Diffusion difference between samples

Thermal conductivity and diffusivity [1.35,1.80,1.81]. At room temperature, the thermal conductivity coefficient X is about 1.75 W cm K . Its temperature dependence is shown in Fig. 1.19. Some typical data are given in Table 1.14. In the low-temperature range, it is strongly dependent on the RRR (residual resistivity ratio Po/PrX which is also an indication of the purity. Table 1.15 shows the differences between samples of varying purity. 

Figure 5 shows the dynamic response of each sensor in the array as it senses the volatiles desorbed from the fibre. The general profile reflects a combination of factors inherent within the measurement system namely desorption characteristics of the volatile species from the SPME fibre, diffusion of the volatiles through the static headspace between the fibre and the sensors and the response characteristics of the sensors to the volatiles. Different portions of the general response profiles are potentially of use in terms of resolving differences between samples and thus classifying sample types. 

We note one difference between the behavior of our preparation of CsPuF6 and that of Penneman et al. (1). Our samples did not decompose (in sealed x-ray capillaries) after 3 months. Three weeks in a dry-box atmosphere (ca. 10 ppm H2O in N2) did leave a product with weaker, more diffuse CsPuF6 lines. 

A dry combustion-direct injection apparatus was applied to water samples by Van Hall et al. [51 ]. The carbon dioxide was measured with a non-dispersive infrared gas analyser. Later developments included a total carbon analyser [97], a diffusion unit for the elimination of carbonates [98], and finally a dual tube which measured total carbon by combustion through one pathway and carbonate carbon through another. Total organic carbon was then calculated as the difference between the two measurements [99]. 

An interesting difference between the samples is found in the behaviour of the samples during destabilisation. While for the sample under a deuterium pressure, LiBD4 is not destabilised until both phases are molten, in the vacuum sample LiD is destabilised in the solid phase, and at much lower temperatures (ca. 360°C). This is primarily due to the improved diffusion kinetics of Li species over that of LiBD4. There is also the possibility that LiD precipitation out of the liquid phase at nucleation sites on the Mg particles allows improved mixing over the solid Mg and liquid LiBD4 observed in the sealed sample. 

Sampling rates for the case of total boundary layer-control can be expected to be nearly independent of temperature, since both the diffusion coefficients in air, and the kinematic viscosity of air are only weak functions of temperature (Shoeib and Harner, 2002). This leaves the air-flow velocity as the major factor that can be responsible for the seasonal differences among sampling rates observed by Ockenden et al. (1998). The absence of large R differences between indoor and outdoor exposures may be indicative of membrane-control, but it may also reflect the efficient damping of high flow velocities by the deployment devices used for SPMD air exposures (Ockenden et al., 2001). 

Although Rs values of high Ks compounds derived from Eq. 3.68 may have been partly influenced by particle sampling, it is unlikely that the equation can accurately predict the summed vapor plus particulate phase concentrations, because transport rates through the boundary layer and through the membrane are different for the vapor-phase fraction and the particle-bound fraction, due to differences in effective diffusion coefficients between molecules and small particles. In addition, it will be difficult to define universally applicable calibration curves for the sampling rate of total (particle -I- vapor) atmospheric contaminants. At this stage of development, results obtained with SPMDs for particle-associated compounds provides valuable information on source identification and temporal... 

Early studies, which did not include many high-order reflections, revealed systematic differences between spherical-atom X-ray- and neutron-temperature factors (Coppens 1968). Though the spherical-atom approximation of the X-ray treatment is an important contributor to such discrepancies, differences in data-collection temperature (for studies at nonambient temperatures) and systematic errors due to other effects cannot be ignored. For instance, thermal diffuse scattering (TDS) is different for neutrons and X-rays. As the effect of TDS on the Bragg intensities can be mimicked by adjustment of the thermal parameters, systematic differences may occur. Furthermore, since neutron samples must be... 

Wool and O Connor [33] stated that the self-diffusion coefficient should follow a Williams-Landel-Ferry (WLF) temperature dependence providing that the mode of failure remains the same between samples healed at different temperatures between Tg and Tg + 100°C. Using a reference temperature of 196°C (469 K), the WLF relationship for PI700 polysulfone can be written as follows [38] ... 

Figures 3 and 4 display the paired results obtained with Ab-cor and 3M diffusion-type samplers, respectively. These results were analyzed through use of the "t" test for paired samples and the calculation of correlation coefficients and regression equations, with the results of these analyses shown in Table I. A statistically significant correlation is seen between the data set for each type of diffusion sampler and the corresponding tube/ pump sample data set, and the "t" test fails to refute the null hypothesis that there is no significant systematic difference between each of the diffusion sampler data set and the corresponding tube/pump data set.

Figures 8-11 display the difference between each diffusion sampler and its corresponding tube/pump sample, plotted against the tube/pump sample result. These figures show the difference as a function of the magnitude of the nominally "true" (or, at least, accepted) value. Lines indicating the bounds of 10% and 25% of the tube/pump sample are shown.

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