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The Silicon Charge Detector (SCD) is a subdetector of the High Energy Cosmic Radiation Detection payload. The dynamic range of the silicon microstrip detector can be extended by the capacitive coupling effect, which is related to the interstrip capacitance and the coupling capacitance. A detector prototype with several sets of parameters was designed and tested in the ion beams at the CERN Super Proton Synchrotron. The capacitive coupling fractions with readout strip and floating strip incidences were studied using the beam test data and SPICE simulation.

To investigate the influence of inertia and slip on the instability of a liquid film on a fibre, a theoretical framework based on the axisymmetric Navier-Stokes equations is proposed via linear instability analysis. The model reveals that slip significantly enhances perturbation growth in viscous film flows, whereas it exerts minimal influence on flows dominated by inertia. Moreover, under no-slip boundary conditions, the dominant instability mode of thin films remains unaltered by inertia, closely aligning with predictions from a no-slip lubrication model. Conversely, when slip is introduced, the dominant wavenumber experiences a noticeable reduction as inertia decreases. This trend is captured by an introduced lubrication model with giant slip. Direct numerical simulations of the Navier-Stokes equations are then performed to further confirm the theoretical findings at the linear stage. For the nonlinear dynamics, no-slip simulations show complex vortical structures within films, driven by fluid inertia near surfaces. Additionally, in scenarios with weak inertia, a reduction in the volume of satellite droplets is observed due to slip, following a power-law relationship.

The ground state search of the Ising model can be used to solve many combinatorial optimization problems. Under the current computer architecture, an Ising ground state search algorithm suitable for hardware computing is necessary for solving practical problems. Inspired by the potential energy conversion of springs, we propose a point convolutional neural network algorithm for ground state search based on spring vibration model, called Spring-Ising Algorithm. Spring-Ising Algorithm regards the spin as a moving mass point connected to a spring and establish the equation of motion for all spins. Spring-Ising Algorithm can be mapped on the GPU or AI chips through the basic structure of the neural network for fast and efficient parallel computing. The algorithm has very productive results for solving the Ising model and has been test in the recognized test benchmark K2000. The algorithm introduces the concept of dynamic equilibrium to achieve a more detailed local search by dynamically adjusting the weight of the Ising model in the spring oscillation model. Finally, there is the simple hardware test speed evaluation. Spring-Ising Algorithm can provide the possibility to calculate the Ising model on a chip which focuses on accelerating neural network calculations.

As a new member of GECAM mission, GECAM-C (also named High Energy Burst Searcher, HEBS) was launched onboard the SATech-01 satellite on July 27th, 2022, which is capable to monitor gamma-ray transients from $\sim$ 6 keV to 6 MeV. As the main detector, there are 12 gamma-ray detectors (GRDs) equipped for GECAM-C. In order to verify the GECAM-C GRD detector performance and to validate the Monte Carlo simulations of detector response, comprehensive on-ground calibration experiments have been performed using X-ray beam and radioactive sources, including Energy-Channel relation, energy resolution, detection efficiency, SiPM voltage-gain relation and the non-uniformity of positional response. In this paper, the detailed calibration campaigns and data analysis results for GECAM-C GRDs are presented, demonstrating the excellent performance of GECAM-C GRD detectors.

As a new member of GECAM mission, the GECAM-C (also called High Energy Burst Searcher, HEBS) is a gamma-ray all-sky monitor onboard SATech-01 satellite, which was launched on July 27th, 2022 to detect gamma-ray transients from 6 keV to 6 MeV, such as Gamma-Ray Bursts (GRBs), high energy counterpart of Gravitational Waves (GWs) and Fast Radio Bursts (FRBs), and Soft Gamma-ray Repeaters (SGRs). Together with GECAM-A and GECAM-B launched in December 2020, GECAM-C will greatly improve the monitoring coverage, localization, as well as temporal and spectral measurements of gamma-ray transients. GECAM-C employs 12 SiPM-based Gamma-Ray Detectors (GRDs) to detect gamma-ray transients . In this paper, we firstly give a brief description of the design of GECAM-C GRDs, and then focus on the on-ground tests and in-flight performance of GRDs. We also did the comparison study of the SiPM in-flight performance between GECAM-C and GECAM-B. The results show GECAM-C GRD works as expected and is ready to make scientific observations.

More than a century after the performance of the oil drop experiment, the possible existence of fractionally charged particles FCP still remains unsettled. The search for FCPs is crucial for some extensions of the Standard Model in particle physics. Most of the previously conducted searches for FCPs in cosmic rays were based on experiments underground or at high altitudes. However, there have been few searches for FCPs in cosmic rays carried out in orbit other than AMS-01 flown by a space shuttle and BESS by a balloon at the top of the atmosphere. In this study, we conduct an FCP search in space based on on-orbit data obtained using the DArk Matter Particle Explorer (DAMPE) satellite over a period of five years. Unlike underground experiments, which require an FCP energy of the order of hundreds of GeV, our FCP search starts at only a few GeV. An upper limit of $6.2\times 10^{-10}~~\mathrm{cm^{-2}sr^{-1} s^{-1}}$ is obtained for the flux. Our results demonstrate that DAMPE exhibits higher sensitivity than experiments of similar types by three orders of magnitude that more stringently restricts the conditions for the existence of FCP in primary cosmic rays.

In low-permeability porous media, the velocity of a fluid flow exhibits a nonlinear dependence on the imposed pressure gradient. This non-Darcian flow behavior has important implications to geological disposal of nuclear waste, hydrocarbon extraction from shale, and flow and transport in clay-rich aquifers. Temperature has been postulated to affect the threshold pressure gradient of a non-Darcian flow; however, the supporting data is very limited. In this study we for the first time report a systematic measurement of the threshold pressure gradient under various permeabilities and temperatures. The results show that a higher temperature leads to a lower threshold pressure gradient under the same permeability and a faster reduction of the threshold pressure gradient with increasing permeability. The experimental data are fitted to a two-parameter model to determine the parameters, h0 and a, which characterize the interfacial fluid-solid interactions and the transition between the Darcy and non-Darcian regimes.

The GECAM mission consists of two identical microsatellites (GECAM-A and GECAM-B). Each satellite is equipped with 25 gamma-ray detectors (GRD) and 8 charged particle detectors (CPD). The main scientific objective of the GECAM mission is to detect gamma-ray bursts (GRBs) associated with the gravitational wave events produced by the merging of binary compact stars. After the launch on Dec. 10, 2020 , we carried out a series of on orbit tests. This paper introduces the test results of the GECAM-B satellite. According to the in-flight performance, the energy band for gamma-ray detection of GECAM-B is from about 7 keV to 3.5 MeV. GECAM-B can achieve prompt localization of GRBs. For the first time, GECAM-B realized a quasi-real-time transmission of trigger information using Beidou-3 RDSS. Keywords GECAM, gamma-ray burst, gravitational wave, GRD, CPD

Each satellite of the Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor (GECAM, mission) consists of 25 SiPM based gamma-ray detectors (GRDs). Although SiPM based GRD has merits of compact size and low bias-voltage, the drift of the SiPM gain with temperature is a severe problem for GRD performance. An adaptive voltage supply source was designed to automatically adjust the SiPM bias voltage to compensate the temperature effects and keep the gain stable. This approach has been proved to be effective during both the on-ground and in-flight tests. The in-flight measured variation of the SiPM gain is within 2%. To reduce the gain non-uniformity of GRDs, an iterative bias voltage adjustment approach is proposed and implemented. The gain non-uniformity is reduced from 17% to 0.6%. In this paper, the gain stabilization and consistency correction approach are presented and discussed in detail.

The exciton-phonon coupling in highly oriented pyrolytic graphite is studied using resonant inelastic X-ray scattering (RIXS) spectroscopy. With ~ 70 meV energy resolution, multiple low energy excitations associated with coupling to phonons can be clearly resolved in RIXS spectra. Using resonance dependence and the closed form for RIXS cross-section without considering the intermediate state mixing of phonon modes, the dimensionless coupling constant g is determined to be 5 and 0.4, corresponding to the coupling strength of 0.42 eV +/- 40 meV and 0.21 eV +/- 30 meV, for zone center and boundary phonons respectively. The reduced g value for zone-boundary phonon may be related to its double resonance nature.

We report the application of machine learning methods for predicting the effective diffusivity (De) of two-dimensional porous media from images of their structures. Pore structures are built using reconstruction methods and represented as images, and their effective diffusivity is computed by lattice Boltzmann (LBM) simulations. The datasets thus generated are used to train convolutional neural network (CNN) models and evaluate their performance. The trained model predicts the effective diffusivity of porous structures with computational cost orders of magnitude lower than LBM simulations. The optimized model performs well on porous media with realistic topology, large variation of porosity (0.28-0.98), and effective diffusivity spanning more than one order of magnitude (), e.g., >95% of predicted De have truncated relative error of <10% when the true De is larger than 0.2. The CNN model provides better prediction than the empirical Bruggeman equation, especially for porous structure with small diffusivity. The relative error of CNN predictions, however, is rather high for structures with De < 0.1. To address this issue, the porosity of porous structures is encoded directly into the neural network but the performance is enhanced marginally. Further improvement, i.e., 70% of the CNN predictions for structures with true De < 0.1 have relative error <30%, is achieved by removing trapped regions and dead-end pathways using a simple algorithm. These results suggest that deep learning augmented by field knowledge can be a powerful technique for predicting the transport properties of porous media. Directions for future research of machine learning in porous media are discussed based on detailed analysis of the performance of CNN models in the present work.

One of the main purposes of the DArk Matter Particle Explorer (DAMPE) is to measure the cosmic ray nuclei up to several tens of TeV or beyond, whose origin and propagation remains a hot topic in astrophysics. The Plastic Scintillator Detector (PSD) on top of DAMPE is designed to measure the charges of cosmic ray nuclei from H to Fe and serves as a veto detector for discriminating gamma-rays from charged particles. We propose in this paper a charge reconstruction procedure to optimize the PSD performance in charge measurement. Essentials of our approach, including track finding, alignment of PSD, light attenuation correction, quenching and equalization correction are described detailedly in this paper after a brief description of the structure and operational principle of the PSD. Our results show that the PSD works very well and almost all the elements in cosmic rays from H to Fe are clearly identified in the charge spectrum.

DAMPE (DArk Matter Particle Explorer) is a spaceborne high-energy cosmic ray and gamma-ray detector, successfully launched in December 2015. It is designed to probe astroparticle physics in the broad energy range from few GeV to 100 TeV. The scientific goals of DAMPE include the identification of possible signatures of Dark Matter annihilation or decay, the study of the origin and propagation mechanisms of cosmic-ray particles, and gamma-ray astronomy. DAMPE consists of four sub-detectors: a plastic scintillator strip detector, a Silicon-Tungsten tracKer-converter (STK), a BGO calorimeter and a neutron detector. The STK is composed of six double layers of single-sided silicon micro-strip detectors interleaved with three layers of tungsten for photon conversions into electron-positron pairs. The STK is a crucial component of DAMPE, allowing to determine the direction of incoming photons, to reconstruct tracks of cosmic rays and to estimate their absolute charge (Z). We present the in-flight performance of the STK based on two years of in-flight DAMPE data, which includes the noise behavior, signal response, thermal and mechanical stability, alignment and position resolution.

DArk Matter Particle Explorer (DAMPE) is a general purpose high energy cosmic ray and gamma ray observatory, aiming to detect high energy electrons and gammas in the energy range 5 GeV to 10 TeV and hundreds of TeV for nuclei. This paper provides a method using machine learning to identify electrons and separate them from gammas,protons,helium and heavy nuclei with the DAMPE data from 2016 January 1 to 2017 June 30, in energy range from 10 to 100 GeV.

The DArk Matter Particle Explorer (DAMPE) is one of the four satellites within the Strategic Pioneer Research Program in Space Science of the Chinese Academy of Science (CAS). The Silicon-Tungsten Tracker (STK), which is composed of 768 singled-sided silicon microstrip detectors, is one of the four subdetectors in DAMPE, providing track reconstruction and charge identification for relativistic charged particles. The charge response of DAMPE silicon microstrip detectors is complicated, depending on the incident angle and impact position. A new charge reconstruction algorithm for the DAMPE silicon microstrip detector is introduced in this paper. This algorithm can correct the complicated charge response, and was proved applicable by the ion test beam.

The DArk Matter Particle Explorer (DAMPE) is a space-borne particle detector designed to probe electrons and gamma-rays in the few GeV to 10 TeV energy range, as well as cosmic-ray proton and nuclei components between 10 GeV and 100 TeV. The silicon-tungsten tracker-converter is a crucial component of DAMPE. It allows the direction of incoming photons converting into electron-positron pairs to be estimated, and the trajectory and charge (Z) of cosmic-ray particles to be identified. It consists of 768 silicon micro-strip sensors assembled in 6 double layers with a total active area of 6.6 m$^2$. Silicon planes are interleaved with three layers of tungsten plates, resulting in about one radiation length of material in the tracker. Internal alignment parameters of the tracker have been determined on orbit, with non-showering protons and helium nuclei. We describe the alignment procedure and present the position resolution and alignment stability measurements.

The DArk Matter Particle Explorer (DAMPE), one of the four scientific space science missions within the framework of the Strategic Pioneer Program on Space Science of the Chinese Academy of Sciences, is a general purpose high energy cosmic-ray and gamma-ray observatory, which was successfully launched on December 17th, 2015 from the Jiuquan Satellite Launch Center. The DAMPE scientific objectives include the study of galactic cosmic rays up to $\sim 10$ TeV and hundreds of TeV for electrons/gammas and nuclei respectively, and the search for dark matter signatures in their spectra. In this paper we illustrate the layout of the DAMPE instrument, and discuss the results of beam tests and calibrations performed on ground. Finally we present the expected performance in space and give an overview of the mission key scientific goals.

The DArk Matter Particle Explorer (DAMPE) is one of the four satellites within Strategic Pioneer Research Program in Space Science of the Chinese Academy of Science (CAS). DAMPE can detect electrons, photons in a wide energy range (5 GeV to 10 TeV) and ions up to iron (100GeV to 100 TeV). Silicon-Tungsten Tracker (STK) is one of the four subdetectors in DAMPE, providing photon-electron conversion, track reconstruction and charge identification for ions. Ion beam test was carried out in CERN with 60GeV/u Lead primary beams. Charge reconstruction and charge resolution of STK detectors were investigated.

Ultrafast electron pulses, combined with laser-pump and electron-probe technologies, allow for various forms of ultrafast microscopy and spectroscopy to elucidate otherwise challenging to observe physical and chemical transitions. However, the pursuit of simultaneous ultimate spatial and temporal resolution has been largely subdued by the low monochromaticity of the electron pulses and their poor phase synchronization to the optical excitation pulses. State-of-the-art photon-driven sources have good monochromaticity but poor phase synchronization. In contrast, field-driven photoemission has much higher light phase synchronization, due to the intrinsic sub-cycle emission dynamics, but poor monochromaticity. Such sources suffer from larger electron energy spreads (3 - 100 eV) attributed to the relatively low field enhancement of the conventional metal tips which necessitates long pump wavelengths (> 800 nm) in order to gain sufficient ponderomotive potential to access the field-driven regime. In this work, field-driven photoemission from ~1 nm radius carbon nanotubes excited by a femtosecond laser at a short wavelength of 410 nm has been realized. The energy spread of field-driven electrons is effectively compressed to 0.25 eV outperforming all conventional ultrafast electron sources. Our new nanotube-based ultrafast electron source opens exciting prospects for attosecond imaging and emerging light-wave electronics.

The DArk Matter Particle Explorer (DAMPE) is one of the four satellites within Strategic Pioneer Research Program in Space Science of the Chinese Academy of Science (CAS). DAMPE can detect electrons, photons and ions in a wide energy range (5 GeV to 10 TeV) and ions up to iron (100GeV to 100 TeV). Plastic Scintillator Detector (PSD) is one of the four payloads in DAMPE, providing e/\gamma separation and charge identification up to Iron. An ion beam test was carried out for the Qualification Model of PSD in CERN with 40GeV/u Argon primary beams. The Birk's saturation and charge resolution of PSD were investigated.

Understanding the recovery of gas from reservoirs featuring pervasive nanopores is essential for effective shale gas extraction. Classical theories cannot accurately predict such gas recovery and many experimental observations are not well understood. Here we report molecular simulations of the recovery of gas from single nanopores, explicitly taking into account molecular gas-wall interactions. We show that, in very narrow pores, the strong gas-wall interactions are essential in determining the gas recovery behavior both quantitatively and qualitatively. These interactions cause the total diffusion coefficients of the gas molecules in nanopores to be smaller than those predicted by kinetic theories, hence slowing down the rate of gas recovery. These interactions also lead to significant adsorption of gas molecules on the pore walls. Because of the desorption of these gas molecules during gas recovery, the gas recovery from the nanopore does not exhibit the usual diffusive scaling law (i.e., the accumulative recovery scales as $R \sim t^{1/2}$ but follows a super-diffusive scaling law $R \sim t^n$ ($n>0.5$), which is similar to that observed in some field experiments. For the system studied here, the super-diffusive gas recovery scaling law can be captured well by continuum models in which the gas adsorption and desorption from pore walls are taken into account using the Langmuir model.

The Silicon Tracker (STK) is a detector of the DAMPE satellite to measure the incidence direction of high energy cosmic ray. It consists of 6 X-Y double layers of silicon micro-strip detectors with 73,728 readout channels. It's a great challenge to readout the channels and process the huge volume of data in the critical space environment. 1152 Application Specific Integrated Circuits (ASIC) and 384 ADCs are adopted to readout the detector channels. The 192 Tracker Front-end Hybrid (TFH) modules and 8 identical Tracker Readout Board (TRB) modules are designed to control and digitalize the front signals. In this paper, the design of the readout electronics for STK and its performance will be presented in detail.

The Dark Matter Particle Explorer (DAMPE) is an upcoming scientific satellite mission for high energy gamma-ray, electron and cosmic rays detection. The silicon tracker (STK) is a sub detector of the DAMPE payload with an excellent position resolution (readout pitch of 242um), which measures the incident direction of particles, as well as charge. The STK consists 12 layers of Silicon Micro-strip Detector (SMD), equivalent to a total silicon area of 6.5m$^2$. The total readout channels of the STK are 73728, which leads to a huge amount of raw data to be dealt. In this paper, we focus on the on-board data compression algorithm and procedure in the STK, which was initially verified by cosmic-ray measurements.

Dissipative particle dynamics (DPD) is a novel particle method for mesoscale modeling of complex fluids. DPD particles are often thought to represent packets of real atoms, and the physical scale probed in DPD models are determined by the mapping of DPD variables to the corresponding physical quantities. However, the non-uniqueness of such mapping has led to difficulties in setting up simulations to mimic real systems and in interpreting results. For modeling transport phenomena where thermal fluctuations are important (e.g., fluctuating hydrodynamics), an area particularly suited for DPD method, we propose that DPD fluid particles should be viewed as only 1) to provide a medium in which the momentum and energy are transferred according to the hydrodynamic laws and 2) to provide objects immersed in the DPD fluids the proper random "kicks" such that these objects exhibit correct fluctuation behaviors at the macroscopic scale. We show that, in such a case, the choice of system temperature and mapping of DPD scales to physical scales are uniquely determined by the level of coarse-graining and properties of DPD fluids. We also verified that DPD simulation can reproduce the macroscopic effects of thermal fluctuation in particulate suspension by showing that the Brownian diffusion of solid particles can be computed in DPD simulations with good accuracy.