Revision of Singlet Quantum Yields in the Catalyzed Decomposition of Cyclic Peroxides

The chemiluminescence of cyclic peroxides activated by oxidizable fluorescent dyes is an example of chemically initiated electron exchange luminescence (CIEEL), which has been used also to explain the efficient bioluminescence of fireflies. Diphenoyl peroxide and dimethyl-1,2-dioxetanone were used as model compounds for the development of this CIEEL mechanism. However, the chemiexcitation efficiency of diphenoyl peroxide was found to be much lower than originally described. In this work, we redetermine the chemiexcitation quantum efficiency of dimethyl-1,2-dioxetanone, a more adequate model for firefly bioluminescence, and found a singlet quantum yield (Phi(s)) of 0.1%, a value at least 2 orders of magnitude lower than previously reported. Furthermore, we synthesized two other 1,2-dioxetanone derivatives and confirm the low chemiexcitation efficiency (Phi(s) < 0.1%) of the intermolecular CIEEL-activated decomposition of this class of cyclic. peroxides. These results are compared with other chemiluminescent reactions, supporting the general trend that intermolecular CIEEL systems are much less efficient in generating singlet excited states than analogous intramolecular processes (Phi(s) approximate to 50%), with the notable exception of the peroxyoxalate reaction (Phi(s) approximate to 60%).

Kinetics of thermal decomposition of niobium hydride

High-purity niobium powders can be obtained from the well-known hydride-dehydride (HDH) process. The aim of this work was the investigation of the structural phase transition of the niobium hydride to niobium metal as function of temperature, heating rate and time. The niobium powder used in this work was obtained by high-temperature hydriding of niobium machining chips followed by conventional ball milling and sieving. X-ray diffraction measurements were carried out in vacuum using a high-temperature chamber coupled to an X-ray diffractometer. During the dehydriding process, it is possible to follow the phase transition from niobium hydride to niobium metal starting at about 380 degrees C for a heating rate of 20 degrees C/min. The heating rate was found to be an important parameter, since complete dehydriding was obtained at 490 degrees C for a heating rate of 20 degrees C/min. The higher dehydriding rate was found at 500 degrees C. Results contribute to a better understanding of the kinetics of thermal decomposition of niobium hydride to niobium metal. (C) 2011 Elsevier Ltd. All rights reserved.

Development of Plurimetallic Electrocatalysts Prepared by Decomposition of Polymeric Precursors for EtOH/O-2 Fuel Cell

This work aimed to develop plurimetallic electrocatalysts composed of Pt, Ru, Ni, and Sn supported on C by decomposition of polymeric precursors (DPP), at a constant metal: carbon ratio of 40:60 wt.%, for application in direct ethanol fuel cell (DEFC). The obtained nanoparticles were physico-chemically characterized by X-ray diffraction (XRD) and energy dispersive X-ray spectroscopy (EDX). XRD results revealed a face-centered cubic crystalline Pt with evidence that Ni, Ru, and Sn atoms were incorporated into the Pt structure. Electrochemical characterization of the nanoparticles was accomplished by cyclic voltammetry (CV) and chronoamperometry (CA) in slightly acidic medium (0.05 mol L-1 H2SO4), in the absence and presence of ethanol. Addition of Sn to PtRuNi/C catalysts significantly shifted the ethanol and CO onset potentials toward lower values, thus increasing the catalytic activity, especially for the quaternary composition Pt64Sn15Ru13Ni8/C. Electrolysis of ethanol solutions at 0.4 V vs. RHE allowed determination of acetaldehyde and acetic acid as the main reaction products. The presence of Ru in alloys promoted formation of acetic acid as the main product of ethanol oxidation. The Pt64Sn15Ru13Ni8/C catalyst displayed the best performance for DEFC.

Development of plurimetallic electrocatalysts prepared by decomposition of polymeric precursors for EtOH/O2 fuel cell

This work aimed to develop plurimetallic electrocatalysts composed of Pt, Ru, Ni, and Sn supported on C by decomposition of polymeric precursors (DPP), at a constant metal:carbon ratio of 40:60 wt.%, for application in direct ethanol fuel cell (DEFC). The obtained nanoparticles were physico-chemically characterized by X-ray diffraction (XRD) and energy dispersive X-ray spectroscopy (EDX). XRD results revealed a face-centered cubic crystalline Pt with evidence that Ni, Ru, and Sn atoms were incorporated into the Pt structure. Electrochemical characterization of the nanoparticles was accomplished by cyclic voltammetry (CV) and chronoamperometry (CA) in slightly acidic medium (0.05 mol L-1 H2SO4), in the absence and presence of ethanol. Addition of Sn to PtRuNi/C catalysts significantly shifted the ethanol and CO onset potentials toward lower values, thus increasing the catalytic activity, especially for the quaternary composition Pt64Sn15Ru13Ni8/C. Electrolysis of ethanol solutions at 0.4 V vs. RHE allowed determination of acetaldehyde and acetic acid as the main reaction products. The presence of Ru in alloys promoted formation of acetic acid as the main product of ethanol oxidation. The Pt64Sn15Ru13Ni8/C catalyst displayed the best performance for DEFC.

Calculation of the eigenfunctions of the two-group neutron diffusion equation and application to modal decomposition of BWR instabilities

In this thesis, numerical methods aiming at determining the eigenfunctions, their adjoint and the corresponding eigenvalues of the two-group neutron diffusion equations representing any heterogeneous system are investigated. First, the classical power iteration method is modified so that the calculation of modes higher than the fundamental mode is possible. Thereafter, the Explicitly-Restarted Arnoldi method, belonging to the class of Krylov subspace methods, is touched upon. Although the modified power iteration method is a computationally-expensive algorithm, its main advantage is its robustness, i.e. the method always converges to the desired eigenfunctions without any need from the user to set up any parameter in the algorithm. On the other hand, the Arnoldi method, which requires some parameters to be defined by the user, is a very efficient method for calculating eigenfunctions of large sparse system of equations with a minimum computational effort. These methods are thereafter used for off-line analysis of the stability of Boiling Water Reactors. Since several oscillation modes are usually excited (global and regional oscillations) when unstable conditions are encountered, the characterization of the stability of the reactor using for instance the Decay Ratio as a stability indicator might be difficult if the contribution from each of the modes are not separated from each other. Such a modal decomposition is applied to a stability test performed at the Swedish Ringhals-1 unit in September 2002, after the use of the Arnoldi method for pre-calculating the different eigenmodes of the neutron flux throughout the reactor. The modal decomposition clearly demonstrates the excitation of both the global and regional oscillations. Furthermore, such oscillations are found to be intermittent with a time-varying phase shift between the first and second azimuthal modes.

Extracting evolutionary information from the spectral decomposition of early-type galaxies.

Holding the major share of stellar mass in galaxies and being also old and passively evolving, early-type galaxies (ETGs) are the primary probes in investigating these various evolution scenarios, as well as being useful means to provide insights on cosmological parameters. In this thesis work I focused specifically on ETGs and on their capability in constraining galaxy formation and evolution; in particular, the principal aims were to derive some of the ETGs evolutionary parameters, such as age, metallicity and star formation history (SFH) and to study their age-redshift and mass-age relations. In order to infer galaxy physical parameters, I used the public code STARLIGHT: this program provides a best fit to the observed spectrum from a combination of many theoretical models defined in user-made libraries. the comparison between the output and input light-weighted ages shows a good agreement starting from SNRs of ∼ 10, with a bias of ∼ 2.2% and a dispersion 3%. Furthermore, also metallicities and SFHs are well reproduced. In the second part of the thesis I performed an analysis on real data, starting from Sloan Digital Sky Survey (SDSS) spectra. I found that galaxies get older with cosmic time and with increasing mass (for a fixed redshift bin); absolute light-weighted ages, instead, result independent from the fitting parameters or the synthetic models used. Metallicities, instead, are very similar from each other and clearly consistent with the ones derived from the Lick indices. The predicted SFH indicates the presence of a double burst of star formation. Velocity dispersions and extinctiona are also well constrained, following the expected behaviours. As a further step, I also fitted single SDSS spectra (with SNR∼ 20), to verify that stacked spectra gave the same results without introducing any bias: this is an important check, if one wants to apply the method at higher z, where stacked spectra are necessary to increase the SNR. Our upcoming aim is to adopt this approach also on galaxy spectra obtained from higher redshift Surveys, such as BOSS (z ∼ 0.5), zCOSMOS (z 1), K20 (z ∼ 1), GMASS (z ∼ 1.5) and, eventually, Euclid (z 2). Indeed, I am currently carrying on a preliminary study to estabilish the applicability of the method to lower resolution, as well as higher redshift (z 2) spectra, just like the Euclid ones.

The role of rotation thromboelastometry in early prediction of massive transfusion

Early prediction of massive transfusion (MT) is critical in the management of severely injured trauma patients. Variables available early after injury including physiologic, laboratory, and rotation thromboelastometric (ROTEM) parameters were evaluated as predictors for the need of MT.

A multi-level decomposition of variance in somatic symptom reporting in families with adolescent children

OBJECTIVES: This paper examines four different levels of possible variation in symptom reporting: occasion, day, person and family. DESIGN: In order to rule out effects of retrospection, concurrent symptom reporting was assessed prospectively using a computer-assisted self-report method. METHODS: A decomposition of variance in symptom reporting was conducted using diary data from families with adolescent children. We used palmtop computers to assess concurrent somatic complaints from parents and children six times a day for seven consecutive days. In two separate studies, 314 and 254 participants from 96 and 77 families, respectively, participated. A generalized multilevel linear models approach was used to analyze the data. Symptom reports were modelled using a logistic response function, and random effects were allowed at the family, person and day level, with extra-binomial variation allowed for on the occasion level. RESULTS: Substantial variability was observed at the person, day and occasion level but not at the family level. CONCLUSIONS: To explain symptom reporting in normally healthy individuals, situational as well as person characteristics should be taken into account. Family characteristics, however, would not help to clarify symptom reporting in all family members.

DECOMPOSITION OF REGRESSION ESTIMATORS TO EXPLORE THE INFLUENCE OF "UNMEASURED" TIME-VARYING CONFOUNDERS

In environmental epidemiology, exposure X and health outcome Y vary in space and time. We present a method to diagnose the possible influence of unmeasured confounders U on the estimated effect of X on Y and to propose several approaches to robust estimation. The idea is to use space and time as proxy measures for the unmeasured factors U. We start with the time series case where X and Y are continuous variables at equally-spaced times and assume a linear model. We define matching estimator b(u)s that correspond to pairs of observations with specific lag u. Controlling for a smooth function of time, St, using a kernel estimator is roughly equivalent to estimating the association with a linear combination of the b(u)s with weights that involve two components: the assumptions about the smoothness of St and the normalized variogram of the X process. When an unmeasured confounder U exists, but the model otherwise correctly controls for measured confounders, the excess variation in b(u)s is evidence of confounding by U. We use the plot of b(u)s versus lag u, lagged-estimator-plot (LEP), to diagnose the influence of U on the effect of X on Y. We use appropriate linear combination of b(u)s or extrapolate to b(0) to obtain novel estimators that are more robust to the influence of smooth U. The methods are extended to time series log-linear models and to spatial analyses. The LEP plot gives us a direct view of the magnitude of the estimators for each lag u and provides evidence when models did not adequately describe the data.

Observation and identification of metabolites emerging during postmortem decomposition of brain tissue by means of in situ 1H-magnetic resonance spectroscopy

Postmortem decomposition of brain tissue was investigated by (1)H-magnetic resonance spectroscopy (MRS) in a sheep head model and selected human cases. Aiming at the eventual estimation of postmortem intervals in forensic medicine, this study focuses on the characterization and identification of newly observed metabolites. In situ single-voxel (1)H-MRS at 1.5 T was complemented by multidimensional homo- and heteronuclear high-resolution NMR spectroscopy of an extract of sheep brain tissue. The inclusion of spectra of model solutions in the program LC Model confirmed the assignments in situ. The first postmortem phase was characterized mainly by changes in the concentrations of metabolites usually observed in vivo and by the appearance of previously reported decay products. About 3 days postmortem, new metabolites, including free trimethylammonium, propionate, butyrate, and iso-butyrate, started to appear in situ. Since the observed metabolites and the time course is comparable in sheep and human brain tissue, the model system seems to be appropriate.

Effects of elevated atmospheric CO2 and O3 on wood density, antomical proerties and decomposition of Northern Hardwoods

Anthropogenic activities continue to drive atmospheric CO2 and O3 concentrations to levels higher than during the pre-industrial era. Accumulating evidence indicates that both elevated CO2 and elevated O3 could modify the quantity and biochemistry of woody plant biomass. Anatomical properties of woody plants are largely influenced by the activity of the cambium and the growth characteristics of wood cells, which are in turn influenced by a range of environmental factors. Hence, alterations in the concentrations of atmospheric CO2 and / or O3 could also impact wood anatomical properties. Many fungi derive their metabolic resources for growth from plant litter, including woody tissue, and therefore modifications in the quantity, biochemistry and anatomical properties of woody plants in response to elevated CO2 and / or O3 could impact the community of wood-decaying fungi and rates of wood decomposition. Consequently carbon and nutrient cycling and productivity of terrestrial ecosystem could also be impacted. Alterations in wood structure and biochemistry of woody plants could also impact wood density and subsequently impact wood quality. This dissertation examined the long term effects of elevated CO2 and / or O3 on wood anatomical properties, wood density, wood-decaying fungi and wood decomposition of northern hardwood tree species at the Aspen Free-Air CO2 and O3 Enrichment (Aspen FACE) project, near Rhinelander, WI, USA. Anatomical properties of wood varied significantly with species and aspen genotypes and radial position within the stem. Elevated CO2 did not have significant effects on wood anatomical properties in trembling aspen, paper birch or sugar maple, except for marginally increasing (P < 0.1) the number of vessels per square millimeter. Elevated O3 marginally or significantly altered vessel lumen diameter, cell wall area and vessel lumen area proportions depending on species and radial position. In line with the modifications in the anatomical properties, elevated CO2 and O3, alone, significantly modified wood density but effects were species and / or genotype specific. However, the effects of elevated CO2 and O3, alone, on wood anatomical properties and density were ameliorated when in combination. Wood species had a much greater impact on the wood-decaying fungal community and initial wood decomposition rate than did growth or decomposition of wood in elevated CO2 and / or O3. Polyporales, Agaricales, and Russulales were the dominant orders of fungi isolated. Based on the current results, future higher levels of CO2 and O3 may have moderate effects on wood quality of northern hardwoods, but for utilization purposes these may not be considered significant. However, wood-decaying fungal community composition and decomposition of northern hardwoods may be altered via shifts in species and / or genotype composition under future higher levels of CO2 and O3.

Topographic time-frequency decomposition of the EEG

Frequency-transformed EEG resting data has been widely used to describe normal and abnormal brain functional states as function of the spectral power in different frequency bands. This has yielded a series of clinically relevant findings. However, by transforming the EEG into the frequency domain, the initially excellent time resolution of time-domain EEG is lost. The topographic time-frequency decomposition is a novel computerized EEG analysis method that combines previously available techniques from time-domain spatial EEG analysis and time-frequency decomposition of single-channel time series. It yields a new, physiologically and statistically plausible topographic time-frequency representation of human multichannel EEG. The original EEG is accounted by the coefficients of a large set of user defined EEG like time-series, which are optimized for maximal spatial smoothness and minimal norm. These coefficients are then reduced to a small number of model scalp field configurations, which vary in intensity as a function of time and frequency. The result is thus a small number of EEG field configurations, each with a corresponding time-frequency (Wigner) plot. The method has several advantages: It does not assume that the data is composed of orthogonal elements, it does not assume stationarity, it produces topographical maps and it allows to include user-defined, specific EEG elements, such as spike and wave patterns. After a formal introduction of the method, several examples are given, which include artificial data and multichannel EEG during different physiological and pathological conditions.