Boron Nitride Nanosheets for Metal Protection

Although the high impermeability of graphene makes it an excellent barrier to inhibit metal oxidation and corrosion, graphene can form a galvanic cell with the underlying metal that promotes corrosion of the metal in the long term. Boron nitride (BN) nanosheets which have a similar impermeability could be a better choice as protective barrier, because they are more thermally and chemically stable than graphene and, more importantly, do not cause galvanic corrosion due to their electrical insulation. In this study, the performance of commercially available BN nanosheets grown by chemical vapor deposition as a protective coating on metal has been investigated. The heating of the copper foil covered with the BN nanosheet at 250 {\deg}C in air over 100 h results in dramatically less oxidation than the bare copper foil heated for 2 h under the same conditions. The electrochemical analyses reveal that the BN nanosheet coating can increase open circuit potential and possibly reduce oxidation of the underlying copper foil in sodium chloride solution. These results indicate that BN nanosheets are a good candidate for oxidation and corrosion protection, although conductive atomic force microscopy analyses show that the effectiveness of the protection relies on the quality of BN nanosheets.Comment: With Supporting Informatio

Atomically Thin Boron Nitride: Unique Properties and Applications

Atomically thin boron nitride (BN) is an important two-dimensional (2D) nanomaterial, with many properties distinct from graphene. In this feature article, these unique properties and associated applications often not possible from graphene are outlined. The article starts with characterization and identification of atomically thin BN. It is followed by demonstrating their strong oxidation resistance at high temperatures and applications in protecting metals from oxidation and corrosion. As flat insulators, BN nanosheets are ideal dielectric substrates for surface enhanced Raman spectroscopy (SERS) and electronic devices based on 2D heterostructures. The light emission of BN nanosheets in the deep ultraviolet (DUV) and ultraviolet (UV) regions are also included for its scientific and technological importance. The last part is dedicated to synthesis, characterization, and optical properties of BN nanoribbons, a special form of nanosheets

Analysis of Factors Inducing Alatae in Aphis glycines

The effects of crowding, quality of host plant, temperature, and aphid types on the induction of alatae in soybean aphid (Aphis glycines) were investigated in this paper. This study shows that the crowding in the apterous adults is the major factor causing the formation of the alatae aphids in the next generation. In low aphid densities, the crowding effect is strengthened as the density increases, whereas overcrowding weakens the crowding effect. However, the crowding in young apterae would not develop them into alatae. The quality of host plant has an impact on the apterae’s reaction to crowding. With the crowding density of two apterae per bottle, the percentage of winged offspring is higher when treated with mature leaflets than treated with young leaflets or control treatment (without leaflets). Furthermore, starvation cannot enhance the formation of alatae. A. glycines wing induction is also influenced by temperature in that 30oC and 25oC have a stronger inhibitory effect on the production of alatae than 21oC. The ability of producing winged morphs varies among different types of parent A. glycines. Crowding in the alatae also can induce alatae in offspring, although the sensitivity to crowding is lower than that of apterae.Originating text in Chinese.Citation: Lãœ, Li-Hua, Chen, Rui-Lu. (1993). Analysis of Factors Inducing Alatae in Aphis glycines. Kun chong xue bao.Acta entomologica Sinica, 36, 143-149

An approach to fault diagnosis for gearbox based on order tracking and extreme learning machine

Varying speed machinery condition detection and fault diagnosis are more difficult due to non-stationary machine dynamics and vibration. In this paper, an intelligent fault diagnosis method based on order analysis and extreme learning machine (ELM) is proposed. Order tracking, easily identifying speed-related vibrations, is useful for machine condition monitoring, which could obtain the resampling signal of constant increment angle. Then, the power spectrum (PS) of characteristic orders, as the fault feature vectors, is extracted and normalized from the de-noising signal. Last, in order to diagnose the faults of the gearbox automatically, ELM, provided better generalization performance at a much faster learning speed and with least human intervene, is applied to identify and classify the faults. From the result of experiment, the approach of this paper is effective to judge the fault type under variable speed conditions