Using atomistic models and molecular dynamics simulations, interlayer corrugation and resistant force in a biwalled carbon nanotube are shown to be strongly dependent upon the morphology combination of the bitube. Consequently, energy dissipation in a commensurate (e.g., armchair/armchair or zigzag/zigzag) bitube oscillator is found to be much larger than that in an incommensurate (e.g., zigzag/armchair) oscillator, resulting in a decay of oscillation amplitude within a few nanoseconds in the commensurate bitube and several tens of nanoseconds in the incommensurate bitube.

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Our first-principles calculations reveal surprisingly high sensitivity of the field-induced energy gap of bilayer graphene to changes in its interlayer spacing. Small adjustments in the interlayer spacing near its equilibrium value produce large modulations in the gap over a wide range of field strength. We elucidate the mechanism for the extremely effective gap tuning by examining the interlayer charge redistribution driven by the coupled electric field and nanomechanical effect.

In recent years, Deep Learning (DL), such as the algorithms of Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN) and Generative Adversarial Networks (GAN), has been widely studied and applied in various fields including agriculture. Researchers in the fields of agriculture often use software frameworks without sufficiently examining the ideas and mechanisms of a technique. This article provides a concise summary of major DL algorithms, including concepts, limitations, implementation, training processes, and example codes, to help researchers in agriculture to gain a holistic picture of major DL techniques quickly. Research on DL applications in agriculture is summarized and analyzed, and future opportunities are discussed in this paper, which is expected to help researchers in agriculture to better understand DL algorithms and learn major DL techniques quickly, and further to facilitate data analysis, enhance related research in agriculture, and thus promote DL applications effectively.

Designing smart surfaces with tunable wettability has drawn much attention in recent years for academic research and practical applications. Most of the previous methods to achieve such surfaces demand some particular materials that inherently have special features or complicated structures which are usually not easy to obtain. A novel strategy to achieve such smart surfaces is proposed by using the surface patterned shape memory polymers of chemically crosslinked polycyclooctene which shows a giant deformability of up to ≈730% strain. The smart surfaces possess the ability to continuously tune the wettability by controlling the recovery temperature and/or time. Coating the modified titanium dioxide nanoparticles onto such surfaces renders the surface superhydrophobicity and expands the tunable range of contact angles (CAs). Theoretical calculations of the CAs at different strains via modified Cassie model well explain the tunable wettability behaviors of such smart surfaces.

The encapsulated copper atoms inside a defected single-walled carbon nanotube escape from the tube through the defect hole as the temperature increases. This causes the partially confined copper nanowires (CNWs) to undergo special structural transformations from a solid to a distinguishable helical layered structure and finally to the liquid state. The defect has a vital function in automatically adjusting the internal pressure and copper atom density. The critical structural transformation temperature of the CNW is significantly influenced by the confinement conditions of the carbon nanotube.

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