Chinese Physics B

Kuang, Hao; Yu, Junxiao; Chen, Jie; Elsayed-Ali, H. E.; Li, Runze; Rentzepis, Peter M.

doi: 10.1088/1674-1056/ad1983pmid: N/A

With the integration of ultrafast reflectivity and polarimetry probes, we observed carrier relaxation and spin dynamics induced by ultrafast laser excitation of Ni (111) single crystals. The carrier relaxation time within the linear excitation range reveals that electron–phonon coupling and dissipation of photon energy into the bulk of the crystal take tens of picoseconds. On the other hand, the observed spin dynamics indicate a longer time of about 120 ps. To further understand how the lattice degree of freedom is coupled with these dynamics may require the integration of an ultrafast diffraction probe.

He, Xiang-Lei; Mao, Ao-Hua; Sun, Meng-Meng; Zou, Ji-Tong; Wang, Xiao-Gang

doi: 10.1088/1674-1056/ad0b02pmid: N/A

Magnetic reconnection processes in three-dimensional (3D) complex field configurations have been investigated in different magneto-plasma systems in space, laboratory, and astrophysical systems. Two-dimensional (2D) features of magnetic reconnection have been well developed and applied successfully to systems with symmetrical property, such as toroidal fusion plasmas and laboratory experiments with an axial symmetry. But in asymmetric systems, the 3D features are inevitably different from those in the 2D case. Magnetic reconnection structures in multiple celestial body systems, particularly star–planet–Moon systems, bring fresh insights to the understanding of the 3D geometry of reconnection. Thus, we take magnetic reconnection in an ancient solar–lunar terrestrial magneto-plasma system as an example by using its crucial parameters approximately estimated already and also some specific applications in pathways for energy and matter transports among Earth, ancient Moon, and the interplanetary magnetic field (IMF). Then, magnetic reconnection of the ancient lunar–terrestrial magnetospheres with the IMF is investigated numerically in this work. In a 3D simulation for the Earth–Moon–IMF system, topological features of complex magnetic reconnection configurations and dynamical characteristics of magnetic reconnection processes are studied. It is found that a coupled lunar-terrestrial magnetosphere is formed, and under various IMF orientations, multiple X-points emerge at distinct locations, showing three typical magnetic reconnection structures in such a geometry, i.e., the X-line, the triple current sheets, and the A–B null pairs. The results can conduce to further understanding of reconnection physics in 3D for plasmas in complex magnetic configurations, and also a possible mechanism for energy and matters transport in evolutions of similar astrophysical systems.

Wang, Chunyun; Yao, Yuan; Shi, Haosen; Yu, Hongfu; Ma, Longsheng; Jiang, Yanyi

doi: 10.1088/1674-1056/ad1986pmid: N/A

We construct a power enhancement cavity to form an optical lattice in an ytterbium optical clock. It is demonstrated that the intra-cavity lattice power can be increased by about 45 times, and the trap depth can be as large as 1400Er when laser light with a power of only 0.6 W incident to the lattice cavity. Such high trap depths are the key to accurate evaluation of the lattice-induced light shift with an uncertainty down to ∼1 × 10−18. By probing the ytterbium atoms trapped in the power-enhanced optical lattice, we obtain a 4.3 Hz-linewidth Rabi spectrum, which is then used to feedback to the clock laser for the close loop operation of the optical lattice clock. We evaluate the density shift of the Yb optical lattice clock based on interleaving measurements, which is –0.46(62) mHz. This result is smaller compared to the density shift of our first Yb optical clock without lattice power enhancement cavity mainly due to a larger lattice diameter of 344 μm.

Xie, Xiao-Xiao; Huo, Liang-An; Dong, Ya-Fang; Cheng, Ying-Ying

doi: 10.1088/1674-1056/ad1176pmid: N/A

While the interaction between information and disease in static networks has been extensively investigated, many studies have ignored the characteristics of network evolution. In this study, we construct a new two-layer coupling model to explore the interactions between information and disease. The upper layer describes the diffusion of disease-related information, and the lower layer represents the disease transmission. We then use power-law distributions to examine the influence of asymmetric activity levels on dynamic propagation, revealing a mapping relationship characterizing the interconnected propagation of information and diseases among partial nodes within the network. Subsequently, we derive the disease outbreak threshold by using the microscopic Markov-chain approach (MMCA). Finally, we perform extensive Monte Carlo (MC) numerical simulations to verify the accuracy of our theoretical results. Our findings indicate that the activity levels of individuals in the disease transmission layer have a more significant influence on disease transmission compared with the individual activity levels in the information diffusion layer. Moreover, reducing the damping factor can delay disease outbreaks and suppress disease transmission, while improving individual quarantine measures can contribute positively to disease control. This study provides valuable insights into policymakers for developing outbreak prevention and control strategies.

Tang, Jian-Feng; Xia, Lei; Li, Guang-Li; Fu, Jun; Duan, Shukai; Wang, Lidan

doi: 10.1088/1674-1056/aceeeapmid: N/A

Neuromorphic computing, inspired by the human brain, uses memristor devices for complex tasks. Recent studies show that self-organizing random nanowires can implement neuromorphic information processing, enabling data analysis. This paper presents a model based on these nanowire networks, with an improved conductance variation profile. We suggest using these networks for temporal information processing via a reservoir computing scheme and propose an efficient data encoding method using voltage pulses. The nanowire network layer generates dynamic behaviors for pulse voltages, allowing time series prediction analysis. Our experiment uses a double stochastic nanowire network architecture for processing multiple input signals, outperforming traditional reservoir computing in terms of fewer nodes, enriched dynamics and improved prediction accuracy. Experimental results confirm the high accuracy of this architecture on multiple real-time series datasets, making neuromorphic nanowire networks promising for physical implementation of reservoir computing.

Xu, Hao-Hang; Liu, Qing-Yuan; Xin, Chao; Shen, Qin-Xin; Luo, Jun; Zhou, Rui; Cheng, Jin-Guang; Liu, Jian; Tao, Ling Ling; Liu, Zhi-Guo; Huo, Ming-Xue; Wang, Xian-Jie; Sui, Yu

doi: 10.1088/1674-1056/ad1381pmid: N/A

Quasi-one-dimensional (1D) antiferromagnets are known to display intriguing phenomena especially when there is a spin gap in their spin-excitation spectra. Here we demonstrate that a spin gap exists in the quasi-1D Heisenberg antiferromagnet CoTi2O5 with highly ordered Co2+/Ti4+ occupation, in which the Co2+ ions with S = 3/2 form a 1D spin chain along the a-axis. CoTi2O5 undergoes an antiferromagnetic transition at TN ∼ 24 K and exhibits obvious anisotropic magnetic susceptibility even in the paramagnetic region. Although a gapless magnetic ground state is usually expected in a quasi-1D Heisenberg antiferromagnet with half-integer spins, by analyzing the specific heat, the thermal conductivity, and the spin-lattice relaxation rate (1/T1) as a function of temperature, we found that a spin gap is opened in the spin-excitation spectrum of CoTi2O5 around TN, manifested by the rapid decrease of magnetic specific heat to zero, the double-peak characteristic in thermal conductivity, and the exponential decay of 1/T1 below TN. Both the magnetic measurements and the first-principles calculations results indicate that there is spin-orbit coupling in CoTi2O5, which induces the magnetic anisotropy in CoTi2O5, and then opens the spin gap at low temperature.

Liang, Qian; Luo, Xiangyan; Qian, Guolin; Wang, Yuanfan; Liang, Yongchao; Xie, Quan

doi: 10.1088/1674-1056/acef04pmid: N/A

Recently, the newly synthesized septuple-atomic layer two-dimensional (2D) material MoSi2N4 (MSN) has attracted attention worldwide. Our work delves into the effect of vacancies and external electric fields on the electronic properties of the MSN/graphene (Gr) heterostructure using first-principles calculation. We find that four types of defective structures, N-in, N-out, Si and Mo vacancy defects of monolayer MSN and MSN/Gr heterostructure are stable in air. Moreover, vacancy defects can effectively modulate the charge transfer at the interface of the MSN/Gr heterostructure as well as the work function of the pristine monolayer MSN and MSN/Gr heterostructure. Finally, the application of an external electric field enables the dynamic switching between n-type and p-type Schottky contacts. Our work may offer the possibility of exceeding the capabilities of conventional Schottky diodes based on MSN/Gr heterostructures.

Tang, Yiming; Yang, Zhongyuan; Yao, Yifei; Zhou, Yun; Tan, Yuan; Wang, Zichao; Pan, Tong; Xiong, Rui; Sun, Junli; Wei, Guanghong

doi: 10.1088/1674-1056/ad1a92pmid: N/A

The rapid advancement and broad application of machine learning (ML) have driven a groundbreaking revolution in computational biology. One of the most cutting-edge and important applications of ML is its integration with molecular simulations to improve the sampling efficiency of the vast conformational space of large biomolecules. This review focuses on recent studies that utilize ML-based techniques in the exploration of protein conformational landscape. We first highlight the recent development of ML-aided enhanced sampling methods, including heuristic algorithms and neural networks that are designed to refine the selection of reaction coordinates for the construction of bias potential, or facilitate the exploration of the unsampled region of the energy landscape. Further, we review the development of autoencoder based methods that combine molecular simulations and deep learning to expand the search for protein conformations. Lastly, we discuss the cutting-edge methodologies for the one-shot generation of protein conformations with precise Boltzmann weights. Collectively, this review demonstrates the promising potential of machine learning in revolutionizing our insight into the complex conformational ensembles of proteins.