We review the physics of monolayer graphene in a strong magnetic field, with emphasis on highly collective states that emerge from the weakly interacting system because of correlations (emergent states). After reviewing the general properties of graphene and of electrons in a magnetic field, we give a brief introduction to the integer quantum Hall effect (IQHE) and the fractional quantum Hall effect (FQHE) in a 2D electron gas as foundation to show that monolayer graphene in a magnetic field exhibits both effects, but with properties modified by the influence of the graphene crystal. After giving an introduction to standard methods of dealing with emergent states for this system, we show that an SO(8) fermion dynamical symmetry governs the emergent degrees of freedom and that the algebraic and group properties of the dynamical symmetry provide a new view of strongly correlated states observed in monolayer graphene subject to a strong magnetic field.

Localized surface plasmon resonance (LSPR) is an intriguing phenomenon that can break diffraction limitations and exhibit excellent light-confinement abilities, making it an attractive strategy for enhancing the light absorption capabilities of photodetectors. However, the complex mechanism behind this enhancement is still plaguing researchers, especially for hot-electron injection process, which inhibits further optimization and development. A clear guideline for basic physical model, enhancement mechanism, material selection and architectural design for LSPR photodetector are still required. This review firstly describes the mainstream understanding of fundamental physical modes of LSPR and related enhancement mechanism for LSPR photodetectors. Then, the universal strategies for tuning the LSPR frequency are introduced. Besides, the state-of-the-art progress in the development of LSPR photodetectors is briefly summarized. Finally, we highlight the remaining challenges and issues needed to be resolved in the future research.

Variational quantum algorithms have been widely demonstrated in both experimental and theoretical contexts to have extensive applications in quantum simulation, optimization, and machine learning. However, the exponential growth in the dimension of the Hilbert space results in the phenomenon of vanishing parameter gradients in the circuit as the number of qubits and circuit depth increase, known as the barren plateau phenomena. In recent years, research in non-equilibrium statistical physics has led to the discovery of the realization of many-body localization. As a type of floquet system, many-body localized floquet system has phase avoiding thermalization with an extensive parameter space coverage and has been experimentally demonstrated can produce time crystals. We applied this circuit to the variational quantum algorithms for the calculation of many-body ground states and studied the variance of gradient for parameter updates under this circuit. We found that this circuit structure can effectively avoid barren plateaus. We also analyzed the entropy growth, information scrambling, and optimizer dynamics of this circuit. Leveraging this characteristic, we designed a new type of variational ansatz, called the “many-body localization ansatz”. We applied it to solve quantum many-body ground states and examined its circuit properties. Our numerical results show that our ansatz significantly improved the variational quantum algorithm.

Vortex wave and plane wave, as two most fundamental forms of wave propagation, are widely applied in various research fields. However, there is currently a lack of basic mechanism to enable arbitrary conversion between them. In this paper, we propose a new paradigm of extremely anisotropic acoustic metasurface (AM) to achieve the efficient conversion from 2D vortex waves with arbitrary orbital angular momentum (OAM) to plane waves. The underlying physics of this conversion process is ensured by the symmetry shift of AM medium parameters and the directional compensation of phase. Moreover, this novel phenomenon is further verified by analytical calculations, numerical demonstrations, and acoustic experiments, and the deflection angle and direction of the converted plane waves are qualitatively and quantitatively confirmed by a simple formula. Our work provides new possibilities for arbitrary manipulation of acoustic vortex, and holds potential applications in acoustic communication and OAM-based devices.

Atomically thin two-dimensional (2D) semiconductors are attractive channel materials for next-generation field-effect transistors (FETs). The high-performance 2D electronics requires high-quality integration of high-\kappa dielectrics, which however remains a significant challenge. In this mini-review, we provide a brief introduction on recent progress in the van der Waals (vdW) integration of high-\kappa dielectrics onto 2D semiconductors. We first highlight the importance of high-\kappa dielectric integration for 2D FETs. Next, we summarize the recent breakthroughs in the various vdW integrations of high-\kappa dielectrics with 2D semiconductors, along with their interfaces’ properties. Additionally, we examine the quasi-vdW integration of conventional high-\kappa dielectrics onto 2D semiconductors. Finally, we discuss the challenges and potential future research directions in this field.

Laser-induced breakdown spectroscopy (LIBS) is a spectroscopic analytic technique with great application potential because of its unique advantages for online/in-situ detection. However, due to the spatially inhomogeneity and drastically temporal varying nature of its emission source, the laser-induced plasma, it is difficult to find or hard to generate an appropriate spatiotemporal window for high repeatable signal collection with lower matrix effects. The quantification results of traditional physical principle based calibration model are unsatisfactory since these models were not able to compensate for complicate matrix effects as well as signal fluctuation. Machine learning is an emerging approach, which can intelligently correlated the complex LIBS spectral data with its qualitative or/and quantitative composition by establishing multivariate regression models with greater potential to reduce the impacts of signal fluctuation and matrix effects, therefore achieving relatively better qualitative and quantitative performance. In this review, the progress of machine learning application in LIBS is summarized from two main aspects: i) Pre-processing data for machine learning model, including spectral selection, variable reconstruction, and denoising to improve qualitative/quantitative performance; ii) Machine learning methods for better quantification performance with reduction of the impact of matrix effect as well as LIBS spectra fluctuations. The review also points out the issues that researchers need to address in their future research on improving the performance of LIBS analysis using machine learning algorithms, such as restrictions on training data, the disconnect between physical principles and algorithms, the low generalization ability and massive data processing ability of the model.

Superconducting critical temperature is the most attractive material property due to its impact on the applications of electricity transmission, railway transportation, strong magnetic fields for nuclear fusion and medical imaging, quantum computing, etc. The ability to predict its value is a constant pursuit for condensed matter physicists. We developed a new hierarchical neural network (HNN) AI algorithm to resolve the contradiction between the large number of descriptors and the small number of datasets always faced by neural network AI approaches to materials science. With this new HNN-based AI model, a much-increased number of 909 universal descriptors for inorganic compounds, and a dramatically cleaned database for conventional superconductors, we achieved high prediction accuracy with a test R² score of 95.6%. The newly developed HNN model accurately predicted T_{\mathrm{c}} of 45 new high-entropy alloy superconductors with a mean absolute percent error below 6% compared to the experimental data. This demonstrated a significant potential for predicting other properties of inorganic materials.

Since the isolation of graphene, two-dimensional (2D) materials have attracted increasing interest because of their excellent chemical and physical properties, as well as promising applications. Nonetheless, particular challenges persist in their further development, particularly in the effective identification of diverse 2D materials, the domains of large-scale and high-precision characterization, also intelligent function prediction and design. These issues are mainly solved by computational techniques, such as density function theory and molecular dynamic simulation, which require powerful computational resources and high time consumption. The booming deep learning methods in recent years offer innovative insights and tools to address these challenges. This review comprehensively outlines the current progress of deep learning within the realm of 2D materials. Firstly, we will briefly introduce the basic concepts of deep learning and commonly used architectures, including convolutional neural and generative adversarial networks, as well as U-net models. Then, the characterization of 2D materials by deep learning methods will be discussed, including defects and materials identification, as well as automatic thickness characterization. Thirdly, the research progress for predicting the unique properties of 2D materials, involving electronic, mechanical, and thermodynamic features, will be evaluated succinctly. Lately, the current works on the inverse design of functional 2D materials will be presented. At last, we will look forward to the application prospects and opportunities of deep learning in other aspects of 2D materials. This review may offer some guidance to boost the understanding and employing novel 2D materials.

Microwave-enhanced laser-induced breakdown spectroscopy (ME-LIBS) is a promising analysis technique for trace element detection with the advantage of high signal intensity. However, the shot-to-shot repeatability of the ME-LIBS signal is relatively low, which affects the precision of the result and limits quantification performance. A cavity confinement microwave-enhanced laser-induced plasma (CC-ME-LIP) modulation method is proposed to improve the repeatability of the ME-LIBS signal. During the plasma evolution, cavity confinement provides an environment that regulates plasma around the microwave probe, controls plasma expansion, and minimizes interaction with the atmosphere. This behavior enhances the stability of the plasma morphology, leading to improved signal repeatability. In addition, confinement increases the energy transfer process within the plasma by the superimposition of two methods, resulting in a stronger signal intensity. The CC-ME-LIP modulation method is applied to the brass sample. The relative standard deviation (RSD) of the different copper and zinc lines has been reduced, along with an improvement of the intensity enhancement factor (IEF). For example, Cu 521.820 nm line RSD reduced from 29.11% (ME-LIBS) to 17.12% (CC-ME-LIBS) with an IEF of 1.08. The result demonstrated that the proposed approach significantly improves the repeatability of the ME-LIBS signal, thereby increasing the overall signal quality. To gain a deeper understanding, a detailed analysis of the mechanisms behind the increased signal intensity and improved repeatability was further investigated.

We study mathematical, physical and computational aspects of the stabilizer formalism arising in quantum information and quantum computation. The measurement process of Pauli observables with its algorithm is given. It is shown that to detect genuine entanglement we need a full set of stabilizer generators and the stabilizer witness is coarser than the GHZ (Greenberger–Horne–Zeilinger) witness. We discuss stabilizer codes and construct a stabilizer code from a given linear code. We also discuss quantum error correction, error recovery criteria and syndrome extraction. The symplectic structure of the stabilizer formalism is established and it is shown that any stabilizer code is unitarily equivalent to a trivial code. The structure of graph codes as stabilizer codes is identified by obtaining the respective stabilizer generators. The distance of embeddable stabilizer codes in lattices is obtained. We discuss the Knill−Gottesman theorem, tableau representation and frame representation. The runtime of simulating stabilizer gates is obtained by applying stabilizer matrices. Furthermore, an algorithm for updating global phases is given. Resolution of quantum channels into stabilizer channels is shown. We discuss capacity achieving codes to obtain the capacity of the quantum erasure channel. Finally, we discuss the shadow tomography problem and an algorithm for constructing classical shadow is given.

In our study, we explore high-order exceptional points (EPs), which are crucial for enhancing the sensitivity of open physical systems to external changes. We utilize the Hilbert−Schmidt speed (HSS), a measure of quantum statistical speed, to accurately identify EPs in non-Hermitian systems. These points are characterized by the simultaneous coalescence of eigenvalues and their associated eigenstates. One of the main benefits of using HSS is that it eliminates the need to diagonalize the evolved density matrix, simplifying the identification process. Our method is shown to be effective even in complex, multi-dimensional and interacting Hamiltonian systems. In certain cases, a generalized evolved state may be employed over the conventional normalized state. This necessitates the use of a metric operator to define the inner product between states, thereby introducing additional complexity. Our research confirms that HSS is a reliable and practical tool for detecting EPs, even in these demanding situations.

Many-body localization (MBL) of a disordered interacting boson system in one dimension is studied numerically at the filling faction one-half. The von Neumann entanglement entropy S_{\mathrm{v}\mathrm{N}} is commonly used to detect the MBL phase transition but remains challenging to be directly measured. Based on the U(1) symmetry from the particle number conservation, S_{\mathrm{v}\mathrm{N}} can be decomposed into the particle number entropy S_N and the configuration entropy S_C. In light of the tendency that the eigenstate’s S_C nears zero in the localized phase, we introduce a quantity describing the deviation of S_N from the ideal thermalization distribution; finite-size scaling analysis illustrates that it shares the same phase transition point with S_{\mathrm{v}\mathrm{N}} but displays the better critical exponents. This observation hints that the phase transition to MBL might largely be determined by S_N and its fluctuations. Notably, the recent experiments [A. Lukin, et al., Science 364, 256 (2019); J. Léonard, et al., Nat. Phys. 19, 481 (2023)] demonstrated that this deviation can potentially be measured through the S_N measurement. Furthermore, our investigations reveal that the thermalized states primarily occupy the low-energy section of the spectrum, as indicated by measures of localization length, gap ratio, and energy density distribution. This low-energy spectrum of the Bose model closely resembles the entire spectrum of the Fermi (or spin XXZ) model, accommodating a transition from the thermalized to the localized states. While, owing to the bosonic statistics, the high-energy spectrum of the model allows the formation of distinct clusters of bosons in the random potential background. We analyze the resulting eigenstate properties and briefly summarize the associated dynamics. To distinguish between the phase regions at the low and high energies, a probing quantity based on the structure of S_{\mathrm{v}\mathrm{N}} is also devised. Our work highlights the importance of symmetry combined with entanglement in the study of MBL. In this regard, for the disordered Heisenberg XXZ chain, the recent pure eigenvalue analyses in [J. Šuntajs, et al., Phys. Rev. E 102, 062144 (2020)] would appear inadequate, while methods used in [A. Morningstar, et al., Phys. Rev. B 105, 174205 (2022)] that spoil the U(1) symmetry could be misleading.

Single-photon detections (SPDs) represent a highly sensitive light detection technique capable of detecting individual photons at extremely low light intensity levels. This technology mainly relies on the mainstream SPDs, such as photomultiplier tubes (PMTs), avalanche photodiodes (SAPD), superconducting nanowire single-photon detectors (SNSPDs), superconducting transition-edge sensor (TES), and hybrid lead halide perovskite. However, the complexity and high manufacturing cost, coupled with the requirement of special conditions like a low-temperature environment, pose significant challenges to the wide adoption of SPDs. To address the challenges faced by SPDs, significant efforts have been devoted to enhancing their performance. In this review, we first summarize the principles and technical challenges of several SPDs. Conductors, superconductors, semiconductors, 3D bulk materials, 2D film materials, 1D nanowires, and 0D quantum dots have all been discussed for single-photon detectors. Methods such as special optical structure, waveguide integration, and strain engineering have been employed to elevate the performance of single-photon detectors. These techniques enhance light absorption and modulate the band structure of the material, thereby improving the single-photon sensitivity. By providing an overview of the current situation and future challenges of SPDs, this review aims to propose potential solutions for photon detection technology.

Heterostructures composed of two-dimensional van der Waals (vdW) materials allow highly controllable stacking, where interlayer twist angles introduce a continuous degree of freedom to alter the electronic band structures and excitonic physics. Motivated by the discovery of Mott insulating states and superconductivity in magic-angle bilayer graphene, the emerging research fields of “twistronics” and moiré physics have aroused great academic interests in the engineering of optoelectronic properties and the exploration of new quantum phenomena, in which moiré superlattice provides a pathway for the realization of artificial excitonic crystals. Here we systematically summarize the current achievements in twistronics and moiré excitonic physics, with emphasis on the roles of lattice rotational mismatches and atomic registries. Firstly, we review the effects of the interlayer twist on electronic and photonic physics, particularly on exciton properties such as dipole moment and spin-valley polarization, through interlayer interactions and electronic band structures. We also discuss the exciton dynamics in vdW heterostructures with different twist angles, like formation, transport and relaxation processes, whose mechanisms are complicated and still need further investigations. Subsequently, we review the theoretical analysis and experimental observations of moiré superlattice and moiré modulated excitons. Various exotic moiré effects are also shown, including periodic potential, moiré miniband, and varying wave function symmetry, which result in exciton localization, emergent exciton peaks and spatially alternating optical selection rule. We further introduce the expanded properties of moiré systems with external modulation factors such as electric field, doping and strain, showing that moiré lattice is a promising platform with high tunability for optoelectronic applications and in-depth study on frontier physics. Lastly, we focus on the rapidly developing field of correlated electron physics based on the moiré system, which is potentially related to the emerging quantum phenomena.

In materials science, data-driven methods accelerate material discovery and optimization while reducing costs and improving success rates. Symbolic regression is a key to extracting material descriptors from large datasets, in particular the Sure Independence Screening and Sparsifying Operator (SISSO) method. While SISSO needs to store the entire expression space to impose heavy memory demands, it limits the performance in complex problems. To address this issue, we propose a RF-SISSO algorithm by combining Random Forests (RF) with SISSO. In this algorithm, the Random Forests algorithm is used for prescreening, capturing non-linear relationships and improving feature selection, which may enhance the quality of the input data and boost the accuracy and efficiency on regression and classification tasks. For a testing on the SISSO’s verification problem for 299 materials, RF-SISSO demonstrates its robust performance and high accuracy. RF-SISSO can maintain the testing accuracy above 0.9 across all four training sample sizes and significantly enhancing regression efficiency, especially in training subsets with smaller sample sizes. For the training subset with 45 samples, the efficiency of RF-SISSO was 265 times higher than that of original SISSO. As collecting large datasets would be both costly and time-consuming in the practical experiments, it is thus believed that RF-SISSO may benefit scientific researches by offering a high predicting accuracy with limited data efficiently.

Efficiently and fast seeking specific lattices with targeted phonon thermal conductivity {\kappa }_L plays an important role in the thermal design and thermal management of materials. How to efficiently and accurately evaluate the phonon lifetime determined by anharmonicity becomes a critical bottleneck when high-throughput measuring {\kappa }_L. Here, we propose a method of fast evaluating three-phonon scattering induced lifetime based on the many-body theory of phonon gas. In the high temperature limit, the phonon scattering rate is simply determined by the product of only two anharmonic parameters: the square of Grüneisen parameter and the phase space size of three-phonon scattering, both of which can be quickly derived from the harmonic phonon properties. We demonstrate the effectiveness of the method in high-throughput evaluating the {\kappa }_L in first-principles calculation, which exhibits a good consistence with our collected experimental data. This method shows promising application potential in exploring material screening of the targeted {\kappa }_L, which by improving the ability of characterizing phonon anharmonicity will further enhance the performance of {\kappa }_L prediction.

One of unique features of non-Hermitian systems is the extreme sensitive to their boundary conditions, e.g., the emergence of non-Hermitian skin effect (NHSE) under the open boundary conditions, where most of bulk states become localized at the boundaries. In the presence of impurities, the scale-free localization can appear, which is qualitatively distinct from the NHSE. Here, we experimentally design a disordered non-Hermitian electrical circuits in the presence of a single non-Hermitian impurity and the nonreciprocal hopping. We observe the anomalous scale-free accumulation of eigenstates, opposite to the bulk hopping direction. The experimental results open the door to further explore the anomalous skin effects in non-Hermitian electrical circuits.

Non-Hermitian systems with parity−time (PT)-symmetry have been extensively studied and rapidly developed in resonance wireless power transfer (WPT). The WPT system that satisfies PT-symmetry always has real eigenvalues, which promote efficient energy transfer. However, meeting the condition of PT-symmetry is one of the most puzzling issues. Stable power transfer under different transmission conditions is also a great challenge. Bound state in the continuum (BIC) supporting extreme quality-factor mode provides an opportunity for efficient WPT. Here, we propose theoretically and demonstrate experimentally that BIC widely exists in resonance-coupled systems without PT-symmetry, and it can even realize more stable and efficient power transfer than PT-symmetric systems. Importantly, BIC for efficient WPT is universal and suitable in standard second-order and even high-order WPT systems. Our results not only extend non-Hermitian physics beyond PT-symmetry, but also bridge the gap between BIC and practical application engineering, such as high-performance WPT, wireless sensing and communications.

Laser-induced breakdown spectroscopy (LIBS) is regarded as the future superstar for analytical chemistry and widely applied in various fields. Improving the quality of LIBS signal is fundamental to achieving accurate quantification and large-scale commercialization of LIBS. To propose control methods that improve LIBS signal quality, it is essential to have a comprehensive understanding of the influence of key parameters, such as ambient gas pressure, temperature, and sample temperature on LIBS signals. To date, extensive research has been carried out. However, different researchers often yield significantly different experimental results for LIBS, preventing the formation of consistent conclusions. This greatly prevents the understanding of influencing laws of key parameters and the improvement of LIBS quantitative performance. Taking ambient gas pressure as an example, this paper compares the effects of ambient gas pressure under different optimization conditions, reveals the influence of spatiotemporal window caused by inherent characteristics of LIBS signal sources, i.e., intense temporal changes and spatial non-uniformity of laser-induced plasmas, on the impact patterns of key parameters. From the perspective of plasma spatiotemporal evolution, the paper elucidates the influence patterns of ambient gas pressure on LIBS signals, clarifying seemingly contradictory research results in the literature.

The generalized time-dependent generator coordinate method (TD-GCM) is extended to include pairing correlations. The correlated GCM nuclear wave function is expressed in terms of time-dependent generator states and weight functions. The particle−hole channel of the effective interaction is determined by a Hamiltonian derived from an energy density functional, while pairing is treated dynamically in the standard BCS approximation with time-dependent pairing tensor and single-particle occupation probabilities. With the inclusion of pairing correlations, various time-dependent phenomena in open-shell nuclei can be described more realistically. The model is applied to the description of saddle-to-scission dynamics of induced fission. The generalized TD-GCM charge yields and total kinetic energy distribution for the fission of ²⁴⁰Pu, are compared to those obtained using the standard time-dependent density functional theory (TD-DFT) approach, and with available data.

Results of the Raman scattering experiments, heat capacity measurements, ab initio simulations of the Raman spectra and pressure-induced phase transition in UTe₂ single crystal are reported. Assignment of symmetries to particular Raman-active phonons follows directly from a comparative analysis of the measured and calculated Raman spectra. Theoretically determined lattice contribution to the specific heat of UTe₂ allows for better description of its heat capacity measured over the temperatures ranging from 30 to 400 K. The orthorhombic-to-tetragonal phase transition pressure of 3.8 GPa is predicted at room temperature in very good agreement with the recent experimental studies. The phase transition remains almost phonon-independent with the transition pressure weakly temperature-dependent below 500 K. The strong local Coulomb correlations between U-5f electrons and spin−orbit interaction are shown to be important for realistic theoretical description of phonons and pressure-induced phase transition in UTe₂.

We study the topology and localization properties of a generalized Su−Schrieffer−Heeger (SSH) model with a quasi-periodic modulated hopping. It is found that the interplay of off-diagonal quasi-periodic modulations can induce topological Anderson insulator (TAI) phases and reentrant topological Anderson insulator (RTAI), and the topological phase boundaries can be uncovered by the divergence of the localization length of the zero-energy mode. In contrast to the conventional case that the TAI regime emerges in a finite range with the increase of disorder, the TAI and RTAI are robust against arbitrary modulation amplitude for our system. Furthermore, we find that the TAI and RTAI can induce the emergence of reentrant localization transitions. Such an interesting connection between the reentrant localization transition and the TAI/RTAI can be detected from the wave-packet dynamics in cold atom systems by adopting the technique of momentum-lattice engineering.

In this study, TiN/NbO_x/Pt memristor devices with short-term memory (STM) and self-rectifying characteristics are used for reservoir computing. The STM characteristics of the device are detected using direct current sweep and pulse transients. The self-rectifying characteristics of the device can be explained by the work function differences between the TiN and Pt electrodes. Furthermore, neural network simulations were conducted for pattern recognition accuracy when the conductance was used as the synaptic weight. The emulation of synaptic memory and forgetfulness by short-term memory effects are demonstrated using paired-pulse facilitation and excitatory postsynaptic potential. The efficient training reservoir computing consisted of all 16 states (4-bit) in the memristor device as a physical reservoir and the artificial neural network simulation as a read-out layer and yielded a pattern recognition accuracy of 92.34% for the modified National Institute of Standards and Technology dataset. Finally, it is found that STM and long-term memory in the device coexist by adjusting the intensity of pulse stimulation.

The van der Waals interface structures and behaviors are of great importance in determining the physical properties of two-dimensional atomic crystals and their heterostructures. The delicate interfacial properties are sensitively dependent on the mechanical behaviors of atomically thin films under external strain. Here, we investigated the strain-engineered rippling structures at the CVD-grown bilayer-MoS₂ interface with advanced atomic force microscopy (AFM). The in-plane compressive strain is sequentially introduced into the 1L-substrate and 2L-1L interface of bilayer-MoS₂ flakes via a fast-cooling process. The thermal strain-engineered rippling structures were directly visualized at the central 2H- and 3R-MoS₂ bilayer regions with friction force microscopy (FFM) and bimodal AFM techniques. These rippling structures can be further artificially manipulated into the beating-like rippling features and fully erased via the contact mode AFM scanning. Our results shed lights on the strain-engineered interfacial structures of two-dimensional materials and also inspire the further investigation on the interface engineering of their electronic and optical properties.

Two-dimensional (2D) ferroelectric materials, which possess electrically switchable spontaneous polarization and can be easily integrated with semiconductor technologies, is of utmost importance in the advancement of high-integration low-power nanoelectronics. Despite the experimental discovery of certain 2D ferroelectric materials such as CuInP₂S₆ and In₂Se₃, achieving stable ferroelectricity at room temperature in these materials continues to present a significant challenge. Herein, stable ferroelectric order at room temperature in the 2D limit is demonstrated in van der Waals SnP₂S₆ atom layers, which can be fabricated via mechanical exfoliation of bulk SnP₂S₆ crystals. Switchable polarization is observed in thin SnP₂S₆ of ~7 nm. Importantly, a van der Waals ferroelectric field-effect transistor (Fe-FET) with ferroelectric SnP₂S₆ as top-gate insulator and p-type WTe_0.6Se_1.4 as the channel was designed and fabricated successfully, which exhibits a clear clockwise hysteresis loop in transfer characteristics, demonstrating ferroelectric properties of SnP₂S₆ atomic layers. In addition, a multilayer graphene/SnP₂S₆/multilayer graphene van der Waals vertical heterostructure phototransistor was also fabricated successfully, exhibiting improved optoelectronic performances with a responsivity (R) of 2.9 A/W and a detectivity (D) of 1.4 × 10¹² Jones. Our results show that SnP₂S₆ is a promising 2D ferroelectric material for ferroelectric-integrated low-power 2D devices.

Incorporating valley as a degree of freedom into quantum anomalous Hall systems offers a novel approach to manipulating valleytronics in electronic transport. Using the Kane−Mele monolayer as a concrete model, we comprehensively explore the various topological phases in the presence of inequivalent exchange fields and reveal the roles of the interfacial Rashba effect and external electric field in tuning topological valley-polarized states. We find that valley-polarized states can be realized by introducing Kane−Mele spin−orbit coupling and inequivalent exchange fields. Further introducing Rashba spin−orbit coupling and an electric field into the system can lead to diverse topological states, such as the valley-polarized quantum anomalous Hall effect with \mathcal{C}=\pm 1,\pm 2 and valley-contrasting states with \mathcal{C}=0. Remarkably, different valley-polarized topological states can be continuously tuned by varying the strength and direction of the external electric field in a fixed system. Our work demonstrates the tunability of topological states in valley-polarized quantum anomalous Hall systems and provides an ideal platform for applications in electronic transport devices in topological valleytronics.

The rapid rise of artificial intelligence (AI) has catalyzed advancements across various trades and professions. Developing large-scale AI models is now widely regarded as one of the most viable approaches to achieving general-purpose intelligent agents. This pressing demand has made the development of more advanced computing accelerators an enduring goal for the rapid realization of large-scale AI models. However, as transistor scaling approaches physical limits, traditional digital electronic accelerators based on the von Neumann architecture face significant bottlenecks in energy consumption and latency. Optical computing accelerators, leveraging the high bandwidth, low latency, low heat dissipation, and high parallelism of optical devices and transmission over waveguides or free space, offer promising potential to overcome these challenges. In this paper, inspired by the generic architectures of digital electronic accelerators, we conduct a bottom-up review of the principles and applications of optical computing accelerators based on the basic element of computing accelerators − the multiply-accumulate (MAC) unit. Then, we describe how to solve matrix multiplication by composing calculator arrays from different MAC units in diverse architectures, followed by a discussion on the two main applications where optical computing accelerators are reported to have advantages over electronic computing. Finally, the challenges of optical computing and our perspective on its future development are presented. Moreover, we also survey the current state of optical computing in the industry and provide insights into the future commercialization of optical computing.

Quantum secure direct communication (QSDC) can transmit secret messages without keys, making it an important branch of quantum communication. We present a hybrid entanglement-based quantum secure direct communication (HE-QSDC) protocol with simple linear optical elements, combining the benefits of both continuous variables (CV) and discrete variables (DV) encoding. We analyze the security and find that the QSDC protocol has a positive security capacity when the bit error rate is less than 0.073. Compared with previous DV QSDC protocols, our protocol has higher communication efficiency due to performing nearly deterministic Bell-state measurement. On the other hand, compared with CV QSDC protocol, this protocol has higher fidelity with large \alpha. Based on these advantages, our protocol may provide an alternative approach to realize secure communication.

Currently, magnetic storage devices are encountering the problem of achieving lightweight and high integration in mobile computing devices during the information age. As a result, there is a growing urgency for two-dimensional half-metallic materials with a high Curie temperature (T_C). This study presents a theoretical investigation of the fundamental electromagnetic properties of the monolayer hexagonal lattice of Mn₂X₃ (X = S, Se, Te). Additionally, the potential application of Mn₂X₃ as magneto-resistive components is explored. All three of them fall into the category of ferromagnetic half-metals. In particular, the Monte Carlo simulations indicate that the T_C of Mn₂S₃ reachs 381 K, noticeably greater than room temperature. These findings present notable advantages for the application of Mn₂S₃ in spintronic devices. Hence, a prominent spin filtering effect is apparent when employing non-equilibrium Green’s function simulations to examine the transport parameters. The resulting current magnitude is approximately 2 × 10⁴ nA, while the peak gigantic magnetoresistance exhibits a substantial value of 8.36 × 10¹⁶ %. It is noteworthy that the device demonstrates a substantial spin Seebeck effect when the temperature differential between the electrodes is modified. In brief, Mn₂X₃ exhibits outstanding features as a high T_C half-metal, exhibiting exceptional capabilities in electrical and thermal drives spin transport. Therefore, it holds great potential for usage in spintronics applications.