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Carbon nanotubes (CNTs) are semimetallic while boron nitride nanotubes (BNNTs) are wide band gap insulators. Despite the discrepancy in their electrical properties, a comparison between the mechanical and thermal properties of CNTs and BNNTs has a significant research value for their potential applications. In this work, molecular dynamics simulations are performed to systematically investigate the mechanical and thermal properties of CNTs and BNNTs. The calculated Young's modulus is about 1.1 TPa for CNTs and 0.72 TPa for BNNTs under axial compressions. The critical bucking strain and maximum stress are inversely proportional to both diameter and length-diameter ratio and CNTs are identified axially stiffer than BNNTs. Thermal conductivities of (10, 0) CNTs and (10, 0) BNNTs follow similar trends with respect to length and temperature and are lower than that of their two-dimensional counterparts, graphene nanoribbons (GNRs) and BN nanoribbons (BNNRs), respectively. As the temperature falls below 200 K (130 K) the thermal conductivity of BNNTs (BNNRs) is larger than that of CNTs (GNRs), while at higher temperature it is lower than the latter. In addition, thermal conductivities of a (10, 0) CNT and a (10, 0) BNNT are further studied and analyzed under various axial compressive strains. Low-frequency phonons which mainly come from flexure modes are believed

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This paper presents a micropulse calorimeter for heat capacity measurement of thin films. Optimization of the structure and data processing methods of the microcalorimeter improved the thermal isolation and temperature uniformity and reduced the heat capacity measurement errors. Heat capacities of copper thin films with thicknesses from 20 nm to 340 nm are measured in the temperature range from 300 K to 420 K in vacuum of 1 mPa. The specific heat of the 340 nm Cu film is close to the literature data of bulk Cu. For the thinner films, the data shows that the specific heat increases with the decreasing of film thickness (or the average crystalline size).

Low-concentration formaldehyde (HCHO) together with ethanol/toluene/acetone/α-pinene (as an interference gas of HCHO) is detected with a micro gas sensor array, composed of eight tin oxide (SnO2) thin film gas sensors with Au, Cu, Pt or Pd metal catalysts. The characteristics of the multi-dimensional signals from the eight sensors are evaluated. A multilayer neural network with an error backpropagation (BP) learning algorithm, plus the principal component analysis (PCA) technique, is implemented to recognize these indoor volatile organic compounds (VOC). The results show that the micro gas sensor array, plus the multilayer neural network, is very effective in recognizing 0.06 ppm HCHO in single gas component and in binary gas mixtures, toluene/ethanol/α-pinene with small relative error.