Method of Forming Composite Materials including Conjugated Materials attached to Carbon Nanotubes or Graphenes (CIP)

A method of forming composite materials includes dispersing a conjugated material, a solvent for the conjugated material, and a plurality of carbon nanotubes (CNTs) or graphene including structures having an outer surface to form a dispersion. The solvent is evaporated from the dispersion to yield a CNT or graphene composite including a plurality of crystalline supramolecular structures having the conjugated material non-covalently secured to the outer surface of the CNT or the graphene including structure. The supramolecular structures have an average length which extends outward in a length direction from the outer surface of the CNT or graphene including structure, where the average length is greater than an average width of the supramolecular structures

Supramolecular Structures Comprising At Least Partially Conjugated Polymers Attached to Carbon Nanotubes and Graphenes

A composition of matter includes at least one carbon nanotube (CNT) or grapheme type structure having an outer surface and a plurality of crystalline polymer supramolecular structures that includes a conjugated polymer that are non-covalently secured to the outer surface of the CNTs or the grapheme type structure. The conjugated polymer can be a conjugated homopolymer or a block copolymer including at least one conjugated block. The supramolecular structures extend outward from the outer surface of the CNTs or grapheme type structures

Efficient excitation and tuning of toroidal dipoles within individual homogenous nanoparticles

We revisit the fundamental topic of light scattering by single homogenous nanoparticles from the new perspective of excitation and manipulation of toroidal dipoles. It is revealed that besides within all-dielectric particles, toroidal dipoles can also be efficiently excited within homogenous metallic nanoparticles. Moreover, we show that those toroidal dipoles excited can be spectrally tuned through adjusting the radial anisotropy parameters of the materials, which paves the way for further more flexible manipulations of the toroidal responses within photonic systems. The study into toroidal multipole excitation and tuning within nanoparticles deepens our understanding of the seminal problem of light scattering, and may incubate many scattering related fundamental researches and applications.Comment: Four Figures,Ten Pages and Comments Welcome

Semi-supervised Online Multiple Kernel Learning Algorithm for Big Data

In order to improve the performance of machine learning in big data, online multiple kernel learning algorithms are proposed in this paper. First, a supervised online multiple kernel learning algorithm for big data (SOMK_bd) is proposed to reduce the computational workload during kernel modification. In SOMK_bd, the traditional kernel learning algorithm is improved and kernel integration is only carried out in the constructed kernel subset. Next, an unsupervised online multiple kernel learning algorithm for big data (UOMK_bd) is proposed. In UOMK_bd, the traditional kernel learning algorithm is improved to adapt to the online environment and data replacement strategy is used to modify the kernel function in unsupervised manner. Then, a semi-supervised online multiple kernel learning algorithm for big data (SSOMK_bd) is proposed. Based on incremental learning, SSOMK_bd makes full use of the abundant information of large scale incomplete labeled data, and uses SOMK_bd and UOMK_bd to update the current reading data. Finally, experiments are conducted on UCI data set and the results show that the proposed algorithms are effective