Study of axial strain induced torsion of single wall carbon nanotubes by 2D continuum anharmonic anisotropic elastic model

Recent molecular dynamic simulations have found chiral single wall carbon nanotubes (SWNTs) twist during stretching, which is similar to the motion of a screw. Obviously this phenomenon, as a type of curvature-chirality effect, can not be explained by usual isotropic elastic theory of SWNT. More interestingly, with larger axial strains (before buckling), the axial strain induced torsion (a-SIT) shows asymmetric behaviors for axial tensile and compressing strains, which suggests anharmonic elasticity of SWNTs plays an important role in real a-SIT responses. In order to study the a-SIT of chiral SWNTs with actual sizes, and avoid possible deviations of computer simulation results due to the finite-size effect, we propose a 2D analytical continuum model which can be used to describe the the SWNTs of arbitrary chiralities, curvatures, and lengths, with the concerning of anisotropic and anharmonic elasticity of SWNTs. This elastic energy of present model comes from the continuum limit of lattice energy based on Second Generation Reactive Empirical Bond Order potential (REBO-II), a well-established empirical potential for solid carbons. Our model has no adjustable parameters, except for those presented in REBO-II, and all the coefficients in the model can be calculated analytically. Using our method, we obtain a-SIT responses of chiral SWNTs with arbitrary radius, chiralities and lengthes. Our results are in reasonable agreement with recent molecular dynamic simulations. [Liang {\it et. al}, Phys. Rev. Lett, ${\bf 96}$, 165501 (2006).] Our approach can also be used to calculate other curvature-chirality dependent anharmonic mechanic responses of SWNTs.Comment: 14 pages, 2 figure

Predictive Coding Based Multiscale Network with Encoder-Decoder LSTM for Video Prediction

We present a multi-scale predictive coding model for future video frames prediction. Drawing inspiration on the ``Predictive Coding" theories in cognitive science, it is updated by a combination of bottom-up and top-down information flows, which can enhance the interaction between different network levels. However, traditional predictive coding models only predict what is happening hierarchically rather than predicting the future. To address the problem, our model employs a multi-scale approach (Coarse to Fine), where the higher level neurons generate coarser predictions (lower resolution), while the lower level generate finer predictions (higher resolution). In terms of network architecture, we directly incorporate the encoder-decoder network within the LSTM module and share the final encoded high-level semantic information across different network levels. This enables comprehensive interaction between the current input and the historical states of LSTM compared with the traditional Encoder-LSTM-Decoder architecture, thus learning more believable temporal and spatial dependencies. Furthermore, to tackle the instability in adversarial training and mitigate the accumulation of prediction errors in long-term prediction, we propose several improvements to the training strategy. Our approach achieves good performance on datasets such as KTH, Moving MNIST and Caltech Pedestrian. Code is available at https://github.com/Ling-CF/MSPN

Designing a Double-Pole Nanoscale Relay Based on a Carbon Nanotube: A Theoretical Study

We theoretically investigate a novel and powerful double-pole nanoscale relay based on a carbon nanotube, which is one of the nanoelectromechanical switches being able to work under the strong nuclear radiation, and analyze the physical mechanism of the operating stages in the operation, including “pull in,” “connection,” and “pull back,” as well as the key factors influencing the efficiency of the devices. We explicitly provide the analytical expression of the two important operation voltages, V[subscript pull in] and V[subscript pull back], therefore clearly showing the dependence of the material properties and geometry of the present devices by the analytical method from basic physics, avoiding complex numerical calculations. Our method is easy to use in preparing the design guide for fabricating the present device and other nanoelectromechanical devices

Study of PDMS based magnetorheological elastomers

The fabrication of conventional magnetorheological elastomers (MRE) is usually taken more than 1 day because the conventional matrixes such as natural rubber and silicone rubber need long curing time to become solid state. This study presents a rapid method for fabricating MRE within 90 minutes by using poly(dimethylsiloxane) (PDMS) as the matrix thanks to the rapid curing of PDMS in high temperature. A total of four PDMS based MRE samples were fabricated. Their mechanical and rheological properties under both steady-state and dynamic loading conditions were tested with a parallel-plate rheometer. Additionally, the microstructures of the PDMS based MREs were also observed by SEM and compared with the silicone rubber based MRE