Juan C. Burgos

Carbon nanotubes: engineering biomedical applications.

Carbon nanotubes (CNTs) are cylinder-shaped allotropic forms of carbon, most widely produced under chemical vapor deposition. They possess astounding chemical, electronic, mechanical, and optical properties. Being among the most promising materials in nanotechnology, they are also likely to revolutionize medicine. Among other biomedical applications, after proper functionalization carbon nanotubes can be transformed into sophisticated biosensing and biocompatible drug-delivery systems, for specific targeting and elimination of tumor cells. This chapter provides an introduction to the chemical and electronic structure and properties of single-walled carbon nanotubes, followed by a description of the main synthesis and post-synthesis methods. These sections allow the reader to become familiar with the specific characteristics of these materials and the manner in which these properties may be dependent on the specific synthesis and post-synthesis processes. The chapter ends with a review of the current biomedical applications of carbon nanotubes, highlighting successes and challenges.

A scalable method for parallelizing sampling-based motion planning algorithms

This paper describes a scalable method for parallelizing sampling-based motion planning algorithms. It subdivides configuration space (C-space) into (possibly overlapping) regions and independently, in parallel, uses standard (sequential) sampling-based planners to construct roadmaps in each region. Next, in parallel, regional roadmaps in adjacent regions are connected to form a global roadmap. By subdividing the space and restricting the locality of connection attempts, we reduce the work and inter-processor communication associated with nearest neighbor calculation, a critical bottleneck for scalability in existing parallel motion planning methods. We show that our method is general enough to handle a variety of planning schemes, including the widely used Probabilistic Roadmap (PRM) and Rapidly-exploring Random Trees (RRT) algorithms. We compare our approach to two other existing parallel algorithms and demonstrate that our approach achieves better and more scalable performance. Our approach achieves almost linear scalability on a 2400 core LINUX cluster and on a 153,216 core Cray XE6 petascale machine.

Nanocatalyst shape and composition during nucleation of single-walled carbon nanotubes

The dynamic evolution of nanocatalyst particle shape and carbon composition during the initial stages of single-walled carbon nanotube growth by chemical vapor deposition synthesis is investigated. Classical reactive and ab initio molecular dynamics simulations are used, along with environmental transmission electron microscope video imaging analyses. A clear migration of carbon is detected from the nanocatalyst/substrate interface, leading to a carbon gradient showing enrichment of the nanocatalyst layers in the immediate vicinity of the contact layer. However, as the metal nanocatalyst particle becomes saturated with carbon, a dynamic equilibrium is established, with carbon precipitating on the surface and nucleating a carbon cap that is the precursor of nanotube growth. A carbon composition profile decreasing towards the nanoparticle top is clearly revealed by the computational and experimental results that show a negligible amount of carbon in the nanoparticle region in contact with the nucleating cap. The carbon composition profile inside the nanoparticle is accompanied by a well-defined shape evolution of the nanocatalyst driven by the various opposing forces acting upon it both from the substrate and from the nascent carbon nanostructure. This new understanding suggests that tuning the nanoparticle/substrate interaction would provide unique ways of controlling the nanotube synthesis.

Toward realistic pursuit-evasion using a roadmap-based approach

In this work, we describe an approach for modeling and simulating group behaviors for pursuit-evasion that uses a graph-based representation of the environment and integrates multi-agent simulation with roadmap-based path planning. Our approach can be applied to more realistic scenarios than are typically studied in most previous work, including agents moving in 3D environments such as terrains, multi-story buildings, and dynamic environments. We also support more realistic three-dimensional visibility computations that allow evading agents to hide in crowds or behind hills. We demonstrate the utility of this approach on mobile robots and in simulation for a variety of scenarios including pursuit-evasion and tag on terrains, in multi-level buildings, and in crowds.

Direct evidence of atomic-scale structural fluctuations in catalyst nanoparticles

Rational catalyst design requires an atomic scale mechanistic understanding of the chemical pathways involved in the catalytic process. A heterogeneous catalyst typically works by adsorbing reactants onto its surface, where the energies for specific bonds to dissociate and/or combine with other species (to form desired intermediate or final products) are lower. Here, using the catalytic growth of single-walled carbon nanotubes (SWCNTs) as a prototype reaction, we show that the chemical pathway may in-fact involve the entire catalyst particle, and can proceed via the fluctuations in the formation and decomposition of metastable phases in the particle interior. We record in situ and at atomic resolution, the dynamic phase transformations occurring in a Cobalt catalyst nanoparticle during SWCNT growth, using a state-of-the-art environmental transmission electron microscope (ETEM). The fluctuations in catalyst carbon content are quantified by the automated, atomic-scale structural analysis of the time-resolved ETEM images and correlated with the SWCNT growth rate. We find the fluctuations in the carbon concentration in the catalyst nanoparticle and the fluctuations in nanotube growth rates to be of complementary character. These findings are successfully explained by reactive molecular dynamics (RMD) simulations that track the spatial and temporal evolution of the distribution of carbon atoms within and on the surface of the catalyst particle. We anticipate that our approach combining real-time, atomic-resolution image analysis and molecular dynamics simulations will facilitate catalyst design, improving reaction efficiencies and selectivity towards the growth of desired structure.

Engineering preferential adsorption of single-walled carbon nanotubes on functionalized ST-cut surfaces of quartz.

Horizontal alignment during synthesis of single-walled carbon nanotubes has been found experimentally along certain directions of well-defined quartz surfaces. The reasons for such alignment are here examined using first-principles computational analysis, as a function of structure and chemistry of the specific exposed facet, presence and location of OH and H functional groups, and degree of hydration of the surface. It is found that selective functionalization of low-coordinated surface sites may cause exposure of low-coordinated Si atoms that bond strongly to nanotube walls. On the other hand, saturation of low-coordinated oxygen also favors carbon nanotube adhesion to the substrate. As found previously on bare silica surfaces, a chirality preference is confirmed on functionalized surfaces toward zigzag over armchair nanotubes. Magnetization effects on the surface originated by the presence of adsorbed functional groups are found to enhance adsorption of arm-chair nanotubes compared to that on clean surfaces. On the basis of the findings, it is suggested that surfaces may be engineered to favor horizontal adsorption of specific chiralities along preferential directions.