Ultrasensitive Mechanical Sensor Using Tunable Ordered Array of Metallic and Insulating States in Vanadium Dioxide

Detecting tiny deformations or vibrations, particularly those associated with strains below 1%, is essential in various technological applications. Traditional intrinsic materials, including metals and semiconductors, face challenges in simultaneously achieving initial metallic state and strain-induced insulating state, hindering the development of highly sensitive mechanical sensors. Here we report an ultrasensitive mechanical sensor based on a strain-induced tunable ordered array of metallic and insulating states in the single-crystal bronze-phase vanadium dioxide [VO_(2)(B)] quantum material. It is shown that the initial metallic state in the VO_(2)(B) flake can be tuned to the insulating state by applying a weak uniaxial tensile strain. Such a unique property gives rise to a record-high gauge factor of above 607970, surpassing previous values by an order of magnitude, with excellent linearity and mechanical resilience as well as durability. As a proof-of-concept application, we use our proposed mechanical sensor to demonstrate precise sensing of the micro piece, gentle airflows and water droplets. We attribute the superior performance of the sensor to the strain-induced continuous metal-insulator transition in the single-crystal VO_(2)(B) flake, evidenced by experimental and simulation results. Our findings highlight the potential of exploiting correlated quantum materials for next-generation ultrasensitive flexible mechanical sensors, addressing critical limitations in traditional materials.

Moiré Synaptic Transistor for Homogeneous-Architecture Reservoir Computing

Reservoir computing has been considered as a promising intelligent computing paradigm for effectively processing complex temporal information.Exploiting tunable and reproducible dynamics in the single electronic device have been desired to implement the “reservoir” and the “readout” layer of reservoir computing system.Two-dimensional moiré materials,with an artificial lattice constant many times larger than the atomic length scale,are one type of most studied artificial quantum materials in community of material science and condensed-matter physics over the past years.These materials are featured with gate-tunable periodic potential and electronic correlation,thus varying the electric field allows the electrons in the moiré potential per unit cell to exhibit distinct and reproducible dynamics,showing great promise in robust reservoir computing.Here,we report that a moiré synaptic transistor can be used to implement the reservoir computing system with a homogeneous reservoir-readout architecture.The synaptic transistor is fabricated based on an h-BN/bilayer graphene/h-BN moiré heterostructure,exhibiting ferroelectricity-like hysteretic gate voltage dependence of resistance.Varying the magnitude of the gate voltage enables the moiré transistor to switch between long-term memory and shortterm memory with nonlinear dynamics.By employing the short-and long-term memories as the reservoir nodes and weights of the readout layer,respectively,we construct a full-moiré physical neural network and demonstrate that the classification accuracy of 90.8% can be achieved for the MNIST(Modified National Institute of Standards and Technology) handwritten digits database.Our work would pave the way towards the development of neuromorphic computing based on moiré materials.

Moiré heterostructures:highly tunable platforms for quantum simulation and future computing

Moiré heterostructures, where the constituent 2D materials are stacked vertically at a small angle, feature longwavelength interference pattern at the van der Waals interfaces. One typical example is twisted bilayer graphene. In this moiré heterostructure, the emergence of moiré superlattices can effectively reconstruct the energy bands into dispersionless flat bands at a special twisted angle(so-called ‘magic angle’).

上犹客家话音系

本文描写江西省上犹县客家话音系,通过声学分析揭示其一级元音和单字调的格局,并列出同音字汇。