Spectroscopic characterization of protein-wrapped single-wall carbon nanotubes and quantification of their cellular uptake in multiple cell generations

We study the spectral characteristics of bovine serum albumin (BSA) protein conjugated single-wall carbon nanotubes (SWNTs), and quantify their uptake by macrophages. The binding of BSA onto the SWNT surface is found to change the protein structure and to increase the doping of the nanotubes. The G-band Raman intensity follows a well-defined power law for SWNT concentrations of up to 33 μg ml-1 in aqueous solutions. Subsequently, in vitro experiments demonstrate that incubation of BSA-SWNT complexes with macrophages affects neither the cellular growth nor the cellular viability over multiple cell generations. Using wide spot Raman spectroscopy as a fast, non-destructive method for statistical quantification, we observe that macrophages effectively uptake BSA-SWNT complexes, with the average number of nanotubes internalized per cell remaining relatively constant over consecutive cell generations. The number of internalized SWNTs is found to be ∼30 × 106 SWNTs/cell for a 60 mm-2 seeding density and ∼100 × 10 6 SWNTs/cell for a 200 mm-2 seeding density. Our results show that BSA-functionalized SWNTs are an efficient molecular transport system with low cytotoxicity maintained over multiple cell generations. © 2013 IOP Publishing Ltd.

Towards robust design of axial compressors with uncertainty quantification

Operational uncertainties such as throttle excursions, varying inlet conditions and geometry changes lead to variability in compressor performance. In this work, the main operational uncertainties inherent in a transonic axial compressor are quantified to deter- mine their effect on performance. These uncertainties include the effects of inlet distortion, metal expansion, ow leakages and blade roughness. A 3D, validated RANS model of the compressor is utilized to simulate these uncertainties and quantify their effect on polytropic efficiency and pressure ratio. To propagate them, stochastic collocation and sparse pseudospectral approximations are used. We demonstrate that lower-order approximations are sufficient as these uncertainties are inherently linear. Results for epistemic uncertainties in the form of meshing methodologies are also presented. Finally, the uncertainties considered are ranked in order of their effect on efficiency loss. © 2012 AIAA.

Rebuilding the limit order book: Sequential Bayesian inference on hidden states

The limit order book of an exchange represents an information store of market participants' future aims and for many traders the information held in this store is of interest. However, information loss occurs between orders being entered into the exchange and limit order book data being sent out. We present an online algorithm which carries out Bayesian inference to replace information lost at the level of the exchange server and apply our proof of concept algorithm to real historical data from some of the world's most liquid futures contracts as traded on CME GLOBEX, EUREX and NYSE Liffe exchanges. © 2013 © 2013 Taylor & Francis.