This paper proposed a new topology optimization method based on geometry deep learning. The density distribution in design domain is described by deep neural networks. Compared to traditional ...density-based method, using geometry deep learning method to describe the density distribution function can guarantee the smoothness of the boundary and effectively overcome the checkerboard phenomenon. The design variables can be reduced phenomenally based on deep learning representation method. The numerical results for three different kernels including the Gaussian, Tansig, and Tribas are compared. The structural complexity can be directly controlled through the architectures of the neural networks, and minimum length is also controllable for the Gaussian kernel. Several 2-D and 3-D numerical examples are demonstrated in detail to demonstrate the effectiveness of proposed method from minimum compliance to stress-constrained problems.
The COVID-19 pandemic that is still prevalent around the globe each day consumes massive disposable face masks and consequently lays a heavy burden on waste management. Meanwhile, the incineration of ...these medical wastes further escalates the already overwhelming carbon emission that leads to global warming and climate change. To offer a potential solution addressing medical waste and CO
2
emission challenges, we herein develop a synthetic protocol to upgrade face masks into Ni, N-doped graphene (Ni–N-C) sheet catalysts for selectively reducing CO
2
into CO electrochemically. The high specific surface area and the uniform dispersion of Ni active sites of the catalyst derived from a regular disposable face mask enable a near-unity CO Faradaic efficiency (FE) at the current density of 200 mA cm
−2
. This study offers outside-of-the-box thinking to address environmental issues by turning medical wastes into CO
2
reduction catalysts.
We report phase-programmable Gaussian boson sampling (GBS) which produces up to 113 photon detection events out of a 144-mode photonic circuit. A new high-brightness and scalable quantum light source ...is developed, exploring the idea of stimulated emission of squeezed photons, which has simultaneously near-unity purity and efficiency. This GBS is programmable by tuning the phase of the input squeezed states. The obtained samples are efficiently validated by inferring from computationally friendly subsystems, which rules out hypotheses including distinguishable photons and thermal states. We show that our GBS experiment passes a nonclassicality test based on inequality constraints, and we reveal nontrivial genuine high-order correlations in the GBS samples, which are evidence of robustness against possible classical simulation schemes. This photonic quantum computer, Jiuzhang 2.0, yields a Hilbert space dimension up to ∼ 1043, and a sampling rate ∼ 1024 faster than using brute-force simulation on classical supercomputers.
Quantum computational advantage using photons Zhong, Han-Sen; Wang, Hui; Deng, Yu-Hao ...
Science (American Association for the Advancement of Science),
12/2020, Volume:
370, Issue:
6523
Journal Article
Peer reviewed
Open access
Quantum computers promise to perform certain tasks that are believed to be intractable to classical computers. Boson sampling is such a task and is considered a strong candidate to demonstrate the ...quantum computational advantage. We performed Gaussian boson sampling by sending 50 indistinguishable single-mode squeezed states into a 100-mode ultralow-loss interferometer with full connectivity and random matrix-the whole optical setup is phase-locked-and sampling the output using 100 high-efficiency single-photon detectors. The obtained samples were validated against plausible hypotheses exploiting thermal states, distinguishable photons, and uniform distribution. The photonic quantum computer,
, generates up to 76 output photon clicks, which yields an output state-space dimension of 10
and a sampling rate that is faster than using the state-of-the-art simulation strategy and supercomputers by a factor of ~10
.
Hybrid organic–inorganic perovskites have attracted substantial interest as the most favorable prospective material for efficient photovoltaic and optoelectronic devices. However, their extreme ...sensitivity to electron beam radiation makes it difficult to obtain their intrinsic structure by transmission electron microscopy and can even lead to significant misidentifications. In 2018, the coexistence of methylammonium lead iodide (MAPbI3) in the cubic and tetragonal phase using electron microscopy and electron diffraction techniques was reported in article “Self‐Organized Superlattice and Phase Coexistence inside Thin Film Organometal Halide Perovskite”. Herein, however, that claim is challenged by comparing their experimental data to simulated diffraction patterns and arguing that their perovskite samples may have been damaged due to excessive electron beam irradiation. Consequently, true phase coexistence was not observed in that previously reported work, rather merely the decomposition products of MAPbI3.
Organic‐inorganic hybrid perovskites (OIHPs) have recently emerged as groundbreaking semiconductor materials owing to their remarkable properties. Transmission electron microscopy (TEM), as a very ...powerful characterisation tool, has been widely used in perovskite materials for structural analysis and phase identification. However, the perovskites are highly sensitive to electron beams and easily decompose into PbX2 (X = I, Br, Cl) and metallic Pb. The electron dose of general high‐resolution TEM is much higher than the critical dose of MAPbI3, which results in universal misidentifications that PbI2 and Pb are incorrectly labelled as perovskite. The widely existed mistakes have negatively affected the development of perovskite research fields. Here misidentifications of the best‐known MAPbI3 perovskite are summarised and corrected, then the causes of mistakes are classified and ascertained. Above all, a solid method for phase identification and practical strategies to reduce the radiation damage for perovskite materials have also been proposed. This review aims to provide the causes of mistakes and avoid misinterpretations in perovskite research fields in the future.
Levitated optomechanics has great potential in precision measurements, thermodynamics, macroscopic quantum mechanics, and quantum sensing. Here we synthesize and optically levitate silica ...nanodumbbells in high vacuum. With a linearly polarized laser, we observe the torsional vibration of an optically levitated nanodumbbell. This levitated nanodumbbell torsion balance is a novel analog of the Cavendish torsion balance, and provides rare opportunities to observe the Casimir torque and probe the quantum nature of gravity as proposed recently. With a circularly polarized laser, we drive a 170-nm-diameter nanodumbbell to rotate beyond 1 GHz, which is the fastest nanomechanical rotor realized to date. Smaller silica nanodumbbells can sustain higher rotation frequencies. Such ultrafast rotation may be used to study material properties and probe vacuum friction.
In this paper, the motion differential equations of the bi-directional functionally graded Timoshenko beam are established using Hamilton’s principle. The material properties of the beam change ...exponentially in both axial and thickness directions. Using the variable substitution method, state space differential equations of the structure are established. First, the dynamic stiffness matrix is formed using the traditional method. Then, the author proposes a new method to directly form an exact dynamic stiffness matrix by using state space differential equations, and this method is compared with the traditional dynamic stiffness matrix method. At the same time, the natural frequency of the structure is computed by combining the Wittrick–William algorithm with a non-iterative algorithm. The influence of gradient parameters α,β on the fundamental frequency, mode shape and frequency response function is analysed through the establishment of the dynamic stiffness matrix of the overall structure. Finally, using the Lagrange equation and the method of modal superposition, structural dynamic differential equations under a harmonic moving load are derived. Using the precise integration method, the dynamic response of the displacement is computed and the influence of gradient parameters α,β on the dynamic response is analysed.
The key technology of a battery management system is to online estimate the battery states accurately and robustly. For lithium iron phosphate battery, the relationship between state of charge and ...open circuit voltage has a plateau region which limits the estimation accuracy of voltage-based algorithms. The open circuit voltage hysteresis requires advanced online identification algorithms to cope with the strong nonlinear battery model. The available capacity, as a crucial parameter, contributes to the state of charge and state of health estimation of battery, but it is difficult to predict due to comprehensive influence by temperature, aging and current rates. Aim at above problems, the ampere-hour counting with current correction and the dual adaptive extended Kalman filter algorithms are combined to estimate model parameters and state of charge. This combination presents the advantages of less computation burden and more robustness. Considering the influence of temperature and degradation, the data-driven algorithm namely least squares support vector machine is implemented to predict the available capacity. The state estimation and capacity prediction methods are coupled to improve the estimation accuracy at different temperatures among the lifetime of battery. The experiment results verify the proposed methods have excellent state and available capacity estimation accuracy.
•A dual adaptive extended Kalman filter is used to estimate parameters and states.•A correction term is introduced to consider the effect of current rates.•The least square support vector machine is used to predict the available capacity.•The experiment results verify the proposed state and capacity prediction methods.
PDF
