSCF yields superior convergence acceleration and artifact suppression under severe optical aberrations and LED misalignment. This information-centric paradigm provides a versatile, model-agnostic strategy to enhance robustness across diverse computational imaging modalities. △ Less

Submitted 21 May, 2026; originally announced May 2026.

Comments: 15 Pages, 5 figures

Quantum compressed sensing

Authors: Jianyong Hu, Wei Li, Shuxiao Wu, Liwen Zhang, Yongchuang Sun, Jiazhao Tian, Guosheng Feng, Zhixing Qiao, Jianqiang Liu, Changgang Yang, Ruiyun Chen, Chengbing Qin, Guofeng Zhang, Liantuan Xiao, Suotang Jia

Abstract: How many measurements are fundamentally required to capture a signal. Shannon's information theory established the bedrock of this question in 1948, the Nyquist Shannon theorem set the first answer, and compressed sensing (CS) rewrote it in 2006 by reducing the required measurement number to M = O(Klog(N/K)) for a K sparse signal. Here, we propose quantum compressed sensing (QCS), a paradigm that… ▽ More How many measurements are fundamentally required to capture a signal. Shannon's information theory established the bedrock of this question in 1948, the Nyquist Shannon theorem set the first answer, and compressed sensing (CS) rewrote it in 2006 by reducing the required measurement number to M = O(Klog(N/K)) for a K sparse signal. Here, we propose quantum compressed sensing (QCS), a paradigm that reframes signal acquisition as a unitary quantum evolution. By encoding high dimensional signal information into a single quantum probe state, then introducing domain-alignment evolution,a physically realizable unitary transformation that maps the sparse basis directly onto the measurement basis. QCS executes the support-set search at the quantum level without consuming measurement trials. The logarithmic penalty vanishes, compressing the required measurement number from the classical bound to M =O(K) and reducing reconstruction from ill posed optimization to linear estimation. We experimentally validate QCS using frequency and time domain sparse signals, confirming that the measurement number scales linearly with sparsity and decouples entirely from the signal dimension. Our work provides a physical pathway toward ultimate information acquisition efficiency, with broad implications for sensing, imaging, and communication. △ Less

Submitted 15 May, 2026; originally announced May 2026.

Effect of startup modes on cold start performance of PEM fuel cells with different cathode flow fields

Authors: Wenzhe Zhang, Xingxiao Tao, Qifeng Li, Kai Sun, Rui Chen, Zhizhao Che, Tianyou Wang

Abstract: Proton Exchange Membrane Fuel Cell (PEMFC) is widely recognized for its cleanliness and high efficiency, but is still facing challenges in cold environments. At low temperatures, the formation of ice and repeated freezing/thawing cycles may cause cell performance reduction and irreversible degradation. The cathode flow field of PEMFCs has a significant effect on the performance. In contrast to the… ▽ More Proton Exchange Membrane Fuel Cell (PEMFC) is widely recognized for its cleanliness and high efficiency, but is still facing challenges in cold environments. At low temperatures, the formation of ice and repeated freezing/thawing cycles may cause cell performance reduction and irreversible degradation. The cathode flow field of PEMFCs has a significant effect on the performance. In contrast to the conventional ``channel-ridge'' flow field, the metal foam has the advantages of excellent pre-distribution of gases and water drainage, which make it a promising candidate for the cold start. This paper examines the cold start of PEMFCs with metal foam flow field (MFFF) and serpentine flow field (SFF), and the influence of constant current mode, constant voltage mode, and ramping current mode is investigated experimentally through performance test and electrochemical characterization. The results show that lowering the voltage and increasing the current can enhance the cold-start performance of fuel cells. The MFFF fuel cell has superior cold start performance compared to the SFF fuel cell under the constant voltage mode of 0.3 V. Furthermore, the variable current mode is developed by considering the distinct properties of heat and water production during various phases, and the results indicate that increasing the current density at the unsaturated stage leads to an elevated rate of heat production and a reduced rate of water production, which can improve the cold start of PEMFCs. △ Less

Submitted 14 May, 2026; originally announced May 2026.

Journal ref: International Journal of Heat and Mass Transfer, (2024) 225, 125418

Deciphering Neural Reparameterized Full-Waveform Inversion with Neural Sensitivity Kernel and Wave Tangent Kernel

Authors: Ruihua Chen, Yisi Luo, Bangyu Wu, Xile Zhao, Deyu Meng

Abstract: Full-waveform inversion (FWI) estimates unknown parameters in the wave equation from limited boundary measurements. Recent advances in neural reparameterized FWI (NeurFWI) demonstrate that representing the parameters using a neural network can reduce the reliance on the high-quality initial model and wavefield data, at the cost of slow high-resolution convergence. However, its underlying theoretic… ▽ More Full-waveform inversion (FWI) estimates unknown parameters in the wave equation from limited boundary measurements. Recent advances in neural reparameterized FWI (NeurFWI) demonstrate that representing the parameters using a neural network can reduce the reliance on the high-quality initial model and wavefield data, at the cost of slow high-resolution convergence. However, its underlying theoretical mechanism remains unclear. In this study, we establish the neural sensitivity kernel (NSK) and the wave tangent kernel (WTK) to analyze their convergence behavior from both model and data domains. These theoretical frameworks show that the neural tangent kernel (NTK) induced by neural representation adaptively modulates the original sensitivity and wave tangent kernels. This modulation leads to several key outcomes, i.e., the spectral filtering effect, the gradient wavenumber modulation, and the wave frequency bias, connecting the convergence behavior of NeurFWI with the eigen-structures of NSK and WTK. Building on these insights, we propose several enhanced NeurFWI methods with tailored eigen-structures in NSK and WTK to improve inversion performances and efficiency. We numerically validate these theoretical claims and the proposed methods in seismic exploration, and firstly extend their application to medical imaging. △ Less

Submitted 14 May, 2026; originally announced May 2026.

Analysis of Electromagnetic Scattering from Semiconductor Nanostructures by Solving Coupled Volume Integral and Two-fluid Hydrodynamic Equations

Authors: Doolos Aibek Uulu, Meruyert Khamitova, Rui Chen, Liang Chen, Ping Li, Hakan Bagci

Abstract: Semiconductor-based plasmonic nanostructures support localized surface plasmon modes in the infrared region. Unlike metallic nanostructures, they support both free electrons and holes, requiring a two-fluid hydrodynamic Drude equation (HDE) to accurately capture spatial dispersion effects and low-frequency acoustic plasmon modes that cannot be described by single-fluid models. In this work, a volu… ▽ More Semiconductor-based plasmonic nanostructures support localized surface plasmon modes in the infrared region. Unlike metallic nanostructures, they support both free electrons and holes, requiring a two-fluid hydrodynamic Drude equation (HDE) to accurately capture spatial dispersion effects and low-frequency acoustic plasmon modes that cannot be described by single-fluid models. In this work, a volume integral equation (VIE)-based solver is proposed for the analysis of electromagnetic scattering from semiconductor nanostructures. The proposed approach couples the VIE, formulated in terms of the electric flux density and the free-electron and hole polarization currents, with the two-fluid HDE. The coupled system is discretized using a tetrahedral mesh and solved efficiently using a two-level iterative solver. In contrast to finite-element-based methods, the proposed VIE-based approach does not require domain-wide meshing and inherently satisfies the radiation condition, thereby eliminating artificial absorbing boundaries. Numerical results for InSb-type semiconductor nanostructures demonstrate the accuracy and efficiency of the proposed VIE-based solver and its ability to capture unique optical phenomena, such as acoustic plasmon resonances and the blueshift of localized surface plasmon resonances, that cannot be described by the single-fluid HDE or classical Drude-based models. △ Less

Submitted 30 April, 2026; originally announced April 2026.

Numerically-Exact Quantum-Simulation Approach for Two-Dimensional Spectroscopy of Open Quantum Systems

Authors: Yi-Xuan Yao, Hao-Yue Zhang, Cheng-Ge Liu, Rong-Hang Chen, Qing Ai, Franco Nori

Abstract: Two-dimensional spectroscopy (2DS) is a powerful ultrafast technique for probing electronic and vibrational dynamics in complex microscopic systems. Extracting detailed information on system dynamics and system-bath interactions from 2DS experiments requires precise theoretical simulations for comparison, which motivates the development of numerically-exact and computationally-efficient simulation… ▽ More Two-dimensional spectroscopy (2DS) is a powerful ultrafast technique for probing electronic and vibrational dynamics in complex microscopic systems. Extracting detailed information on system dynamics and system-bath interactions from 2DS experiments requires precise theoretical simulations for comparison, which motivates the development of numerically-exact and computationally-efficient simulation approaches. Here, we propose a quantum-simulation approach for 2DS based on the bath-engineering technique (BET), which has been successfully employed in quantum simulations of open quantum dynamics. To demonstrate our approach, we first simulate the 2DS of a driven four-level system in chiral enantiodetection, where we also assess the applicability of the center-line slope (CLS) method for extracting time correlation functions (TCFs) from the 2DS. We further apply our approach to the 2DS of ${\rm Rh(CO)_2C_5H_7O_2}$ (RDC) dissolved in chloroform, where the results reproduce the main spectral patterns observed in experiments. Our work provides a numerically-exact and efficient framework for simulating 2DS, and can offer additional insight into the dynamics of open quantum systems. △ Less

Submitted 28 April, 2026; originally announced April 2026.

Journal ref: Phys. Rev. A 113, 043729 Published 27 April, 2026

Quantum Compressed Sensing Enables Image Classification with a Single Photon

Authors: Yanshan Fan, Jianyong Hu, Shuxiao Wu, Zhixing Qiao, Guosheng Feng, Changgang Yang, Jianqiang Liu, Ruiyun Chen, Chengbing Qin, Guofeng Zhang, Liantuan Xiao, Suotang Jia

Abstract: Image classification is a core task of intelligent sensing, conventionally follows a sequential imaging then processing pipeline. However, redundant high-dimensional image reconstruction is inherently inefficient, especially in photon limited scenarios. Here we report a photon level image classification method using quantum compressed sensing, which reformulates the classification task as a sparse… ▽ More Image classification is a core task of intelligent sensing, conventionally follows a sequential imaging then processing pipeline. However, redundant high-dimensional image reconstruction is inherently inefficient, especially in photon limited scenarios. Here we report a photon level image classification method using quantum compressed sensing, which reformulates the classification task as a sparse signal measurement problem directly oriented toward class labels. By exploiting the parallelism of photonic quantum superposition states, a single photon can be encoded the complete spatial information of a high-dimensional image. Through a diffractive deep neural network, we physically construct a dedicated measurement basis aligned with the class space, enabling signal-dependent adaptive compressive measurement. Ideally, our method can extract class information via a single quantum projective measurement, reducing the required number of measurements from the logarithmic scaling O(Klog(N/K)) of classical compressed sensing to the constant-order information-theoretic limit M = K = 1. Experimental results show that a classification accuracy of 69.0% can be achieved by using a single-photon detection event as the decision criterion, while it increases to 95.0% with four-photon detection events. This work demonstrates image classification at the energy efficiency limit and introduces a measurement as decision framework. It provides a foundation for intelligent sensing systems that operate under extreme photon budgets and harsh environments. △ Less

Submitted 28 April, 2026; originally announced April 2026.

Ultrawide-angle diffraction-limited 2D beam steering via hybrid integrated metasurface-photonic circuit

Authors: Zhiping He, Luigi Ranno, Padraic Burns, Fan Yang, Hung-I Lin, Maarten R. A. Peters, Hanyu Zheng, Rui Chen, Yi Ji Tan, Chuanyu Lian, Nathan Dostart, Hyun Jung Kim, Carlos Ríos, Tian Gu, Juejun Hu

Abstract: Two-dimensional (2D) wide field-of-view (FOV) beam steering is a key enabling capability for emerging free-space optical systems, including inter-satellite optical links, airborne LiDAR, point-to-point optical wireless communications, and collaborative robotic platforms. These applications require rapid acquisition and tracking across both azimuth and elevation; architectures that offer wide scann… ▽ More Two-dimensional (2D) wide field-of-view (FOV) beam steering is a key enabling capability for emerging free-space optical systems, including inter-satellite optical links, airborne LiDAR, point-to-point optical wireless communications, and collaborative robotic platforms. These applications require rapid acquisition and tracking across both azimuth and elevation; architectures that offer wide scanning in only one dimension while maintaining limited coverage in the orthogonal direction constrain link availability, coverage uniformity, and system agility. Here, we demonstrate a chip-scale platform for ultrawide-angle, diffraction-limited 2D beam steering based on hybrid integration of a silicon photonic integrated circuit (PIC) and an optical metasurface. A free-form micro-optical reflector efficiently transforms the guided waveguide mode into an expanded free-space beam that illuminates an analytically optimized ultrawide-FOV metasurface. The integrated system achieves a measured FOV exceeding 160° while maintaining diffraction-limited beam quality over a broad angular range at telecom wavelengths. This hybrid PIC-metasurface architecture provides a compact and scalable route to high-quality 2D beam steering and establishes a practical pathway toward integrated optical projectors for space-based optical communications and other applications requiring agile, wide-angle, high-fidelity beam control. △ Less

Submitted 14 April, 2026; originally announced April 2026.

Comments: 17 pages, 6 figures

Harnessing Photonics for Machine Intelligence

Authors: Hanqing Zhu, Shupeng Ning, Hongjian Zhou, Ziang Yin, Ray T. Chen, Jiaqi Gu, David Z. Pan

Abstract: The exponential growth of machine-intelligence workloads is colliding with the power, memory, and interconnect limits of the post-Moore era, motivating compute substrates that scale beyond transistor density alone. Integrated photonics is emerging as a candidate for artificial intelligence (AI) acceleration by exploiting optical bandwidth and parallelism to reshape data movement and computation. T… ▽ More The exponential growth of machine-intelligence workloads is colliding with the power, memory, and interconnect limits of the post-Moore era, motivating compute substrates that scale beyond transistor density alone. Integrated photonics is emerging as a candidate for artificial intelligence (AI) acceleration by exploiting optical bandwidth and parallelism to reshape data movement and computation. This review reframes photonic computing from a circuits-and-systems perspective, moving beyond building-block progress toward cross-layer system analysis and full-stack design automation. We synthesize recent advances through a bottleneck-driven taxonomy that delineates the operating regimes and scaling trends where photonics can deliver end-to-end sustained benefits. A central theme is cross-layer co-design and workload-adaptive programmability to sustain high efficiency and versatility across evolving application domains at scale. We further argue that Electronic-Photonic Design Automation (EPDA) will be pivotal, enabling closed-loop co-optimization across simulation, inverse design, system modeling, and physical implementation. By charting a roadmap from laboratory prototypes to scalable, reproducible electronic-photonic ecosystems, this review aims to guide the CAS community toward an automated, system-centric era of photonic machine intelligence. △ Less

Submitted 12 April, 2026; originally announced April 2026.

Comments: 20 pages

Increased endurance of nonvolatile photonics enabled by nanostructured phase-change materials

Authors: Jayita Dutta, Andrew Tang, Brian Mills, Rui Chen, Arnab Manna, Gokul Nath SJ, Virat Tara, Dennis Callahan, Cosmin Constantin Popescu, Juejun Hu, Arka Majumdar

Abstract: The rapid rise of artificial intelligence, and in-memory computing has reinvigorated research on scalable, energy-efficient, and reconfigurable photonic hardware. Non-volatile phase-change materials (PCMs) are attractive, as they offer large refractive index contrast, wavelength-scale footprints, and zero static power consumption. However, current PCM-based electrically controlled photonic devices… ▽ More The rapid rise of artificial intelligence, and in-memory computing has reinvigorated research on scalable, energy-efficient, and reconfigurable photonic hardware. Non-volatile phase-change materials (PCMs) are attractive, as they offer large refractive index contrast, wavelength-scale footprints, and zero static power consumption. However, current PCM-based electrically controlled photonic devices are plagued by high insertion loss and low endurance. One prevalent hypothesis for these material limitations come from electromagnetic scattering in the interface and large programming volumes, respectively. Here, we validate this hypothesis by showing that nano-structuring of PCM minimizes optical loss and enhances the endurance. By tapering both ends of a wide bandgap PCM Sb2Se3 segment on a silicon waveguide, we suppressed the insertion loss by ~94% (resulting in a loss of ~0.1 dB per π phase shift). Through combining tapering and segmentation, we achieved high optical modulation amplitude (~70%), low loss (~0.5 dB per π phase shift), low-voltage (< 5V) actuation, and record high endurance greater than 100 million cycles. This work showcases the substantial advantage of nanopatterning PCMs to attain low loss and high cyclability. △ Less

Submitted 9 April, 2026; originally announced April 2026.

Dataset Distillation for Machine Learning Force Field in Phase Transition Regime

Authors: Ruiyang Chen, Qingyuan Zhang, Ji Chen

Abstract: Machine learning force field (MLFF) has emerged as a powerful data-driven tool for atomistic simulations, enabling large-scale and complex atomic systems to be simulated with accuracy comparable to \textit{ab initio} methods. However, MLFFs often suffer from low training efficiency in the phase transition regime, where structural fluctuations are significantly elevated. To address this challenge,… ▽ More Machine learning force field (MLFF) has emerged as a powerful data-driven tool for atomistic simulations, enabling large-scale and complex atomic systems to be simulated with accuracy comparable to \textit{ab initio} methods. However, MLFFs often suffer from low training efficiency in the phase transition regime, where structural fluctuations are significantly elevated. To address this challenge, we propose a Central-Peripheral Distillation (CPD) algorithm for training dataset distillation. By strategically integrating representative samples with critical corner cases, the CPD algorithm ensures that the distilled dataset retains maximum structural diversity. We validated the efficacy of the CPD method on the liquid-liquid phase transition of dense hydrogen. Results show that, with the CPD approach, only 200 configurations are sufficient to train a MLFF that can fully reproduce the structural and dynamical properties of liquid hydrogen in the vicinity of its phase transition regime. This work paves the way for high-fidelity labeling of the MLFF training datasets, for instance by adopting high-level \textit{ab initio} calculations beyond the standard density functional theory, thereby enhancing the predictive accuracy of MLFFs. △ Less

Submitted 3 April, 2026; originally announced April 2026.

Effects of gas diffusion layer thickness on PEM fuel cells with composite foam-rib flow fields

Authors: Wei Gao, Qifeng Li, Kai Sun, Rui Chen, Zhizhao Che, Tianyou Wang

Abstract: Gas diffusion layers (GDLs) play a crucial role for the performance of proton exchange membrane fuel cells (PEMFCs). The utilization of composite foam-rib flow fields (CFRFFs) can alter the reactant gas transfer pattern, hence improving the efficiency of under-rib reactant gas transfer and water drainage. The impact of the cathode and anode GDL thicknesses (h_{c,GDL} and h_{a,GDL}) on the performa… ▽ More Gas diffusion layers (GDLs) play a crucial role for the performance of proton exchange membrane fuel cells (PEMFCs). The utilization of composite foam-rib flow fields (CFRFFs) can alter the reactant gas transfer pattern, hence improving the efficiency of under-rib reactant gas transfer and water drainage. The impact of the cathode and anode GDL thicknesses (h_{c,GDL} and h_{a,GDL}) on the performance of CFRFF design is investigated by three-dimensional multiphase non-isothermal numerical simulation in this study. The results indicate that for the conventional rib flow field (CRFF) design, there is an optimal h_{c,GDL} for optimal cell performance, while for the CFRFF design, as h_{c,GDL} becomes thinner, the cell performance increases, and the trend is dominated by the variation of the oxygen concentration. Under a thin GDL, the rib width of the CRFF design should be as small as possible to minimize concentration polarization loss, while the rib width of the CFRFF design can be slightly larger. Furthermore, by decreasing the thickness of h_{a,GDL} in both the CRFF and CFRFF designs, there is an increase in the dissolved water content in the ionomer of the cathode CL and a subsequent decrease in the Ohmic polarization loss. △ Less

Submitted 2 April, 2026; originally announced April 2026.

Journal ref: International Communications in Heat and Mass Transfer. 153 (2024) 107394

The electricity system value of the local acceptance of onshore wind in Europe

Authors: James Price, Guillermo Valenzuela-Venegas, Oskar Vågerö, Marianne Zeyringer, Monika Bucha, Ruihong Chen, Adrienne Etard, Andrea N. Hahmann, Alena Lohrmann, Russell McKenna, Christian Mikovits, Evangelos Panos, Meixi Zhang, Luis Ramirez Camargo

Abstract: The large-scale deployment of wind power is central to Europe`s energy transition but faces challenges due to its social and environmental impacts on communities. Here we assess how the tolerance of local stakeholders to such impacts translates across spatial scales to shape the cost and design of the continent`s net-zero electricity system using a soft-linked modelling framework. We find that low… ▽ More The large-scale deployment of wind power is central to Europe`s energy transition but faces challenges due to its social and environmental impacts on communities. Here we assess how the tolerance of local stakeholders to such impacts translates across spatial scales to shape the cost and design of the continent`s net-zero electricity system using a soft-linked modelling framework. We find that lower impact tolerance can reduce the role of onshore wind in Europe reaching net-zero by up to 84% relative to a future where wind enjoys higher acceptance, with other low carbon sources needing to be scaled up to compensate. This translates into total European electricity system costs increasing by between 2-14% while some countries see costs escalating by 20% or more. Our results show that the local acceptance of onshore wind is a key structural driver of the system and highlight the system value of policies to promote it. △ Less

Submitted 30 March, 2026; originally announced March 2026.

Unveiling the Mechanism of Continuous Representation Full-Waveform Inversion: A Wave Based Neural Tangent Kernel Framework

Authors: Ruihua Chen, Yisi Luo, Bangyu Wu, Deyu Meng

Abstract: Full-waveform inversion (FWI) estimates physical parameters in the wave equation from limited measurements and has been widely applied in geophysical exploration, medical imaging, and non-destructive testing. Conventional FWI methods are limited by their notorious sensitivity to the accuracy of the initial models. Recent progress in continuous representation FWI (CR-FWI) demonstrates that represen… ▽ More Full-waveform inversion (FWI) estimates physical parameters in the wave equation from limited measurements and has been widely applied in geophysical exploration, medical imaging, and non-destructive testing. Conventional FWI methods are limited by their notorious sensitivity to the accuracy of the initial models. Recent progress in continuous representation FWI (CR-FWI) demonstrates that representing parameter models with a coordinate-based neural network, such as implicit neural representation (INR), can mitigate the dependence on initial models. However, its underlying mechanism remains unclear, and INR-based FWI shows slower high-frequency convergence. In this work, we investigate the general CR-FWI framework and develop a unified theoretical understanding by extending the neural tangent kernel (NTK) for FWI to establish a wave-based NTK framework. Unlike standard NTK, our analysis reveals that wave-based NTK is not constant, both at initialization and during training, due to the inherent nonlinearity of FWI. We further show that the eigenvalue decay behavior of the wave-based NTK can explain why CR-FWI alleviates the dependency on initial models and shows slower high-frequency convergence. Building on these insights, we propose several CR-FWI methods with tailored eigenvalue decay properties for FWI, including a novel hybrid representation combining INR and multi-resolution grid (termed IG-FWI) that achieves a more balanced trade-off between robustness and high-frequency convergence rate. Applications in geophysical exploration on Marmousi, 2D SEG/EAGE Salt and Overthrust, 2004 BP model, and the more realistic 2014 Chevron models show the superior performance of our proposed methods compared to conventional FWI and existing INR-based FWI methods. △ Less

Submitted 22 March, 2026; originally announced March 2026.

Comments: Published as a conference paper at ICLR 2026 (poster)

Journal ref: In Proceedings of the 14th International Conference on Learning Representations (ICLR 2026), Rio de Janeiro, Brazil

Efficient photo-Nernst terahertz emission in single heavy-metal films

Authors: Lei Wang, Linxuan Song, Elbert E. M. Chia, Peijie Sun, Jianlin Luo, Rongyan Chen, Yong-Chang Lau, Xinbo Wang

Abstract: State-of-the-art metallic terahertz (THz) emitters rely predominantly on spintronic heterostructures, where heavy metals serve as passive spin-to-charge converters. Here, we demonstrate efficient THz radiation from standalone Pt nanofilms at cryogenic temperatures and under external magnetic fields. The governing mechanism is identified as the ultrafast photo-Nernst effect, wherein a transient the… ▽ More State-of-the-art metallic terahertz (THz) emitters rely predominantly on spintronic heterostructures, where heavy metals serve as passive spin-to-charge converters. Here, we demonstrate efficient THz radiation from standalone Pt nanofilms at cryogenic temperatures and under external magnetic fields. The governing mechanism is identified as the ultrafast photo-Nernst effect, wherein a transient thermal gradient drives a transverse charge current. The THz emission polarity is directly dictated by the sign of the Nernst coefficient, as verified by the phase reversal observed between Pt and W or Ta. Remarkably, both thickness scaling and alloying-induced suppression of thermal conductivity independently amplify the single-layer emission to levels comparable with benchmark spintronic bilayers. These findings redefine the established role of heavy metals from passive spin-sinks to active THz emitters, uncovering a universal emission paradigm applicable across diverse spintronic and quantum materials. △ Less

Submitted 23 March, 2026; originally announced March 2026.

Comments: 7 pages, 4 figures

Systematically Improvable Numerical Atomic Orbital Basis Using Contracted Truncated Spherical Waves

Authors: Yike Huang, Zuxin Jin, Linfeng Zhang, Mohan Chen, Rui Chen, Ling Li

Abstract: To solve the Kohn-Sham equation within the framework of density functional theory, we develop a scheme to construct numerical atomic orbital (NAO) basis sets by contracting truncated spherical waves (TSWs). The contraction minimizes the trace of the kinetic operator in the residual space, generalizing the spillage minimizing scheme [M. Chen et al., J. Phys. Condens. Matter 22, 445501 (2010); P. Li… ▽ More To solve the Kohn-Sham equation within the framework of density functional theory, we develop a scheme to construct numerical atomic orbital (NAO) basis sets by contracting truncated spherical waves (TSWs). The contraction minimizes the trace of the kinetic operator in the residual space, generalizing the spillage minimizing scheme [M. Chen et al., J. Phys. Condens. Matter 22, 445501 (2010); P. Lin et al., Phys. Rev. B 103, 235131 (2021)]. In addition to the systematic improvability inherited from previous schemes, the use of TSW instead of plane waves as the expansion basis bridges reference states and NAOs more effectively, and eliminates spurious interactions between periodic images, thereby enabling better transferability through the inclusion of extensive reference states. Benchmarks demonstrate that the constructed NAO achieves satisfactory precision for various properties of both molecules and bulk systems, including total energy, bond length, atomization energy, lattice constant, cohesive energy, band gap, and energy-level alignment. By incorporating unoccupied states, the improved transferability in describing the conduction band is demonstrated to be effective and substantial. △ Less

Submitted 9 April, 2026; v1 submitted 14 March, 2026; originally announced March 2026.

Deformation mechanisms and compressive response of NbTaTiZr alloy via machine learning potentials

Authors: Hongyang Liu, Bo Chen, Rong Chen, Dongdong Kang, Jiayu Dai

Abstract: Refractory multi-principal element alloys (MPEAs) are key research focus for excellent high-temp properties and engineering potential. Deformation mechanisms/mechanical behaviors of quaternary NbTaTiZr MPEA under high strain rates/extreme temps remain unclear. We built a variable-composition ML potential for NbTaTiZr, combined with MD simulations to study effects of crystal orientation, strain rat… ▽ More Refractory multi-principal element alloys (MPEAs) are key research focus for excellent high-temp properties and engineering potential. Deformation mechanisms/mechanical behaviors of quaternary NbTaTiZr MPEA under high strain rates/extreme temps remain unclear. We built a variable-composition ML potential for NbTaTiZr, combined with MD simulations to study effects of crystal orientation, strain rate, temp, composition on compressive mechanics. NbTaTiZr shows structural/mechanical anisotropy in compression [111] max yield strength, [110] min (prone to twinning), [100] via local disorder/dislocation slip (dominant 1/2<111> dislocations). At 10^10 s^-1, yield strength rises sharply, disordered structures increase; high strain rates suppress dislocations to promote disordering. Retains high strength at 2100 K. Higher Nb/Ta boosts yield strength, Ti/Zr reduce it. Reveals MPEA mechanical anisotropy and strain-rate-dependent disordering, guiding high-performance refractory alloy design. △ Less

Submitted 28 February, 2026; originally announced March 2026.

Comments: 12 figures

Journal ref: Acta Phys. Sin., 2025, 74(19): 196102

General linear correction method for DFT+X energy: application to U-M (M=Al, Ga, In) alloys under high pressure

Authors: X. L. Pan, H. X. Song, Y. Sun, F. C. Wu, H. Wang, Y. F. Wang, Y. Chen, X. R. Chen, Hua Y. Geng

Abstract: DFT+X methods, such as DFT+U and DFT+DMFT, are important supplements to standard density functional theory when strong on-site Coulomb interactions are present. However, the involvement of external parameters in the underlying model Hamiltonian introduces intrinsic ambiguity when comparing the total energies obtained with different model parameters. This renders DFT+X approaches semi-empirical and… ▽ More DFT+X methods, such as DFT+U and DFT+DMFT, are important supplements to standard density functional theory when strong on-site Coulomb interactions are present. However, the involvement of external parameters in the underlying model Hamiltonian introduces intrinsic ambiguity when comparing the total energies obtained with different model parameters. This renders DFT+X approaches semi-empirical and severely hinders their capability to describe phase ordering and phase stability, especially when reliable experimental benchmarks are unavailable, such as under high pressure. In this work, we resolve this longstanding problem by proposing a general linear correction method that eliminates the ambiguous energy contributions introduced by the model Hamiltonian in DFT+X approaches, thereby enabling direct comparison of their energies calculated with different interaction parameters. The method is demonstrated and validated within the framework of DFT+U, an important member of the DFT+X family. It is then applied to important nuclear materials of uranium-based binaries U-M (M=Al, Ga, In) alloys. With this approach, we resolve the long-standing discrepancy between theoretical predictions and experimental observations of phase stability with unprecedented accuracy, and predict several previously unknown stable intermetallic compounds under high pressure. The broad applicability of the method is further confirmed by accurate predictions of formation enthalpies for diverse systems, including Np-Al, U-Si, and Cu-O binaries, the ternary MnSnAu compound, and oxygen adsorption on the Cu(111) surface. This work establishes linear-corrected DFT+U as a fully first-principles approach and validates the linear correction method as a robust and general scheme that can be readily extended to other DFT+X methods. △ Less

Submitted 28 February, 2026; originally announced March 2026.

Comments: 45 pages, 5 figures, with Supplementary Material

Journal ref: Acta Materialia 306, 121935 (2026)

Designing the Haystack: Programmable Chemical Space for Generative Molecular Discovery

Authors: Yuchen Zhu, Donghai Zhao, Yangyang Zhang, Yitong Li, Xiaorui Wang, Shuwang Li, Yue Kong, Beichen Zhang, Ricki Chen, Chang Liu, Xingcai Zhang, Tingjun Hou, Chang-Yu Hsieh

Abstract: Chemical space exploration underlies drug discovery, yet most generative models treat chemical space as a fixed, implicitly learned distribution, focusing on sampling molecules rather than deliberately designing the space itself. We introduce SpaceGFN, a generative framework that elevates chemical space to a programmable computational object: a controllable degree of freedom enabling explicit cons… ▽ More Chemical space exploration underlies drug discovery, yet most generative models treat chemical space as a fixed, implicitly learned distribution, focusing on sampling molecules rather than deliberately designing the space itself. We introduce SpaceGFN, a generative framework that elevates chemical space to a programmable computational object: a controllable degree of freedom enabling explicit construction and adaptive traversal of structured molecular universes. SpaceGFN decouples space definition from exploration. Users specify building blocks and reaction rules to construct chemically and synthetically coherent spaces, while a GFlowNet performs efficient, property-biased sampling within them. In Discovery mode, we demonstrate programmable space design through two strategies. A pseudo-natural product space assembles natural product-like architectures. An evolution-inspired (Evo) space recombines endogenous metabolite fragments via enzyme-consistent transformations, introducing an evolutionary prior into chemical generation. This bias yields favorable shifts in predicted metabolic and toxicological profiles while preserving pharmacological diversity, supported by broad docking enrichment across therapeutic targets. In Editing mode, SpaceGFN enables reaction-consistent lead optimization through a curated toolkit of executable synthetic transformations, allowing local, synthesis-aware modification of existing compounds instead of unrestricted graph mutation. Across 96 drug targets, SpaceGFN achieves strong optimization performance while maintaining structural diversity under synthetic constraints. By integrating programmable chemical universe construction with flow-based exploration and reaction-level editing, SpaceGFN establishes a general paradigm for deliberate navigation of therapeutic chemical space. △ Less

Submitted 28 February, 2026; originally announced March 2026.

Limits and Trade-Offs of Shift-Invariant Meta-Optical Encoders for Image Compression

Authors: Yubo Zhang, Rui Chen, Zhihao Zhou, Arka Majumdar

Abstract: Meta-optical encoders can reduce image data before electronic readout or transmission, but engineered point-spread functions (PSFs) do not automatically outperform conventional imaging. We study scene-agnostic, shift-invariant, linear optical encoders using both a fixed total-variation (TV) reconstruction backend and a learned YOLOv8 detection backend. Under a measurement-budget definition of comp… ▽ More Meta-optical encoders can reduce image data before electronic readout or transmission, but engineered point-spread functions (PSFs) do not automatically outperform conventional imaging. We study scene-agnostic, shift-invariant, linear optical encoders using both a fixed total-variation (TV) reconstruction backend and a learned YOLOv8 detection backend. Under a measurement-budget definition of compression ratio that counts all sensed samples across all channels, we compare lens imaging with spatial binning, positive random multi-channel PSFs, signed random kernels, and orthogonal multi-channel kernels. In the low-noise regime, lens-binning gives the highest reconstruction fidelity and strongest YOLOv8 detection metrics at the same compression ratio. Multi-channel encoders, however, degrade more slowly under measurement noise because the measurements are distributed across complementary channels. These results show that, for scene-agnostic incoherent imaging, engineered convolutional PSFs should be justified primarily by robustness, multiplexing, or downstream system constraints, rather than by an expectation that generic wavefront coding will outperform lens-based binning. △ Less

Submitted 30 July, 2026; v1 submitted 20 February, 2026; originally announced February 2026.

Comments: 4 pages, 4 figures

Correlation between 2D Square Ice and 3D Bulk Ice by Critical Crystallization Pressure

Authors: Zhen Zeng, Kai Sun, Rui Chen, Mengshan Suo, Zhizhao Che, Tianyou Wang

Abstract: Low-dimensional ice trapped in nanocapillaries is a fascinating phenomenon and is ubiquitous in our daily lives. As a decisive factor of the confinement effect, the size of nanocapillary significantly affects the critical crystallization pressure and crystalline structure, especially for multi-layered ices. By choosing square ice as a typical two-dimensional (2D) multi-layered ice pattern and usin… ▽ More Low-dimensional ice trapped in nanocapillaries is a fascinating phenomenon and is ubiquitous in our daily lives. As a decisive factor of the confinement effect, the size of nanocapillary significantly affects the critical crystallization pressure and crystalline structure, especially for multi-layered ices. By choosing square ice as a typical two-dimensional (2D) multi-layered ice pattern and using all-atom molecular dynamics simulations, we further unveil the variation mechanism of critical crystallization pressure with the nanocapillary size. The results show a strong dependence of the critical crystallization pressure on the size of the graphene sheet for monolayer, bilayer, and trilayer square ice. The quasi-macroscopic crystallization pressure, the actual pressure of water molecules, and the freezable region between them are all strongly dependent on the nanocapillary width. As the size of the capillary becomes larger in all three directions, the critical crystallization pressure converges to the true macroscopic crystallization pressure, which is very close to the value of the crystallization pressure for bulk ice. A direct correlation is established between 2D square ice and three-dimensional (3D) bulk ice by the critical crystallization pressure. There is an unfreezable threshold for crystallizing spontaneously in practice when the quasi-macroscopic crystallization pressure is equal to the actual pressure, which can explain the limit of nanocapillary width for multi-layered ice. △ Less

Submitted 1 February, 2026; originally announced February 2026.

Journal ref: J. Phys. Chem. C 2024, 128, 33, 14007-14016

Self-sustained microcomb lasing in an integrated hybrid oscillator

Authors: Bitao Shen, Huajin Chang, Junhao Han, Yimeng Wang, Xuguang Zhang, Haoyu Wang, Zihan Tao, Ruixuan Chen, Yandong He, Haowen Shu, Xingjun Wang

Abstract: Microcavity optical frequency combs (microcombs) are compact, coherent light sources whose chip-scale integrability is poised to drive advances in metrology, communications, and sensing. Among available microcomb generation methods, hybrid cavities uniquely co-locate gain and Kerr dynamics, where the lasing mode directly resonates in the nonlinear microcavity, simultaneously enabling self-sustaine… ▽ More Microcavity optical frequency combs (microcombs) are compact, coherent light sources whose chip-scale integrability is poised to drive advances in metrology, communications, and sensing. Among available microcomb generation methods, hybrid cavities uniquely co-locate gain and Kerr dynamics, where the lasing mode directly resonates in the nonlinear microcavity, simultaneously enabling self-sustained and highly efficient microcomb generation. However, their implementation is often limited by partial integration or the need for external injection, which complicates operation architecture, raises power and hampers system miniaturization. In this work, we present a fully integrated hybrid cavity for self-sustained microcomb generation, relying solely on the co-oscillation of lasing and Kerr nonlinearity without external driving. The system collapses the pump laser, nonlinear resonator and feedback loops into a minimalist on-chip two-element cavity, consisting of a high-Q microresonator with engineered intracavity reflection and a reflective semiconductor optical amplifier (RSOA). The scheme delivers self-starting operation and stable performance without active feedback. The generated coherent microcomb achieves intrinsic linewidths below 1 kHz and integrated linewidths around 100 kHz, with self-sustained operation exceeding 24 hours. This ultra-compact architecture provides a practical path toward scalable, coherent multi-wavelength sources for integrated photonic systems. △ Less

Submitted 15 December, 2025; originally announced December 2025.

A High-Order Discretization Scheme for Surface Integral Equations for Analyzing the Electroencephalography Forward Problem

Authors: Rui Chen, Viviana Giunzioni, Adrien Merlini, Francesco P. Andriulli

Abstract: A Nystrom-based high-order (HO) discretization scheme for surface integral equations (SIEs) for analyzing the electroencephalography (EEG) forward problem is proposed in this work. We use HO surface elements and interpolation functions for the discretization of the interfaces of the head volume and the unknowns on the elements, respectively. The advantage of this work over existing isoparametric H… ▽ More A Nystrom-based high-order (HO) discretization scheme for surface integral equations (SIEs) for analyzing the electroencephalography (EEG) forward problem is proposed in this work. We use HO surface elements and interpolation functions for the discretization of the interfaces of the head volume and the unknowns on the elements, respectively. The advantage of this work over existing isoparametric HO discretization schemes resides in the fact that the interpolation points are different from the mesh nodes, allowing for the flexible manipulation of the order of the basis functions without regenerating the mesh of the interfaces. Moreover, the interpolation points are chosen from the quadrature rules with the same number of points on the elements simplifying the numerical computation of the surface integrals for the far-interaction case. In this contribution, we extend the implementation of the HO discretization scheme to the double-layer and the adjoint double-layer formulations, as well as to the isolated-skull-approach for the double-layer formulation and to the indirect adjoint double-layer formulation, employed to improve the solution accuracy in case of high conductivity contrast models, which requires the development of different techniques for the singularity treatment. Numerical experiments are presented to demonstrate the accuracy, flexibility, and efficiency of the proposed scheme for the four SIEs for analyzing the EEG forward problem. △ Less

Submitted 4 December, 2025; originally announced December 2025.

Comments: 9 pages, 7 figures

Two-phase flow in porous metal foam flow fields of PEM fuel cells

Authors: Xingxiao Tao, Kai Sun, Rui Chen, Mengshan Suo, Huaiyu Liu, Zhizhao Che, Tianyou Wang

Abstract: Porous metal foam (PMF) flow field is a potential option for proton exchange membrane fuel cells (PEMFCs) due to its excellent capabilities in gas distribution and water drainage. However, the gas-liquid two-phase flow in the PMF flow field on the pore scale is still unclear. In this study, we investigate the gas-liquid two-phase flow in the PMF flow field. Film, plug, and ligament flows are found… ▽ More Porous metal foam (PMF) flow field is a potential option for proton exchange membrane fuel cells (PEMFCs) due to its excellent capabilities in gas distribution and water drainage. However, the gas-liquid two-phase flow in the PMF flow field on the pore scale is still unclear. In this study, we investigate the gas-liquid two-phase flow in the PMF flow field. Film, plug, and ligament flows are found in the hydrophilic PMF flow field, while slug and droplet flows are found in the hydrophobic PMF flow field. The results suggest that optimizing the pore size, increasing the metal foam surface hydrophobicity, and optimizing the operating condition are helpful for the water management of the PMF flow field. The frequency analysis of the pressure drop also shows that the dominant frequency can be used as an indicator to analyze the transition between different flow patterns. △ Less

Submitted 1 December, 2025; originally announced December 2025.

Journal ref: Chemical Engineering Science, 282, 2023, 119270

Mass transfer and water management in proton exchange membrane fuel cells with a composite foam-rib flow field

Authors: Wei Gao, Qifeng Li, Kai Sun, Rui Chen, Zhizhao Che, Tianyou Wang

Abstract: Mass transfer capability of reactants and hydrothermal management is important for the performance and durability of proton exchange membrane fuel cells. In the conventional rib flow field, the oxygen transport is affected by the accumulation of under-rib liquid water which causes excessive concentration loss and limits cell performance. To improve the cell performance, a composite foam-rib flow f… ▽ More Mass transfer capability of reactants and hydrothermal management is important for the performance and durability of proton exchange membrane fuel cells. In the conventional rib flow field, the oxygen transport is affected by the accumulation of under-rib liquid water which causes excessive concentration loss and limits cell performance. To improve the cell performance, a composite foam-rib flow field structure is proposed by combining the metal foam flow field and the conventional rib flow field. The proposed design is simulated by using a three-dimensional homogeneous non-isothermal numerical model. The results show that the composite foam-rib flow field, by improving the oxygen transfer and water removal capabilities under the ribs, can improve the oxygen concentration and current density without increasing the pumping power, thus improving the cell performance under different conditions. The key parameters of the composite foam-rib flow field are optimized. With the optimal metal foam filling ratio of 0.75 and porosity of 0.85, the peak power density and the limiting current density for the composite foam-rib flow field are higher than the conventional rib flow field by 5.20% and 22.68%. △ Less

Submitted 1 December, 2025; originally announced December 2025.

Journal ref: International Journal of Heat and Mass Transfer, Volume 216, 1 December 2023, 124595

Diffuse Laser Cooling Based on the $6\mathrm{P}_{3/2}$ Excited State of Rubidium Atoms via 420 nm Blue Light

Authors: Jia Zhang, Xun Gao Zheng Xiao, Xiaolei Guan Ruihang Chen, Mengyuan Han Tiantian Shi, Jingbiao Chen

Abstract: To date, the laser cooling of rubidium atoms has inevitably relied on 780 nm cooling light corresponding to the first excited state $5\mathrm{P}_{3/2}$. Surprisingly, we demonstrate laser cooling directly utilizing 420 nm blue light for active optical clock, which corresponds to the high excited state $6\mathrm{P}_{3/2}$ of Rb atom. Experimentally, we successfully apply the 420 nm diffuse laser co… ▽ More To date, the laser cooling of rubidium atoms has inevitably relied on 780 nm cooling light corresponding to the first excited state $5\mathrm{P}_{3/2}$. Surprisingly, we demonstrate laser cooling directly utilizing 420 nm blue light for active optical clock, which corresponds to the high excited state $6\mathrm{P}_{3/2}$ of Rb atom. Experimentally, we successfully apply the 420 nm diffuse laser cooling technique to prepare a cold $^{87}\mathrm{Rb}$ atomic cloud with a length of up to one meter, and measure the cold-atom absorption spectroscopy. The cold atom number is approximately $4.4\times10^{7}$. We systematically compare the cooling effects of 420 nm and 780 nm diffuse laser cooling, and verify the feasibility of blue light cooling using high excited state. This work directly employs blue light to cool and manipulate ground-state Rb atoms to the 6P excited state, providing a new and efficient approach for the cold-atom active optical clock. It is also expected to open up research directions and application prospects in frontier fields such as Rydberg atoms, ultracold quantum gases Bose-Einstein condensation, quantum information, and so on. △ Less

Submitted 20 November, 2025; originally announced November 2025.

Comments: 11 pages, 7 figures

Quantum Biology, Quantum Simulation and Quantum Coherent Devices

Authors: Rong-Hang Chen, Jing Dong, Wen Yang, Qing Ai, Gui-Lu Long

Abstract: Many living organisms can exploit quantum mechanical effects to gain distinct biological advantages. In plants, photosynthesis uses quantum coherence to achieve near 100% efficiency in energy transfer. With advances in experimental techniques, two-dimensional electronic spectroscopy can reveal dynamic processes such as coherence and coupling within a system, and it plays an important role in study… ▽ More Many living organisms can exploit quantum mechanical effects to gain distinct biological advantages. In plants, photosynthesis uses quantum coherence to achieve near 100% efficiency in energy transfer. With advances in experimental techniques, two-dimensional electronic spectroscopy can reveal dynamic processes such as coherence and coupling within a system, and it plays an important role in studying energy transfer in photosynthesis. On the theory side, methods such as the generalized Bloch-Redfield theory and the hierarchical equations of motion are used to model photosynthetic systems. Quantum simulation, as a high-efficiency and low-complexity approach, has also made progress across various platforms in the study of photosynthesis. In recent years, a series of studies has introduced quantum coherence into artificial systems to enhance energy transfer efficiency, laying the groundwork for the design of coherent devices with efficient energy transport. Birds can use the weak geomagnetic field and spin-dependent chemical reactions to detect direction. Theoretical frameworks for animal navigation include magnetite-based mechanisms, magnetoreceptor genes, and the radical-pair mechanism. Quantum simulations of navigation have also advanced on multiple platforms. Inspired by animal navigation, diverse quantum effects have been applied to improve sensing and to support navigation tasks. This paper presents a comprehensive review of progress on quantum coherence in photosynthesis and avian navigation, along with related theoretical methods, quantum simulation approaches, and research on quantum coherent devices. △ Less

Submitted 18 November, 2025; originally announced November 2025.

Midinfrared Semiconductor Photonics - A Roadmap

Authors: J. R. Meyer, I. Vurgaftman, S. -Q. Yu, R. Q. Yang, A. M. Andrews, G. Strasser, B. Schwarz, M. Razeghi, L. Shterengas, G. Kipshidze, G. Belenky, L. Sterczewski, W. Zhou, S. Lee, M. Pan, R. Szedlak, N. Schäfer, J. Koeth, R. Weih, A. Rogalski, A. Piotrowski, J. Sobieski, P. Leszcz, J. Piotrowski, M. R. Mirzaei , et al. (34 additional authors not shown)

Abstract: Semiconductor photonic devices operating in the midwave infrared (mid-IR, which we roughly define here as wavelengths spanning 3 to 14 microns) uniquely address a wide range of current practical needs. These include chemical sensing, environmental monitoring, industrial process control, medical diagnostics, thermal imaging, LIDAR, free space optical communication, and security monitoring. However,… ▽ More Semiconductor photonic devices operating in the midwave infrared (mid-IR, which we roughly define here as wavelengths spanning 3 to 14 microns) uniquely address a wide range of current practical needs. These include chemical sensing, environmental monitoring, industrial process control, medical diagnostics, thermal imaging, LIDAR, free space optical communication, and security monitoring. However, mid-IR device technologies are currently still works in progress that are generally much less mature than their near infrared and visible counterparts. Not only are most of the relevant materials more difficult to grow and process, but attainment of the desired optical device performance is often fundamentally more challenging. This Roadmap will review the leading applications for mid-IR optoelectronics, summarize the status and deficiencies of current device technologies, and then suggest possible roadmaps for improving and maturing the performance, manufacturability, and cost of each device type so the critical needs that are uniquely addressed by mid-IR photonics can be satisfied. △ Less

Submitted 5 November, 2025; originally announced November 2025.

Comments: Submitted to Advances in Optics and Photonics

2D Addressable Mid-infrared Metasurface Spatial Light Modulator

Authors: Cosmin-Constantin Popescu, Maarten Robbert Anton Peters, Oleg Maksimov, Harish Bhandari, Rashi Sharma, Kathleen Richardson, Arka Majumdar, Hyun Jung Kim, Rui Chen, Khoi Phuong Dao, Luigi Ranno, Brian Mills, Dennis Calahan, Tian Gu, Juejun Hu

Abstract: Active metasurfaces enable dynamic control of light for applications in beam steering, pixelated holography, and adaptive optics, but demonstrations of two-dimensional (2D) electrically addressable arrays have so far been limited. Here we introduce a scalable 2D architecture based on phase-change materials (PCMs) integrated metasurfaces and apply it to realize the first transmissive mid-infrared (… ▽ More Active metasurfaces enable dynamic control of light for applications in beam steering, pixelated holography, and adaptive optics, but demonstrations of two-dimensional (2D) electrically addressable arrays have so far been limited. Here we introduce a scalable 2D architecture based on phase-change materials (PCMs) integrated metasurfaces and apply it to realize the first transmissive mid-infrared (mid-IR) spatial light modulator (SLM). The device is fabricated through standard silicon photonic foundry processing combined with backend-of-line (BEOL) integration and employs multilayer backend metal interconnects to implement a crossbar addressing scheme. Each pixel is integrated with a silicon diode selector to suppress sneak-path currents, a feature essential for scaling to large arrays. The result establishes a foundry-compatible route to high-density, large-area active metasurfaces with independently tunable pixels. △ Less

Submitted 5 November, 2025; originally announced November 2025.

Widely tunable cavity-enhanced backward difference-frequency generation

Authors: Ming-Yuan Gao, Yue-Wei Song, Ren-Hui Chen, Yin-Hai Li, Zhi-Yuan Zhou, Bao-Sen Shi

Abstract: Difference-frequency generation (DFG) is a powerful technique for generating widely tunable infrared radiation. However, conventional phase-matching schemes may require tuning multiple parameters-such as the wavelengths, crystal temperature, crystal angle, and poling period-to achieve wide tunability, which increases the complexity of practical operation. In this work, we employ a backward quasi-p… ▽ More Difference-frequency generation (DFG) is a powerful technique for generating widely tunable infrared radiation. However, conventional phase-matching schemes may require tuning multiple parameters-such as the wavelengths, crystal temperature, crystal angle, and poling period-to achieve wide tunability, which increases the complexity of practical operation. In this work, we employ a backward quasi-phase-matching scheme with distinctive tuning characteristics and demonstrate pump-enhanced continuous-wave DFG output tunable from 1751 nm to 2451 nm (700 nm range) in a bulk crystal. The tuning is achieved solely by varying the pump wavelength and the signal wavelength (less than 5 nm), enabling continuous, rapid, and room-temperature operation. The tuning characteristics, power-scaling behavior, and output stability are experimentally verified with the idler wavelength set at 2000 nm. The approach offers a new paradigm for widely tunable infrared radiation generation and holds promise for applications in spectroscopy and biomedical sensing. △ Less

Submitted 17 October, 2025; originally announced October 2025.

Enhancing Diffusion-Based Sampling with Molecular Collective Variables

Authors: Juno Nam, Bálint Máté, Artur P. Toshev, Manasa Kaniselvan, Rafael Gómez-Bombarelli, Ricky T. Q. Chen, Brandon Wood, Guan-Horng Liu, Benjamin Kurt Miller

Abstract: Diffusion-based samplers learn to sample complex, high-dimensional distributions using energies or log densities alone, without training data. Yet, they remain impractical for molecular sampling because they are often slower than molecular dynamics and miss thermodynamically relevant modes. Inspired by enhanced sampling, we encourage exploration by introducing a sequential bias along bespoke, info… ▽ More Diffusion-based samplers learn to sample complex, high-dimensional distributions using energies or log densities alone, without training data. Yet, they remain impractical for molecular sampling because they are often slower than molecular dynamics and miss thermodynamically relevant modes. Inspired by enhanced sampling, we encourage exploration by introducing a sequential bias along bespoke, information-rich, low-dimensional projections of atomic coordinates known as collective variables (CVs). We introduce a repulsive potential centered on the CVs from recent samples, which pushes future samples towards novel CV regions and effectively increases the temperature in the projected space. Our resulting method improves efficiency, mode discovery, enables the estimation of free energy differences, and retains independent sampling from the approximate Boltzmann distribution via reweighting by the bias. On standard peptide conformational sampling benchmarks, the method recovers diverse conformational states and accurate free energy profiles. We are the first to demonstrate reactive sampling using a diffusion-based sampler, capturing bond breaking and formation with universal interatomic potentials at near-first-principles accuracy. The approach resolves reactive energy landscapes at a fraction of the wall-clock time of standard sampling methods, advancing diffusion-based sampling towards practical use in molecular sciences. △ Less

Submitted 30 December, 2025; v1 submitted 13 October, 2025; originally announced October 2025.

Neuromorphic heat transport effects in a molecular junction

Authors: Renai Chen, Galen T. Craven

Abstract: Understanding energy transport at the nanoscale is an open and fundamental challenge in the molecular sciences with direct implications for the design of new electronics, computing devices, and materials. While nanoscale energy transport under steady-state conditions has been studied extensively, there is much less known about energy transport under time-dependent driving forces, particularly in t… ▽ More Understanding energy transport at the nanoscale is an open and fundamental challenge in the molecular sciences with direct implications for the design of new electronics, computing devices, and materials. While nanoscale energy transport under steady-state conditions has been studied extensively, there is much less known about energy transport under time-dependent driving forces, particularly in the far-from-equilibrium regime. In this work, we use nonequilibrium molecular dynamics simulations and stochastic thermodynamics to investigate energy transport in a well-studied nanoscale system, a molecular junction, subjected to a time-periodic temperature gradient. The primary observation is that molecular junctions can exhibit heat transport hysteresis, a phenomenon in which the heat flux through a system depends not only on the instantaneous value of a time-dependent temperature bias but also on the temporal history of that bias. The presented findings illustrate that molecular junctions can exhibit the specific memory effect, heat transport hysteresis, that is essential for the design of thermal neuromorphic computers. This work elucidates a potential pathway toward the realization of such devices. △ Less

Submitted 13 October, 2025; originally announced October 2025.

Journal ref: J. Chem. Phys. 163, 054115 (2025)

Climate-Adaptive and Cascade-Constrained Machine Learning Prediction for Sea Surface Height under Greenhouse Warming

Authors: Tianmu Zheng, Ru Chen, Xin Su, Julian Mak, Gang Huang, Bingzheng Yan

Abstract: Machine learning (ML) has achieved remarkable success in climate and marine science. Given that greenhouse warming fundamentally reshapes ocean conditions such as stratification, circulation patterns and eddy activity, evaluating the climate adaptability of the ML models is crucial. While physical constraints have been shown to enhance the performance of ML models, kinetic energy (KE) cascade has… ▽ More Machine learning (ML) has achieved remarkable success in climate and marine science. Given that greenhouse warming fundamentally reshapes ocean conditions such as stratification, circulation patterns and eddy activity, evaluating the climate adaptability of the ML models is crucial. While physical constraints have been shown to enhance the performance of ML models, kinetic energy (KE) cascade has not been used as a constraint despite its importance in regulating multi-scale ocean motions. Here we develop two sea surface height (SSH) prediction models (with and without KE cascade constraint) and quantify their climate adaptability at the Kuroshio Extension. Both models exhibit only slight performance degradation under greenhouse warming conditions. Incorporating the KE cascade as a physical constraint significantly improves the model performance, reducing eddy kinetic energy errors by 14.7% in the present climate and 15.9% under greenhouse warming. Additional validations using satellite observations and in the Gulf Stream region further confirm the robustness of the proposed models. Compared with the KE spectrum constraint, both constraints improve the cross-scale transfer and spectrum of KE, but the KE cascade constraint yields larger improvements in the cross-scale transfer. This work presents the first application of the KE cascade as a physical constraint for ML-based ocean state prediction and demonstrates its robust adaptability across climates, offering guidance for the further development of global ML models for both present and future conditions. △ Less

Submitted 23 January, 2026; v1 submitted 23 September, 2025; originally announced September 2025.

Monitoring Nitric Oxide in Trigeminal Neuralgia Rats with a Cerium Single-Atom Nanozyme Electrochemical Biosensor

Authors: Kangling Tian, Fuhua Li, Ran Chen, Shihong Chen, Wenbin Wei, Yihang Shen, Muzi Xu, Chunxian Guo, Luigi G. Occhipinti, Hong Bin Yang, Fangxin Hu

Abstract: Trigeminal neuralgia (TN) is the most common neuropathic disorder; however, its pathogenesis remains unclear. A prevailing theory suggests that nitric oxide (NO) may induce nerve compression and irritation via vascular dilation, thereby being responsible for the condition, making real-time detection of generated NO critical. However, traditional evaluations of NO rely on indirect colorimetric or c… ▽ More Trigeminal neuralgia (TN) is the most common neuropathic disorder; however, its pathogenesis remains unclear. A prevailing theory suggests that nitric oxide (NO) may induce nerve compression and irritation via vascular dilation, thereby being responsible for the condition, making real-time detection of generated NO critical. However, traditional evaluations of NO rely on indirect colorimetric or chemiluminescence techniques, which offer limited sensitivity and spatial resolution for its real-time assessment in biological environments. Herein, we reported the development of a highly sensitive NO electrochemical biosensor based cerium single-atom nanozyme (Ce1-CN) with ultrawide linear range from 1.08 nM to 143.9 μM, and ultralow detection limit of 0.36 nM, which enables efficient and real-time evaluation of NO in TN rats. In-situ attenuated total reflection surface-enhanced infrared spectroscopy combined with density functional theory calculations revealed the high-performance biosensing mechanism, whereby the Ce centers in Ce1-CN nanoenzymes adsorb NO and subsequently react with OH- to form *HNO2. Results demonstrated that NO concentration was associated with TN onset. Following carbamazepine treatment, NO production from nerves decreased, accompanied by an alleviation of pain. These findings indicate that the biosensor serves as a valuable tool for investigating the pathogenesis of TN and guiding subsequent therapeutic strategies. △ Less

Submitted 22 September, 2025; originally announced September 2025.

Simulation of radiation environment for the beam monitor of CEE experiment

Authors: Qian Wang, Hulin Wang, Chaosong Gao, Jun Liu, Xianglun Wei, Junshuai Liu, Zhen Wang, Ran Chen, Peng Ma, Haibo Yang, Chengxin Zhao, Mingmei Xu, Shusu Shi, Xiangming Sun, Feng Liu

Abstract: The cooling storage ring external-target experiment is a large-scale nuclear physics experiment, which aims to study the physics of heavy-ion collisions at low temperatures and high baryon densities. A beam monitor (BM) is placed in the beam line to monitor the beam status and to improve the reconstruction resolution of the primary vertices. The radiation dose and particle fluence stemming from th… ▽ More The cooling storage ring external-target experiment is a large-scale nuclear physics experiment, which aims to study the physics of heavy-ion collisions at low temperatures and high baryon densities. A beam monitor (BM) is placed in the beam line to monitor the beam status and to improve the reconstruction resolution of the primary vertices. The radiation dose and particle fluence stemming from the beam interactions with gases and detector materials affect the performance of the sensors and electronics of BM. This paper uses FLUKA Monte Carlo code to simulate the radiation environment of BM detector. Radiation quantities including the total ionizing dose, 1 MeV neutron equivalent fluence, high-energy hadron flux, thermal neutron flux, and nuclear fragment flux are presented. Results of alternative simulation setups, including adding shielding layers inside the BM, are also investigated. △ Less

Submitted 14 September, 2025; originally announced September 2025.

Comments: 15 pages, 12 figures

Journal ref: 2025 JINST 20 P09020