Helical insertion of polyphenylene chains into confined cylindrical slits composed of two carbon nanotubes.

The helical insertion behavior of poly(-phenylene) (PP) chains into confined cylindrical slits constructed by two carbon nanotubes (CNTs) with different diameters is studied by molecular dynamics simulations. The contribution of system energy and each energy component to helical self-assembly is discussed to further explain the conditions, driving force and mechanism. The width and length of the slit, the diameter of the outer tube and the temperature have a great impact on the helical insertion of PP chains.

Nanoparticle induced limitless spiral of polyacetylene isomers.

Helical nanomaterials represent an emerging group of nanostructures because of their multiple functionalities enabled by unique spiral geometry and nanoscale dimensions. This study demonstrates that several trans-transoid polyacetylene (Tt-PA) chains can self-spiral limitlessly over the whole length of polymers to form regular multiple helices under the inducement of water cluster, fullerene ball and metallic nanoparticles (NPs). Multi-helices possess random chirality selection which have equal probability of left-handedness and right-handedness.

Confinement of carbon nanotube enabled multi-strand helices of polyacetylenes.

Molecular dynamics simulations demonstrate that several polyacetylene (PA) chains can encapsulate and self-assemble into multi-stranded helical structures in confined inner space of carbon nanotubes (SWCNTs). The driving van der Waals force and curvature provided by the tube wall enable polymers to bend and spiral to maximize the π-π stacking area with the tube wall when filling the inside of the SWCNT. Structural forms and knitting patterns of multiple helices are influenced by the combined effect of the tube space, the number of PA chains and the temperature.

Defect enabled formation of multilayered funnel from isolated graphene nanoring.

Molecular dynamics simulations demonstrate that the cut defect can induce and guide the self-assembly of an isolated graphene nanoring (GNR) to form multi-layered funnel morphology. The vdW force is the driving force; the tangent component drives the self-assembly of GNR and the normal component adjusts and maintains the vertical distance between graphene layers, which is two times of the vdW radius. Moreover, the offset π-π stacking aids the adjacent layers to achieve the lowest energy of AB stacking.

Nanohelices from planar polymer self-assembled in carbon nanotubes.

The polymer possessing with planar structure can be activated and guided to encapsulate the inner space of SWNT and form a helix through van der Waals interaction and the π-π stacking effect between the polymer and the inner surface of SWNT. The SWNT size, the nanostructure and flexibility of polymer chain are all determine the final structures. The basic interaction between the polymer and the nanotubes is investigated, and the condition and mechanism of the helix-forming are explained particularly.

Novel scroll peapod produced by spontaneous scrolling of graphene onto fullerene string.

Novel scroll peapods are fabricated simply by utilizing the spontaneous scrolling mechanism of graphene onto fullerene string. The basic interaction between the graphene and the fullerene string is investigated, and the mechanism of the formation of the scroll peapod is explained in particular. The formation of the scroll peapod and its formation time are influenced by the combined effects of fullerene number, diameter and graphene size.

A multiple-feature and multiple-kernel scene segmentation algorithm for humanoid robot.

This technical correspondence presents a multiple-feature and multiple-kernel support vector machine (MFMK-SVM) methodology to achieve a more reliable and robust segmentation performance for humanoid robot. The pixel wise intensity, gradient, and C1 SMF features are extracted via the local homogeneity model and Gabor filter, which would be used as inputs of MFMK-SVM model. It may provide multiple features of the samples for easier implementation and efficient computation of MFMK-SVM model.

A three-domain fuzzy support vector regression for image denoising and experimental studies.

A novel three-domain fuzzy support vector regression (3DFSVR) is proposed, where the three-domain fuzzy kernel function (3DFKF) provides a solution to process uncertainties and input-output data information simultaneously. When compared with traditional two-domain SVR (2DSVR), the major advantage of 3DFSVR is able to use the prior knowledge via the novel fuzzy domain to analyze uncertain data and signals, which will enhance the potentials of 2DSVR. The 3DFKF is presented to integrate the kernel and fuzzy membership functions into a three-domain function.