Risk assessment of fault water inrush during deep mining

With the gradual depletion of shallow coal resources,the Yanzhou mine in China will enter the lower coal seam mining phase.However,as mining depth increases,lower coal seam mining in Yanzhou is threatened by water inrush in the Benxi Formation limestone and Ordovician limestone.The existing prediction models for the water burst at the bottom of the coal seam are less accurate than expected owing to various controlling factors and their intrinsic links.By analyzing the hydrogeological exploration data of the Baodian lower seam and combining the results of the water inrush coefficient method and the Yanzhou mine pressure seepage test,an evaluation model of the seepage barrier capacity of the fault was established.The evaluation results show the water of the underlying limestone aquifer in the Baodian mine area mainly threatens the lower coal mining through the fault fracture zone.The security of mining above confined aquifer in the Baodian mine area gradually decreases from southwest to northeast.By comparing the water inrush coefficient method and the evaluation model of fault impermeability,the results show the evaluation model based on seepage barrier conditions is closer to the actual situation when analyzing the water breakout situation at the working face.

Temperature-mediated structural evolution of vapor–phase deposited cyclosiloxane polymer thin films for enhanced mechanical properties and thermal conductivity

Polymer-derived ceramic(PDC) thin films are promising wear-resistant coatings for protecting metals and carbon-carbon composites from corrosion and oxidation.However,the high pyrolysis temperature hinders the applications on substrate materials with low melting points.We report a new synthesis route for PDC coatings using initiated chemical vapor deposited poly(1,3,5-trivinyl-1,3,5-trimethylcyclotrisiloxane)(pV_3D_3) as the precurs or.We investigated the changes in siloxane moieties and the network topology,and proposed a three-stage mechanism for the thermal annealing process.The rise of the connectivity number for the structures obtained at increased annealing temperatures was found with strong correlation to the enhanced mechanical properties and thermal conductivity.Our PDC films obtained via annealing at 850℃ exhibit at least 14.6% higher hardness than prior reports for PDCs synthesized below 1100℃.Furthermore,thermal conductivity up to 1.02 W(mK)^(-1) was achieved at the annealing temperature as low as 700℃,which is on the same order of magnitude as PDCs obtained above 1100℃.Using minimum thermal conductivity models,we found that the thermal transport is dominated by diffusons in the films below the percolation of rigidity,while ultra-short mean-free path phonons contribute to the thermal conductivity of the films above the percolation threshold.The findings of this work provide new insights for the development of wear-resistant and thermally conductive PDC thin films for durable protection coatings.

High-sensitive and fast MXene/silicon photodetector forsingle-pixel X-ray imaging

The demand for high-performance X-ray detectors leads to material innovationfor efficient photoelectric conversion and carrier transfer. However, currentX-ray detectors are often susceptible to chemical and irradiation instability,complex fabrication processes, hazardous components, and difficult compatibility.Here, we investigate a two-dimensional (2D) material with a relativelylow atomic number, Ti_(3)C_(2)T_(x) MXenes, and single crystal silicon for X-ray detectionand single-pixel imaging (SPI). We fabricate a Ti_(3)C_(2)T_(x) MXene/Si X-raydetector demonstrating remarkable optoelectronic performance. This detectorexhibits a sensitivity of 1.2 × 10^(7) μC Gyair^(-1) cm^(-2), a fast response speed with arise time of 31 μs, and an incredibly low detection limit of 2.85 nGyair s^(-1).These superior performances are attributed to the unique charge couplingbehavior under X-ray irradiation via intrinsic polaron formation. The deviceremains stable even after 50 continuous hours of high-dose X-ray irradiation.Our device fabrication process is compatible with silicon-based semiconductortechnology. Our work suggests new directions for eco-friendly X-ray detectorsand low-radiation imaging system.

Recent progress in the development of dielectric elastomer materials and their multilayer actuators

Dielectric elastomers(DEs)have emerged as one of the most promising artificial muscle technologies,due to their exceptional properties such as large actuation strain,fast response,high energy density,and flexible processibility for various configurations.Over the past two decades,researchers have been working on developing DE materials with improved properties and exploring innovative applications of dielectric elastomer actuators(DEAs).This review article focuses on two main topics:recent material innovation of DEs and development of multilayer stacking processes for DEAs,which are important to promoting commercialization of DEs.It begins by explaining the working principle of a DEA.Then,recently developed strategies for preparing new DE materials are introduced,including reducing mechanical stiffness,increasing dielectric permittivity,suppressing viscoelasticity loss,and mitigating electromechanical instability without pre-stretching.In the next section,different multilayer stacking methods for fabricating multilayer DEAs are discussed,including conventional dry stacking,wet stacking,a novel dry stacking method,and micro-fabrication-enabled stacking techniques.This review provides a comprehensive and up-to-date overview of recent developments in high-performance DE materials and multilayer stacking methods.It highlights the progress made in the field and also discusses potential future directions for further advancements.

Urban rail transit disruption management: Research progress and future directions

Urban rail transit (URT) disruptions present considerable challenges due to several factors: i) a high probability of occurrence, arising from facility failures, disasters, and vandalism;ii) substantial negative effects, notably the delay of numerous passengers;iii) an escalating frequency, attributable to the gradual aging of facilities;and iv) severe penalties, including substantial fines for abnormal operation. This article systematically reviews URT disruption management literature from the past decade, categorizing it into pre-disruption and post-disruption measures. The pre-disruption research focuses on reducing the effects of disruptions through network analysis, passenger behavior analysis, resource allocation for protection and backup, and enhancing system resilience. Conversely, post-disruption research concentrates on restoring normal operations through train rescheduling and bus bridging services. The review reveals that while post-disruption strategies are thoroughly explored, pre-disruption research is predominantly analytical, with a scarcity of practical pre-emptive solutions. Moreover, future research should focus more on increasing the interchangeability of transport modes, reinforcing redundancy relationships between URT lines, and innovating post-disruption strategies.

Uncovering the CO_(2)emissions of vehicles:A well-to-wheel approach

Carbon dioxide(CO_(2))from road traffic is a non-negligible part of global greenhouse gas(GHG)emissions,and it is a challenge for the world today to accurately estimate road traffic CO_(2)emissions and formulate effective emission reduction policies.Current emission inventories for vehicles have either low-resolution,or limited coverage,and they have not adequately focused on the CO_(2)emission produced by new energy vehicles(NEV)considering fuel life cycle.To fill the research gap,this paper proposed a framework of a high-resolution well-to-wheel(WTW)CO_(2)emission estimation for a full sample of vehicles and revealed the unique CO_(2)emission characteristics of different categories of vehicles combined with vehicle behavior.Based on this,the spatiotemporal characteristics and influencing factors of CO_(2)emissions were analyzed with the geographical and temporal weighted regression(GTWR)model.Finally,the CO_(2)emissions of vehicles under different scenarios are simulated to support the formulation of emission reduction policies.The results show that the distribution of vehicle CO_(2)emissions shows obvious heterogeneity in time,space,and vehicle category.By simply adjusting the existing NEV promotion policy,the emission reduction effect can be improved by 6.5%-13.5%under the same NEV penetration.If combined with changes in power generation structure,it can further release the emission reduction potential of NEVs,which can reduce the current CO_(2)emissions by 78.1%in the optimal scenario.

Autonomous aeroamphibious invisibility cloak with stochastic-evolution learning

Being invisible ad libitum has long captivated the popular imagination,particularly in terms of safeguarding modern high-end instruments from potential threats.Decades ago,the advent of metamaterials and transformation optics sparked considerable interest in invisibility cloaks,which have been mainly demonstrated in ground and waveguide modalities.However,an omnidirectional flying cloak has not been achieved,primarily due to the challenges associated with dynamic synthesis of metasurface dispersion.We demonstrate an autonomous aeroamphibious invisibility cloak that incorporates a suite of perception,decision,and execution modules,capable of maintaining invisibility amidst kaleidoscopic backgrounds and neutralizing external stimuli.The physical breakthrough lies in the spatiotemporal modulation imparted on tunable metasurfaces to sculpt the scattering field in both space and frequency domains.To intelligently control the spatiotemporal metasurfaces,we introduce a stochastic-evolution learning that automatically aligns with the optimal solution through maximum probabilistic inference.In a fully self-driving experiment,we implement this concept on an unmanned drone and showcase adaptive invisibility in three canonical landscapes-sea,land,and air-with a similarity rate of up to 95%.Our work extends the family of invisibility cloaks to flying modality and inspires other research on material discoveries and homeostatic meta-devices.

Brillouin Klein space and half-turn space in three-dimensional acoustic crystals

The Bloch band theory and Brillouin zone(BZ)that characterize wave-like behaviors in periodic mediums are two cornerstones of contemporary physics,ranging from condensed matter to topological physics.Recent theoretical breakthrough revealed that,under the projective symmetry algebra enforced by artificial gauge fields,the usual two-dimensional(2D)BZ(orientable Brillouin two-torus)can be fundamentally modified to a non-orientable Brillouin Klein bottle with radically distinct manifold topology.However,the physical consequence of artificial gauge fields on the more general three-dimensional(3D)BZ(orientable Brillouin three-torus)was so far missing.Here,we theoretically discovered and experimentally observed that the fundamental domain and topology of the usual 3D BZ can be reduced to a non-orientable Brillouin Klein space or an orientable Brillouin half-turn space in a 3D acoustic crystal with artificial gauge fields.We experimentally identify peculiar 3D momentum-space non-symmorphic screw rotation and glide reflection symmetries in the measured band structures.Moreover,we experimentally demonstrate a novel stacked weak Klein bottle insulator featuring a nonzero Z2 topological invariant and self-collimated topological surface states at two opposite surfaces related by a nonlocal twist,radically distinct from all previous 3D topological insulators.Our discovery not only fundamentally modifies the fundamental domain and topology of 3D BZ,but also opens the door towards a wealth of previously overlooked momentum-space multidimensional manifold topologies and novel gaugesymmetry-enriched topological physics and robust acoustic wave manipulations beyond the existing paradigms.

On the Directivity of Radiating Quantum Electromagnetic Systems

We explore the spatial directivity of radiating quantum source systems,which are defined as any generic source capable of producing photon emission and directing it to specific regions in space.We present a comprehensive definition of quantum directivity,inspired by both classical antenna theory and photon detection theory.Through an in-depth conceptual and mathematical analysis,we identify and address several critical challenges associated with characterizing the directive properties of a general quantum source system.Our approach essentially presents a computational model that relies solely on the density operator of the radiation field as input.

Bayesian operational modal analysis of a long-span cable-stayed sea-crossing bridge

Sea-crossing bridges have attracted considerable attention in recent years as an increasing number of projects have been constructed worldwide.Situated in the coastal area,sea-crossing bridges are subjected to a harsh environment(e.g.strong winds,possible ship collisions,and tidal waves)and their performance can deteriorate quickly and severely.To enhance safety and serviceability,it is a routine process to conduct vibration tests to identify modal properties(e.g.natural frequencies,damping ratios,and mode shapes)and to monitor their long-term variation for the purpose of early-damage alert.Operational modal analysis(OMA)provides a feasible way to investigate the modal properties even when the cross-sea bridges are in their operation condition.In this study,we focus on the OMA of cable-stayed bridges,because they are usually long-span and flexible to have extremely low natural frequencies.It challenges experimental capability(e.g.instrumentation and budgeting)and modal identification techniques(e.g.low frequency and closely spaced modes).This paper presents a modal survey of a cable-stayed sea-crossing bridge spanning 218 m+620 m+218 m.The bridge is located in the typhoon-prone area of the northwestern Pacific Ocean.Ambient vibration data was collected for 24 h.A Bayesian fast Fourier transform modal identification method incorporating an expectation-maximization algorithm is applied for modal analysis,in which the modal parameters and associated identification uncertainties are both addressed.Nineteen modes,including 15 translational modes and four torsional modes,are identified within the frequency range of[0,2.5 Hz].

Performing optical logic operations by a diffractive neural network

Optical logic operations lie at the heart of optical computing,and they enable many applications such as ultrahighspeed information processing.However,the reported optical logic gates rely heavily on the precise control of input light signals,including their phase difference,polarization,and intensity and the size of the incident beams.Due to the complexity and difficulty in these precise controls,the two output optical logic states may suffer from an inherent instability and a low contrast ratio of intensity.Moreover,the miniaturization of optical logic gates becomes difficult if the extra bulky apparatus for these controls is considered.As such,it is desirable to get rid of these complicated controls and to achieve full logic functionality in a compact photonic system.Such a goal remains challenging.Here,we introduce a simple yet universal design strategy,capable of using plane waves as the incident signal,to perform optical logic operations via a diffractive neural network.Physically,the incident plane wave is first spatially encoded by a specific logic operation at the input layer and further decoded through the hidden layers,namely,a compound Huygens’metasurface.That is,the judiciously designed metasurface scatters the encoded light into one of two small designated areas at the output layer,which provides the information of output logic states.Importantly,after training of the diffractive neural network,all seven basic types of optical logic operations can be realized by the same metasurface.As a conceptual illustration,three logic operations(NOT,OR,and AND)are experimentally demonstrated at microwave frequencies.

Photonic matrix multiplication lights up photonicaccelerator and beyond

Matrix computation,as a fundamental building block of information processing in science and technology,contributes most of the computational overheads in modern signal processing and artificial intelligence algorithms.Photonic accelerators are designed to accelerate specific categories of computing in the optical domain,especially matrix multiplication,to address the growing demand for computing resources and capacity.Photonic matrix multiplication has much potential to expand the domain of telecommunication,and artificial intelligence benefiting from its superior performance.Recent research in photonic matrix multiplication has flourished and may provide opportunities to develop applications that are unachievable at present by conventional electronic processors.In this review,we first introduce the methods of photonic matrix multiplication,mainly including the plane light conversion method,Mach–Zehnder interferometer method and wavelength division multiplexing method.We also summarize the developmental milestones of photonic matrix multiplication and the related applications.Then,we review their detailed advances in applications to optical signal processing and artificial neural networks in recent years.Finally,we comment on the challenges and perspectives of photonic matrix multiplication and photonic acceleration.

Realizing transmitted metasurface cloak by a tandem neural network

Being invisible at will has been a long-standing dream for centuries, epitomized by numerous legends;humans have never stopped their exploration steps to realize this dream. Recent years have witnessed a breakthrough in this search due to the advent of transformation optics, metamaterials, and metasurfaces. However, the previous metasurface cloaks typically work in a reflection manner that relies on a high-reflection background, thus limiting the applications. Here, we propose an easy yet viable approach to realize the transmitted metasurface cloak, just composed of two planar metasurfaces to hide an object inside, such as a cat. To tackle the hard-to-converge issue caused by the nonuniqueness phenomenon, we deploy a tandem neural network(T-NN) to efficiently streamline the inverse design. Once pretrained, the T-NN can work for a customer-desired electromagnetic response in one single forward computation, saving a great amount of time. Our work opens a new avenue to realize a transparent invisibility cloak, and the tandem-NN can also inspire the inverse design of other metamaterials and photonics.

Demonstration of topological wireless power transfer

Recent advances in non-radiative wireless power transfer(WPT)technique essentially relying on magnetic resonance and near-field coupling have successfully enabled a wide range of applications.However,WPT systems based on double resonators are severely limited to short-or mid-range distance,due to the deteriorating efficiency and power with long transfer distance.WPT systems based on multi-relay resonators can overcome this problem,which,however,suffer from sensitivity to perturbations and fabrication imperfections.Here,we experimentally demonstrate a concept of topological wireless power transfer(TWPT),where energy is transferred efficiently via the near-field coupling between two topological edge states localized at the ends of a one-dimensional radiowave topological insulator.Such a TWPT system can be modelled as a parity-time-symmetric Su-Schrieffer-Heeger(SSH)chain with complex boundary potentials.Besides,the coil configurations are judiciously designed,which significantly suppress the unwanted cross-couplings between nonadjacent coils that could break the chiral symmetry of the SSH chain.By tuning the inter-and intra-cell coupling strengths,we theoretically and experimentally demonstrate high energy transfer efficiency near the exceptional point of the topological edge states,even in the presence of disorder.The combination of topological metamaterials,non-Hermitian physics,and WPT techniques could promise a variety of robust,efficient WPT applications over long distances in electronics,transportation,and industry.

Protective Coatings Containing ZnMOF-BTA Metal–Organic Framework for Active Protection of AA2024-T3

ZnMOF-BTA,a new metal–organic framework with excellent anti-corrosion properties was prepared and characterized.Polarization and immersion tests in 3.5 wt%NaCl were performed on AA2024-T3 alloy to assess the corrosion inhibition ability of ZnMOF-BTA.It showed an inhibition efficiency of more than 90%,indicating excellent corrosion inhibition of ZnMOF-BTA on AA 2024-T3 in NaCl.Moreover,ZnMOF-BTA particles were incorporated into polyurethane coatings to create corrosion-resistant coatings.Electrochemical tests and neutral salt spray analysis were used to assess the corrosion protection ability of ZnMOF-BTA-laden polyurethane coatings.The results of electrochemical impedance spectra clearly showed the outstanding corrosion resistance and durability of ZnMOF-BTA coatings after 1440 h of immersion with a high pore resistance(Rpo)of 1.76×10^(10)Ωcm^(2).In addition,during the cross-cut adhesion test,the coating did not detach from the substrate,and after the impact test,there was scarcely any indication of a fracture,which further supports the notion that the coating has strong adhesion to the substrate.

Nonlinear all-optical modulator based on non-Hermitian PT symmetry

All-optical modulators with ultrahigh speed are in high demand due to the rapid development of optical interconnection and computation. However, due to weak photon–photon interaction, the advancement of all-optical modulators is consequently hampered by the large footprint and high power consumption. In this work, the enhanced sensitivity around an exceptional point(EP) from parity-time(PT) symmetry theory is initiatively introduced into a nonlinear all-optical modulator design. Further, a non-Hermitian all-optical modulator based on PT symmetry is proposed, which utilizes the large Kerr nonlinearity from indium tin oxide(ITO) in its epsilon-near-zero(ENZ) region. The whole system is expected to operate around EP, giving rise to the advantages of nanoscale integration and large modulation depth. This presented modulator with high efficiency and high-speed all-optical control can be commendably extended to the design methodology of various nanostructures and further prompt the development of all-optical signal processing.

Plasmon resonance-enhanced graphene nanofilmbased dual-band infrared silicon photodetector

Graphene-based photodetectors have attracted much attention due to their unique properties,such as high-speed and wide-band detection capability.However,they suffer from very low external quantum efficiency in the infrared(IR)region and lack spectral selectivity.Here,we construct a plasmon-enhanced macro-assembled graphene nanofilm(nMAG)based dual-band infrared silicon photodetector.The Au plasmonic nanostructures improve the absorption of long-wavelength photons with energy levels below the Schottky barrier(between metal and Si)and enhance the interface transport of electrons.Combined with the strong photo-thermionic emission(PTI)effect of nMAG,the n MAG–Au–Si heterojunctions show strong dual-band detection capability with responsivities of52.9 mA/W at 1342 nm and 10.72 mA/W at 1850 nm,outperforming IR detectors without plasmonic nanostructures by 58–4562 times.The synergy between plasmon–exciton resonance enhancement and the PTI effect opens a new avenue for invisible light detection.

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.

Integrating social neuroscience into human-machine mutual behavioral understanding for autonomous driving

Autonomous vehicles(AVs)are advertised to free human drivers,providing a safer and more efficient transport mode.After decades of extensive investment and invention,various types of AVs have been unveiled,but they are still restricted to limited application scenarios because of potential safety concerns.Despite rare sensing or detection failures from corner cases,one of the significant concerns primarily questions whether AVs would interact appropriately with surrounding human-driven vehicles on public roads.

Macroscopic assembled graphene nanofilms based room temperature ultrafast mid-infrared photodetectors

Graphene with linear energy dispersion and weak electron-phonon interaction is highly anticipated to harvest hot electrons in a broad wavelength range.However,the limited absorption and serious backscattering of hot-electrons result in inadequate quantum yields,especially in the mid-infrared range.Here,we report a macroscopic assembled graphene(nMAG)nanofilm/silicon heterojunction for ultrafast mid-infrared photodetection.The assembled Schottky diode works in 1.5-4.0μm at room temperature with fast response(20-30 ns,rising time,4 mm2 window)and high detectivity(1.61011 to 1.9109 Jones from 1.5 to 4.0μm)under the pulsed laser,outperforming single-layer-graphene/silicon photodetectors by 2-8 orders.These performances are attributed to the greatly enhanced photo-thermionic effect of electrons in nMAG due to its high light absorption(~40%),long carrier relaxation time(~20 ps),low work function(4.52 eV),and suppressed carrier number fluctuation.The nMAG provides a long-range platform to understand the hot-carrier dynamics in bulk 2D materials,leading to broadband and ultrafast MIR active imaging devices at room temperature.