Actinometry of Hydrogen Plasmas

Optical emission spectroscopy (OES) can be used to map the electron energy distribution of hydrogen plasmas. Using actinometry, a type of OES where trace amounts of noble gases are introduced, the effect of discharge power on the electron temperature of hydrogen plasmas was explored. This was done using argon and krypton as actinometers for low pressure hydrogen plasmas. It was determined that the electron temperature decreased with respect to power supplied to the discharge

Approximation of Bayesian inverse problems for PDEs

Inverse problems are often ill posed, with solutions that depend sensitively on data. In any numerical approach to the solution of such problems, regularization of some form is needed to counteract the resulting instability. This paper is based on an approach to regularization, employing a Bayesian formulation of the problem, which leads to a notion of well posedness for inverse problems, at the level of probability measures. The stability which results from this well posedness may be used as the basis for quantifying the approximation, in finite dimensional spaces, of inverse problems for functions. This paper contains a theory which utilizes this stability property to estimate the distance between the true and approximate posterior distributions, in the Hellinger metric, in terms of error estimates for approximation of the underlying forward problem. This is potentially useful as it allows for the transfer of estimates from the numerical analysis of forward problems into estimates for the solution of the related inverse problem. It is noteworthy that, when the prior is a Gaussian random field model, controlling differences in the Hellinger metric leads to control on the differences between expected values of polynomially bounded functions and operators, including the mean and covariance operator. The ideas are applied to some non-Gaussian inverse problems where the goal is determination of the initial condition for the Stokes or Navier–Stokes equation from Lagrangian and Eulerian observations, respectively

Gamma-ray Novae: Rare or Nearby?

Classical Novae were revealed as a surprise source of gamma-rays in Fermi LAT observations. During the first 8 years since the LAT was launched, 6 novae in total have been detected to > 5 sigma in gamma-rays, in contrast to the 69 discovered optically in the same period. We attempt to resolve this discrepancy by assuming all novae are gamma-ray emitters, and assigning peak one-day fluxes based on a flat distribution of the known emitters to a simulated population. To determine optical parameters, the spatial distribution and magnitudes of bulge and disc novae in M31 are scaled to the Milky Way, which we approximate as a disc with a 20 kpc radius and elliptical bulge with semi major axis 3 kpc and axis ratios 2:1 in the xy plane. We approximate Galactic reddening using a double exponential disc with vertical and radial scale heights of r_d = 5 kpc and z_d = 0.2 kpc, and demonstrate that even such a rudimentary model can easily reproduce the observed fraction of gamma-ray novae, implying that these apparently rare sources are in fact nearby and not intrinsically rare. We conclude that classical novae with m_R < 12 and within ~8 kpc are likely to be discovered in gamma-rays using the Fermi LAT.Comment: Accepted by MNRAS, 10 pages, 7 figure

Variational data assimilation using targetted random walks

The variational approach to data assimilation is a widely used methodology for both online prediction and for reanalysis (offline hindcasting). In either of these scenarios it can be important to assess uncertainties in the assimilated state. Ideally it would be desirable to have complete information concerning the Bayesian posterior distribution for unknown state, given data. The purpose of this paper is to show that complete computational probing of this posterior distribution is now within reach in the offline situation. In this paper we will introduce an MCMC method which enables us to directly sample from the Bayesian\ud posterior distribution on the unknown functions of interest, given observations. Since we are aware that these\ud methods are currently too computationally expensive to consider using in an online filtering scenario, we frame this in the context of offline reanalysis. Using a simple random walk-type MCMC method, we are able to characterize the posterior distribution using only evaluations of the forward model of the problem, and of the model and data mismatch. No adjoint model is required for the method we use; however more sophisticated MCMC methods are available\ud which do exploit derivative information. For simplicity of exposition we consider the problem of assimilating data, either Eulerian or Lagrangian, into a low Reynolds number (Stokes flow) scenario in a two dimensional periodic geometry. We will show that in many cases it is possible to recover the initial condition and model error (which we describe as unknown forcing to the model) from data, and that with increasing amounts of informative data, the uncertainty in our estimations reduces

Improved surface quality of anisotropically etched silicon {111} planes for mm-scale integrated optics

We have studied the surface quality of millimeter-scale optical mirrors produced by etching CZ and FZ silicon wafers in potassium hydroxide to expose the $\{111\}$ planes. We find that the FZ surfaces have four times lower noise power at spatial frequencies up to $500\, {mm}^{-1}$. We conclude that mirrors made using FZ wafers have higher optical quality

A study in hybrid vehicle architectures : comparing efficiency and performance

Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2009."June 2009." Cataloged from PDF version of thesis.Includes bibliographical references (p. 19).This paper presents a comparison of performance and efficiencies for four vehicle power architectures; the internal combustion engine (ICE), the parallel hybrid (i.e. Toyota Prius), the serial hybrid (i.e. Chevrolet Volt), and the electric vehicle (i.e. Chevrolet EV-1). These four power schemes represent the most prominent power architecture options available to automotive designers and engineers today. Experimentation was preformed using a one-man power scooter, a five horsepower ICE, an alternator, three 12 volt batteries, and an electric motor. Data was collected using an accelerometer and timing device. The ICE architecture transmits power to the wheels from only from the engine, the parallel hybrid from both the ICE and the electric motor, the serial hybrid from only the electric motor with the ICE and alternator acting as a generator, and the electric vehicle (EV) from only the electric motor. Performance was quantified through top speed and acceleration numbers for each respective architecture. Each power scheme was modeled analytically to determine theoretical efficiencies and performance numbers. These theoretical numbers were then compared to experimental data for validation. Results from testing, as well as the factors represent the ratio of each attribute to the lowest value within that category (given the value 1), are shown in figure 1 below. ICE Series Parallel EV 25.6 14.1 25.6 14.1 2.5 3.7 3.7 3.7 32.4 62.7 54.3 74.0 1.8 1 1.8 1 1.5 1.5 1.5 1.0 1.9 1.7 2.3 Figure 1: Performance and Efficiency Values for Experimental Power Schemes.(cont.) These conclusions would allow, given desired output efficiencies or performance values, an automotive designer to determine which architecture(s) would best suit their needs.by Gavin M. Cotter.S.B

Matter-wave grating distinguishing conservative and dissipative interactions

We propose an optical grating for matter waves that separates molecules depending on whether their interaction with the light is conservative or dissipative. Potential applications include fundamental tests of quantum mechanics, measurement of molecular properties and the ability to selectively prepare matter waves with different internal temperatures

Biotechnological applications of functional metagenomics in the food and pharmaceutical industries

peer-reviewedMicroorganisms are found throughout nature, thriving in a vast range of environmental conditions. The majority of them are unculturable or difficult to culture by traditional methods. Metagenomics enables the study of all microorganisms, regardless of whether they can be cultured or not, through the analysis of genomic data obtained directly from an environmental sample, providing knowledge of the species present, and allowing the extraction of information regarding the functionality of microbial communities in their natural habitat. Function-based screenings, following the cloning and expression of metagenomic DNA in a heterologous host, can be applied to the discovery of novel proteins of industrial interest encoded by the genes of previously inaccessible microorganisms. Functional metagenomics has considerable potential in the food and pharmaceutical industries, where it can, for instance, aid (i) the identification of enzymes with desirable technological properties, capable of catalyzing novel reactions or replacing existing chemically synthesized catalysts which may be difficult or expensive to produce, and able to work under a wide range of environmental conditions encountered in food and pharmaceutical processing cycles including extreme conditions of temperature, pH, osmolarity, etc; (ii) the discovery of novel bioactives including antimicrobials active against microorganisms of concern both in food and medical settings; (iii) the investigation of industrial and societal issues such as antibiotic resistance development. This review article summarizes the state-of-the-art functional metagenomic methods available and discusses the potential of functional metagenomic approaches to mine as yet unexplored environments to discover novel genes with biotechnological application in the food and pharmaceutical industries.Science Foundation Ireland(SFI)Grant Number 13/SIRG/215