Non-Invasive Self-Adaptive Information States' Acquisition inside Dynamic Scattering Spaces.

Pushing the information states' acquisition efficiency has been a long-held goal to reach the measurement precision limit inside scattering spaces. Recent studies have indicated that maximal information states can be attained through engineered modes; however, partial intrusion is generally required. While non-invasive designs have been substantially explored across diverse physical scenarios, the non-invasive acquisition of information states inside dynamic scattering spaces remains challenging due to the intractable non-unique mapping problem, particularly in the context of multi-target scenarios. Here, we establish the feasibility of non-invasive information states' acquisition experimentally for the first time by introducing a tandem-generated adversarial network framework inside dynamic scattering spaces. To illustrate the framework's efficacy, we demonstrate that efficient information states' acquisition for multi-target scenarios can achieve the Fisher information limit solely through the utilization of the external scattering matrix of the system. Our work provides insightful perspectives for precise measurements inside dynamic complex systems.

3D Intelligent Cloaked Vehicle Equipped with Thousand-Level Reconfigurable Full-Polarization Metasurfaces.

A crucial aspect in shielding a variety of advanced electronic devices from electromagnetic detection involves controlling the flow of electromagnetic waves, akin to invisibility cloaks. Decades ago, the exploration of transformation optics heralded the dawn of modern invisibility cloaks, which has stimulated immense interest across various physical scenarios. However, most prior research is simplified to low-dimensional and stationary hidden objects, limiting their practical applicability in a dynamically changing world. This study develops a 3D large-scale intelligent cloak capable of remaining undetectable even in non-stationary conditions. By employing thousand-level reconfigurable full-polarization metasurfaces, this work has achieved an exceptionally high degree of freedom in sculpting the scattering waves as desired. Serving as the core computational unit, a hybrid inverse design enables the cloaked vehicle to respond in real-time, with a rapid reaction time of just 70 ms. These experiments integrate the cloaked vehicle with a perception-decision-control-execution system and evaluate its performance under random static positions and dynamic travelling trajectories, achieving a background scattering matching degree of up to 93.3%. These findings establish a general paradigm for the next generation of intelligent meta-devices in real-world settings, potentially paving the way for an era of "Electromagnetic Internet of Things."

Eye accommodation-inspired neuro-metasurface focusing.

The human eye, which relies on a flexible and controllable lens to focus light onto the retina, has inspired many scientific researchers to understand better and imitate the biological vision system. However, real-time environmental adaptability presents an enormous challenge for artificial eye-like focusing systems. Inspired by the mechanism of eye accommodation, we propose a supervised-evolving learning algorithm and design a neuro-metasurface focusing system. Driven by on-site learning, the system exhibits a rapid response to ever-changing incident waves and surrounding environments without any human intervention. Adaptive focusing is achieved in several scenarios with multiple incident wave sources and scattering obstacles. Our work demonstrates the unprecedented potential for real-time, fast, and complex electromagnetic (EM) wave manipulation for various purposes, such as achromatic, beam shaping, 6 G communication, and intelligent imaging.

Polarization-Orthogonal Nondegenerate Plasmonic Higher-Order Topological States.

Photonic topological states, providing light-manipulation approaches in robust manners, have attracted intense attention. Connecting photonic topological states with far-field degrees of freedom (d.o.f.) has given rise to fruitful phenomena. Recently emerged higher-order topological insulators (HOTIs), hosting boundary states two or more dimensions lower than those of bulk, offer new paradigms to localize or transport light topologically in extended dimensionalities. However, photonic HOTIs have not been related to d.o.f. of radiation fields yet. Here, we report the observation of polarization-orthogonal second-order topological corner states at different frequencies on a designer-plasmonic kagome metasurface in the far field. Such phenomenon stands on two mechanisms, i.e., projecting the far-field polarizations to the intrinsic parity d.o.f. of lattice modes and the parity splitting of the plasmonic corner states in spectra. We theoretically and numerically show that the parity splitting originates from the underlying interorbital coupling. Both near-field and far-field experiments verify the polarization-orthogonal nondegenerate second-order topological corner states. These results promise applications in robust optical single photon emitters and multiplexed photonic devices.

Inverse design of a photonic moiré lattice waveguide towards improved slow light performances.

Slow light waveguides in photonic crystals are engineered using a conventional method or a deep learning (DL) method, which is data-intensive and suffers from data inconsistency, and both methods result in overlong computation time with low efficiency. In this paper, we overcome these problems by inversely optimizing the dispersion band of a photonic moiré lattice waveguide using automatic differentiation (AD). The AD framework allows the creation of a definite target band to which a selected band is optimized, and a mean square error (MSE) as an objective function between the selected and the target bands is used to efficiently compute gradients using the autograd backend of the AD library. Using a limited-memory Broyden-Fletcher-Goldfarb-Shanno minimizer algorithm, the optimization converges to the target band, with the lowest MSE value of 9.844×10-7, and a waveguide that produces the exact target band is obtained. The optimized structure supports a slow light mode with a group index of 35.3, a bandwidth of 110 nm, and a normalized-delay-bandwidth-product of 0.805, which is a 140.9% and 178.9% significant improvement if compared to conventional and DL optimization methods, respectively. The waveguide could be utilized in slow light devices for buffering.

MagTetris: A simulator for fast magnetic field and force calculation for permanent magnet array designs.

In this paper, a simulator named "MagTetris" is proposed for fast magnetic field (B-field) and force calculation for permanent magnet arrays (PMAs) designs consisting of cuboid and arc-shaped magnets (approximated by cuboids) with arbitrary configurations. The proposed simulator can compute the B-field of a PMA on arbitrary observation planes and the magnetic force acting on any magnet/group of magnets. An accelerated calculation method for B-fields of PMAs is developed based on the current model of permanent magnet, which is further extended to magnetic force calculation. The proposed method and the associated codes were validated with numerical simulation and experimental results. The calculation speed of "MagTetris" is at least 500 times higher than that using finite-element method (FEM)-based software with uncompromised accuracy. Compared with a freeware in Python, Magpylib, "MagTetris" has a calculation acceleration of greater than 50% using the same language. "MagTetris" has a simple data structure, which can be easily migrated to other programming languages maintaining similar performances. This proposed simulator can accelerate a PMA design and/or allow designs with high flexibility considering the B-field and force simultaneously. It can facilitate and accelerate innovations of magnet designs to advance dedicated portable MRI in terms of compactness, weight, and performance.

A knowledge-inherited learning for intelligent metasurface design and assembly.

Recent breakthroughs in deep learning have ushered in an essential tool for optics and photonics, recurring in various applications of material design, system optimization, and automation control. Deep learning-enabled on-demand metasurface design has been the subject of extensive expansion, as it can alleviate the time-consuming, low-efficiency, and experience-orientated shortcomings in conventional numerical simulations and physics-based methods. However, collecting samples and training neural networks are fundamentally confined to predefined individual metamaterials and tend to fail for large problem sizes. Inspired by object-oriented C++ programming, we propose a knowledge-inherited paradigm for multi-object and shape-unbound metasurface inverse design. Each inherited neural network carries knowledge from the "parent" metasurface and then is freely assembled to construct the "offspring" metasurface; such a process is as simple as building a container-type house. We benchmark the paradigm by the free design of aperiodic and periodic metasurfaces, with accuracies that reach 86.7%. Furthermore, we present an intelligent origami metasurface to facilitate compatible and lightweight satellite communication facilities. Our work opens up a new avenue for automatic metasurface design and leverages the assemblability to broaden the adaptability of intelligent metadevices.

Broadband Flexible Microstrip Antenna Array with Conformal Load-Bearing Structure.

To enhance the load-bearing mechanical properties and broadband electromagnetic characteristics of the conformal antenna, a broadband microstrip antenna array with a conformal load-bearing structure is proposed in this paper, which consists of three flexible substrate layers and two honeycomb core layers stacked on each other. By combining the antenna and honeycomb core layer in a structural perspective, the antenna array is implemented in the composition function of surface conformability and load-bearing. Additionally, the sidelobe level of the antenna is suppressed based on the reflection surface loaded. Meanwhile, an equivalent model of a honeycomb core layer has been established and applied in the design of a conformal antenna with a load-bearing structure. The presented model increases the accuracy of simulated results and reduces the memory consumption and time of the simulation. The overall size of the proposed antenna array is 32.84 × 36.65 × 4.9 mm (1.36 λ0 × 1.52 λ0 × 0.2 λ0, λ0 is the wavelength at 12.5 GHz). The proposed antenna element and array have been fabricated and measured in the flat state and under other various bending states. Experiment results show the operating relative bandwidth of the antenna array is 20.68% (11.67-13.76 GHz and 14.33-14,83 GHz) in the flat state. Under different bending conditions, the proposed antenna array covers 24.16% (11.08-14.1 GHz), 23.82% (10.63-13.5 GHz), and 23.12% with 30°, 60°, and 90° in the xoz plane (11.55-14.33 GHz). In terms of mechanical load bearing, the structure has better performance than the traditional single-layer honeycomb core load-bearing structure antenna.

Radiative anti-parity-time plasmonics.

Space and guided electromagnetic waves, as widely known, are two crucial cornerstones in extensive wireless and integrated applications respectively. To harness the two cornerstones, radiative and integrated devices are usually developed in parallel based on the same physical principles. An emerging mechanism, i.e., anti-parity-time (APT) symmetry originated from non-Hermitian quantum mechanics, has led to fruitful phenomena in harnessing guided waves. However, it is still absent in harnessing space waves. Here, we propose a radiative plasmonic APT design to harness space waves, and experimentally demonstrate it with subwavelength designer-plasmonic structures. We observe two exotic phenomena unrealized previously. Rotating polarizations of incident space waves, we realize polarization-controlled APT phase transition. Tuning incidence angles, we observe multi-stage APT phase transition in higher-order APT systems, constructed by using the scalability of leaky-wave couplings. Our scheme shows promise in demonstrating novel APT physics, and constructing APT-symmetry-empowered radiative devices.

High-performance permanent magnet array design by a fast genetic algorithm (GA)-based optimization for low-field portable MRI.

Lightweight and compact permanent magnet arrays (PMAs) are suitable for portable dedicated magnetic resonance imaging (MRI). It is worth exploring different PMA design possibilities and optimization methods with an adequate balance between weight, size, and performance, in addition to Halbach arrays and C-shaped/H-shaped magnets which are widely used. In this paper, the design and optimization of a sparse high-performance inward-outward ring-pair PMA consisting of magnet cuboids is presented for portable imaging of the brain. The design is lightweight (151kg) and compact (inner bore diameter: 270mm, outer diameter: 616mm, length: 480mm, 5-Gauss range: 1840×1840×2340mm3). The optimization framework is based on the genetic algorithm with a consideration of both field properties and simulated image quality. The resulting PMA design has an average field strength of 101.5 mT and a field pattern with a built-in linear readout gradient. Subtracting the best fit to the linear gradient target resulted in a residual deviation from the target field of 0.76mT and an average linear regression coefficient of 0.85 to the linear gradient. The required radiofrequency bandwidth is 6.9% within a field of view (FoV) with a diameter of 200mm and a length of 125mm. It has a magnetic field generation efficiency of 0.67mT/kg, which is high among the sparse PMAs that were designed for an FoV with a diameter of 200mm. The field can be used to supply gradients in one direction working with gradient coils in the other two directions, or can be rotated to encode signals for imaging with axial slice selection. The encoding capability of the designed PMA was examined through the simulated reconstructed images. The force experienced by each magnet in the design was calculated, and the feasibility of a physical implementation was confirmed. The design can offer an increased field strength, and thus, an increased signal-to-noise ratio. It has a longitudinal field direction that allows the application of technologies developed for solenoidal magnets. This proposed design can be a promising alternative to supplying the main and gradient fields in combination for dedicated portable MRI. Lastly, the design is resulted from a fast genetic algorithm-based optimization in which fast magnetic field calculation was applied and high design flexibility was feasible. Within optimization iterations, image quality metrics were used for the encoding field of a magnet configuration to guide the design of the magnet array.

MAP-SNN: Mapping spike activities with multiplicity, adaptability, and plasticity into bio-plausible spiking neural networks.

Spiking Neural Networks (SNNs) are considered more biologically realistic and power-efficient as they imitate the fundamental mechanism of the human brain. Backpropagation (BP) based SNN learning algorithms that utilize deep learning frameworks have achieved good performance. However, those BP-based algorithms partially ignore bio-interpretability. In modeling spike activity for biological plausible BP-based SNNs, we examine three properties: multiplicity, adaptability, and plasticity (MAP). Regarding multiplicity, we propose a Multiple-Spike Pattern (MSP) with multiple-spike transmission to improve model robustness in discrete time iterations. To realize adaptability, we adopt Spike Frequency Adaption (SFA) under MSP to reduce spike activities for enhanced efficiency. For plasticity, we propose a trainable state-free synapse that models spike response current to increase the diversity of spiking neurons for temporal feature extraction. The proposed SNN model achieves competitive performances on the N-MNIST and SHD neuromorphic datasets. In addition, experimental results demonstrate that the proposed three aspects are significant to iterative robustness, spike efficiency, and the capacity to extract spikes' temporal features. In summary, this study presents a realistic approach for bio-inspired spike activity with MAP, presenting a novel neuromorphic perspective for incorporating biological properties into spiking neural networks.

Photonic Moiré lattice waveguide with a large slow light bandwidth and delay-bandwidth product.

We proposed an effective approach to enlarge the slow light bandwidth and normalized-delay-bandwidth product in an optimized moiré lattice-based photonic crystal waveguide that exhibits intrinsic mid-band characteristics. A flatband corresponding to a nearly constant group index of 34 over a wide bandwidth of 82 nm centered at 1550 nm with near-zero group velocity dispersion was achieved. A large normalized-delay-bandwidth product of 0.5712 with a relative dispersion of 0.114%/µm was obtained, which is a significant improvement if compared with previous results. Our results indicate that the photonic moiré lattice waveguide could advance slow light applications.

Experimental Realization of a Superdispersion-Enabled Ultrabroadband Terahertz Cloak.

Invisibility has been a topic of long-standing interest owing to the advent of metamaterials and transformation optics, but still faces open challenges after its tremendous development in recent decades. One of the big challenges is the narrow bandwidth, as the realization of an invisibility cloak is usually based on a metamaterial-an artificial composite material composed of subwavelength resonator structures that are always associated with dispersion. Different from previous works that have tried to eliminate the material dispersion to enhance the bandwidth of an invisibility cloak, here, it is found that by judiciously harnessing the material dispersion, the bandwidth of the cloak can still be significantly increased. Interestingly, the material dispersion does not violate the law of causality. As a proof of concept, an ultrabroadband terahertz (THz) carpet cloak is experimentally demonstrated through an array of superdispersive microparticles, rendering the target object invisible to detection by both time- and frequency-domain wideband systems. The work presents a feasible invisibility strategy that is closer to practical applications and may pave a brand-new way for the development of dispersion-dominated ultrabroadband metadevices.

Breaking the fundamental scattering limit with gain metasurfaces.

A long-held tenet in physics asserts that particles interacting with light suffer from a fundamental limit to their scattering cross section, referred to as the single-channel scattering limit. This notion, appearing in all one, two, and three dimensions, severely limits the interaction strength between all types of passive resonators and photonic environments and thus constrains a plethora of applications in bioimaging, sensing, and photovoltaics. Here, we propose a route to overcome this limit by exploiting gain media. We show that when an excited resonance is critically coupled to the desired scattering channel, an arbitrarily large scattering cross section can be achieved in principle. From a transient analysis, we explain the formation and relaxation of this phenomenon and compare it with the degeneracy-induced multi-channel superscattering, whose temporal behaviors have been usually overlooked. To experimentally test our predictions, we design a two-dimensional resonator encircled by gain metasurfaces incorporating negative- resistance components and demonstrate that the scattering cross section exceeds the single- channel limit by more than 40-fold. Our findings verify the possibility of stronger scattering beyond the fundamental scattering limit and herald a novel class of light-matter interactions enabled by gain metasurfaces.

Homeostatic neuro-metasurfaces for dynamic wireless channel management.

The physical basis of a smart city, the wireless channel, plays an important role in coordinating functions across a variety of systems and disordered environments, with numerous applications in wireless communication. However, conventional wireless channel typically necessitates high-complexity and energy-consuming hardware, and it is hindered by lengthy and iterative optimization strategies. Here, we introduce the concept of homeostatic neuro-metasurfaces to automatically and monolithically manage wireless channel in dynamics. These neuro-metasurfaces relieve the heavy reliance on traditional radio frequency components and embrace two iconic traits: They require no iterative computation and no human participation. In doing so, we develop a flexible deep learning paradigm for the global inverse design of large-scale metasurfaces, reaching an accuracy greater than 90%. In a full perception-decision-action experiment, our concept is demonstrated through a preliminary proof-of-concept verification and an on-demand wireless channel management. Our work provides a key advance for the next generation of electromagnetic smart cities.

Dynamic recognition and mirage using neuro-metamaterials.

Breakthroughs in the field of object recognition facilitate ubiquitous applications in the modern world, ranging from security and surveillance equipment to accessibility devices for the visually impaired. Recently-emerged optical computing provides a fundamentally new computing modality to accelerate its solution with photons; however, it still necessitates digital processing for in situ application, inextricably tied to Moore's law. Here, from an entirely optical perspective, we introduce the concept of neuro-metamaterials that can be applied to realize a dynamic object- recognition system. The neuro-metamaterials are fabricated from inhomogeneous metamaterials or transmission metasurfaces, and optimized using, such as topology optimization and deep learning. We demonstrate the concept in experiments where living rabbits play freely in front of the neuro-metamaterials, which enable to perceive in light speed the rabbits' representative postures. Furthermore, we show how this capability enables a new physical mechanism for creating dynamic optical mirages, through which a sequence of rabbit movements is converted into a holographic video of a different animal. Our work provides deep insight into how metamaterials could facilitate a myriad of in situ applications, such as illusive cloaking and speed-of-light information display, processing, and encryption, possibly ushering in an "Optical Internet of Things" era.

First-principles investigation of copper diffusion barrier performance in defective 2D layered materials.

This paper investigates the diffusion barrier performance of 2D layered materials with pre-existing vacancy defects using first-principles density functional theory. Vacancy defects in 2D materials may give rise to a large amount of Cu accumulation, and consequently, the defect becomes a diffusion path for Cu. Five 2D layered structures are investigated as diffusion barriers for Cu, i.e. graphene with C vacancy, hBN with B/N vacancy, and MoS2with Mo/2S vacancy. The calculated energy barriers using climbing image-nudged elastic band show that MoS2-V2Shas the highest diffusion energy barrier among other 2D layers, followed by hBN-VNand graphene. The obtained energy barrier of Cu on defected layer is found to be proportional to the length of the diffusion path. Moreover, the diffusion of Cu through vacancy defects is found to modulate the electronic structures and magnetic properties of the 2D layer. The charge density difference shows that there exists a considerable charge transfer between Cu and barrier layer as quantified by Bader charge. Given the current need for an ultra-thin diffusion barrier layer, the obtained results contribute to the field of application of 2D materials as Cu diffusion barrier in the presence of mono-vacancy defects.

STSC-SNN: Spatio-Temporal Synaptic Connection with temporal convolution and attention for spiking neural networks.

Spiking neural networks (SNNs), as one of the algorithmic models in neuromorphic computing, have gained a great deal of research attention owing to temporal information processing capability, low power consumption, and high biological plausibility. The potential to efficiently extract spatio-temporal features makes it suitable for processing event streams. However, existing synaptic structures in SNNs are almost full-connections or spatial 2D convolution, neither of which can extract temporal dependencies adequately. In this work, we take inspiration from biological synapses and propose a Spatio-Temporal Synaptic Connection SNN (STSC-SNN) model to enhance the spatio-temporal receptive fields of synaptic connections, thereby establishing temporal dependencies across layers. Specifically, we incorporate temporal convolution and attention mechanisms to implement synaptic filtering and gating functions. We show that endowing synaptic models with temporal dependencies can improve the performance of SNNs on classification tasks. In addition, we investigate the impact of performance via varied spatial-temporal receptive fields and reevaluate the temporal modules in SNNs. Our approach is tested on neuromorphic datasets, including DVS128 Gesture (gesture recognition), N-MNIST, CIFAR10-DVS (image classification), and SHD (speech digit recognition). The results show that the proposed model outperforms the state-of-the-art accuracy on nearly all datasets.

Long-term stability of arsenic calcium residue (ACR) treated with FeSO4 and H2SO4: Function of H+ and Fe(â ¡).

Arsenic calcium residue (ACR) generated from the As-bearing wastewater treatment is highly hazardous due to high content of available As, which was seeking a suitable method for safe disposal such as stabilization treatment. In this study, the stabilization of available As in ACR was performed by combined treatment with FeSO4 and H2SO4. After stabilization treatment, the As leaching concentrations extracted by China Standard Leaching Test (CSLT, HJ/T299-2007) decreased significantly from 162 mg/L to less than the Chinese regulation limit of 1.2 mg/L. And FeSO4-H2SO4 treated ACR could maintain good long-term stability even after cured for 365 days. The stabilization mechanism for available As in ACR using leaching tests, sequential extraction analysis, XPS, XRD, and SEM-EDS was investigated. H+ from H2SO4 and Fe(Ⅱ) hydrolysis was committed to the full release of available As. Reactive oxygen species (ROSs) produced from Fe(Ⅱ) oxygenation drove the oxidation of As(Ⅲ) to As(Ⅴ). The release As was stabilized by forming stable Fe-O-As complexes (FeAsO4·xFe(OH)3). Moreover, this study also presented an effective and feasible method for ACR disposal.