Morphology Controllable Synthesis of NiO/NiFe2O4 Hetero-Structures for Ultrafast Lithium-Ion Battery

Rational design of high performance anode material with outstanding rate capability and cycling stability is of great importance for lithium ion batteries (LIBs). Herein, a series of NiO/NiFe2O4 hetero-structures with adjustable porosity, particle size, and shell/internal structure have been synthesized via a controllable annealing process. The optimized NiO/NiFe2O4 (S-NFO) is hierarchical hollow nanocube that is composed of ~5 nm subunits and high porosity. When being applied as anode for LIBs, the S-NFO exhibits high rate capability and excellent cycle stability, which remains high capacity of 1,052 mAh g−1 after 300 cycles at 5.0 A g−1 and even 344 mAh g−1 after 2,000 cycles at 20 A g−1. Such impressive electrochemical performance of S-NFO is mainly due to three reasons. One is high porosity of its hierarchical hollow shell, which not only promotes the penetration of electrolyte, but also accommodates the volume change during cycling. Another is the small particle size of its subunits, which can effectively shorten the electron/ion diffusion distance and provide more active sites for Li+ storage. Besides, the hetero-interfaces between NiO and NiFe2O4 also contribute toitsfast charge transport

Pyrite-Type CoS2 Nanoparticles Supported on Nitrogen-Doped Graphene for Enhanced Water Splitting

It is extremely meaningful to develop cheap, highly efficient, and stable bifunctional electrocatalysts for both hydrogen and oxygen evolution reactions (HER and OER) to promote large-scale application of water splitting technology. Herein, we reported the preparation of CoS2 nanoparticles supported on nitrogen-doped graphene (CoS2@N-GN) by one-step hydrothermal method and the enhanced electrochemical efficacy for catalyzing hydrogen and oxygen in water electrolysis. The CoS2@N-GN composites are composed of nitrogen-doped graphene and CoS2 nanocrystals with the average size of 73.5 nm. Benefitting from the improved electronic transfer and synergistic effect, the as-prepared CoS2@N-GN exhibits remarkable OER and HER performance in 1.0 M KOH, with overpotentials of 243 mV for OER and 204 mV for HER at 10 mA cm−2, and the corresponding Tafel slopes of 51.8 and 108 mV dec−1, respectively. Otherwise, the CoS2@N-GN hybrid also presents superior long-term catalytic durability. Moreover, an alkaline water splitting device assembled by CoS2@N-GN as both anode and cathode can achieve a low cell voltage of 1.53 V at 60 °C with a high faraday efficiency of 100% for overall water splitting. The tremendously enhanced electrochemical behaviors arise from favorable factors including small sized, homogenously dispersed novel CoS2 nanocrystals and coupling interaction with the underlying conductive nitrogen-doped graphene, which would provide insight into the rational design of transition metal chalcogenides for highly efficient and durable hydrogen and oxygen-involved electrocatalysis

Photonic Floquet time crystals

The public and scientists constantly have different perspectives. While on a time crystal, they stand in line and ask: What is a time crystal? Show me a material that is spontaneously crystalline in time? This study synthesizes a photonic material of Floquet time crystals and experimentally observes its indicative period-2T beating. We explicitly reconstruct a discrete time-crystalline ground state and reveal using an appropriately-designed photonic Floquet simulator the rigid period-doubling as a signature of the spontaneous breakage of the discrete time-translational symmetry. Unlike the result of the exquisite many-body interaction, the photonic time crystal is derived from a single-particle topological phase that can be extensively accessed by many pertinent nonequilibrium and periodically-driven platforms. Our observation will drive theoretical and technological interests toward condensed matter physics and topological photonics, and demystify time crystals for the non-scientific public.Comment: 39 pages, 5 figures, supplementary materials, 6 suppl. figure

Generalized differential morphological profiles for remote sensing image classification

Differential morphological profiles (DMPs) are widely used for the spatial/structural feature extraction and classification of remote sensing images. They can be regarded as the shape spectrum, depicting the response of the image structures related to different scales and sizes of the structural elements (SEs). DMPs are defined as the difference of morphological profiles (MPs) between consecutive scales. However, traditional DMPs can ignore discriminative information for features that are across the scales in the profiles. To solve this problem, we propose scale-span differential profiles, i.e., generalized DMPs (GDMPs), to obtain the entire differential profiles. GDMPs can describe the complete shape spectrum and measure the difference between arbitrary scales, which is more appropriate for representing the multiscale characteristics and complex landscapes of remote sensing image scenes. Subsequently, the random forest (RF) classifier is applied to interpret GDMPs considering its robustness for high-dimensional data and ability of evaluating the importance of variables. Meanwhile, the RF "out-of-bag" error can be used to quantify the importance of each channel of GDMPs and select the most discriminative information in the entire profiles. Experiments conducted on three well-known hyperspectral data sets as well as an additional World View-2 data are used to validate the effectiveness of GDMPs compared to the traditional DMPs. The results are promising as GDMPs can significantly outperform the traditional one, as it is capable of adequately exploring the multiscale morphological information

DETC2008-49059 RESEARCH ON PARAMETRIC SIMULATION TECHNOLOGY BASED ON COMPLICATED MECHANISM DESIGN

ABSTRACT A new method is proposed, which is the core of parametric simulation based on complicated mechanism design. Furthermore, the connotation of parametric simulation is affirmed and the flow of parametric simulation based on complicated mechanism design is put forward. At last, taking a parallel mechanism as example, the technology of parametric simulation proposed is applied to the simulation study used ADAMS, which consist of analyzing mechanism, parametric modeling, creating GUI, creating menu and parametric simulation. The practice indicates that the method is effectual. INTRODUCTION The mechanism design is key part of the mechanical design. During the mechanism design, the rationality of the structure parameter decides the comprehensive function of the whole mechanical product directly. Therefore, how to select the structure parameter is a very important problem. Usually, these parameters will be confirmed through the calculation of the kinetics and dynamics. For the complicated construction (for example, the space parallel mechanism), these analytic calculation processes are very complicated. And we usually can't acquire ideal solution because it's hard to establish the correct analytic mathematics model. The above questions can be resolved by simulation method effectively. Simulation method is that kinematical model is set up according to mechanism kinematical theory and then researched for confirming reasonable structure parameter. However, during the practical design, the structure parameters of the model usually need to be modified constantly and simulated repeatedly for getting ideal solution. The efficiency of simulation study is decreased by fussy operation. If mechanism model and relative constraint are completely parametric, namely by building parametric model, can improve the efficiency of simulation evidently. In the paper, a new method about mechanism design is proposed, which is the core of parametric simulation. On the basis of it, a 3-TPT parallel mechanism is taken as example, the technology of parametric simulation proposed is applied to the simulation study used ADAMS. THE CONTENT OF PARAMETRIC SIMULATION The parametric simulation includes two meanings: one is the parametric model, that is the totally parameterization of the shape and constraint to make the whole simulation model completely confirming by a few parameters, so that it is easy to reconstruct the model; The other is the whole parametric simulation flow, namely in the process of simulation, dynamically change the value of parametric variables for determining the optimum parameter in the condition of satisfying constraint. It is clear that the parametric simulation model is the precondition of parametric simulation process. For the mechanical product, which has the complicated mechanism, it is usually difficult to assure suitable structure parameter at the first stage. But the application of parametric simulation technology can make the dimension as parameter, further establishing parametric simulation model to carry on simulation research. As a result, the parametric simulation is particularly suitable for the complicated mechanism design

Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI

Heterogeneous data is endemic due to the use of diverse models and settings of devices by hospitals in the field of medical imaging. However, there are few open-source frameworks for federated heterogeneous medical image analysis with personalization and privacy protection simultaneously without the demand to modify the existing model structures or to share any private data. In this paper, we proposed PPPML-HMI, an open-source learning paradigm for personalized and privacy-preserving federated heterogeneous medical image analysis. To our best knowledge, personalization and privacy protection were achieved simultaneously for the first time under the federated scenario by integrating the PerFedAvg algorithm and designing our novel cyclic secure aggregation with the homomorphic encryption algorithm. To show the utility of PPPML-HMI, we applied it to a simulated classification task namely the classification of healthy people and patients from the RAD-ChestCT Dataset, and one real-world segmentation task namely the segmentation of lung infections from COVID-19 CT scans. For the real-world task, PPPML-HMI achieved $\sim$5\% higher Dice score on average compared to conventional FL under the heterogeneous scenario. Meanwhile, we applied the improved deep leakage from gradients to simulate adversarial attacks and showed the solid privacy-preserving capability of PPPML-HMI. By applying PPPML-HMI to both tasks with different neural networks, a varied number of users, and sample sizes, we further demonstrated the strong robustness of PPPML-HMI

Energy-efficient electrochemical ammonia production from dilute nitrate solution

Highly efficient electrochemical nitrate reduction could become a key process for sustainable ammonia production overcoming many limitations of the Haber–Bosch process. Current state-of-the-art electrocatalysts have severe drawbacks regarding yield, selectivity and energy efficiency when dealing with dilute nitrate solutions. Herein, we report a layered double hydroxide (LDH)/Cu foam hybrid electrocatalyst that offers a potential solution to this challenge. The [Ni0.75Fe0.25(OH)2](CO3)0.125 (Ni3Fe–CO3 LDH) exhibits an appropriate kinetic energy barrier for the Volmer step generating hydrogen radicals as well as suppressing H–H bond formation by inhibition of the Heyrovsky step. The electrochemically generated hydrogen radicals transfer to a Cu surface enabling NO3− reduction to NH3. The Ni3Fe–CO3 LDH/Cu foam hybrid electrode exhibits an 8.5-fold higher NH3 yield compared to a pristine Cu surface, while exhibiting an NH3 selectivity of 95.8% at 98.5% NO3− conversion. The best half-cell energy efficiency (36.6%) was recorded while achieving 96.8% faradaic efficiency at −0.2 V in 5 mM NO3−(aq)