Cold-gas chemical vapor deposition to identify the key precursor for rapidly growing vertically-aligned single-wall and few-wall carbon nanotubes from pyrolyzed ethanol

Vertically-aligned carbon nanotubes (VA-CNTs) were rapidly grown from ethanol and their chemistry has been studied using a "cold-gas" chemical vapor deposition (CVD) method. Ethanol vapor was preheated in a furnace, cooled down and then flowed over cobalt catalysts upon ribbon-shaped substrates at 800 °C, while keeping the gas unheated. CNTs were obtained from ethanol on a sub-micrometer scale without preheating, but on a millimeter scale with preheating at 1000 °C. Acetylene was predicted to be the direct precursor by gas chromatography and gas-phase kinetic simulation, and actually led to millimeter-tall VA-CNTs without preheating when fed with hydrogen and water. There was, however a difference in CNT structure, i.e. mainly few-wall tubes from pyrolyzed ethanol and mainly single-wall tubes for unheated acetylene, and the by-products from ethanol pyrolysis possibly caused this difference. The "cold-gas" CVD, in which the gas-phase and catalytic reactions are separately controlled, allowed us to further understand CNT growth. © 2012 Elsevier Ltd. All rights reserved.

Disentangling the Complexity of Early-Stage Technology Acquisitions

While tools have been developed to assist firms' decision making for bringing known products and components into the supply chain, fewer tools are available to guide the acquisition of earlier-stage technologies, which is a riskier proposition due to higher technological and market uncertainties. Through synthesis of literature in technology sourcing, open innovation, alliances, mergers and acquisitions, outsourcing, and technology and knowledge transfer and consultation with industry, this paper identifies critical issues that decision makers should consider before making an early-stage technology acquisition. Sixteen questions emerge to guide decision making, comprising internal, technology, and partner assessments. These questions allow a firm to disentangle the complexity of early-stage technology acquisitions and select the most appropriate targets.

A Study of Fan-Distortion Interaction Within the NASA Rotor 67 Transonic Stage

The performance of a transonic fan operating within nonuniform inlet flow remains a key concern for the design and operability of a turbofan engine. This paper applies computational methods to improve the understanding of the interaction between a transonic fan and an inlet total pressure distortion. The test case studied is the NASA rotor 67 stage operating with a total pressure distortion covering a 120-deg sector of the inlet flow field. Full-annulus, unsteady, three-dimensional CFD has been used to simulate the test rig installation and the full fan assembly operating with inlet distortion. Novel post-processing methods have been applied to extract the fan performance and features of the interaction between the fan and the nonuniform inflow. The results of the unsteady computations agree well with the measurement data. The local operating condition of the fan at different positions around the annulus has been tracked and analyzed, and this is shown to be highly dependent on the swirl and mass flow redistribution that the rotor induces ahead of it due to the incoming distortion. The upstream flow effects lead to a variation in work input that determines the distortion pattern seen downstream of the fan stage. In addition, the unsteady computations also reveal more complex flow features downstream of the fan stage, which arise due to the three dimensionality of the flow and unsteadiness. © 2012 American Society of Mechanical Engineers.

Technology confidence in early stage development of medical devices

Innovation is a critical factor in ensuring commercial success within the area of medical technology. Biotechnology and Healthcare developments require huge financial and resource investment, in-depth research and clinical trials. Consequently, these developments involve a complex multidisciplinary structure, which is inherently full of risks and uncertainty. In this context, early technology assessment and 'proof of concept' is often sporadic and unstructured. Existing methodologies for managing the feasibility stage of medical device development are predominantly suited to the later phases of development and favour detail in optimisation, validation and regulatory approval. During these early phases, feasibility studies are normally conducted to establish whether technology is potentially viable. However, it is not clear how this technology viability is currently measured. This paper aims to redress this gap through the development of a technology confidence scale, as appropriate explicitly to the feasibility phase of medical device design. These guidelines were developed from analysis of three recent innovation studies within the medical device industry.

Making a shallow network deep: Conversion of a boosting classifier into a decision tree by boolean optimisation

This paper presents a novel way to speed up the evaluation time of a boosting classifier. We make a shallow (flat) network deep (hierarchical) by growing a tree from decision regions of a given boosting classifier. The tree provides many short paths for speeding up while preserving the reasonably smooth decision regions of the boosting classifier for good generalisation. For converting a boosting classifier into a decision tree, we formulate a Boolean optimization problem, which has been previously studied for circuit design but limited to a small number of binary variables. In this work, a novel optimisation method is proposed for, firstly, several tens of variables i.e. weak-learners of a boosting classifier, and then any larger number of weak-learners by using a two-stage cascade. Experiments on the synthetic and face image data sets show that the obtained tree achieves a significant speed up both over a standard boosting classifier and the Fast-exit-a previously described method for speeding-up boosting classification, at the same accuracy. The proposed method as a general meta-algorithm is also useful for a boosting cascade, where it speeds up individual stage classifiers by different gains. The proposed method is further demonstrated for fast-moving object tracking and segmentation problems. © 2011 Springer Science+Business Media, LLC.

HTGR fuel element depletion benchmark: Stage three results

Recently, a new numerical benchmark exercise for High Temperature Gas Cooled Reactor (HTGR) fuel depletion was defined. The purpose of this benchmark is to provide a comparison basis for different codes and methods applied to the burnup analysis of HTGRs. The benchmark specifications include three different models: (1) an infinite lattice of tristructural isotropic (TRISO) fuel particles, (2) an infinite lattice of fuel pebbles, and (3) a prismatic fuel including fuel and coolant channels. In this paper, we present the results of the third stage of the benchmark obtained with MCNP based depletion code BGCore and deterministic lattice code HELIOS 1.9. The depletion calculations were performed for three-dimensional model of prismatic fuel with explicitly described TRISO particles as well as for two-dimensional model, in which double heterogeneity of the TRISO particles was eliminated using reactivity equivalent physical transformation (RPT). Generally, good agreement in the results of the calculations obtained using different methods and codes was observed.

HTGR fuel element depletion benchmark: Stage one results

In this paper, we reported the results of the first stage of HTGR fuel element depletion benchmark obtained with BGCore and HELIOS depletion codes. The results of the k-inf are generally in good agreement. However, significant deviation in concentrations of several nuclides between MCNP based and HELIOS codes was observed.