Yanshan University

About: Yanshan University is a education organization based out in Qinhuangdao, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 19544 authors who have published 16904 publications receiving 184378 citations. The organization is also known as: Yānshān dàxué.

Nanotwinned diamond with unprecedented hardness and stability

Abstract: Nanotwinned diamond synthesized with onion carbon nanoparticles as precursors has much higher hardness and thermal stability than natural diamond; its enhanced hardness is due to the reduced size of its twin structures. Even diamond has its limitations when used in tools to cut and shape the hardest of materials. Materials scientists have therefore sought to synthesize materials that are harder than natural diamond, preferably with increased thermal stability. In air, natural diamond starts to oxidize at about 800 °C, leading to severe wear at high temperatures. Attempts to increase the hardness of diamond by decreasing its grain size have succeeded, but at the cost of even poorer thermal stability. Yongjun Tian and colleagues report the synthesis of synthetic diamond that is both ultrahard and has a dramatically enhanced thermal stability with an oxidization temperature of more than 1,000 °C. The material is synthesized using onion carbon nanoparticles as precursors and owes its enhanced hardness to a nanoscale structure consisting not of tiny grains, but of crystal 'twins' — domains of the crystal lattice related by symmetry. This result, which follows similar success with nanotwinned cubic boron nitride, suggests a general approach to making new, advanced carbon-based materials with exceptional properties. Although diamond is the hardest material for cutting tools, poor thermal stability has limited its applications, especially at high temperatures. Simultaneous improvement of the hardness and thermal stability of diamond has long been desirable. According to the Hall−Petch effect1,2, the hardness of diamond can be enhanced by nanostructuring (by means of nanograined and nanotwinned microstructures), as shown in previous studies3,4,5,6,7. However, for well-sintered nanograined diamonds, the grain sizes are technically limited to 10−30 nm (ref. 3), with degraded thermal stability4 compared with that of natural diamond. Recent success in synthesizing nanotwinned cubic boron nitride (nt-cBN) with a twin thickness down to ∼3.8 nm makes it feasible to simultaneously achieve smaller nanosize, ultrahardness and superior thermal stability5. At present, nanotwinned diamond (nt-diamond) has not been fabricated successfully through direct conversions of various carbon precursors3,6,7 (such as graphite, amorphous carbon, glassy carbon and C60). Here we report the direct synthesis of nt-diamond with an average twin thickness of ∼5 nm, using a precursor of onion carbon nanoparticles at high pressure and high temperature, and the observation of a new monoclinic crystalline form of diamond coexisting with nt-diamond. The pure synthetic bulk nt-diamond material shows unprecedented hardness and thermal stability, with Vickers hardness up to ∼200 GPa and an in-air oxidization temperature more than 200 °C higher than that of natural diamond. The creation of nanotwinned microstructures offers a general pathway for manufacturing new advanced carbon-based materials with exceptional thermal stability and mechanical properties.

Multiscale Convolutional Neural Networks for Fault Diagnosis of Wind Turbine Gearbox

Abstract: This paper proposes a novel intelligent fault diagnosis method to automatically identify different health conditions of wind turbine (WT) gearbox. Unlike traditional approaches, where feature extraction and classification are separately designed and performed, this paper aims to automatically learn effective fault features directly from raw vibration signals while classify the type of faults in a single framework, thus providing an end-to-end learning-based fault diagnosis system for WT gearbox without additional signal processing and diagnostic expertise. Considering the multiscale characteristics inherent in vibration signals of a gearbox, a new multiscale convolutional neural network (MSCNN) architecture is proposed to perform multiscale feature extraction and classification simultaneously. The proposed MSCNN incorporates multiscale learning into the traditional CNN architecture, which has two merits: 1) high-level fault features can be effectively learned by the hierarchical learning structure with multiple pairs of convolutional and pooling layers; and 2) multiscale learning scheme can capture complementary and rich diagnosis information at different scales. This greatly improves the feature learning ability and enables better diagnosis performance. The proposed MSCNN approach is evaluated through experiments on a WT gearbox test rig. Experimental results and comprehensive comparison analysis with respect to the traditional CNN and traditional multiscale feature extractors have demonstrated the superiority of the proposed method.

Ultrahigh volumetric capacitance and cyclic stability of fluorine and nitrogen co-doped carbon microspheres

Abstract: Highly porous nanostructures with large surface areas are typically employed for electrical double-layer capacitors to improve gravimetric energy storage capacity; however, high surface area carbon-based electrodes result in poor volumetric capacitance because of the low packing density of porous materials. Here, we demonstrate ultrahigh volumetric capacitance of 521 F cm−3 in aqueous electrolytes for non-porous carbon microsphere electrodes co-doped with fluorine and nitrogen synthesized by low-temperature solvothermal route, rivaling expensive RuO2 or MnO2 pseudo-capacitors. The new electrodes also exhibit excellent cyclic stability without capacitance loss after 10,000 cycles in both acidic and basic electrolytes at a high charge current of 5 A g−1. This work provides a new approach for designing high-performance electrodes with exceptional volumetric capacitance with high mass loadings and charge rates for long-lived electrochemical energy storage systems. Carbon-based supercapacitors often suffer from poor volumetric capacitance due to the low packing density which arises from attempts to increase the electrode surface area. Here, in contrast, the authors fabricate N and F co-doped non-porous solid carbon spheres and achieve exceptional performances.

Delay-dependent guaranteed cost control for T-S fuzzy systems with time delays

Abstract: This study introduces a guaranteed cost control method for nonlinear systems with time-delays which can be represented by Takagi-Sugeno (T-S) fuzzy models with time-delays. The state feedback and generalized dynamic output feedback approaches are considered. The generalized dynamic output feedback controller is presented by a new fuzzy controller architecture which is of dual indexed rule base. It considers both the dynamic part and the output part of T-S fuzzy model which guarantees that the controller without any delay information can stabilize time-delay T-S fuzzy systems. Based on delay-dependent Lyapunov functional approach, some sufficient conditions for the existence of state feedback controller are provided via parallel distributed compensation (PDC) first. Second, the corresponding conditions are extended into the generalized dynamic output feedback closed-loop system via so-called generalized PDC technique. The upper bound of time-delay can be obtained using convex optimization such that the system can be stabilized for all time-delays whose sizes are not larger than the bound. The minimizing method is also proposed to search the suboptimal upper bound of guaranteed cost function. The effectiveness of the proposed method can be shown by the simulation examples.

Semimetallic Two-Dimensional Boron Allotrope with Massless Dirac Fermions

Abstract: It has been widely accepted that planar boron structures, composed of triangular and hexagonal motifs are the most stable two-dimensional (2D) phases and likely precursors for boron nanostructures. Here we predict, based on an ab initio evolutionary structure search, a novel 2D boron structure with nonzero thickness, which is considerably, by $50\text{ }\text{ }\mathrm{meV}/\mathrm{atom}$, lower in energy than the recently proposed $\ensuremath{\alpha}$-sheet structure and its analogues. In particular, this phase is identified for the first time to have a distorted Dirac cone, after graphene and silicene the third elemental material with massless Dirac fermions. The buckling and coupling between the two sublattices not only enhance the energetic stability, but also are the key factors for the emergence of the distorted Dirac cone.