Low temperature growth of ultra-high mass density carbon nanotube forests on conductive supports

We grow ultra-high mass density carbon nanotube forests at 450°C on Ti-coated Cu supports using Co-Mo co-catalyst. X-ray photoelectron spectroscopy shows Mo strongly interacts with Ti and Co, suppressing both aggregation and lifting off of Co particles and, thus, promoting the root growth mechanism. The forests average a height of 0.38 μm and a mass density of 1.6 g cm -3. This mass density is the highest reported so far, even at higher temperatures or on insulators. The forests and Cu supports show ohmic conductivity (lowest resistance ∼22 kΩ), suggesting Co-Mo is useful for applications requiring forest growth on conductors. © 2013 AIP Publishing LLC.

Evaluation of bimetallic catalysts for the growth of carbon nanotube forests

We systematically study the growth of carbon nanotube forests by chemical vapor deposition using evaporated monometallic or bimetallic Ni, Co, or Fe films supported on alumina. Our results show two regimes of catalytic activity. When the total thickness of catalyst is larger than nominally 1nm, bimetallic catalysts tend to outperform the equivalent layers of a single metal, yielding taller forests of multi-walled carbon nanotubes (CNTs). In contrast, for layers thinner than ~1nm, bimetallic catalysts are notably less active than individually. However, the amount of small diameter and single-walled CNTs is significantly increased. This possible transition at ~1nm might be related to different catalyst composition after annealing, depending whether or not the films overlap during evaporation and alloy during catalyst formation. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Hybrids of carbon nanotube forests and gold nanoparticles for improved surface plasmon manipulation.

We report the fabrication and characterization of hybrids of vertically-aligned carbon nanotube forests and gold nanoparticles for improved manipulation of their plasmonic properties. Raman spectroscopy of nanotube forests performed at the separation area of nanotube-nanoparticles shows a scattering enhancement factor of the order of 1 × 10(6). The enhancement is related to the plasmonic coupling of the nanoparticles and is potentially applicable in high-resolution scanning near-field optical microscopy, plasmonics, and photovoltaics.

Carbon nanotube forests growth using catalysts from atomic layer deposition

We have grown carbon nanotubes using Fe and Ni catalyst films deposited by atomic layer deposition. Both metals lead to catalytically active nanoparticles for growing vertically aligned nanotube forests or carbon fibres, depending on the growth conditions and whether the substrate is alumina or silica. The resulting nanotubes have narrow diameter and wall number distributions that are as narrow as those grown from sputtered catalysts. The state of the catalyst is studied by in-situ and ex-situ X-ray photoemission spectroscopy. We demonstrate multi-directional nanotube growth on a porous alumina foam coated with Fe prepared by atomic layer deposition. This deposition technique can be useful for nanotube applications in microelectronics, filter technology, and energy storage. © 2014 AIP Publishing LLC.

Formation of composite organic thin film transistors with nanotubes and nanowires

Plastic electronics is a rapidly expanding topic, much of which has been focused on organic semiconductors. However, it is also of interest to find viable ways to integrate nanomaterials, such as silicon nanowires (SiNWs) and carbon nanotubes (CNTs), into this technology. Here, we present methods of fabrication of composite devices incorporating such nanostructured materials into an organic matrix. We investigate the formation of polymer/CNT composites, for which we use the semiconducting polymer poly(3,3‴-dialkyl-quaterthiophene) (PQT). We also report a method of fabricating polymer/SiNW TFTs, whereby sparse arrays of parallel oriented SiNWs are initially prepared on silicon dioxide substrates from forests of as-grown gold-catalysed SiNWs. Subsequent ink-jet printing of PQT on these arrays produces a polymer/SiNW composite film. We also present the electrical characterization of all composite devices. © 2007 Elsevier B.V. All rights reserved.

Plasma enhanced chemical vapour deposited carbon nanotubes for field emission applications

Plasma Enhanced Chemical Vapour Deposition is an extremely versatile technique for directly growing multiwalled carbon nanotubes onto various substrates. We will demonstrate the deposition of vertically aligned nanotube arrays, sparsely or densely populated nanotube forests, and precisely patterned arrays of nanotubes. The high-aspect ratio nanotubes (∼50 nm in diameter and 5 microns long) produced are metallic in nature and direct contact electrical measurements reveal that each nanotube has a current carrying capacity of 107-108 A/cm2, making them excellent candidates as field emission sources. We examined the field emission characteristics of dense nanotube forests as well as sparse nanotube forests and found that the sparse forests had significantly lower turn-on fields and higher emission currents. This is due to a reduction in the field enhancement of the nanotubes due to electric field shielding from adjacent nanotubes in the dense nanotube arrays. We thus fabricated a uniform array of single nanotubes to attempt to overcome these issues and will present the field emission characteristics of this.

Spatio-temporal clustering of probabilistic region trajectories

We propose a novel model for the spatio-temporal clustering of trajectories based on motion, which applies to challenging street-view video sequences of pedestrians captured by a mobile camera. A key contribution of our work is the introduction of novel probabilistic region trajectories, motivated by the non-repeatability of segmentation of frames in a video sequence. Hierarchical image segments are obtained by using a state-of-the-art hierarchical segmentation algorithm, and connected from adjacent frames in a directed acyclic graph. The region trajectories and measures of confidence are extracted from this graph using a dynamic programming-based optimisation. Our second main contribution is a Bayesian framework with a twofold goal: to learn the optimal, in a maximum likelihood sense, Random Forests classifier of motion patterns based on video features, and construct a unique graph from region trajectories of different frames, lengths and hierarchical levels. Finally, we demonstrate the use of Isomap for effective spatio-temporal clustering of the region trajectories of pedestrians. We support our claims with experimental results on new and existing challenging video sequences. © 2011 IEEE.

A simple combinatorial method aiding research on single-walled carbon nanotube growth on substrates

Establishing fabrication methods of carbon nanotubes (CNTs) is essential to realize many applications expected for CNTs. Catalytic growth of CNTs on substrates by chemical vapor deposition (CVD) is promising for direct fabrication of CNT devices, and catalyst nanoparticles play a crucial role in such growth. We have developed a simple method called "combinatorial masked deposition (CMD)", in which catalyst particles of a given series of sizes and compositions are formed on a single substrate by annealing gradient catalyst layers formed by sputtering through a mask. CMD enables preparation of hundreds of catalysts on a wafer, growth of single-walled CNTs (SWCNTs), and evaluation of SWCNT diameter distributions by automated Raman mapping in a single day. CMD helps determinations of the CVD and catalyst windows realizing millimeter-tall SWCNT forest growth in 10 min, and of growth curves for a series of catalysts in a single measurement when combined with realtime monitoring. A catalyst library prepared using CMD yields various CNTs, ranging from individuals, networks, spikes, and to forests of both SWCNTs and multi-walled CNTs, and thus can be used to efficiently evaluate self-organized CNT field emitters, for example. The CMD method is simple yet effective for research of CNT growth methods. © 2010 The Japan Society of Applied Physics.

Silhouette-based object phenotype recognition using 3D shape priors

This paper tackles the novel challenging problem of 3D object phenotype recognition from a single 2D silhouette. To bridge the large pose (articulation or deformation) and camera viewpoint changes between the gallery images and query image, we propose a novel probabilistic inference algorithm based on 3D shape priors. Our approach combines both generative and discriminative learning. We use latent probabilistic generative models to capture 3D shape and pose variations from a set of 3D mesh models. Based on these 3D shape priors, we generate a large number of projections for different phenotype classes, poses, and camera viewpoints, and implement Random Forests to efficiently solve the shape and pose inference problems. By model selection in terms of the silhouette coherency between the query and the projections of 3D shapes synthesized using the galleries, we achieve the phenotype recognition result as well as a fast approximate 3D reconstruction of the query. To verify the efficacy of the proposed approach, we present new datasets which contain over 500 images of various human and shark phenotypes and motions. The experimental results clearly show the benefits of using the 3D priors in the proposed method over previous 2D-based methods. © 2011 IEEE.