Characterisation of commercially CVD grown multi-walled carbon nanotubes for paint applications

AbstractCommercially grown, multi-walled carbon nanotubes (MWNTs), available in kilogram quantities from three commercial suppliers have been characterised using a number of analytical techniques. The catalysts used in the growth of the MWNTs are identified by energy dispersive X-ray spectroscopy (EDX) and different growth mechanisms are postulated to explain the various structures present in the MWNT stock in its as-supplied form. A tightening of the agglomerate structures during purification and functionalisation is shown using scanning electron microscopy (SEM) and confirmed more qualitatively using pore-size distributions obtained using the Brunauer–Emmett–Teller (BET) method and non-local density functional theory (NLDFT) calculations. Differences in thermal stability are shown using thermogravimetric analysis (TGA) and are related back to the residual catalysts present. X-ray photoelectron spectroscopy (XPS) is used to confirm functionalisation of certain grades and Raman spectroscopy is used to investigate the level of defects present

Characterizing the Initial Phase of Epidemic Growth on some Empirical Networks

A key parameter in models for the spread of infectious diseases is the basic reproduction number $R_0$, which is the expected number of secondary cases a typical infected primary case infects during its infectious period in a large mostly susceptible population. In order for this quantity to be meaningful, the initial expected growth of the number of infectious individuals in the large-population limit should be exponential. We investigate to what extent this assumption is valid by performing repeated simulations of epidemics on selected empirical networks, viewing each epidemic as a random process in discrete time. The initial phase of each epidemic is analyzed by fitting the number of infected people at each time step to a generalised growth model, allowing for estimating the shape of the growth. For reference, similar investigations are done on some elementary graphs such as integer lattices in different dimensions and configuration model graphs, for which the early epidemic behaviour is known. We find that for the empirical networks tested in this paper, exponential growth characterizes the early stages of the epidemic, except when the network is restricted by a strong low-dimensional spacial constraint, such as is the case for the two-dimensional square lattice. However, on finite integer lattices of sufficiently high dimension, the early development of epidemics shows exponential growth.Comment: To be included in the conference proceedings for SPAS 2017 (International Conference on Stochastic Processes and Algebraic Structures), October 4-6, 201

CREME: The 2011 Revision of the Cosmic Ray Effects on Micro-Electronics Code

We describe a tool suite, CREME, which combines existing capabilities of CREME96 and CREME86 with new radiation environment models and new Monte Carlo computational capabilities for single event effects and total ionizing dose

Arterial spin labelling reveals an abnormal cerebral perfusion pattern in Parkinson's disease

There is a need for objective imaging markers of Parkinson's disease status and progression. Positron emission tomography and single photon emission computed tomography studies have suggested patterns of abnormal cerebral perfusion in Parkinson's disease as potential functional biomarkers. This study aimed to identify an arterial spin labelling magnetic resonance-derived perfusion network as an accessible, non-invasive alternative. We used pseudo-continuous arterial spin labelling to measure cerebral grey matter perfusion in 61 subjects with Parkinson's disease with a range of motor and cognitive impairment, including patients with dementia and 29 age- and sex-matched controls. Principal component analysis was used to derive a Parkinson's disease-related perfusion network via logistic regression. Region of interest analysis of absolute perfusion values revealed that the Parkinson's disease pattern was characterized by decreased perfusion in posterior parieto-occipital cortex, precuneus and cuneus, and middle frontal gyri compared with healthy controls. Perfusion was preserved in globus pallidus, putamen, anterior cingulate and post- and pre-central gyri. Both motor and cognitive statuses were significant factors related to network score. A network approach, supported by arterial spin labelling-derived absolute perfusion values may provide a readily accessible neuroimaging method to characterize and track progression of both motor and cognitive status in Parkinson's diseas

A satellite data driven biophysical modeling approach for estimating northern peatland and tundra CO\u3csub\u3e2\u3c/sub\u3e and CH\u3csub\u3e4\u3c/sub\u3e fluxes

The northern terrestrial net ecosystem carbon balance (NECB) is contingent on inputs from vegetation gross primary productivity (GPP) to offset the ecosystem respiration (Reco) of carbon dioxide (CO2) and methane (CH4) emissions, but an effective framework to monitor the regional Arctic NECB is lacking. We modified a terrestrial carbon flux (TCF) model developed for satellite remote sensing applications to evaluate wetland CO2 and CH4 fluxes over pan-Arctic eddy covariance (EC) flux tower sites. The TCF model estimates GPP, CO2 and CH4 emissions using in situ or remote sensing and reanalysis-based climate data as inputs. The TCF model simulations using in situ data explained \u3e70% of the r2 variability in the 8 day cumulative EC measured fluxes. Model simulations using coarser satellite (MODIS) and reanalysis (MERRA) records accounted for approximately 69% and 75% of the respective r2 variability in the tower CO2 and CH4 records, with corresponding RWSE uncertainties of 1.3 gCM-2 d-1 (CO2) and 18.2 mg Cm-2 d-1 (CH4). Although the estimated annual CH4 emissions were small (gCm-2 yr-1) relative to Reco (\u3e180 gCm-2 yr-1), they reduced the across-site NECB by 23%and contributed to a global warming potential of approximately 165±128 gCO2eqm−2 yr−1 when considered over a 100 year time span. This model evaluation indicates a strong potential for using the TCF model approach to document landscape-scale variability in CO2 and CH4 fluxes, and to estimate the NECB for northern peatland and tundra ecosystems

The Cosmology Large Angular Scale Surveyor

The Cosmology Large Angular Scale Surveyor (CLASS) is a four telescope array designed to characterize relic primordial gravitational waves from inflation and the optical depth to reionization through a measurement of the polarized cosmic microwave background (CMB) on the largest angular scales. The frequencies of the four CLASS telescopes, one at 38 GHz, two at 93 GHz, and one dichroic system at 145/217 GHz, are chosen to avoid spectral regions of high atmospheric emission and span the minimum of the polarized Galactic foregrounds: synchrotron emission at lower frequencies and dust emission at higher frequencies. Low-noise transition edge sensor detectors and a rapid front-end polarization modulator provide a unique combination of high sensitivity, stability, and control of systematics. The CLASS site, at 5200 m in the Chilean Atacama desert, allows for daily mapping of up to 70\% of the sky and enables the characterization of CMB polarization at the largest angular scales. Using this combination of a broad frequency range, large sky coverage, control over systematics, and high sensitivity, CLASS will observe the reionization and recombination peaks of the CMB E- and B-mode power spectra. CLASS will make a cosmic variance limited measurement of the optical depth to reionization and will measure or place upper limits on the tensor-to-scalar ratio, $r$, down to a level of 0.01 (95\% C.L.)