Sample characteristics

Discontinuity between the physical form of the sample and reference material used can lead to error. This is another manifestation of the matrix effect, but one which has to be considered when analyzing biological and environmental samples. There is no easy answer to the relationship between partide size and homogeneity. It is a popular assumption that the smaller the partide size the less the degree of heterogeneity. In some cases this may be true but there are a number of considerations. 

Evolution of analytical techniques can cause data, once considered to be state of the arf to be shown to be unreliable. A good example is provided by the work of Houba et al. (1995), who demonstrated that a number of older methods for the determination of trace levels of boron in plant materials were subject to the interference by high levels of copper. This and other evidence suggest that older data, even when presented on a certificate, have to be viewed critically see also Section 3.2. The analyst must stay aware of developments and be ready to disregard certified values if the date of certification of the CRM predates the release of new developments and the certification authority concerned cannot confirm that the certified value is good in the light of the new knowledge. 

According to the various theories on DTA, the area under the curve peak is proportional to the heat of reaction or transition, and hence, the mass of reactive sample. In general, the peak area is inversely proportional to the thermal conductivity, k directly proportional to the density, f), and the heat of reaction, AH and independent of the specific heat (59). The relationship can be expressed as 

The effect of sample mass on the peak area will be discussed in greater detail in the sections on quantitative DTA and DSC. 

There are a number of conflicting studies concerning the effect of particle size and particle-size distribution of the sample on the peak areas and Al in values. Speil et al. (2) found that the peak areas under the kaolin dehydration peak varied from 725-2080 mm2 over the particle-size range of 0.05-0.1 to 5-20 ju. It was also found that the A7 in values varied from 580-625°C. However, Norton (89) found that the A7 io values remained essentially constant but that the temperature at which the dehydration reaction was completed varied from 610-670°c ver a particle-size range of 0.1 to 20-44 p. Grimshawet al. (90) agreed with the latter study in that, with particle sizes down to 1 fi, the thermal characteristics of the kaolin samples were independent of particle size. This effect is illustrated in Table 5.5. 

Carthew (91), who also studied the decomposition of kaolinite. in the particle-size range of 2 to 0.25-0.1 ju. agreed with the work of Norton (89) and Grimshaw et al. (90). For particle sizes from 2-1 u to 0.25-0.1 ju. the peak areas were essentially constant, as were the A7 in values. The disagreement with Spiel et al. (2) was attributed to the fact that they obtained their particle-size fractions by a grinding process, which could reduce die degree of crystallinity of the kaolin. 

Langer and Kerr (65) found that an increase in the particle size ofkaolinite produced a peak temperature increase for the dehydration reaction. There 

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ANIMAL SAMPLES, CHARACTERISTICS OF SAMPLING LOCALITY, AND ANALYTICAL METHODS... 

Simulation spectra were generated using parameters that describe the ion beam, target and detector geometry, beam and detector resolution, and sample characteristics. The sample parameters, which include the number of layers and the areal density and atomic composition of each layer were then varied until the simulation conformed to the experimental data. The HIBS spectra were analysed using a modified version of the RBS analysis program. 

In this study, heating value of dry sample [Hu(wf), kJ/kg] and of wet waste [Hu (raw), kJ/kg] was determined with the calculation by formula (1) and (2), respectively. Sample characteristics were also determined by analytic measurement. 

Here again, in the low-noise case of scintillation noise, the absorbance noise is again independent of the reference signal level, and is now independent of the sample characteristics, as well, and depends only on the magnitude of the external noise source. 

Figure 5.6 Interaction of a beam of primary electrons with a thin solid sample, showing the various processes which can take place (Pollard and Heron 1996 51). Various types of electron can be scattered or ejected back towards the source, or transmitted through the sample. Characteristic X-rays and bremsstrahlung can be produced, and also cathodoluminescence. These products form the basis of analytical and imaging electron microscopy, and of a range of other techniques. (After Woldseth 1973 Fig. 4.1 - reproduced by permission of the Royal Society of Chemistry.)...

In some cases, sample preparation for CZE requires only the dilution of the sample, mostly to accommodate detection (for signal and linearity of response). However, as was previously mentioned, sample characteristics such as viscosity, buffer composition (pH and excipients), and salt content can especially affect electrophoretic injection and performance. 

The frequency interpretation of the interval estimates on the unknown amounts is given by the following ( 27 ) With at least 1- a confidence, based on the sampling characteristics of the observations on the standards, at least P proportion of the interval estimates made from a particular calibration will contain the true amounts. The Bonferroni inequality insures the 1-a confidence since the confidence interval about the regression line and the upper bound on cr are each performed using a 1- a/2 confidence coefficient. Hence, the frequency interpretation states that at least (1-a) proportion of the standard calibrations are such that at least P proportion of the intervals produced by the method cover the true unknown amounts. For the remaining a proportion of standard calibrations the proportion of intervals which cover the true unknown values may be less than P. 

Simply stated, fiber-optic cable allows for the transfer of light from one point to another withont going in a straight line, and as such their use in process analytical apphcations is highly seductive. In a likely near-infrared process application there may be multiple sample stream take-offs, each with different sample characteristics, located in positions spread around a process unit, with the only apparently convenient analyzer location some distance away. 

Catalytic tests were carried out following a protocol that allowed establish relationships between the characteristics of fresh and spent catalysts and the catalytic performance. Specifically, within the first 5-10 hours reaction time, the catalytic performance was not stable, due to variations in samples characteristics. Therefore, two different sets of results were taken, the first after less than 1 hour (representative of fresh samples), and the second one after 3-4 hours (representative of spent samples). Only in the case of the equilibrated sample, no difference was found between the characteristics of samples eq and eqsp (Tables 1 and 2) for this catalyst, also the catalytic performance did not vary at all during catalytic tests. 

The choice of the suppression method to be used depends on both the solvent and sample characteristics. Therefore, HPLC-NMR suppression via presaturation... 

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