Dielectric polarization in MgFe_(2)O_(4) coating and bulk doping to enhance high-voltage cycling stability of Na_(2/3)Ni_(1/3)Mn_(2/3)O_(2) cathode material

Charging P2-Na_(2/3)Ni_(1/3)Mn_(2/3)O_(2)to 4.5 V for higher capacity is enticing.However,it leads to severe capacity fading,ascribing to the lattice oxygen evolution and the P2-O2 phase transformation.Here,the Mg Fe_(2)O_(4) coating and Mg,Fe co-doping were constructed simultaneously by Mg,Fe surface treatment to suppress lattice oxygen evolution and P2-O2 phase transformation of P2-Na_(2/3)Ni_(1/3)Mn_(2/3)O_(2)at deep charging.Through ex-situ X-ray diffraction(XRD)tests,we found that the Mg,Fe bulk co-doping could reduce the repulsion between transition metals and Na+/vacancies ordering,thus inhibiting the P2-O2 phase transition and significantly reducing the irreversible volume change of the material.Meanwhile,the internal electric field formed by the dielectric polarization of Mg Fe_(2)O_(4) effectively inhibits the outward migration of oxidized O^(a-)(a<2),thereby suppressing the lattice oxygen evolution at deep charging,confirmed by in situ Raman and ex situ XPS techniques.P2-Na NM@MF-3 shows enhanced high-voltage cycling performance with capacity retentions of 84.8% and 81.3%at 0.1 and 1 C after cycles.This work sheds light on regulating the surface chemistry for Na-layered oxide materials to enhance the high-voltage performance of Na-ion batteries.

Imaginary Time Crystal of Thermal Quantum Matter

Temperature is a fundamental thermodynamic variable for matter.Physical observables are often found to either increase or decrease with it,or show a non-monotonic dependence with peaks signaling underlying phase transitions or anomalies.Statistical Held theory has established connection between temperature and time:a quantum ensemble with inverse temperatureβis formally equivalent to a dynamic system evolving along an imaginary time from 0 to iβin the space one dimension higher.Here we report that a gas of hard-core bosons interacting with a thermal bath manifests an unexpected temperature-periodic oscillation of its macroscopic observables,arising from the microscopic origin of space-time locked translational symmetry breaking and crystalline ordering.Such a temperature crystal,supported by quantum Monte Carlo simulation,generalizes the concept of purely spatial density-wave order to the imaginary time axis for Euclidean action.

HQNN-SFOP:Hybrid Quantum Neural Networks with Signal Feature Overlay Projection for Drone Detection Using Radar Return Signals-A Simulation

With the wide application of drone technology,there is an increasing demand for the detection of radar return signals from drones.Existing detection methods mainly rely on time-frequency domain feature extraction and classical machine learning algorithms for image recognition.This method suffers from the problem of large dimensionality of image features,which leads to large input data size and noise affecting learning.Therefore,this paper proposes to extract signal time-domain statistical features for radar return signals from drones and reduce the feature dimension from 512×4 to 16 dimensions.However,the downscaled feature data makes the accuracy of traditional machine learning algorithms decrease,so we propose a new hybrid quantum neural network with signal feature overlay projection(HQNN-SFOP),which reduces the dimensionality of the signal by extracting the statistical features in the time domain of the signal,introduces the signal feature overlay projection to enhance the expression ability of quantum computation on the signal features,and introduces the quantum circuits to improve the neural network’s ability to obtain the inline relationship of features,thus improving the accuracy and migration generalization ability of drone detection.In order to validate the effectiveness of the proposed method,we experimented with the method using the MM model that combines the real parameters of five commercial drones and random drones parameters to generate data to simulate a realistic environment.The results show that the method based on statistical features in the time domain of the signal is able to extract features at smaller scales and obtain higher accuracy on a dataset with an SNR of 10 dB.On the time-domain feature data set,HQNNSFOP obtains the highest accuracy compared to other conventional methods.In addition,HQNN-SFOP has good migration generalization ability on five commercial drones and random drones data at different SNR conditions.Our method verifies the feasibility and effectiveness of signal detection methods based on quantum computation and experimentally demonstrates that the advantages of quantum computation for information processing are still valid in the field of signal processing,it provides a highly efficient method for the drone detection using radar return signals.