Effective growth of multiwalled boron nitride nanotubes (BNNTs) has been obtained by thermal chemical vapor deposition (CVD). This is achieved by a growth vapor trapping approach as guided by the theory of nucleation. Our results enable the growth of BNNTs in a conventional horizontal tube furnace within an hour at 1200 °C. We found that these BNNTs have an absorption band edge of 5.9 eV, approaching that of single h-BN crystals, which are promising for future nanoscale deep-UV light emitting devices.

For the first time, patterned growth of boron nitride nanotubes is achieved by catalytic chemical vapor deposition (CCVD) at 1200 °C using MgO, Ni, or Fe as the catalysts, and an Al 2 O 3 diffusion barrier as underlayer. The as-grown BNNTs are clean, vertically aligned, and have high crystallinity. Near band-edge absorption ∼6.0 eV is detected, without significant sub-band absorption centers. Electronic transport measurement confirms that these BNNTs are perfect insulators, applicable for future deep-UV photoelectronic devices and high-power electronics.

This article provides a concise review of the recent research advancements in boron nitride nanotubes (BNNTs) with a comprehensive list of references. As the motivation of the field, we first summarize some of the attractive properties and potential applications of BNNTs. Then, latest discoveries on the properties, applications, and synthesis of BNNTs are discussed. In particular, we focus on low-temperature and patterned growth, and mass production of BNNTs, since these are the major challenges that have hindered investigation of the properties and application of BNNTs for the past decade. Finally, perspectives of future research on BNNTs are discussed.

High growth temperatures (>1100 degrees C), low production yield, and impurities have prevented research progress and applications of boron nitride nanotubes (BNNTs) in the past 10 years. Here, we show that BNNTs can be grown on substrates at 600 degrees C. These BNNTs are constructed of high-order tubular structures and can be used without purification. Tunneling spectroscopy indicates that their band gap ranges from 4.4 to 4.9 eV.

One-dimensional arrays of gold quantum dots (QDs) on insulating boron nitride nanotubes (BNNTs) can form conduction channels of tunneling field-effect transistors. We demonstrate that tunneling currents can be modulated at room temperature by tuning the lengths of QD-BNNTs and the gate potentials. Our discovery will inspire the creative use of nanostructured metals and insulators for future electronic devices.

The mechanical properties of individual multiwall boron nitride nanotubes (MWBNNTs) synthesized by a growth-vapor-trapping chemical vapor deposition method are investigated by a three-point bending technique via atomic force microscopy. Multiple locations on suspended tubes are probed in order to determine the boundary conditions of the supported tube ends. The bending moduli (EB) calculated for 20 tubes with diameters ranging from 18 to 58 nm confirm the exceptional mechanical properties of MWBNNTs, with an average EB of 760 ± 30 GPa. For the first time, the bending moduli of MWBNNTs are observed to increase with decreasing diameter, ranging from 100 ± 20 GPa to as high as 1800 ± 300 GPa. This diameter dependence is evaluated by Timoshenko beam theory. The Young's modulus and shear modulus were determined to be 1800 ± 300 and 7 ± 1 GPa, respectively, for a trimmed data set of 16 tubes. The low shear modulus of MWBNNTs is the reason for the detected diameter-dependent bending modulus and is likely due to the presence of interwall shearing between the crystalline and faceted helical nanotube structures of MWBNNTs.

The grey wolf optimizer (GWO) is a novel type of swarm intelligence optimization algorithm. An improved grey wolf optimizer (IGWO) with evolution and elimination mechanism was proposed so as to achieve the proper compromise between exploration and exploitation, further accelerate the convergence and increase the optimization accuracy of GWO. The biological evolution and the “survival of the fittest” (SOF) principle of biological updating of nature are added to the basic wolf algorithm. The differential evolution (DE) is adopted as the evolutionary pattern of wolves. The wolf pack is updated according to the SOF principle so as to make the algorithm not fall into the local optimum. That is, after each iteration of the algorithm sort the fitness value that corresponds to each wolf by ascending order, and then eliminate R wolves with worst fitness value, meanwhile randomly generate wolves equal to the number of eliminated wolves. Finally, 12 typical benchmark functions are used to carry out simulation experiments with GWO with differential evolution (DGWO), GWO algorithm with SOF mechanism (SGWO), IGWO, DE algorithm, particle swarm algorithm (PSO), artificial bee colony (ABC) algorithm and cuckoo search (CS) algorithm. Experimental results show that IGWO obtains the better convergence velocity and optimization accuracy.

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