Showing 1–50 of 120 results for author: Feng, S

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

Diffusion posterior sampling enables zero shot kilometre scale wind forecasting over complex terrain

Authors: Yujiang Cai, Ya Wang, Shuanglei Feng, Bo Wang, Yiming Liu, Hui Yuan, Xinyi Sun, Haijie Li, Shenming Fu

Abstract: Reliable prediction of near surface wind over complex terrain is limited by the mismatch between the spatial resolution of operational forecasts and terrain controlled local wind variability. Here, we develop KiloGen, a diffusion posterior sampling framework for kilometre scale wind forecast enhancement. KiloGen learns a high resolution vector wind prior from Weather Research and Forecasting (WRF)… ▽ More Reliable prediction of near surface wind over complex terrain is limited by the mismatch between the spatial resolution of operational forecasts and terrain controlled local wind variability. Here, we develop KiloGen, a diffusion posterior sampling framework for kilometre scale wind forecast enhancement. KiloGen learns a high resolution vector wind prior from Weather Research and Forecasting (WRF) model simulations and constrains posterior sampling with 25 km forecasts from the European Centre for Medium Range Weather Forecasts (ECMWF) at inference time. This formulation avoids paired ECMWF and WRF training samples and an explicitly learned mapping from coarse to fine resolution. Applied over Shanxi, China, a region with complex mountainous terrain, KiloGen reconstructs terrain organized wind structures and restores high wavenumber variability while retaining the large scale evolution of the operational forecast. Station verification shows that KiloGen achieves the lowest overall wind speed root mean square error (RMSE) among the evaluated products, with larger benefits at elevated and topographically complex sites. The improvement is strongest under strong wind conditions, reducing RMSE by approximately 10% for observed winds above 20 m s^-1. Across 13 distinct strong wind events, KiloGen improves upon the 0.25 degree ECMWF forecast in all cases and outperforms the 0.1 degree ECMWF forecast in most cases. These results show that diffusion posterior sampling provides an effective approach for terrain aware kilometre scale wind forecast enhancement. △ Less

Submitted 23 July, 2026; originally announced July 2026.

Comments: Main manuscript with 7 figures and Supplementary Information with 5 supplementary figures

Final assessment of radioactive impurities in the JUNO detector

Authors: Thomas Adam, Fengpeng An, Costas Andreopoulos, Giuseppe Andronico, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, João Pedro Athayde Marcondes de André, Didier Auguste, Nikita Balashov, Andrea Barresi, Davide Basilico, Eric Baussan, Marco Beretta, Antonio Bergnoli, Nikita Bessonov, Daniel Bick, Lukas Bieger, Svetlana Biktemerova, Thilo Birkenfeld, Simon Blyth, Manuel Böhles, Anastasia Bolshakova, Mathieu Bongrand, Matteo Borghesi , et al. (549 additional authors not shown)

Abstract: The Jiangmen Underground Neutrino Observatory (JUNO) collaboration has completed the construction of the 20,000-ton liquid scintillator detector and the associated muon veto detector system. To meet the physics objectives, the materials used in the detector must exhibit low radioactive contamination. The single-event rate in the fiducial volume (R $<$ 17.2 m) of the scintillator is required to be… ▽ More The Jiangmen Underground Neutrino Observatory (JUNO) collaboration has completed the construction of the 20,000-ton liquid scintillator detector and the associated muon veto detector system. To meet the physics objectives, the materials used in the detector must exhibit low radioactive contamination. The single-event rate in the fiducial volume (R $<$ 17.2 m) of the scintillator is required to be approximately 7 Hz for energies above 0.7 MeV, resulting in an accidental coincidence background of about 1 event per day for reactor neutrino physics analyses. Since the beginning of the construction phase, we have screened the natural radioactivity content of thousands of materials, to select those that meet the design background budget. The radioactive impurity concentrations of the materials ultimately used in the JUNO detector are summarized in this paper. The construction of the entire detector and the subsequent filling of the liquid scintillator were completed in August 2025. From the initial data, the total count rate of natural radioactivity within the detector's fiducial volume has met the requirements and is sufficient to support the reactor antineutrino analysis. △ Less

Submitted 19 July, 2026; originally announced July 2026.

Temporal Fourier Optics Reveals Hidden Hybridized Light-Matter States

Authors: Juan-Feng Zhu, Wenjie Zhou, Shihao Feng, Zicheng Song, Ruicong Zhang, Cheng-Wei Qiu, Rengming Liu, Lin Wu

Abstract: Spectral measurements provide fundamental insights into wave systems by revealing resonances, mode hybridization, and light-matter interactions. However, intrinsic dissipation and measurement-related spectral broadening often obscure the spectral signatures of the underlying hybridized light-matter states. Here, we establish a temporal Fourier optics framework based on a space-time Fourier corresp… ▽ More Spectral measurements provide fundamental insights into wave systems by revealing resonances, mode hybridization, and light-matter interactions. However, intrinsic dissipation and measurement-related spectral broadening often obscure the spectral signatures of the underlying hybridized light-matter states. Here, we establish a temporal Fourier optics framework based on a space-time Fourier correspondence, which interprets spectral broadening as the Fourier counterpart of temporal attenuation. This perspective introduces a temporal point-spread function (TPSF) that enables direct, synthesis-free reconstruction of the underlying spectral response from experimentally measured spectra by compensating for effective temporal decay before transformation back to the frequency domain. We experimentally validate the framework using deterministic single-molecule Au nanosphere dimers and open Au@Ag nanorod- and nanotriangle-based plasmonic nanocavities coupled to J-aggregate excitons. Across these distinct platforms, TPSF consistently resolves hidden upper and lower polaritonic branches, revealing hybridized light-matter states and strong coupling that remain inaccessible in conventional scattering spectra. The reconstructed spectra agree closely with the recently developed complex-frequency formalism while providing a substantially simpler and experimentally accessible implementation. More broadly, temporal Fourier optics establishes a general framework for recovering dissipation-obscured spectral information, opening new opportunities for spectroscopy, imaging, sensing, and inverse wave measurements across photonics and wave physics. △ Less

Submitted 22 July, 2026; v1 submitted 15 July, 2026; originally announced July 2026.

Comments: 27 pages, 4 figures

Demonstrating CBM Capabilities by $Λ$ Baryon Reconstruction in Ni+Ni Collisions with the mCBM Experiment at SIS18 of GSI/FAIR

Authors: CBM Collaboration, A. Agarwal, Z. Ahammed, N. Ahmad, L. J. Ahrens, M. Al-Turany, N. Alam, J. An, J. Andary, A. Andronic, H. Appelshäuser, B. Arnoldi-Meadows, B. Artur, M. D. Azmi, M. Balzer, A. Bandyopadhyay, V. A. Bâsceanu, J. Becker, A. Belousov, A. Bercuci, R. Berendes, D. Bertini, O. Bertini, M. Beyer, O. Bezshyyko , et al. (318 additional authors not shown)

Abstract: The Compressed Baryonic Matter (CBM) experiment at the upcoming Facility for Antiproton and Ion Research (FAIR) is a high-rate fixed-target experiment designed to investigate nuclear matter at extreme baryon densities in relativistic nucleus-nucleus collisions. To enable high-statistics measurements of rare probes, CBM is designed to operate at event rates up to 10 MHz. This necessitates the devel… ▽ More The Compressed Baryonic Matter (CBM) experiment at the upcoming Facility for Antiproton and Ion Research (FAIR) is a high-rate fixed-target experiment designed to investigate nuclear matter at extreme baryon densities in relativistic nucleus-nucleus collisions. To enable high-statistics measurements of rare probes, CBM is designed to operate at event rates up to 10 MHz. This necessitates the development of fast and radiation-tolerant detectors, self-triggered front-end electronics, a free-streaming data acquisition architecture, and real-time event reconstruction capabilities. Prototype versions and pre-series productions of the CBM detector systems have been deployed in the mini-CBM demonstrator setup mCBM - an experimental precursor comprising sub-components of all major CBM systems, installed at the SIS18 facility of GSI/FAIR within the FAIR Phase-0 program. In 2024, Ni+Ni collisions at a kinetic beam energy of 1.93 AGeV and an average interaction rate of about 250 kHz were successfully recorded. This dataset enables a detailed evaluation of the operational performance of the detector systems as well as the complete CBM data chain, while the reconstruction of rare $Λ$ baryons serves as a natural benchmark. This paper presents the first results on $Λ$ signal reconstruction with the mCBM experiment, demonstrating the readiness of the detector technologies and the data chain for the upcoming full-scale CBM experiment. △ Less

Submitted 1 June, 2026; originally announced June 2026.

Poincaré Sphere Representation of Spin-Driven Polarization Encoding in Two-Dimensional Perovskite Light Sources

Authors: Zijian Gan, Shuyue Feng, Camryn J. Gloor, Wei You, Andrew M. Moran

Abstract: Nonlinear optical light sources enable the generation of photons with polarization states that are intrinsically determined by underlying material dynamics, rather than imposed through external modulation. Here, we investigate the fundamental quantum communication performance achievable using four-wave-mixing signal fields emitted by a representative two-dimensional perovskite system. The experime… ▽ More Nonlinear optical light sources enable the generation of photons with polarization states that are intrinsically determined by underlying material dynamics, rather than imposed through external modulation. Here, we investigate the fundamental quantum communication performance achievable using four-wave-mixing signal fields emitted by a representative two-dimensional perovskite system. The experimentally reconstructed signal field is represented by Stokes-vector trajectories on the Poincaré sphere to establish a practical framework for visualizing spin-driven polarization encoding. An empirical nonlinear response model further connects the properties of the signal field to microscopic exciton and biexciton electronic structure, revealing that interference between resonantly enhanced optical transitions governs the accessible polarization states. The model additionally predicts that modest stabilization of the lowest-energy biexciton could substantially improve the polarization-encoding performance and provide a route toward materials optimization. More broadly, these results motivate closer integration of nonlinear spectroscopy, semiconductor materials, and quantum information science in the development of novel light sources for quantum communication. △ Less

Submitted 31 July, 2026; v1 submitted 27 May, 2026; originally announced May 2026.

Polariton-mediated binding of anti-aligned dipolar excitons

Authors: Haifeng Kang, Quanbing Guo, Tianyi Zhou, Shun Feng, Wei Dai, Kenji Watanabe, Takashi Taniguchi, Hongxing Xu, Ting Yu, Xiaoze Liu

Abstract: Interacting bosonic quasiparticles are the cornerstone for exploring many-body physics and nonlinear quantum phenomena in correlated light-matter systems. Strongly interacting dipolar excitons in van der Waals heterostructures have attracted significant interest due to their out-of-plane electric dipole moments and high tunability via the quantum-confined Stark effect (QCSE). However, leveraging t… ▽ More Interacting bosonic quasiparticles are the cornerstone for exploring many-body physics and nonlinear quantum phenomena in correlated light-matter systems. Strongly interacting dipolar excitons in van der Waals heterostructures have attracted significant interest due to their out-of-plane electric dipole moments and high tunability via the quantum-confined Stark effect (QCSE). However, leveraging these tunable dipolar excitons in strongly coupled exciton-photon systems to explore exotic many-body physics and macroscopic quantum phenomena remains experimentally elusive. Here, we report the strong coupling of dipolar excitons in a gated bilayer MoS2 device integrated with a one-dimensional photonic crystal hosting bound-states-in-continuum (BIC). The resulting polaritons hybridize cavity photons with a coherent superposition of two electrically tunable anti-aligned dipolar excitons, effectively binding them into composite quasiparticle states. By tuning the dipolar excitons into non-degenerate states via the QCSE, we realize in situ reconfiguration of the polariton wavefunction and observe an emergent polariton branch exhibiting non-monotonic Stark shifts. Notably, these tunable polaritons allow for customized control over nonlinear interactions through distinct excitonic hybridization and dipolar configurations. This in situ tunability offers a scalable pathway toward electrically programmable quantum fluids of light and correlated polariton phases in on-chip photonic integrated circuits. △ Less

Submitted 13 February, 2026; originally announced February 2026.

Post-Disaster Resource Redistribution and Cooperation Evolution Based on Two-Layer Network Evolutionary Games

Authors: Yu Chen, Genjiu Xu, Sinan Feng, Chaoqian Wang

Abstract: In the aftermath of large-scale disasters, the scarcity of resources and the paralysis of infrastructure raise severe challenges to effective post-disaster recovery. Efficient coordination between shelters and victims plays a crucial role in building community resilience, yet the evolution of two-layer behavioral feedback between these two groups through network coupling remains insufficiently und… ▽ More In the aftermath of large-scale disasters, the scarcity of resources and the paralysis of infrastructure raise severe challenges to effective post-disaster recovery. Efficient coordination between shelters and victims plays a crucial role in building community resilience, yet the evolution of two-layer behavioral feedback between these two groups through network coupling remains insufficiently understood. Here, this study develops a two-layer network to capture the cross-layer coupling between shelters and victims. The upper layer uses a post-disaster emergency resource redistribution model within the framework of the public goods game, while the lower layer adopts a cooperative evolutionary game to describe internal victim interactions. Monte Carlo simulations on scale-free networks reveal threshold effects of incentives: moderate public goods enhancement and subsidies promote cooperation, whereas excessive incentives induce free-riding. In contrast, credible and well-executed punishment effectively suppresses defection. Targeted punishment of highly connected shelters significantly enhances cooperation under resource constraints. A comparative analysis using a network generated from the actual coordinates of Beijing shelters confirms the model's generality and practical applicability. The findings highlight the importance of calibrated incentives, enforceable sanctions, and structural targeting in fostering robust cooperation across organizational and individual levels in post-disaster environments. △ Less

Submitted 29 January, 2026; originally announced January 2026.

Comments: 11 pages, 14 figures, accepted for publication in Chaos

Nonlinear Optical Quantum Communication with a Two-Dimensional Perovskite Light Source

Authors: Shuyue Feng, Zijian Gan, Camryn J. Gloor, Wei You, Andrew M. Moran

Abstract: Two-dimensional organic-inorganic hybrid perovskite (2D-OIHP) quantum wells are emerging as promising light sources for quantum communication technologies, owing to their ability to generate polarization-encoded optical signals. In this work, we explore how nonlinear optical phenomena can be exploited for quantum information applications, demonstrating the versatility that arises from resonant cou… ▽ More Two-dimensional organic-inorganic hybrid perovskite (2D-OIHP) quantum wells are emerging as promising light sources for quantum communication technologies, owing to their ability to generate polarization-encoded optical signals. In this work, we explore how nonlinear optical phenomena can be exploited for quantum information applications, demonstrating the versatility that arises from resonant coupling among excited states. By tracking changes in the ellipticities of signal photons on femtosecond timescales in four-wave-mixing experiments, we first establish a method for information encoding based on exciton spin dynamics and biexciton correlations. Using single-photon detection, we then implement a proof-of-principle quantum communication protocol by mapping these polarization states onto binary sequences. While the polarizations of weak coherent pulses are typically manipulated with optical elements in traditional quantum key distribution approaches, the intrinsic electronic structure and spin relaxation processes within the 2D-OIHP system determine the characteristics of the signal photons in our method. As a demonstration, an ASCII message consisting of 56 bits is transmitted through the polarization states of photons emitted by 2D-OIHP quantum wells. These results show that the information transmission efficiency depends strongly on contributions from biexciton states, highlighting the potential of spin-dependent nonlinear optical processes for quantum communication. △ Less

Submitted 9 August, 2026; v1 submitted 26 November, 2025; originally announced November 2025.

Initial performance results of the JUNO detector

Authors: Angel Abusleme, Thomas Adam, Kai Adamowicz, David Adey, Shakeel Ahmad, Rizwan Ahmed, Timo Ahola, Sebastiano Aiello, Fengpeng An, Guangpeng An, Costas Andreopoulos, Giuseppe Andronico, João Pedro Athayde Marcondes de André, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, Burin Asavapibhop, Didier Auguste, Margherita Buizza Avanzini, Andrej Babic, Jingzhi Bai, Weidong Bai, Nikita Balashov, Roberto Barbera, Andrea Barresi , et al. (1114 additional authors not shown)

Abstract: The Jiangmen Underground Neutrino Observatory (JUNO) started physics data taking on 26 August 2025. JUNO consists of a 20-kton liquid scintillator central detector, surrounded by a 35 kton water pool serving as a Cherenkov veto, and almost 1000 m$^2$ of plastic scintillator veto on top. The detector is located in a shallow underground laboratory with an overburden of 1800 m.w.e. This paper present… ▽ More The Jiangmen Underground Neutrino Observatory (JUNO) started physics data taking on 26 August 2025. JUNO consists of a 20-kton liquid scintillator central detector, surrounded by a 35 kton water pool serving as a Cherenkov veto, and almost 1000 m$^2$ of plastic scintillator veto on top. The detector is located in a shallow underground laboratory with an overburden of 1800 m.w.e. This paper presents the performance results of the detector, extensively studied during the commissioning of the water phase, the subsequent liquid scintillator filling phase, and the first physics runs. The liquid scintillator achieved an attenuation length of 20.6 m at 430 nm, while the high coverage PMT system and scintillator together yielded about 1785 photoelectrons per MeV of energy deposit at the detector centre, measured using the 2.223 MeV $γ$ from neutron captures on hydrogen with an Am-C calibration source. The reconstructed energy resolution is 3.4% for two 0.511 MeV $γ$ at the detector centre and 2.9% for the 0.93 MeV quenched Po-214 alpha decays from natural radioactive sources. The energy nonlinearity is calibrated to better than 1%. Intrinsic contaminations of U-238 and Th-232 in the liquid scintillator are below 10$^{-16}$ g/g, assuming secular equilibrium. The water Cherenkov detector achieves a muon detection efficiency better than 99.9% for muons traversing the liquid scintillator volume. During the initial science runs, the data acquisition duty cycle exceeded 97.8%, demonstrating the excellent stability and readiness of JUNO for high-precision neutrino physics. △ Less

Submitted 18 November, 2025; originally announced November 2025.

Comments: 38 pages, 23 figures

Prospects for geoneutrino detection with JUNO

Authors: Thomas Adam, Shakeel Ahmad, Rizwan Ahmed, Fengpeng An, João Pedro Athayde Marcondes de André, Costas Andreopoulos, Giuseppe Andronico, Nikolay Anfimov, Vito Antonelli, Tatiana Antoshkina, Didier Auguste, Marcel Büchner, Weidong Bai, Nikita Balashov, Andrea Barresi, Davide Basilico, Eric Baussan, Marco Beretta, Antonio Bergnoli, Nikita Bessonov, Daniel Bick, Lukas Bieger, Svetlana Biktemerova, Thilo Birkenfeld, Simon Blyth , et al. (605 additional authors not shown)

Abstract: Geoneutrinos, which are antineutrinos emitted during the decay of long-lived radioactive elements inside Earth, serve as a unique tool for studying the composition and heat budget of our planet. The Jiangmen Underground Neutrino Observatory (JUNO) experiment in China, which has recently completed construction, is expected to collect a sample comparable in size to the entire existing world geoneutr… ▽ More Geoneutrinos, which are antineutrinos emitted during the decay of long-lived radioactive elements inside Earth, serve as a unique tool for studying the composition and heat budget of our planet. The Jiangmen Underground Neutrino Observatory (JUNO) experiment in China, which has recently completed construction, is expected to collect a sample comparable in size to the entire existing world geoneutrino dataset in less than a year. This paper presents an updated estimation of sensitivity to geoneutrinos of JUNO using the best knowledge available to date about the experimental site, the surrounding nuclear reactors, the detector response uncertainties, and the constraints expected from the TAO satellite detector. To facilitate comparison with present and future geological models, our results cover a wide range of predicted signal strengths. Despite the significant background from reactor antineutrinos, the experiment will measure the total geoneutrino flux with a precision comparable to that of existing experiments within its first few years, ultimately achieving a world-leading precision of about 8% over ten years. The large statistics of JUNO will also allow separation of the Uranium-238 and Thorium-232 contributions with unprecedented precision, providing crucial constraints on models of formation and composition of Earth. Observation of the mantle signal above the lithospheric flux will be possible but challenging. For models with the highest predicted mantle concentrations of heat-producing elements, a 3-sigma detection over six years requires knowledge of the lithospheric flux to within 15%. Together with complementary measurements from other locations, the geoneutrino results of JUNO will offer cutting-edge, high-precision insights into the interior of Earth, of fundamental importance to both the geoscience and neutrino physics communities. △ Less

Submitted 10 November, 2025; originally announced November 2025.

Comments: 32 pages, with 13 figures and 5 tables

Spectral localization of single-nanoparticle plasmons through photonic substrate engineering

Authors: Shihao Feng, Juan-Feng Zhu, Xinyi Fan, Jindong Ai, Wenjie Zhou, Guangxin Liu, Jing-Feng Liu, Renming Liu, Wei Li, Lijun Guo, Lin Wu

Abstract: Surface plasmon resonances (SPRs) are crucial for confining light beyond the diffraction limit, yet heavy metal losses often limit their spectral localization. Here, we propose a practical strategy for enabling the spectral localization of single-nanoparticle SPRs through photonic substrate engineering, which creates distinct optical pathways (OPs) to tailor the electromagnetic environments around… ▽ More Surface plasmon resonances (SPRs) are crucial for confining light beyond the diffraction limit, yet heavy metal losses often limit their spectral localization. Here, we propose a practical strategy for enabling the spectral localization of single-nanoparticle SPRs through photonic substrate engineering, which creates distinct optical pathways (OPs) to tailor the electromagnetic environments around plasmonic nanoparticles. By analyzing the multiplication factor spectrum of the projected local density of states, we can trace and control these OPs, enabling strong spatial and spectral confinement of single-nanoparticle SPRs. Simulations reveal that a photonic crystal substrate can reduce the mode volume by fivefold and boost the quality factor by over 80 times compared to a metal nanoparticle on a dielectric substrate. Proof-of-concept experiments using two types of leaking Fabry-Perot photonic substrates demonstrate active manipulation of SPRs in both "open" and "closed" OP states. This multidimensional photonic substrate engineering establishes a customizable platform for single-nanoparticle plasmonics, potentially transforming applications that were previously limited by spectral localization. △ Less

Submitted 9 September, 2025; originally announced September 2025.

Mo Atom Rearrangement Drives Layer-Dependent Reactivity in Two-Dimensional MoS2

Authors: Zifan Wang, Jiaxuan Wen, Tina Mihm, Shaopeng Feng, Kelvin Huang, Jing Tang, Tianshu Li, Liangbo Liang, Sahar Sharifzadeh, Keji Lai, Xi Ling

Abstract: Two-dimensional (2D) materials offer a valuable platform for manipulating and studying chemical reactions at atomic level, owing to the ease of controlling their microscopic structure at the nanometer scale. While extensive research has been conducted on the structure-dependent chemical activity of 2D materials, the influence of structural transformation during the reaction remains largely unexplo… ▽ More Two-dimensional (2D) materials offer a valuable platform for manipulating and studying chemical reactions at atomic level, owing to the ease of controlling their microscopic structure at the nanometer scale. While extensive research has been conducted on the structure-dependent chemical activity of 2D materials, the influence of structural transformation during the reaction remains largely unexplored. In this work, we report the layer-dependent chemical reactivity of MoS2 during a nitridation atomic substitution reaction and attribute it to the rearrangement of Mo atoms. Our results show that the chemical reactivity of MoS2 decreases as the number of layers is reduced in the few-layer regime. In particular, monolayer MoS2 exhibits significantly lower reactivity compared to its few-layer and multilayer counterparts. Atomic-resolution transmission electron microscope (TEM) reveals that MoN nanonetworks form as reaction products from monolayer and bilayer MoS2, with the continuity of the MoN crystals increasing with layer number, consistent with the local conductivity mapping data. The layer-dependent reactivity is attributed to the relative stability of the hypothetically formed MoN phase which retain the number of Mo atomic layers present in the precursor. Specifically, the low chemical reactivity of monolayer MoS2 is attributed to the high energy cost associated with Mo atom diffusion and migration necessary to form multi-layer Mo lattices in the thermodynamically stable MoN phase. This study underscores the critical role of lattice rearrangement in governing chemical reactivity and highlights the potential of 2D materials as versatile platforms for advancing the understanding of materials chemistry at atomic scale. △ Less

Submitted 4 September, 2025; originally announced September 2025.

Comments: 41 pages, 20 figures

Observation of nonlinear topological corner states originating from different spectral charges

Authors: Victor O. Kompanets, Suge Feng, Yiqi Zhang, Yaroslav V. Kartashov, Yongdong Li, Sergei A. Zhuravitskii, Nikolay N. Skryabin, Alexander V. Kireev, Ivan V. Dyakonov, Alexander A. Kalinkin, Ce Shang, Sergei P. Kulik, Sergey V. Chekalin, Victor N. Zadkov

Abstract: Higher-order topological insulators (HOTIs) are unique topological materials supporting edge states with the dimensionality at least by two lower than the dimensionality of the underlying structure. HOTIs were observed on lattices with different symmetries, but only in geometries, where truncation of HOTI produces a finite structure with the same order of discrete rotational symmetry as that of th… ▽ More Higher-order topological insulators (HOTIs) are unique topological materials supporting edge states with the dimensionality at least by two lower than the dimensionality of the underlying structure. HOTIs were observed on lattices with different symmetries, but only in geometries, where truncation of HOTI produces a finite structure with the same order of discrete rotational symmetry as that of the unit cell, thereby setting the geometry of insulator edge. Here we experimentally demonstrate a new type of two-dimensional (2D) HOTI based on the Kekule-patterned lattice, whose order of discrete rotational symmetry differs from that of the unit cells of the constituent honeycomb lattice, with hybrid boundaries that help to produce all three possible corners that support effectively 0D corner states of topological origin, especially the one associated with spectral charge 5/6. We also show that linear corner states give rise to rich families of stable hybrid nonlinear corner states bifurcating from them in the presence of focusing nonlinearity of the material. Such new types of nonlinear corner states are observed in hybrid HOTI inscribed in transparent nonlinear dielectric using fs-laser writing technique. Our results complete the class of HOTIs and open the way to observation of topological states with new internal structure and symmetry. △ Less

Submitted 29 August, 2025; originally announced August 2025.

Comments: 21 pages, 6 figures

Journal ref: Advanced Materials 37(30), 2500556 (2025)

Thiol post-translational modifications modulate allosteric regulation of the OpcA-G6PDH complex through conformational gate control

Authors: Hoshin Kim, Song Feng, Pavlo Bohutskyi, Xiaolu Li, Daniel Mejia-Rodriguez, Tong Zhang, Wei-Jun Qian, Margaret S. Cheung

Abstract: Cyanobacteria require ultra-fast metabolic switching to maintain reducing power balance during environmental fluctuations. Glucose-6-phosphate dehydrogenase (G6PDH), catalyzing the rate-limiting step of the oxidative pentose phosphate pathway (OPPP), provides essential NADPH and metabolic intermediates for biosynthetic processes and redox homeostasis. In cyanobacteria, the unique redox-sensitive p… ▽ More Cyanobacteria require ultra-fast metabolic switching to maintain reducing power balance during environmental fluctuations. Glucose-6-phosphate dehydrogenase (G6PDH), catalyzing the rate-limiting step of the oxidative pentose phosphate pathway (OPPP), provides essential NADPH and metabolic intermediates for biosynthetic processes and redox homeostasis. In cyanobacteria, the unique redox-sensitive protein OpcA acts as a metabolic switch for G6PDH, enabling rapid adjustment of reducing power generation from glycogen catabolism and resulting in precise regulation of carbon flux between anabolic and catabolic pathways. While the redox-sensitive cysteine structures of OpcA are known to regulate G6PDH, the detailed mechanisms of how redox post-translational modifications (PTMs) influence OpcA's allosteric effects on G6PDH structures and function remain elusive. To investigate this mechanism, we utilized computational modeling combined with experimental redox proteomics using Synechococcus elongatus PCC 7942 as a model system. Redox proteomics captured modified cysteine residues under light/dark or circadian shifts. Computational simulation revealed that thiol PTMs near the OpcA-G6PDH interface are crucial to allosteric regulation of regions affecting the G6PDH activity, including a potential gate region for substrate ingress and product egress, as well as critical hydrogen bond networks within the active site. These PTMs promote rapid metabolic switching by enhancing G6PDH catalytic activity when OpcA is oxidized. This study provides evidence for novel molecular mechanisms that elucidate the importance of thiol PTMs of OpcA in modulating G6PDH structure and function in an allosteric manner, demonstrating how PTM-level regulation provides a critical control mechanism that enables cyanobacteria to rapidly adapt to environmental fluctuations through precise metabolic fine-tuning. △ Less

Submitted 28 July, 2025; originally announced July 2025.

Comments: 24 pages, 6 figures for main text 14 Pages, 6 figures for SI

Report number: PNNL-SA-214168

Revisiting Sampling Strategies for Molecular Generation

Authors: Yuyan Ni, Shikun Feng, Wei-Ying Ma, Zhi-Ming Ma, Yanyan Lan

Abstract: Sampling strategies in diffusion models are critical to molecular generation yet remain relatively underexplored. In this work, we investigate a broad spectrum of sampling methods beyond conventional defaults and reveal that sampling choice substantially affects molecular generation performance. In particular, we identify a maximally stochastic sampling (StoMax), a simple yet underexplored strateg… ▽ More Sampling strategies in diffusion models are critical to molecular generation yet remain relatively underexplored. In this work, we investigate a broad spectrum of sampling methods beyond conventional defaults and reveal that sampling choice substantially affects molecular generation performance. In particular, we identify a maximally stochastic sampling (StoMax), a simple yet underexplored strategy, as consistently outperforming default sampling methods for generative models DDPM and BFN. Our findings highlight the pivotal role of sampling design and suggest promising directions for advancing molecular generation through principled and more expressive sampling approaches. △ Less

Submitted 19 June, 2025; originally announced June 2025.

Vectorial probability loophole in Bell test

Authors: Sheng Feng, Chaoran Tu

Abstract: Exhaustively identifying all loopholes in the Bell test is demanding for interpreting the results of the relevant experiments, since any loophole if not closed can be catastrophic to our understanding of the nonlocal structure of quantum mechanics. Despite a series of recent Bell experiments that claim to be free of loopholes, a united framework with a sound base is still missing to fully recogniz… ▽ More Exhaustively identifying all loopholes in the Bell test is demanding for interpreting the results of the relevant experiments, since any loophole if not closed can be catastrophic to our understanding of the nonlocal structure of quantum mechanics. Despite a series of recent Bell experiments that claim to be free of loopholes, a united framework with a sound base is still missing to fully recognize all potential loopholes in these experiments, as verified by a recent experiment that has pinned down a detection loophole of a new type in Bell analysis. Here, we reveal another loophole previously unknown in the Bell test through a local theory developed here on the basis of a new mathematical concept of high-dimensional vectorial probability, quantified as a vector with interesting but hidden geometry in the probability space. We show that the statistical property of the stochastic events generated for the Bell test can be well described by the local theory, and, in particular, the correlations of these events that violate Bell's theorem can be closely connected to the geometry of the vectorial probability. To close the loophole, theoretical investigations are highly recommended to search for the statistical nature of the stochastic events in quantum measurements that can distinguish the predictions of quantum mechanics and those of the local theory. △ Less

Submitted 10 June, 2026; v1 submitted 12 June, 2025; originally announced June 2025.

Comments: Major revision; experimental data supplied; 16 pages, 2 figures

OpenPros: A Large-Scale Dataset for Limited View Prostate Ultrasound Computed Tomography

Authors: Hanchen Wang, Yixuan Wu, Yinan Feng, Peng Jin, Luoyuan Zhang, Shihang Feng, James Wiskin, Baris Turkbey, Peter A. Pinto, Bradford J. Wood, Songting Luo, Yinpeng Chen, Emad Boctor, Youzuo Lin

Abstract: Prostate cancer is one of the most prevalent and deadly cancers among men, motivating the development of accurate and accessible imaging technologies for early detection. Ultrasound computed tomography (USCT) reconstructs quantitative tissue parameters such as speed-of-sound (SOS) and is a promising low-cost alternative to existing modalities. However, prostate USCT remains challenging due to limi… ▽ More Prostate cancer is one of the most prevalent and deadly cancers among men, motivating the development of accurate and accessible imaging technologies for early detection. Ultrasound computed tomography (USCT) reconstructs quantitative tissue parameters such as speed-of-sound (SOS) and is a promising low-cost alternative to existing modalities. However, prostate USCT remains challenging due to limited-angle acquisition, strong tissue heterogeneity, bone-induced wave distortion, and the lack of large-scale, anatomically realistic datasets for method development and evaluation. We introduce OPENPROS, the first large-scale benchmark dataset for limited-angle prostate USCT, designed to systematically evaluate machine learning methods for quantitative inverse problems. OPENPROS contains over 280,000 paired samples of realistic 2D SOS maps and corresponding ultrasound full-waveform data, generated from anatomically accurate 3D digital prostate models derived from 4 clinical MRI/CT scans and 62 ex vivo prostate specimens with experimental ultrasound measurements. Wave propagation is simulated under clinically realistic configurations using open-source finite-difference time-domain and Runge-Kutta solvers. We provide standardized training, in-distribution, and out-of-distribution benchmarks and evaluate representative deep learning baselines. While learning-based methods substantially improve inference speed and reconstruction accuracy over physics-based approaches, results highlight persistent challenges in robustness, generalization, and high-resolution reconstruction quality. By publicly releasing OPENPROS, we establish a rigorous benchmark to support research in inverse problems, physics-guided learning, and operator learning, and to bridge the gap between machine learning research and practical USCT deployment. The dataset is available at https://open-pros.github.io/. △ Less

Submitted 1 February, 2026; v1 submitted 18 May, 2025; originally announced May 2025.

Super-Resolution Coherent Diffractive Imaging via Titled-Incidence Multi-Rotation-Angle Fusion Ptychography

Authors: Zhou Youyang, Shi Weiren, Xie Yun, Zhao Bianli, Luo Xinyu, Yao Mingjie, Zhang Rui, Tan Xin, Li Kui, Yang Hao, Liu Qi, Nan Yinggang, Bao Jie, Zhang Yuping, Shu Feng, Li Shaopan, Zhang Xiaoshi

Abstract: Coherent diffractive imaging (CDI) enables lensless imaging with experimental simplicity and a flexible field of view, yet its resolution is fundamentally constrained by the Abbe diffraction limit. To overcome this limitation, we introduce a novel Tilted-Incidence Multi-Rotation-Angle Fusion Ptychography technique. This approach leverages a tilted-incidence geometry to extend the collection angle… ▽ More Coherent diffractive imaging (CDI) enables lensless imaging with experimental simplicity and a flexible field of view, yet its resolution is fundamentally constrained by the Abbe diffraction limit. To overcome this limitation, we introduce a novel Tilted-Incidence Multi-Rotation-Angle Fusion Ptychography technique. This approach leverages a tilted-incidence geometry to extend the collection angle beyond the Abbe limit, achieving up to a -fold resolution enhancement. By acquiring diffraction patterns at multiple sample rotation angles, we capture complementary spatial frequency information. A tilted-incidence multi-rotation-angle fusion ptychographic iterative engine (tmf-PIE) algorithm is then employed to integrate these datasets, enabling super-resolution image reconstruction. Additionally, this method mitigates the anisotropic resolution artifacts inherent to tilted CDI geometries. Our technique represents a novel advancement in super-resolution imaging, providing a novel alternative alongside established methods such as STED, SIM, and SMLM. △ Less

Submitted 13 April, 2025; v1 submitted 6 April, 2025; originally announced April 2025.

Comments: 18 pages, 6 figures

Identifying the Unique Geochemical Fingerprints of Omo Group Beds around Lake Turkana, Kenya by Modified Gaussian Discrimination Analysis

Authors: Gerard T. Schuster, Shihang Feng

Abstract: A modified Gaussian Discriminant Analysis (GDA) is used with an optimal search strategy to identify the unique geochemical fingerprints of six different geological beds in the Lake Turkana area. Three-hundred samples were collected from six different beds in the Omo Group of Lake Turkana, where each sample consisted of the PPMs of 11 different chemical compound. The GDA analysis discovered a uniqu… ▽ More A modified Gaussian Discriminant Analysis (GDA) is used with an optimal search strategy to identify the unique geochemical fingerprints of six different geological beds in the Lake Turkana area. Three-hundred samples were collected from six different beds in the Omo Group of Lake Turkana, where each sample consisted of the PPMs of 11 different chemical compound. The GDA analysis discovered a unique combination of three tuff compounds that can uniquely identify one of the five beds, where a 6th bed is excluded because only 7 samples were collected from it. These geochemical fingerprints are important because any Turkana hominin fossil in the Omo Group can now be efficiently identified by matching the geochemical fingerprint of a fossil's host rock to the fingerprint of the bed in which it is found. In general, the GDA search strategy can be a powerful tool for efficiently identifying unique class fingerprints of high-dimensional data. △ Less

Submitted 2 February, 2025; originally announced February 2025.

Comments: 15 pages, 8 figures

One Pot Synthesis of Cubic Gauche Polymeric Nitrogen

Authors: Runteng Chen, Jun Zhang, Zelong Wang, Ke Lu, Yi Peng, Jianfa Zhao, Shaomin Feng, Changqing Jin

Abstract: The long sought cubic gauche polymeric nitrogen (cg-N) consisting of N-N single bonds has been synthesized by a simple route using sodium azide as a precursor at ambient conditions. The recrystallization process was designed to expose crystal faces with low activation energy that facilitates initiating the polymeric reaction at ambient conditions. The azide was considered as a precursor due to the… ▽ More The long sought cubic gauche polymeric nitrogen (cg-N) consisting of N-N single bonds has been synthesized by a simple route using sodium azide as a precursor at ambient conditions. The recrystallization process was designed to expose crystal faces with low activation energy that facilitates initiating the polymeric reaction at ambient conditions. The azide was considered as a precursor due to the low energy barrier in transforming double bonded N=N to single bonded cg-N. Raman spectrum measurements detected the emerging vibron peaks at 635 cm-1 for the polymerized sodium azide samples, demonstrating the formation of cg-N with N-N single bonds. Different from traditional high pressure technique and recently developed plasma enhanced chemical vapor deposition method, the route achieves the quantitative synthesis of cg-N at ambient conditions. The simple method to synthesize cg-N offers potential for further scale up production as well as practical applications of polymeric nitrogen based materials as high energy density materials. △ Less

Submitted 30 December, 2024; originally announced December 2024.

Comments: 12 papes, 3 figures, 1 table

A facile route to synthesize cubic gauche polymeric nitrogen

Authors: Runteng Chen, Jun Zhang, Zelong Wang, Ke Lu, Yi Peng, Jianfa Zhao, Xiaodong Liu, Shaomin Feng, Ruibin Liu, Chuan Xiao, Changqing Jin

Abstract: In this work, the long-sought cg-N with N-N single bond has been synthesized for the first time by a thermal-driven-only chemical route at ambient conditions. The successful synthesis of cg-N was achieved by first creating a solution of azides, which was then pretreated under vacuum conditions. Following the pretreatment, the resultant concentrated azide was heated at temperatures ranging from 260… ▽ More In this work, the long-sought cg-N with N-N single bond has been synthesized for the first time by a thermal-driven-only chemical route at ambient conditions. The successful synthesis of cg-N was achieved by first creating a solution of azides, which was then pretreated under vacuum conditions. Following the pretreatment, the resultant concentrated azide was heated at temperatures ranging from 260°C to 330°C for a reaction time of 3 hours, ultimately leading to the formation of cg-N. The emergent intense Raman peak characterized of cg-N provides solid evidence that the double bonded nitrogen-nitrogen transforms into a single bond form, which agrees well with cg-N structure. To date, this is the only work achieving the quantity of cg-N synthesized at ambient conditions by a facile route that can be further developed for the scalable synthesis and applications of polymerized nitrogen-based materials as high energy density materials. △ Less

Submitted 28 November, 2024; v1 submitted 15 November, 2024; originally announced November 2024.

Comments: 8 pages, 2 figures, published in Science Bulletin

Journal ref: Science Bulletin, 69(24):3812 (2024)

Graph-GIC: A Smart and Parallelized Geomagnetically Induced Current Modelling Algorithm Based on Graph Theory for Space Weather Applications

Authors: Wen Chen, Ding Yuan, Xueshang Feng, Stefaan Poedts, Zhengyang Zou, Song Feng, Yuxuan Zhu, Tong Yin

Abstract: Geomagnetically Induced Current (GIC) refers to the electromagnetic response of the Earth and its conductive modern infrastructures to space weather and would pose a significant threat to high-voltage power grids designed for the alternative current operation. To assess the impact of space weather on the power grid, one needs to calculate the GIC on a national or continental scale. In this study,… ▽ More Geomagnetically Induced Current (GIC) refers to the electromagnetic response of the Earth and its conductive modern infrastructures to space weather and would pose a significant threat to high-voltage power grids designed for the alternative current operation. To assess the impact of space weather on the power grid, one needs to calculate the GIC on a national or continental scale. In this study, we developed a smart and parallelized GIC modelling algorithm, Graph GIC. This algorithm deploys a graph representing a power grid in a single-line diagram, in which substations/transformers act as nodes and transmission lines as edges. With these denotations, a power grid and its electric parameters are mathematically represented with an adjacency matrix and an admittance matrix. We used sparse matrix and parallelisation techniques to expedite the intensive computation in cases of large-scale power grids. The Graph GIC was validated with a benchmark grid, applied to the GIC calculation of the 500 kV power grid of Guangdong, China, and conducted preliminary analysis on the grid's susceptibility to geomagnetic storms. The Graph GIC algorithm has the advantage of an intuitive and highly scalable graph representation of a power grid at any scale. It achieves high-accuracy calculation and a speedup of about 18 times after parallelisation. This algorithm could be applied to assess the impact of space weather on a power grid up to continental scales and could be incorporated into global space weather modelling frameworks. △ Less

Submitted 29 October, 2024; originally announced November 2024.

Comments: 19 pages, 10 figures

Pre-training with Fractional Denoising to Enhance Molecular Property Prediction

Authors: Yuyan Ni, Shikun Feng, Xin Hong, Yuancheng Sun, Wei-Ying Ma, Zhi-Ming Ma, Qiwei Ye, Yanyan Lan

Abstract: Deep learning methods have been considered promising for accelerating molecular screening in drug discovery and material design. Due to the limited availability of labelled data, various self-supervised molecular pre-training methods have been presented. While many existing methods utilize common pre-training tasks in computer vision (CV) and natural language processing (NLP), they often overlook… ▽ More Deep learning methods have been considered promising for accelerating molecular screening in drug discovery and material design. Due to the limited availability of labelled data, various self-supervised molecular pre-training methods have been presented. While many existing methods utilize common pre-training tasks in computer vision (CV) and natural language processing (NLP), they often overlook the fundamental physical principles governing molecules. In contrast, applying denoising in pre-training can be interpreted as an equivalent force learning, but the limited noise distribution introduces bias into the molecular distribution. To address this issue, we introduce a molecular pre-training framework called fractional denoising (Frad), which decouples noise design from the constraints imposed by force learning equivalence. In this way, the noise becomes customizable, allowing for incorporating chemical priors to significantly improve molecular distribution modeling. Experiments demonstrate that our framework consistently outperforms existing methods, establishing state-of-the-art results across force prediction, quantum chemical properties, and binding affinity tasks. The refined noise design enhances force accuracy and sampling coverage, which contribute to the creation of physically consistent molecular representations, ultimately leading to superior predictive performance. △ Less

Submitted 14 July, 2024; originally announced July 2024.

Visible, Near-, and Mid-infrared Computational Spectrometer Enabled by Single-Spinning Film Encoder

Authors: Junren Wen, Weiming Shi, Cheng Gao, Yujie Liu, Shuaibo Feng, Yu Shao, Haiqi Gao, Yuchuan Shao, Yueguang Zhang, Weidong Shen, Chenying Yang

Abstract: Computational spectrometers are pivotal in enabling low-cost, in-situ and rapid spectral analysis, with potential applications in chemistry, biology, and environmental science. However, filter-based spectral encoding approaches typically use filter arrays, complicating the manufacturing process and hindering device consistency. By capitalizing on the polarization separation effect under oblique in… ▽ More Computational spectrometers are pivotal in enabling low-cost, in-situ and rapid spectral analysis, with potential applications in chemistry, biology, and environmental science. However, filter-based spectral encoding approaches typically use filter arrays, complicating the manufacturing process and hindering device consistency. By capitalizing on the polarization separation effect under oblique incidence (PSEOI), we pioneer the use of a single filter for highly efficient spectral encoding, and propose a novel computational spectrometer spanning visible to mid-infrared wavelengths by combining the Single-Spinning Film Encoder (SSFE) with deep learning-based reconstruction algorithm. The particle swarm optimization (PSO) method is employed to optimize the film configuration of SSFE, achieving low-correlation and high-complexity spectral responses under different polarizations and spinning angles, thereby enhancing both spectral resolution and accuracy of reconstruction across diverse spectral ranges. Spectral resolutions up to 0.5 nm, 2 nm, 10 nm can be realized for single-peak narrowband spectra, and 3 nm, 6 nm, 20 nm for dual-peak narrowband spectra, over the visible, near-, and mid-infrared wavelength ranges, respectively. Moreover, the proposed spectrometer demonstrates an overall 81.38% precision for the classification of 220 chemical compounds, confirming its robustness and precision in practical scenarios, along with the capability for compact, cost-effective spectroscopic solutions. △ Less

Submitted 3 July, 2024; originally announced July 2024.

MoleculeCLA: Rethinking Molecular Benchmark via Computational Ligand-Target Binding Analysis

Authors: Shikun Feng, Jiaxin Zheng, Yinjun Jia, Yanwen Huang, Fengfeng Zhou, Wei-Ying Ma, Yanyan Lan

Abstract: Molecular representation learning is pivotal for various molecular property prediction tasks related to drug discovery. Robust and accurate benchmarks are essential for refining and validating current methods. Existing molecular property benchmarks derived from wet experiments, however, face limitations such as data volume constraints, unbalanced label distribution, and noisy labels. To address th… ▽ More Molecular representation learning is pivotal for various molecular property prediction tasks related to drug discovery. Robust and accurate benchmarks are essential for refining and validating current methods. Existing molecular property benchmarks derived from wet experiments, however, face limitations such as data volume constraints, unbalanced label distribution, and noisy labels. To address these issues, we construct a large-scale and precise molecular representation dataset of approximately 140,000 small molecules, meticulously designed to capture an extensive array of chemical, physical, and biological properties, derived through a robust computational ligand-target binding analysis pipeline. We conduct extensive experiments on various deep learning models, demonstrating that our dataset offers significant physicochemical interpretability to guide model development and design. Notably, the dataset's properties are linked to binding affinity metrics, providing additional insights into model performance in drug-target interaction tasks. We believe this dataset will serve as a more accurate and reliable benchmark for molecular representation learning, thereby expediting progress in the field of artificial intelligence-driven drug discovery. △ Less

Submitted 12 June, 2024; originally announced June 2024.

Superionic surface Li-ion transport in carbonaceous materials

Authors: Jianbin Zhou, Shen Wang, Chaoshan Wu, Ji Qi, Hongli Wan, Shen Lai, Shijie Feng, Tsz Wai Ko, Zhaohui Liang, Ke Zhou, Nimrod Harpak, Nick Solan, Mengchen Liu, Zeyu Hui, Paulina J. Ai, Kent Griffith, Chunsheng Wang, Shyue Ping Ong, Yan Yao, Ping Liu

Abstract: Unlike Li-ion transport in the bulk of carbonaceous materials, little is known about Li-ion diffusion on their surface. In this study, we have discovered an ultra-fast Li-ion transport phenomenon on the surface of carbonaceous materials, particularly when they have limited Li insertion capacity along with a high surface area. This is exemplified by a carbon black, Ketjen Black (KB). An ionic condu… ▽ More Unlike Li-ion transport in the bulk of carbonaceous materials, little is known about Li-ion diffusion on their surface. In this study, we have discovered an ultra-fast Li-ion transport phenomenon on the surface of carbonaceous materials, particularly when they have limited Li insertion capacity along with a high surface area. This is exemplified by a carbon black, Ketjen Black (KB). An ionic conductivity of 18.1 mS cm-1 at room temperature is observed, far exceeding most solid-state ion conductors. Theoretical calculations reveal a low diffusion barrier for the surface Li species. The species is also identified as Li*, which features a partial positive charge. As a result, lithiated KB functions effectively as an interlayer between Li and solid-state electrolytes (SSE) to mitigate dendrite growth and cell shorting. This function is found to be electrolyte agnostic, effective for both sulfide and halide SSEs. Further, lithiated KB can act as a high-performance mixed ion/electron conductor that is thermodynamically stable at potentials near Li metal. A graphite anode mixed with KB instead of a solid electrolyte demonstrates full utilization with a capacity retention of ~85% over 300 cycles. The discovery of this surface-mediated ultra-fast Li-ion transport mechanism provides new directions for the design of solid-state ion conductors and solid-state batteries. △ Less

Submitted 27 May, 2024; originally announced May 2024.

Comments: 21 pages, 6 figures

Data quality control system and long-term performance monitor of the LHAASO-KM2A

Authors: Zhen Cao, F. Aharonian, Axikegu, Y. X. Bai, Y. W. Bao, D. Bastieri, X. J. Bi, Y. J. Bi, W. Bian, A. V. Bukevich, Q. Cao, W. Y. Cao, Zhe Cao, J. Chang, J. F. Chang, A. M. Chen, E. S. Chen, H. X. Chen, Liang Chen, Lin Chen, Long Chen, M. J. Chen, M. L. Chen, Q. H. Chen, S. Chen , et al. (263 additional authors not shown)

Abstract: The KM2A is the largest sub-array of the Large High Altitude Air Shower Observatory (LHAASO). It consists of 5216 electromagnetic particle detectors (EDs) and 1188 muon detectors (MDs). The data recorded by the EDs and MDs are used to reconstruct primary information of cosmic ray and gamma-ray showers. This information is used for physical analysis in gamma-ray astronomy and cosmic ray physics. To… ▽ More The KM2A is the largest sub-array of the Large High Altitude Air Shower Observatory (LHAASO). It consists of 5216 electromagnetic particle detectors (EDs) and 1188 muon detectors (MDs). The data recorded by the EDs and MDs are used to reconstruct primary information of cosmic ray and gamma-ray showers. This information is used for physical analysis in gamma-ray astronomy and cosmic ray physics. To ensure the reliability of the LHAASO-KM2A data, a three-level quality control system has been established. It is used to monitor the status of detector units, stability of reconstructed parameters and the performance of the array based on observations of the Crab Nebula and Moon shadow. This paper will introduce the control system and its application on the LHAASO-KM2A data collected from August 2021 to July 2023. During this period, the pointing and angular resolution of the array were stable. From the observations of the Moon shadow and Crab Nebula, the results achieved using the two methods are consistent with each other. According to the observation of the Crab Nebula at energies from 25 TeV to 100 TeV, the time averaged pointing errors are estimated to be $-0.003^{\circ} \pm 0.005^{\circ}$ and $0.001^{\circ} \pm 0.006^{\circ}$ in the R.A. and Dec directions, respectively. △ Less

Submitted 13 June, 2024; v1 submitted 20 May, 2024; originally announced May 2024.

Comments: 15 pages, 9 figures

Enhanced second harmonic generation in high-$Q$ all-dielectric metasurfaces with backward frequency conversion

Authors: Xu Tu, Siqi Feng, Jiajun Li, Yangguang Xing, Feng Wu, Tingting Liu, Shuyuan Xiao

Abstract: Here we employ the quasi-bound state in the continuum (quasi-BIC) resonance in all-dielectric metasurfaces for efficient nonlinear processes in consideration of the backward frequency conversion. We theoretically study the second-harmonic generation (SHG) from symmetry-broken AlGaAs metasurfaces and reveal the efficiency enhancement empowered by high-$Q$ quasi-BIC resonances. By introducing the co… ▽ More Here we employ the quasi-bound state in the continuum (quasi-BIC) resonance in all-dielectric metasurfaces for efficient nonlinear processes in consideration of the backward frequency conversion. We theoretically study the second-harmonic generation (SHG) from symmetry-broken AlGaAs metasurfaces and reveal the efficiency enhancement empowered by high-$Q$ quasi-BIC resonances. By introducing the correction term of nonlinear polarization at the fundamental wave field to the conventional undepleted approximation, we uncover the effect of backward frequency conversion on the nonlinear conversation efficiency. The SHG efficiency as $2.45\times10^{-2}$ with the developed depleted model, shows a $14.3\%$ decrease compared with $2.86\times10^{-2}$ in conventional undepleted approximation, under the incident intensity of 10 MW/cm$^{2}$. Our results are of significant importance for designing efficient nonlinear metasurfaces supporting high-$Q$ resonances. △ Less

Submitted 11 June, 2024; v1 submitted 29 April, 2024; originally announced April 2024.

Journal ref: Physical Review A 109 (6), 063522 (2024)

On-chip Real-time Hyperspectral Imager with Full CMOS Resolution Enabled by Massively Parallel Neural Network

Authors: Junren Wen, Haiqi Gao, Weiming Shi, Shuaibo Feng, Lingyun Hao, Yujie Liu, Liang Xu, Yuchuan Shao, Yueguang Zhang, Weidong Shen, Chenying Yang

Abstract: Traditional spectral imaging methods are constrained by the time-consuming scanning process, limiting the application in dynamic scenarios. One-shot spectral imaging based on reconstruction has been a hot research topic recently and the primary challenges still lie in both efficient fabrication techniques suitable for mass production and the high-speed, high-accuracy reconstruction algorithm for r… ▽ More Traditional spectral imaging methods are constrained by the time-consuming scanning process, limiting the application in dynamic scenarios. One-shot spectral imaging based on reconstruction has been a hot research topic recently and the primary challenges still lie in both efficient fabrication techniques suitable for mass production and the high-speed, high-accuracy reconstruction algorithm for real-time spectral imaging. In this study, we introduce an innovative on-chip real-time hyperspectral imager that leverages nanophotonic film spectral encoders and a Massively Parallel Network (MP-Net), featuring a 4 * 4 array of compact, all-dielectric film units for the micro-spectrometers. Each curved nanophotonic film unit uniquely modulates incident light across the underlying 3 * 3 CMOS image sensor (CIS) pixels, enabling a high spatial resolution equivalent to the full CMOS resolution. The implementation of MP-Net, specially designed to address variability in transmittance and manufacturing errors such as misalignment and non-uniformities in thin film deposition, can greatly increase the structural tolerance of the device and reduce the preparation requirement, further simplifying the manufacturing process. Tested in varied environments on both static and moving objects, the real-time hyperspectral imager demonstrates the robustness and high-fidelity spatial-spectral data capabilities across diverse scenarios. This on-chip hyperspectral imager represents a significant advancement in real-time, high-resolution spectral imaging, offering a versatile solution for applications ranging from environmental monitoring, remote sensing to consumer electronics. △ Less

Submitted 15 April, 2024; originally announced April 2024.

Efficient photon-pair generation empowered by dual quasi-bound states in the continuum

Authors: Tingting Liu, Meibao Qin, Siqi Feng, Xu Tu, Tianjing Guo, Feng Wu, Shuyuan Xiao

Abstract: Here we demonstrate the efficient photon-pair generation via spontaneous parametric down conversion from a semiconductor metasurface supporting dual quasi-bound states in the continuum (quasi-BICs). In a simple metasurface design composed of AlGaAs ellipse nano-cyclinders, the two high-$Q$ quasi-BIC resonances that coincide with the generated signal and idler frequencies significantly boost the lo… ▽ More Here we demonstrate the efficient photon-pair generation via spontaneous parametric down conversion from a semiconductor metasurface supporting dual quasi-bound states in the continuum (quasi-BICs). In a simple metasurface design composed of AlGaAs ellipse nano-cyclinders, the two high-$Q$ quasi-BIC resonances that coincide with the generated signal and idler frequencies significantly boost the local electric field. This leads to a substantial enhancement in the reverse classical nonlinear process of sum frequency generation and subsequently the remarkable high generation rate of photon pairs under the quantum-classical correspondence principle. Within a narrowband wavelength regime around the quasi-BIC resonances, the rate of pair production is enhanced up to $\sim10^{4}$ Hz, two orders of magnitude larger than that in the Mie resonant AlGaAs nanoantennas. Moreover, the photon pair emission is mainly concentrated in the normal direction with respect to the metasurface, and shows tunable rate with the $Q$ factor by engineering the rotation angle of nano-cylinders. The presented work enables nanoscale sources of high-quality entangled photons which will find applications in advanced quantum imaging and communications. △ Less

Submitted 25 January, 2024; originally announced January 2024.

Journal ref: Physical Review B 109 (15), 155424 (2024)

Rigorous and efficient diffraction modeling between arbitrary planes by angular spectrum rearrangement

Authors: Yiwen Hu, Xin Liu, Shi Feng, Xu Liu, Xiang Hao

Abstract: In computational optics, numerical modeling of diffraction between arbitrary planes offers unparalleled flexibility. However, existing methods suffer from the trade-off between computational accuracy and efficiency. To resolve this dilemma, we present a novel approach that rigorously and efficiently models wave propagation between two arbitrary planes. This is achieved by rearranging the angular s… ▽ More In computational optics, numerical modeling of diffraction between arbitrary planes offers unparalleled flexibility. However, existing methods suffer from the trade-off between computational accuracy and efficiency. To resolve this dilemma, we present a novel approach that rigorously and efficiently models wave propagation between two arbitrary planes. This is achieved by rearranging the angular spectrum of the source field, coupled with linear algebraic computations. Notably, our method achieves comparable computational efficiency to the control method for both scalar and vectorial diffraction modeling, while eliminating nearly all numerical errors. Furthermore, we selectively merge the angular spectrum to further enhance the efficiency at the expense of precision in a controlled manner. Thereafter, the time consumption is reduced to at most 3% of that before merging. △ Less

Submitted 11 December, 2023; originally announced December 2023.

Einstein locality: An ignored core element of quantum mechanics

Authors: Sheng Feng

Abstract: Quantum mechanics is commonly accepted as a complete theory thanks to experimental tests of non-locality based on Bell's theorem. However, we discover that the completeness of the quantum theory practically suffered from detrimental ignorance of a core element -- Einstein locality. Without this element, important experimental results of relevance could hardly receive full understanding or were eve… ▽ More Quantum mechanics is commonly accepted as a complete theory thanks to experimental tests of non-locality based on Bell's theorem. However, we discover that the completeness of the quantum theory practically suffered from detrimental ignorance of a core element -- Einstein locality. Without this element, important experimental results of relevance could hardly receive full understanding or were even completely misinterpreted. Here we present the discovery with a theory of Einstein locality developed to recover the completeness of quantum mechanics. The developed theory provides a unified framework to account for the results of, e.g., Bell experiments (on Bell non-locality) and double-slit experiments with entangled photons (on wave-particle duality). The theory reveals the dynamics of Bell non-locality and the principle of biased sampling in measurement in the double-slit experiments, which otherwise will be impossible tasks without introducing Einstein locality. Worse still, ignorance of this element has caused misinterpretation of observations in the double-slit experiments, leading to perplexing statements of duality violation. Einstein locality also manifests indispensability in theory by its connection to the foundations of other fundamental concepts and topics (e.g., entanglement, decoherence, and quantum measurement) and may advance quantum technology by offering a promising approach to optimizing quantum computing hardware. △ Less

Submitted 19 March, 2024; v1 submitted 6 November, 2023; originally announced November 2023.

Comments: Major revision to highlight the significance of the study, 4 figures

Overview of Physics-Informed Machine Learning Inversion of Geophysical Data

Authors: Gerard T. Schuster, Shihang Feng

Abstract: We review four types of algorithms for physics-informed machine learning (PIML) inversion of geophysical data. The unifying equation is given by the joint objective function $ε$: \begin{eqnarray} ε^{||-PIML}&=&λ_1 \overbrace{||{\bf W}^{ML}({\bf H}_{\bf w} {\bf d}^{obs}-{\bf m})||^2}^{NN} + λ_2 \overbrace{{||{\bf W}^{FWI}({\bf L} {\bf m}-{\bf d}^{obs})||^2}}^{FWI} ~+ \nonumber\\ \nonumber\\ && +… ▽ More We review four types of algorithms for physics-informed machine learning (PIML) inversion of geophysical data. The unifying equation is given by the joint objective function $ε$: \begin{eqnarray} ε^{||-PIML}&=&λ_1 \overbrace{||{\bf W}^{ML}({\bf H}_{\bf w} {\bf d}^{obs}-{\bf m})||^2}^{NN} + λ_2 \overbrace{{||{\bf W}^{FWI}({\bf L} {\bf m}-{\bf d}^{obs})||^2}}^{FWI} ~+ \nonumber\\ \nonumber\\ && + ~~Regularizer, \label{PIML.eq120} \end{eqnarray}where the optimal model ${\bf m}^*$ and weights $\bf w^*$ minimize $ε$. Here, The matrix weights are given by the boldface symbol $\bf W$, and full waveform inversion (FWI) is typically computed using a finite-difference solution of the wave equation, where $\bf L$ represents the forward modeling operation of the wave equation as a function of the model $\bf m$. Also, a fully-connected neural network (NN) is used to compute the model ${\bf H_w}{\bf d}^{obs} \approx \bf m$ from the observed input data ${\bf d}^{obs}$. The selection of weights $λ_i$ and the NN operations determine one of four different PIML algorithms. PIML offers potential advantages over standard FWI through its enhanced ability to avoid local minima and the option to locally train the inversion operator, minimizing the requirement for extensive training data for global applicability. However, the effectiveness of PIML relies on the similarity between the test and trained data. Nevertheless, a possible strategy to overcome this limitation involves initial pretraining of a PIML architecture with data from a broader region, followed by fine-tuning for specific data-a method reminiscent of the way large language models are pretrained and adapted for various tasks. △ Less

Submitted 12 October, 2023; originally announced October 2023.

Comments: 37 pages, 16 figures

Non-Equilibrium Nature of Fracture Determines the Crack Paths

Authors: Pengjie Shi, Shizhe Feng, Zhiping Xu

Abstract: A high-fidelity neural network-based force field, NN-F$^{3}$, is developed to cover the strain states up to material failure and the non-equilibrium, intermediate nature of fracture. Simulations of fracture in 2D crystals using NN-F$^{3}$ reveal spatial complexities from lattice-scale kinks to sample-scale patterns. We find that the fracture resistance cannot be quantified by the energy densities… ▽ More A high-fidelity neural network-based force field, NN-F$^{3}$, is developed to cover the strain states up to material failure and the non-equilibrium, intermediate nature of fracture. Simulations of fracture in 2D crystals using NN-F$^{3}$ reveal spatial complexities from lattice-scale kinks to sample-scale patterns. We find that the fracture resistance cannot be quantified by the energy densities of relaxed edges as in the literature. Instead, the fracture patterns, critical stress intensity factors at the kinks, and energy densities of edges in the intermediate, unrelaxed states offer reasonable measures for the fracture toughness and its anisotropy. △ Less

Submitted 30 July, 2023; originally announced July 2023.

Journal ref: Extreme Mechanics Letters 68, 102151, 2024

An Empirical Study of Large-Scale Data-Driven Full Waveform Inversion

Authors: Peng Jin, Yinan Feng, Shihang Feng, Hanchen Wang, Yinpeng Chen, Benjamin Consolvo, Zicheng Liu, Youzuo Lin

Abstract: This paper investigates the impact of big data on deep learning models to help solve the full waveform inversion (FWI) problem. While it is well known that big data can boost the performance of deep learning models in many tasks, its effectiveness has not been validated for FWI. To address this gap, we present an empirical study that investigates how deep learning models in FWI behave when trained… ▽ More This paper investigates the impact of big data on deep learning models to help solve the full waveform inversion (FWI) problem. While it is well known that big data can boost the performance of deep learning models in many tasks, its effectiveness has not been validated for FWI. To address this gap, we present an empirical study that investigates how deep learning models in FWI behave when trained on OpenFWI, a collection of large-scale, multi-structural, synthetic datasets published recently. In particular, we train and evaluate the FWI models on a combination of 10 2D subsets in OpenFWI that contain 470K pairs of seismic data and velocity maps in total. Our experiments demonstrate that training on the combined dataset yields an average improvement of 13.03% in MAE, 7.19% in MSE and 1.87% in SSIM compared to each split dataset, and an average improvement of 28.60%, 21.55% and 8.22% in the leave-one-out generalization test. We further demonstrate that model capacity needs to scale in accordance with data size for optimal improvement, where our largest model yields an average improvement of 20.06%, 13.39% and 0.72% compared to the smallest one. △ Less

Submitted 24 April, 2024; v1 submitted 28 July, 2023; originally announced July 2023.

Effects of Coronal Magnetic Field Configuration on Particle Acceleration and Release during the Ground Level Enhancement Events in Solar Cycle 24

Authors: Wenlong Liu, Xiangliang Kong, Fan Guo, Lulu Zhao, Shiwei Feng, Feiyu Yu, Zelong Jiang, Yao Chen, Joe Giacalone

Abstract: Ground level enhancements (GLEs) are extreme solar energetic particle (SEP) events that are of particular importance in space weather. In solar cycle 24, two GLEs were recorded on 2012 May 17 (GLE 71) and 2017 September 10 (GLE 72), respectively, by a range of advanced modern instruments. Here we conduct a comparative analysis of the two events by focusing on the effects of large-scale magnetic fi… ▽ More Ground level enhancements (GLEs) are extreme solar energetic particle (SEP) events that are of particular importance in space weather. In solar cycle 24, two GLEs were recorded on 2012 May 17 (GLE 71) and 2017 September 10 (GLE 72), respectively, by a range of advanced modern instruments. Here we conduct a comparative analysis of the two events by focusing on the effects of large-scale magnetic field configuration near active regions on particle acceleration and release. Although the active regions both located near the western limb, temporal variations of SEP intensities and energy spectra measured in-situ display different behaviors at early stages. By combining a potential field model, we find the CME in GLE 71 originated below the streamer belt, while in GLE 72 near the edge of the streamer belt. We reconstruct the CME shock fronts with an ellipsoid model based on nearly simultaneous coronagraph images from multi-viewpoints, and further derive the 3D shock geometry at the GLE onset. The highest-energy particles are primarily accelerated in the shock-streamer interaction regions, i.e., likely at the nose of the shock in GLE 71 and the eastern flank in GLE 72, due to quasi-perpendicular shock geometry and confinement of closed fields. Subsequently, they are released to the field lines connecting to near-Earth spacecraft when the shocks move through the streamer cusp region. This suggests that magnetic structures in the corona, especially shock-streamer interactions, may have played an important role in the acceleration and release of the highest-energy particles in the two events. △ Less

Submitted 22 July, 2023; originally announced July 2023.

Comments: Accepted for publication in ApJ

Fractional Denoising for 3D Molecular Pre-training

Authors: Shikun Feng, Yuyan Ni, Yanyan Lan, Zhi-Ming Ma, Wei-Ying Ma

Abstract: Coordinate denoising is a promising 3D molecular pre-training method, which has achieved remarkable performance in various downstream drug discovery tasks. Theoretically, the objective is equivalent to learning the force field, which is revealed helpful for downstream tasks. Nevertheless, there are two challenges for coordinate denoising to learn an effective force field, i.e. low coverage samples… ▽ More Coordinate denoising is a promising 3D molecular pre-training method, which has achieved remarkable performance in various downstream drug discovery tasks. Theoretically, the objective is equivalent to learning the force field, which is revealed helpful for downstream tasks. Nevertheless, there are two challenges for coordinate denoising to learn an effective force field, i.e. low coverage samples and isotropic force field. The underlying reason is that molecular distributions assumed by existing denoising methods fail to capture the anisotropic characteristic of molecules. To tackle these challenges, we propose a novel hybrid noise strategy, including noises on both dihedral angel and coordinate. However, denoising such hybrid noise in a traditional way is no more equivalent to learning the force field. Through theoretical deductions, we find that the problem is caused by the dependency of the input conformation for covariance. To this end, we propose to decouple the two types of noise and design a novel fractional denoising method (Frad), which only denoises the latter coordinate part. In this way, Frad enjoys both the merits of sampling more low-energy structures and the force field equivalence. Extensive experiments show the effectiveness of Frad in molecular representation, with a new state-of-the-art on 9 out of 12 tasks of QM9 and on 7 out of 8 targets of MD17. △ Less

Submitted 26 February, 2024; v1 submitted 20 July, 2023; originally announced July 2023.

$\mathbf{\mathbb{E}^{FWI}}$: Multi-parameter Benchmark Datasets for Elastic Full Waveform Inversion of Geophysical Properties

Authors: Shihang Feng, Hanchen Wang, Chengyuan Deng, Yinan Feng, Yanhua Liu, Min Zhu, Peng Jin, Yinpeng Chen, Youzuo Lin

Abstract: Elastic geophysical properties (such as P- and S-wave velocities) are of great importance to various subsurface applications like CO$_2$ sequestration and energy exploration (e.g., hydrogen and geothermal). Elastic full waveform inversion (FWI) is widely applied for characterizing reservoir properties. In this paper, we introduce $\mathbf{\mathbb{E}^{FWI}}$, a comprehensive benchmark dataset that… ▽ More Elastic geophysical properties (such as P- and S-wave velocities) are of great importance to various subsurface applications like CO$_2$ sequestration and energy exploration (e.g., hydrogen and geothermal). Elastic full waveform inversion (FWI) is widely applied for characterizing reservoir properties. In this paper, we introduce $\mathbf{\mathbb{E}^{FWI}}$, a comprehensive benchmark dataset that is specifically designed for elastic FWI. $\mathbf{\mathbb{E}^{FWI}}$ encompasses 8 distinct datasets that cover diverse subsurface geologic structures (flat, curve, faults, etc). The benchmark results produced by three different deep learning methods are provided. In contrast to our previously presented dataset (pressure recordings) for acoustic FWI (referred to as OpenFWI), the seismic dataset in $\mathbf{\mathbb{E}^{FWI}}$ has both vertical and horizontal components. Moreover, the velocity maps in $\mathbf{\mathbb{E}^{FWI}}$ incorporate both P- and S-wave velocities. While the multicomponent data and the added S-wave velocity make the data more realistic, more challenges are introduced regarding the convergence and computational cost of the inversion. We conduct comprehensive numerical experiments to explore the relationship between P-wave and S-wave velocities in seismic data. The relation between P- and S-wave velocities provides crucial insights into the subsurface properties such as lithology, porosity, fluid content, etc. We anticipate that $\mathbf{\mathbb{E}^{FWI}}$ will facilitate future research on multiparameter inversions and stimulate endeavors in several critical research topics of carbon-zero and new energy exploration. All datasets, codes and relevant information can be accessed through our website at https://efwi-lanl.github.io/ △ Less

Submitted 7 September, 2023; v1 submitted 21 June, 2023; originally announced June 2023.

Comments: 20 pages, 11 figures

Enhanced sum-frequency generation from etchless lithium niobate empowered by dual quasi-bound states in the continuum

Authors: Siqi Feng, Tingting Liu, Wenya Chen, Feng Wu, Shuyuan Xiao

Abstract: The miniaturization of nonlinear light sources is central to the integrated photonic platform, driving a quest for high-efficiency frequency generation and mixing at the nanoscale. In this quest, the high-quality ($Q$) resonant dielectric nanostructures hold great promise, as they enhance nonlinear effects through the resonantly local electromagnetic fields overlapping the chosen nonlinear materia… ▽ More The miniaturization of nonlinear light sources is central to the integrated photonic platform, driving a quest for high-efficiency frequency generation and mixing at the nanoscale. In this quest, the high-quality ($Q$) resonant dielectric nanostructures hold great promise, as they enhance nonlinear effects through the resonantly local electromagnetic fields overlapping the chosen nonlinear materials. Here, we propose a method for the enhanced sum-frequency generation (SFG) from etcheless lithium niobate (LiNbO$_{3}$) by utilizing the dual quasi-bound states in the continuum (quasi-BICs) in a one-dimensional resonant grating waveguide structure. Two high-$Q$ guided mode resonances corresponding to the dual quasi-BICs are respectively excited by two near-infrared input beams, generating a strong visible SFG signal with a remarkably high conversion efficiency of $3.66\times10^{-2}$ (five orders of magnitude higher than that of LiNbO$_{3}$ films of the same thickness) and a small full-width at half-maximum less than 0.2 nm. The SFG efficiency can be tuned via adjusting the grating geometry parameter or choosing the input beam polarization combination. Furthermore, the generated SFG signal can be maintained at a fixed wavelength without the appreciable loss of efficiency by selectively exciting the angle-dependent quasi-BICs, even if the wavelengths of input beams are tuned within a broad spectral range. Our results provide a simple but robust paradigm of high-efficiency frequency conversion on an easy-fabricated platform, which may find applications in nonlinear light sources and quantum photonics. △ Less

Submitted 5 June, 2023; v1 submitted 29 May, 2023; originally announced May 2023.

Journal ref: SCIENCE CHINA Physics, Mechanics & Astronomy 66 (12), 124214 (2023)

Fourier-DeepONet: Fourier-enhanced deep operator networks for full waveform inversion with improved accuracy, generalizability, and robustness

Authors: Min Zhu, Shihang Feng, Youzuo Lin, Lu Lu

Abstract: Full waveform inversion (FWI) infers the subsurface structure information from seismic waveform data by solving a non-convex optimization problem. Data-driven FWI has been increasingly studied with various neural network architectures to improve accuracy and computational efficiency. Nevertheless, the applicability of pre-trained neural networks is severely restricted by potential discrepancies be… ▽ More Full waveform inversion (FWI) infers the subsurface structure information from seismic waveform data by solving a non-convex optimization problem. Data-driven FWI has been increasingly studied with various neural network architectures to improve accuracy and computational efficiency. Nevertheless, the applicability of pre-trained neural networks is severely restricted by potential discrepancies between the source function used in the field survey and the one utilized during training. Here, we develop a Fourier-enhanced deep operator network (Fourier-DeepONet) for FWI with the generalization of seismic sources, including the frequencies and locations of sources. Specifically, we employ the Fourier neural operator as the decoder of DeepONet, and we utilize source parameters as one input of Fourier-DeepONet, facilitating the resolution of FWI with variable sources. To test Fourier-DeepONet, we develop three new and realistic FWI benchmark datasets (FWI-F, FWI-L, and FWI-FL) with varying source frequencies, locations, or both. Our experiments demonstrate that compared with existing data-driven FWI methods, Fourier-DeepONet obtains more accurate predictions of subsurface structures in a wide range of source parameters. Moreover, the proposed Fourier-DeepONet exhibits superior robustness when handling data with Gaussian noise or missing traces and sources with Gaussian noise, paving the way for more reliable and accurate subsurface imaging across diverse real conditions. △ Less

Submitted 24 July, 2023; v1 submitted 26 May, 2023; originally announced May 2023.

Robust Optical Data Encryption by Projection-Photoaligned Polymer-Stabilized-Liquid-Crystals

Authors: Siying Liu, Saleh Alfarhan, Wenbo Wang, Shuai Feng, Yuxiang Zhu, Luyang Liu, Kenan Song, Sui Yang, Kailong Jin, Xiangfan Chen

Abstract: The emerging Internet of Things (IoTs) invokes increasing security demands that require robust encryption or anti-counterfeiting technologies. Albeit being acknowledged as efficacious solutions in processing elaborate graphical information via multiple degrees of freedom, optical data encryption and anti-counterfeiting techniques are typically inept in delivering satisfactory performance without c… ▽ More The emerging Internet of Things (IoTs) invokes increasing security demands that require robust encryption or anti-counterfeiting technologies. Albeit being acknowledged as efficacious solutions in processing elaborate graphical information via multiple degrees of freedom, optical data encryption and anti-counterfeiting techniques are typically inept in delivering satisfactory performance without compromising the desired ease-of-processibility or compatibility, thus leading to the exploration of novel materials and devices that are competent. Here, a robust optical data encryption technique is demonstrated utilizing polymer-stabilized-liquid-crystals (PSLCs) combined with projection photoalignment and photopatterning methods. The PSLCs possess implicit optical patterns encoded via photoalignment, as well as explicit geometries produced via photopatterning. Furthermore, the PSLCs demonstrate improved robustness against harsh chemical environments and thermal stability, and can be directly deployed onto various rigid and flexible substrates. Based on this, it is demonstrated that single PSLC is apt to carry intricate information, or serve as exclusive watermark with both implicit features and explicit geometries. Moreover, a novel, generalized design strategy is developed, for the first time, to encode intricate and exclusive information with enhanced security by spatially programming the photoalignment patterns of a pair of cascade PSLCs, which further illustrates the promising capabilies of PSLCs in optical data encryption and anti-counterfeiting. △ Less

Submitted 24 May, 2023; originally announced May 2023.

Auto-Linear Phenomenon in Subsurface Imaging

Authors: Yinan Feng, Yinpeng Chen, Peng Jin, Shihang Feng, Zicheng Liu, Youzuo Lin

Abstract: Subsurface imaging involves solving full waveform inversion (FWI) to predict geophysical properties from measurements. This problem can be reframed as an image-to-image translation, with the usual approach being to train an encoder-decoder network using paired data from two domains: geophysical property and measurement. A recent seminal work (InvLINT) demonstrates there is only a linear mapping be… ▽ More Subsurface imaging involves solving full waveform inversion (FWI) to predict geophysical properties from measurements. This problem can be reframed as an image-to-image translation, with the usual approach being to train an encoder-decoder network using paired data from two domains: geophysical property and measurement. A recent seminal work (InvLINT) demonstrates there is only a linear mapping between the latent spaces of the two domains, and the decoder requires paired data for training. This paper extends this direction by demonstrating that only linear mapping necessitates paired data, while both the encoder and decoder can be learned from their respective domains through self-supervised learning. This unveils an intriguing phenomenon (named Auto-Linear) where the self-learned features of two separate domains are automatically linearly correlated. Compared with existing methods, our Auto-Linear has four advantages: (a) solving both forward and inverse modeling simultaneously, (b) applicable to different subsurface imaging tasks and achieving markedly better results than previous methods, (c)enhanced performance, especially in scenarios with limited paired data and in the presence of noisy data, and (d) strong generalization ability of the trained encoder and decoder. △ Less

Submitted 21 May, 2024; v1 submitted 27 April, 2023; originally announced May 2023.

Why "solar tsunamis" rarely leave their imprints in the chromosphere

Authors: Ruisheng Zheng, Yihan Liu, Wenlong Liu, Bing Wang, Zhenyong Hou, Shiwei Feng, Xiangliang Kong, Zhenghua Huang, Hongqiang Song, Hui Tian, Pengfei Chen, Robertus Erdélyi, Yao Chen

Abstract: Solar coronal waves frequently appear as bright disturbances that propagate globally from the eruption center in the solar atmosphere, just like the tsunamis in the ocean on Earth. Theoretically, coronal waves can sweep over the underlying chromosphere and leave an imprint in the form of Moreton wave, due to the enhanced pressure beneath their coronal wavefront. Despite the frequent observations o… ▽ More Solar coronal waves frequently appear as bright disturbances that propagate globally from the eruption center in the solar atmosphere, just like the tsunamis in the ocean on Earth. Theoretically, coronal waves can sweep over the underlying chromosphere and leave an imprint in the form of Moreton wave, due to the enhanced pressure beneath their coronal wavefront. Despite the frequent observations of coronal waves, their counterparts in the chromosphere are rarely detected. Why the chromosphere rarely bears the imprints of solar tsunamis remained a mystery since their discovery three decades ago. To resolve this question, all coronal waves and associated Moreton waves in the last decade have been initially surveyed, though the detection of Moreton waves could be hampered by utilising the low-quality H$α$ data from Global Oscillations Network Group. Here, we present 8 cases (including 5 in Appendix) of the coexistence of coronal and Moreton waves in inclined eruptions where it is argued that the extreme inclination is key to providing an answer to address the question. For all these events, the lowest part of the coronal wavefront near the solar surface appears very bright, and the simultaneous disturbances in the solar transition region and the chromosphere predominantly occur beneath the bright segment. Therefore, evidenced by observations, we propose a scenario for the excitation mechanism of the coronal-Moreton waves in highly inclined eruptions, in which the lowest part of a coronal wave can effectively disturb the chromosphere even for a weak (e.g., B-class) solar flare. △ Less

Submitted 28 April, 2023; originally announced April 2023.

Comments: 24 pages, 14 figures

Lattice-aligned gallium oxynitride nanolayer for GaN surface enhancement and function extension

Authors: Junting Chen, Junlei Zhao, Sirui Feng, Li Zhang, Yan Cheng, Hang Liao, Zheyang Zheng, Xiaolong Chen, Zhen Gao, Kevin J. Chen, Mengyuan Hua

Abstract: Gallium nitride (GaN), as a promising alternative semiconductor to silicon, is of well-established use in photoelectronic and electronic technology. However, the vulnerable GaN surface has been a critical restriction that hinders the development of GaN-based devices, especially regarding device stability and reliability. Here, we overcome this challenge by converting the GaN surface into a gallium… ▽ More Gallium nitride (GaN), as a promising alternative semiconductor to silicon, is of well-established use in photoelectronic and electronic technology. However, the vulnerable GaN surface has been a critical restriction that hinders the development of GaN-based devices, especially regarding device stability and reliability. Here, we overcome this challenge by converting the GaN surface into a gallium oxynitride (GaON) epitaxial nanolayer through an in-situ two-step "oxidation-reconfiguration" process. The oxygen plasma treatment overcomes the chemical inertness of the GaN surface, and the sequential thermal annealing manipulates the kinetic-thermodynamic reaction pathways to create a metastable GaON nanolayer with wurtzite lattice. This GaN-derived GaON nanolayer is a tailored structure for surface reinforcement and possesses several advantages, including wide bandgap, high thermodynamic stability, and large valence band offset with GaN substrate. These enhanced physical properties can be further leveraged to enable GaN-based applications in new scenarios, such as complementary logic integrated circuits, photoelectrochemical water splitting, and ultraviolet photoelectric conversion, making GaON a versatile functionality extender. △ Less

Submitted 28 September, 2022; originally announced September 2022.

Comments: 13 pages, 6 figures, submitted under review

Solving Seismic Wave Equations on Variable Velocity Models with Fourier Neural Operator

Authors: Bian Li, Hanchen Wang, Shihang Feng, Xiu Yang, Youzuo Lin

Abstract: In the study of subsurface seismic imaging, solving the acoustic wave equation is a pivotal component in existing models. The advancement of deep learning enables solving partial differential equations, including wave equations, by applying neural networks to identify the mapping between the inputs and the solution. This approach can be faster than traditional numerical methods when numerous insta… ▽ More In the study of subsurface seismic imaging, solving the acoustic wave equation is a pivotal component in existing models. The advancement of deep learning enables solving partial differential equations, including wave equations, by applying neural networks to identify the mapping between the inputs and the solution. This approach can be faster than traditional numerical methods when numerous instances are to be solved. Previous works that concentrate on solving the wave equation by neural networks consider either a single velocity model or multiple simple velocity models, which is restricted in practice. Instead, inspired by the idea of operator learning, this work leverages the Fourier neural operator (FNO) to effectively learn the frequency domain seismic wavefields under the context of variable velocity models. We also propose a new framework paralleled Fourier neural operator (PFNO) for efficiently training the FNO-based solver given multiple source locations and frequencies. Numerical experiments demonstrate the high accuracy of both FNO and PFNO with complicated velocity models in the OpenFWI datasets. Furthermore, the cross-dataset generalization test verifies that PFNO adapts to out-of-distribution velocity models. Moreover, PFNO has robust performance in the presence of random noise in the labels. Finally, PFNO admits higher computational efficiency on large-scale testing datasets than the traditional finite-difference method. The aforementioned advantages endow the FNO-based solver with the potential to build powerful models for research on seismic waves. △ Less

Submitted 19 February, 2023; v1 submitted 25 September, 2022; originally announced September 2022.

Comments: 26 pages, 17 figures

MSC Class: 68T07 (Primary) 86-08 (Secondary)

Using fluorescent beads to emulate single flurophores

Authors: Luis A. Aleman-Castaneda, Sherry Yi-Ting Feng, Rodrigo Gutierrez-Cuevas, Isael Herrera, Thomas G. Brown, Sophie Brasselet, Miguel A. Alonso

Abstract: In this work, we study the conditions under which fluorescent beads can be used to emulate single fluorescent molecules in the calibration of optical microscopes. Although beads are widely used due to their brightness and easy manipulation, there can be notable differences between the point spread functions (PSFs) they produce and those for single-molecule fluorophores, caused by their different e… ▽ More In this work, we study the conditions under which fluorescent beads can be used to emulate single fluorescent molecules in the calibration of optical microscopes. Although beads are widely used due to their brightness and easy manipulation, there can be notable differences between the point spread functions (PSFs) they produce and those for single-molecule fluorophores, caused by their different emission pattern and their size. We study theoretically these differences for various scenarios, e.g. with or without polarization channel splitting, to determine the conditions under which the use of beads as a model for single molecules is valid. We also propose methods to model the blurring due to the size difference and compensate for it to produce PSFs that are more similar to those for single molecules. △ Less

Submitted 6 December, 2022; v1 submitted 19 September, 2022; originally announced September 2022.

Journal ref: J. Opt. Soc. Am. A 39, C167-C178 (2022)

Analyses of Flight Time During Solar Proton Events and Solar Flares

Authors: X. H. Xu, Y. Wang, F. S. Wei, X. S. Feng, M. H. Bo, H. W. Tang, D. S. Wang, B. Lei, B. Y. Wang, P. B. Zuo, C. W. Jiang, X. J. Xu, Z. L. Zhou, Z. Li, P. Zou, L. D. Wang, Y. X. Gu, Y. L. Chen, W. Y. Zhang, P. Sun

Abstract: Analyzing the effects of space weather on aviation is a new and developing topic. It has been commonly accepted that the flight time of the polar flights may increase during solar proton events because the flights have to change their route to avoid the high-energy particles. However, apart from such phenomenon, researches related to the flight time during space weather events is very rare. Based… ▽ More Analyzing the effects of space weather on aviation is a new and developing topic. It has been commonly accepted that the flight time of the polar flights may increase during solar proton events because the flights have to change their route to avoid the high-energy particles. However, apart from such phenomenon, researches related to the flight time during space weather events is very rare. Based on the analyses of 39 representative international air routes around westerlies, it is found that 97.44% (94.87%) of the commercial airplanes on the westbound (eastbound) air routes reveal shorter (longer) flight time during solar proton events compared to those during quiet periods, and the averaged magnitude of change in flight time is ~10 min or 0.21%-4.17% of the total flight durations. Comparative investigations reassure the certainty of such phenomenon that the directional differences in flight time are still incontrovertible regardless of over-land routes (China-Europe) or over-sea routes (China-Western America). Further analyses suggest that the solar proton events associated atmospheric heating will change the flight durations by weakening certain atmospheric circulations, such as the polar jet stream. While the polar jet stream will not be obviously altered during solar flares so that the directional differences in flight time are not found. Besides the conventional space weather effects already known, this paper is the first report that indicates a distinct new scenario of how the solar proton events affect flight time. These analyses are also important for aviation since our discoveries could help the airways optimize the air routes to save passenger time costs, reduce fuel costs and even contribute to the global warming issues. △ Less

Submitted 15 September, 2022; originally announced September 2022.

Comments: submitted to Scientific Reports

Characteristics of Flight Delays during Solar Flares

Authors: X. H. Xu, Y. Wang, F. S. Wei, X. S. Feng, M. H. Bo, H. W. Tang, D. S. Wang, L. Bian, B. Y. Wang, W. Y. Zhang, Y. S. Huang, Z. Li, J. P. Guo, P. B. Zuo, C. W. Jiang, X. J. Xu, Z. L. Zhou, P. Zou

Abstract: Solar flare is one of the severest solar activities on the sun, and it has many important impacts on the near-earth space. It has been found that flight arrival delays will increase during solar flare. However, the detailed intrinsic mechanism of how solar flares influence the delays is still unknown. Based on 5-years huge amount of flight data, here we comprehensively analyze the flight departure… ▽ More Solar flare is one of the severest solar activities on the sun, and it has many important impacts on the near-earth space. It has been found that flight arrival delays will increase during solar flare. However, the detailed intrinsic mechanism of how solar flares influence the delays is still unknown. Based on 5-years huge amount of flight data, here we comprehensively analyze the flight departure delays during 57 solar flares. It is found that the averaged flight departure delay time during solar flares increased by 20.68% (7.67 min) compared to those during quiet periods. It is also shown that solar flare related flight delays reveal apparent time and latitude dependencies. Flight delays during dayside solar flares are more serious than those during nightside flares, and the longer (shorter) delays tend to occur in the lower (higher) latitude airport. Further analyses suggest that flight delay time and delay rate would be directly modulated by the solar intensity (soft X-ray flux) and the Solar Zenith Angle. For the first time, these results indicate that the communication interferences caused by solar flares will directly affect flight departure delay time and delay rate. This work also expands our conventional understandings to the impacts of solar flares on human society, and it could also provide us with brand new views to help prevent or cope with flight delays. △ Less

Submitted 15 September, 2022; originally announced September 2022.

Comments: submitted to APJL

The Effects of Space Weather on Flight Delays

Authors: Y. Wang, X. H. Xu, F. S. Wei, X. S. Feng, M. H. Bo, H. W. Tang, D. S. Wang, L. Bian, B. Y. Wang, W. Y. Zhang, Y. S. Huang, Z. Li, J. P. Guo, P. B. Zuo, C. W. Jiang, X. J. Xu, Z. L. Zhou, P. Zou

Abstract: Although the sun is really far away from us, some solar activities could still influence the performance and reliability of space-borne and ground-based technological systems on Earth. Those time-varying conditions in space caused by the sun are also called space weather, as the atmospheric conditions that can affect weather on the ground. It is known that aviation activities can be affected durin… ▽ More Although the sun is really far away from us, some solar activities could still influence the performance and reliability of space-borne and ground-based technological systems on Earth. Those time-varying conditions in space caused by the sun are also called space weather, as the atmospheric conditions that can affect weather on the ground. It is known that aviation activities can be affected during space weather events, but the exact effects of space weather on aviation are still unclear. Especially how the flight delays, the top topic concerned by most people, will be affected by space weather has never been thoroughly researched. By analyzing huge amount of flight data (~5X106 records), for the first time, we demonstrate that space weather events could have systematically modulating effects on flight delays. The average arrival delay time and 30-minute delay rate during space weather events are significantly increased by 81.34% and 21.45% respectively compared to those during quiet periods. The evident negative correlation between the yearly flight regularity rate and the yearly mean total sunspot number during 22 years also confirms such delay effects. Further studies indicate that the interference in communication and navigation caused by geomagnetic field fluctuations and ionospheric disturbances associated with the space weather events will increase the flight delay time and delay rate. These results expand the traditional field of space weather research and could also provide us with brand new views for improving the flight delay predications. △ Less

Submitted 15 September, 2022; originally announced September 2022.

Comments: submitted to science advances