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Correlation of spark source mass spectrometric sensitivity calibration factors with the element-sensitive physicochemical properties
International Journal of Mass Spectrometty and Ion Processes, 68 (1986) 219-237 Elsevier Science Publishers B.V.. Amsterdam - Printed in The Netherlands
219
CORRELATION OF SPARK SOURCE MASS SPECTROMETRIC SENSITIVITY CALIBRATION FACTORS WITH THE ELEMENTSENSITIVE PHYSICOCHEMICAL PROPERTIES
B.P. DAmA
and H.C. JAIN
Fuel Chemistry Division, Bhabha Atomic Research Centre, Bomhqv - 4000X5 (India) (First received
11 February
1985; in final form 15 July 1985)
ABSTRACT The sensitivity calibration factor commonly expressed as relative sensitivity coefficient (RSC) is used in quantitative spark source mass spectrometry. The RSC was studied from the viewpoint of element-sensitive physicochemical properties and a linear correlation was attempted. Statistical analysis of the experimental data was carried out to justify the linear correlation. This permits quantitative spark source mass spectrometric analysis of trace constituents whose relative sensitivity coefficients have not been experimentally determined. The variation of RSC with respect to specific element-sensitive properties was shown to be the same for certain oxide/graphite-type matrices.
INTRODUCTION
In the field of trace analysis, spark source mass spectrometry (SSMS) is a well-known multi-elemental technique and its characteristic features have been described by several authors [l-6]. However, the ion beam produced from a spark ion source which reaches the detector system does not truly represent the composition of the sample being sparked. This is due to the fact that production and transmission of the ion beam from a spark source depends upon the experimental conditions, the nature of the major or matrix element(s) and the trace constituents. The effect of the experimental conditions can be minimised by using controlled and optimum sparking parameters and keeping intact the geometry of ion production and transmission to the best possible reproducibility. The sensitivity of ion production, even for a particular matrix, is not the same for all the elements. This is because, irrespective of the actual mechanism of ion production and dispersal of the beam from the point of sparking, the element-sensitive volatilisation and ionization processes govern the beam composition in a spark ion source. Hence, to make a feasible accurate SSMS analysis it is necessary to establish
016%1176/86/$03.50
0 1986 Elsevier Science Publishers
B.V.
220
the element-sensitive RSC a priori under a fixed set of experimental conditions and using proper trace constituent standards. The RSC which relates the composition of the sample in atoms with the composition of the ion beam reaching the detector, is defined as [(RSC)&
= [ $1,
(1)
where [( RSC) t] M is the relative sensitivity coefficient of element A with reference to B in matrix M. The parameter ur$ is the experimentally measured concentration of element A with respect to B, and xt is the true concentration of A with respect to B. However, reliable multi-elemental trace constituent standards, in particular the standards containing the desired traces, are not readily available. In order to overcome this difficulty it is essential to have an alternative approach to arrive at the RSC. Ramendik et al. [7,8] have been trying to ascertain the conditions of spark discharge which are critical to produce an ion beam closely resembling the sample composition. They have shown that the degree of dependence of RSC on the matrix is dictated by the physicochemical properties of the matrix elements and the corresponding source parameters used. In this paper, we present the linear regressional analysis of RSC with respect to the element-sensitive properties and the correlation (for a given matrix and experimental conditions) is justified statistically. This alternative approach helps considerably in understanding the elemental parameter that could be responsible for the RSC. Our observations could be used for predictive purposes. A brief analysis of the RSC values reported from different laboratories is presented to illustrate the conformity of their results with our observations. This paper deals mainly with the experiences gained on the U,O,-graphite matrix using both the photoplate and electrical detection modes. Generally, U,O, is an important material in nuclear technology and spark source mass spectrometry is a highly sensitive.technique suitable for the quality control of nuclear-grade fuels. EXPERIMENTAL
The instrument used is a MattauchhHerzog-type double focusing RF-spark source mass spectrometer [25] equipped with both the photoplate and electrical detection systems. For the photoplate detection (PD) mode, the operating conditions and slit settings were: power amplifier anode voltage, 2.5 kV; pulse width, 20 psec; pulse repetition rate, 1000 set-‘; acceleration voltage, 30 kV; object slit width, 50 pm; (Yand /3 slits width, 0.8 mm. In the
221
case of the electrical detection (ED) system, the slit widths were increased to 200 pm for the object slit and 1.5 mm for both the divergence angle defining slit (a) and the energy defining slit (/3) to ensure a stable ion current and to increase the sensitivity. The acceleration voltage used for the electrical mode was 25 kV and the data were collected by the peak switching mode. Seven NBL-98-&O, impurity standards, procured from the National Brunswick Laboratory (NBL, U.S.A.), for general elements and four synthetically doped U,O, reference samples for rare earth elements were used for the determination of relative sensitivity coefficients. Further details of samples, experimentation, methodology and calculation procedure are all given elsewhere [9,10]. A BRIEF
ANALYSIS
Several workers in this field have devised different empirical equations containing the physicochemical parameters of the measured element and that of the reference or/and matrix to calculate the theoretical RSC [ll]. Several authors [12-171 have also made attempts to relate their experimental RSC to the various element-sensitive physicochemical properties by some sort of regressional means. But no generalised theme is available and further studies are necessary on this line. In this work, it is not intended to calculate the RSC (,Y) from the element-specific properties (x) using empirical means, because, this implies fitting of the type y = mx with slope (nz) equal to unity. It means that, if the reference element is fixed, RSC is independent of the experimental conditions and of the nature of the matrix. Moreover, even for a particular matrix, the RSC of an element with respect to a fixed reference element may significantly vary depending upon the sparking and other experimental settings [17,18]. This is further supported by the work of Van Hoye et al. [19] where it was shown that the ratios of the theoretical RSC and the experimental RSC values are not, in general, unity but deviate by more than 50%. In a spark ion source, the plasma features are governed by the properties of the major or the matrix element(s). The dependence of RSC on elementsensitive properties is highly sensitive to the plasma characteristics of the matrix through their relation to the rate of evaporation and ionization of the . trace elements. We may try an ansatz [ (RSC),] M = mx, + c
(2)
where [(RSC) ,lM stands for the RSC of the element (I) in the matrix (M), and x, relates the physicochemical property of the measured element (I). Both m and c’ are constants for a given matrix and fixed experimental conditions.
222
If it is at all possible to express the RSC in the form of Eq. (2) then the constant (c) defines the total contribution of the matrix as well as experimental conditions or/and instrumental bias factors towards the RSC, while the constant (m) defines the rate of change of RSC in the given matrix and experimental features. In the present work, we assume, under the controlled and optimum conditions of operation, the transmission coefficients of all the elemental ions of the same charge are nearly the same. With this presumption, the RSC values can be considered to represent the ion source yields in accordance with the specificities of the measured elements relative to that of the reference. The source yield and, subsequently, the RSC, irrespective of the actual ionization mechanism in the spark plasma, are a measure of the relative atomic volatility coupled with the relative ionization capability. By atomic volatility, is meant the formation of atomic species which may be formed by direct evaporation from the matrix (a process that probably represents the case from the metallic matrices) or by evaporation of some suitable species such as IO from the oxide matrices followed by rupture of the bond. The parameters such as boiling point (BP) or heat of formation of gaseous atoms at the standard state (Hr) are considered for the evaporation process. In an oxide matrix, depending on its method of preparation, an element may exist in different oxidation states and different bond rupture processes may occur depending upon the thermodynamic characteristics of the process. Again, the bond energy may be different depending upon the molecule considered for the calculation. Therefore, it is assumed that the bond strength (II,) of the IO species is a measure of the energy characteristics involved in the bond rupture process. The experimental RSC values used for correlation were determined for singly charged ions 193. So the first ionization potential ( IP) is used hereafter. There is no sufficient reason to believe that any elemental parameter should be raised to some exponent or should be weighted while studying the correlation. Our aim is not to establish the best fitted curve, but to check whether the experimental observations are compatible with certain linear relationships between RSC and elementspecific properties. As a simplifying assumption, some element-sensitive properties, such as the formation of polymerised species ions and the charge distribution, molecular ions, are ignored. Unless these properties are also well accounted in the calculation of RSC or considered for the fitting, the correlation is never expected to be perfect. However, the uncertainties observed on the RSC values are assumed to overshadow the element-sensitive variation in the charge distribution etc.
223 RESULTS
AND DISCUSSION
The relative sensitivity coefficients observed in the U,O,-graphite matrix for the rare earths and other elements relative to uranium using both the electrical and photoplate detection systems [9] are shown in Table 1. The RSC was calculated as the slope (m) of the line y = mx [cf. Eq. (l)], where y is the experimentally observed concentration and x is the certified concentration. For the evaluation of the photoplates, the following considerations were taken into account [9]: (1) the line width (IV,) of mass (m,) peak,
TABLE
1
Experimental relative sensitivity coefficients reference element a and their physicochemical Element
Photoplate detection system
Electrical detection system
RSC
RSC
RSD ’
Cd Ce Nd Sm EU Gd DY Er Lu W Pb
0.3 1.4 2 2.8 2.5 2.6 2.4 4 2.2 2.8 1.7 1.8 2.3 2.6 1.6 2.0 2.1 1.9 1.6 3.0
U
Reference
MO
50.0 39.0 53.1 27.3 28.6 31.4 30.5 29.0 15.3 13.8 6.0 18.7 19.4 25.5 11.0 18.6 24.1 14.6 10.0 16.0
Physicochemical
with respect
to uranium
data h
RSD
B,
(4%)
(“C)
B* (kcal mol-’
I, (ev)
HP (kcal mol-‘)
0.53 1.07 1.46 1.6 1.3 1.4 1.2
14.0 7.7 5.2 22.6 15.2 11.2 11.0
1.2 2.5 1.6 1.8 2.0 2.0 1.4 1.6 1.6 1.37 1.02 1.7
23.8 10.6 10.0 13.2 13.0 14.6 11.5 16.8 11.8 5.5 3.6 15.2
2550 280 2672 1962 2750 2870 2732 907 4612 765 3257 3127 1778 1597 3233 2335 2510 3315 5660 1740
192.7 142.6 102 96 97.7 88 93.6 67.9 145.1 88 190 168 148 133 171 146 146 166 156 90.3
8.298 10.486 6.766 7.435 7.87 7.86 7.635 9.394 7.099 8.993 5.47 5.49 5.63 5.67 6.14 5.93 6.10 5.246 7.98 7.416
134.5 75.2 94.8 67.1 99.5 101.5 102.7 31.245 157.3 26.77 101.0 78.3 49.4 41.9 95.0 69.4 75.8 102.2 203.0 46.6
3818
181.9
6.05
(S) B P Cr Mn Fe co Ni Zn
of some elements properties.
element
a Ref. 9. b The data were taken from ref. 26. ’ RSD is the relative standard deviation
at 68% confidence
limit.
128
as
224
2.60
2.10 : IY I .60
I
.oo
-0.30
I.36
I .41
I.75
2.13
2.50
2.66
3.25
3.63
2.52
3.63
4.74
5.66
6.97
6.06
CURVE(C) 3 .65
::
2.45
LL I ,25 0
Fig. 1. Plots of RSC obtained
from the photoplate
detection
system
against
(B,B,I,),,/
(qAI,)l.
corrected as (w, a WI~.~);(2) mass dependence of photoplate emulsion blackening, corrected by using the factor (wz;“~); and (3) background contribution on the line blackening employing the method of Franzen et al. [20]. For the ED mode no correction is applied. The absolute RSC values obtained from the two detection systems are not corrected for the effect of the different experimental conditions (namely, slit widths, path length, energy and mass response characteristics). However, the trend in the RSC values among the elements for the two detection modes is similar. This reflects the fact that the difference in the RSC values between the two detection systems is systematic. The plots of the experimental relative sensitivity coefficients vs. the inverse of the product of the boiling point ( Bp), bond strength (B,) and ionization potential ( lP) of the measured elements with respect to the reference element uranium are shown in Fig. 1. In Fig. 1, curve (A) relates to the rare earth elements, curve (B) refers to the elements other than rare earths, and the variation of the relative sensitivity coefficients for both the
225
1.10
I.85
2.23
2.60
2.98
3.35
3.73
2.41
3.47
4.53
5.59
6.64
7.70
2.41
3.47
4.53
5.59
6.64
7.70
(Bp.B,.Ip
ju
1.48
CURVE
(6)
2.28-
0.30
1.36
CURVE
(C)
I 0.30
Fig. 2. Plots
1.36
ABp.B,
of RSC obtained
from
‘Ip
)I
the electrical
detection
system
against
(B,B,I,),,/
(B,BI,),.
rare earths and other elements taken together are plotted in curve (C). The RSC values in Fig. 1 were obtained from the PD system (Table 1); the corresponding behaviours obtained for the ED system are exhibited in Fig. 2. In Figs. 1 and 2 the continuous line represents the regression line while the broken lines on either side of the fitted line show the standard error bars of the estimates. Regressional analysis with weighting (l/square of the standard deviation of the RSC as weighting factor) and without weighting are performed. The details of the fitted parameters are tabulated in Table 2 (blocks A,B,C are for the PD system and blocks D,E,F refer to the ED system). To avoid confusion in the figures, only the fitted curves without weights are shown. Nevertheless, every detailed feature of the weighted case is furnished in Table 2. An analysis of the figures and Table 2 reveals the following characteristic features of the correlations. (i) The regression coefficients (slopes and intercepts) are nearly the same for the two lines, namely, for the regression line with and without weighting. This indicates that the RSC values without the error bars, on the average,
226
follow the same trend among the elements as they would including the error limits. Hence, either of the regression equations can be used for predictive purposes. The errors on the slope and the intercept generally lie within the error bars of the experimental RSC values. However, at 95% confidence level, the intervais at which the regression coefficients lie are quite high. This is mainly because fewer points have been used for the correlation calculations. For a particular detection made, even for the different groups of elements (i.e. for the rare earths, other elements and all the elements taken together), there is no significant difference in the slopes and intercepts of the regression lines. This means the regression line obtained for the rare earth elements can still be used to predict the RSC of other elements and vice-versa. A notable difference is observed in the correlation of the RSC values of the rare earths alone in the PD mode. This is because too small a number of elements of very similar properties are involved in the correlation calculations. Thus a small change in the values can appear to significantly affect the regression coefficients. However, at the 95% confidence level the intervals within which the slope and intercept lie for this set of data for the rare earths easily include the regression coefficients obtained using other groups of elements. It is interesting to note that for the two detection systems the slopes of the regression lines remain practically unaltered. This implies that the trends in the RSC values among the elements for the two detection systems are very similar. That is to say, under the slightly different experimental conditions for the two detection modes the beam composition (i.e. charge and energy distributions of the measured elements with respect to the reference) remains nearly the same. It is noted that the difference in the experimenta bias factors for the two detection modes is reflected in the different intercept values. (ii) In any of the cases cited above, the evaluated correlation coefficient ( RXJI) is positive (see column 9) and it exceeds the appropriate critical value of Rxy {column 10) at the 95% confidence level. This gives an indication that the elemental RSC values and the inverse (x) of the product of the chosen physicochemical properties of the measured elements relative to the reference are significantly correlated. (iii) In general, the deviations of the experimental RSC values from the fitted line are low and within the uncertainty level observed on the RSC values. The highest deviation from the regression line is observed for neodymium in the correlation of the RSC values obtained, using the ED mode (Table 2, block F). A comparison of the RSC values (Table 1) of the rare earth elements between the two detection modes shows that the RSC of neodymium obtained from the ED system is somewhat higher than that expected from the general trend.
221
TABLE
2
Details of the regressional analysis of RSC against the inverse of the product measured elements with respect to the reference Block no.
A
Total no. of
8
Nature of
u
0.39 (10.7) 0.41 (14.9) 0.23 (24.6) 0.23 (23.6) 0.27 (16.1) 0.25 (17.3) 0.24 (29.1) 0.29 (33.5) 0.23
W
(7.1) 0.23
u w
B
10
u w
C
18
u w
D
8
U
w
E
F
9
17
Slope (m)
U W
(7.7) 0.20 (17.4) 0.26 (13.8)
Critical 95%h [N-Z
Intercept Y%RSD)
1.24
0.49 > M > 0.29 2.45 0.56 > m > 0.26
(7.0) 1.18
(BP&I,)
(c) Confidence interval
1.45>,>1.03 1.40 > c 3 0.96
0.35 > m 3 0.11
(7.6) 1.9 (11.5) 1.6
0.36 > m 2 0.18
(8.7) 1.6
1.9>.>1.3
(8.5) 1.45
1.6>c31.28
0.36 > m a 0.10 2.30
2.12 0.34 > m $ 0.16 0.41 > m 3 0.07 2.45 0.53 > m 3 0.05 0.27 > m > 0.19 2.36
(5.6) 1.2 (12.2) 1.0 (15.6) 0.89
of the
2.4hcal.4 1.9ac31.3
1.6 > c 3 0.84 1.43~20.62 1.01 3 c > 0.77
0.27>m>O.19
(5.8) 0.88
0.95 > c > 0.8
0.27 3 m + 0.13
(3.2) 1.1
1.3>cco.9
(8.7) 0.91
l.O>,,>O.S
2.13 0.34 3 m > 0.18
(6.1)
’ u and w indicate unweighted and weighted fittings. respectively. h I:?‘, is the critical r value at 95% confidence limit for (N - 2) degrees of freedam.
As one would expect, the deviations are further reduced if separate correlations for elements having more close physicochemical properties are considered (see column 11). (iv) The stand ar d errors of estimates (column 12) at 68% confidence level are relatively high (weighted cases). However, all the experimental points lie within plus or minus one standard error of the estimate values given by the regression equations. The error bars on the RSC values, of course, account well for the higher values of the standard error. This is supplemented by the fact that the standard errors of the estimates are less if one does not consider
228 TABLE 2 (continued) Block no.
Nature of fit a
Correlation coefficient (W
Evaluated RXJ
)
Max. absolute deviation w.r.t. the Signiffitted icance value level for Rxv t, (Relative)
A
U
0.967 0.707
B
w
0.937
u
0.821 0.632
c
w
0.832
u
0.841 0.468
D
w
0.823
u
0.815 0.707
E
w
0.773
U
0.983 0.666
F
w
0.980
U
0.829 0.482 0.882
w
Standard error of estimate at 68% confidence level
Null Test
F(P-l,,Y-
EvaIuated I for nl=O
Evaluated t for c=O
variate
P) with
p = 2c Evaluated F,I.!-2,
Critical F(I.‘\~-‘)
(%) 0.13
0.09
9.4
14.2
87.4
(6.5) 0.15
0.32
6.7
13.2
45
0.4
4.1
8.7
16.5
0.9
4.2
11.5
17.9
0.33
6.2
11.8
38.8
0.77
5.8
17.7
33.5
0.15
3.4
8.2
Il.8
0.91
3.0
6.4
0.09
14.1
17.4
199
0.60
13.0
31.7
170
0.21
5.7
Il.5
32.9
I.3
7.3
16.3
52.7
(7.4) 0.7 (19.6) 0.73 (22.4) 0.72 (20.4) 0.72 (21.8) 0.25 (16) 0.33 (22.7) 0.18 (13.1) 0.18 (13.1) 0.40 (28.4) 0.52 (40.2)
5.99
5.32
4.49
5.99 8.9
5.59
4.54
.’ u and w indicate unweighted and weighted fittings. respectively. h Critical value of the correlation coefficient (R-x;v) for I%’points at 95% confidence ’ Critical F value for 2 parameters and ( I%’- 2) degrees of freedom.
level.
the uncertainties of the RSC values for fitting. In that case, as mentioned earlier, the regression coefficients are not affected significantly. (v) The null tests for the slope and intercept show that the calculated t-values (columns 13 and 14) are always higher than the corresponding critical t-value at 95% confidence level (column 6). This rules out the possibility of either of the parameters being zero and this statement is made with 95% confidence. This means that there exists a relationship between the
229
measured RSC and the elemental parameters considered, and the experimental bias factor (c) is never zero. (vi) In any of the cases cited, the evaluated F( P - 1, N - P) variate (column 15) (where parameter P equals 2 and N is the total number of points or elements considered), which is the ratio of the mean square due to regression and the mean square of the regression, exceeds the appropriate critical F-value (column 16) with 95% confidence and hence statistically significant regression has been obtained. This implies that, if the experiments are repeated for the identical set of elements (and hence for the same x-values), the proportion of variation in the newly observed data is not likely to exceed, within the 95% confidence level, the variation observed in the data set used and which has been accounted for by the linear equation ( y = mx t-
Ce
1.0
10.6
Nd Sm ELI Cd DY Lu
1.03 1.2 1.20 0.95 1.05 0.97
1.3 13.3 6.X 2.55 4.9 9.73
d Ref. 9.
B P Cr Ml1 Fe co Zn MO Cd W Pb
0.1 0.7 1.0 1.1 1.1 1.1 1.7 0.8 1.x 0.67 1.4
33.3 1.4 17.1 13.2 15.4 15.4 2X.4 35.1 42.3 12.0 1x.3
0.80
1
I
I
0.50
0.70
0.90
I
I
I.10
I.30
(Bp.B,~Ip)Er
/
( BP’
I
I .50
I
I. 70
I I ,90
B,~Ip)l
2.09
” In lx
I .53
0.96
W 0.40 0 .I0
0.62 (BP
I
I
I
I.13
I.65
2.17
.Bs.QJNi
/
I 2.66
I
I
3.20
3.71
( t3p.B,.Ip)l
Fig. 3. Plots of RSC of rare earths with references to erbium reference to nickel as a function of the product (B,B,I,).
and of other
metals
with
erbium as an internal standard, and the RSC values of other elements with reference to nickel as the internal standard using the PD system [9]. A comparison of Table 3 with Table 1 shows that the precision of SSMS analysis is improved by a factor of two if the reference and the measured elements resemble each other closely with respect to mass and physicochemical properties. The regressional analysis (see Fig. 3 and Table 4, A for the rare earths and B for the other group) of these data would lead one to the same conclusions (wherever applicable) drawn from Table 2. These RSC values, as expected, yield an improved fitting. The correlation coefficients are very close to unity and the deviations are less. Trial fittings are also done replacing boiling point (BP) by the heat of formation of gaseous atoms in the standard state (H:‘), for all the cases stated above. Blocks C and D in Table 4 and curves (A) and (B) in Fig. 4 show the results of fitting for the PD and ED systems, respectively. after taking the RSC values, with reference to uranium, for all the elements together. It appears that the use of the formation energy of gaseous atoms is as good a measure as boiling point for volatility. It is noted that in replacing (BP) by (El,!‘), the RSC of phosphorus is well fitted for both the detection modes. This may account for the discrepancy in the case of phosphorus mentioned earlier.
231 TABLE 4 Parameters Block no.
of regressional
Total no. of elements /points
analysis
Nature Slope ( m ) of fitting a M Confidence (%RSD)
(N) A
I
U
w
B
9
U
w
C
19
U
w
D
18
u w
0.21 (14.3) 0.22 (11.8) 0.33 (9.8) 0.41 (12.3) 0.28 (16.5) 0.25 (16.3) 0.21 (13.6) 0.26 (11.5)
Critical
Intercept
(e)
45% h-2
c
Confidence interval
h
(%RSD)
interval WIf t=g N-Z%,
(’ f
t;?“zq
0.84
0.93 2 c 2 0.75
0.29 > m > 0.15
(4.0) 0.81
0.87 > c >, 0.75
0.41 > m > 0.25
(2.7) 0.70
0.84 > c > 0.56
0.53 > m > 0.29
(8.4) 0.60
0.72 > (’ 2, 0.48
0.38 > m > 0.18
(8.4) 1.5
1.8>c>1.2
0.29 > m > 0.13 2.57
2.36
2.11 0.34 > m > 0.16
(9.6) 1.42
0.27 > m > 0.15
(5.7) 1.03
1.2O>c>O.86
(7.9) 0.89
0.99 > c > 0.79
2.12 0.32 > m > 0.20
(5.2)
a u and w indicate unweighted and weighted fittings respectively. h ti5T2 is the critical t value at 95% confidence limit for (N - 2) degrees of freedom.
So far, the RSC has been explained in the light of the product of three elemental parameters. However, for metallic matrices and metal alloys, the atomic volatility is expected to be governed by the boiling point or the heat of formation of gaseous atoms alone. Whether a similar expectation is valid for oxide matrices may be checked by studying the correlations of the RSC against ( B,I,) ,J( B,I,), and ( &“I,) a/( H,!‘I,) ,. The RSC values obtained from the ED system can be described by the product of two parameters. But for the PD system the uncertainties in the regression coefficients are as high as 30% and the correlation coefficient is low ( = 0.6). Similar analyses using (B,I,) show that the product of (Z$‘B,) or (II,&) is more appropriate to use in plots against the atomic volatility than the bond strength or boiling point or heat of formation of gaseous atoms alone. Many authors [13,14,16,17] have shown that their experimental RSC values assume a good relation even with a single parameter such as ionization potential, melting point, boiling point or heat of formation of gaseous atoms, etc. However, most of these authors found it to be true for elements with similar properties, such as the halogens, the alkali metals etc. Much of the published data from different laboratories for different matrices as well
232 TABLE 4 (continued) Block no.
A
Nature of fitting ”
U
Correlation coefficient( Evaluated RX,V
Max.
Rxy ) absolute
Significance level for Rxy h
0.952 0.154
B
D
~~~~~ (relative)
W
0.829
u
0.879 0.468
W
0.909
Evaluated
Critical
F;,.,.
1)
0.41
8.5
36.8
71.7
0.1
10.2
11.9
105
0.53
8.1
11.9
65
0.36
6.1
10.4
36.9
0.74
6.1
17.5
37.5
0.18
1.4
12.6
54.2
1.10
8.7
19.2
75.9
(5.3) 0.15
0.456 0.81
uated t for c=O
48.8
0.968
0.827
rn = 0
(12 ) 0.27 (13.6) 0.60 (17.7) (25.4) 0.35 (24.2) 0.41 (29.1)
P)
“p”“;”
24.8
u
u
t for
F(P-l,NEva,_
7.0
0.967
0.950
Eva,_ uated
0.34
W
W
Null test
0.05 (4.0) 0.06
0.666
c
deviation w.r.t. the
Standard error of estimate at 68% confidence level
with
6.61
5.59
4.45
4.49
a u and w indicate unweighted and weighted fittings, respectively. h Critical value of the correlation coefficient (Rxy) for N points at 95% confidence ’ Critical F value for 2 parameters and (N - 2) degrees of freedom.
level.
by using any single as our results are not, in general, explainable physicochemical property of the elements. However, our data plotted vs. the heat of formation of gaseous atoms alone, gives a reasonable linear relationship. This means that the heat of formation of gaseous atoms is a very important parameter in determining the RSC, and thus supports the fact that a considerable fraction of ionization in a spark plasma occurs via the formation of neutral atoms [3,7,8]. However, one can achieve the same RSC for two or more elements with different II,? values. At this stage, one might hope to reproduce better fittings using the heat of formation of the gaseous ions instead of taking the product of the heat of formation of gaseous atoms and the ionization potential. Unfortunately, the results do not support this. The uncertainties on the regression coefficients are high. Finally, to judge the usefulness of the correlations arrived at in the present studies, we have selected some elements which are not used in the fittings. The RSC of these elements with reference to uranium are evaluated from the
233 4
70 CURVE
(A) 9”
0.30
2.62
1.39
2.47
3.56
4.64
5.73
6 81
7.90
r CURVE
(B
1
:: ix
0 .60 0.30
I.32
2.33 1 H;
.B;I&,
I
I
I
I
3.35
4.36
5.38
6 ,36
/_(
H;
7-41
‘B,‘Lp)I
Fig. 4. Plots of RSC against ( H~‘B,/,) [,/( H:‘B,/,) ,.
photoplates using NBL-98-U,O, reference materials. Table 5 shows the experimental RSC values and the RSC values predicted from the following two pairs of regression equations (according to block C in Tables 2 and 4) RSC = 0.27x + 1.6
(3)
RSC = 0.25x + 1.45
(4)
where x stands for (B, B,Z,),,/
BpBJrJl~
and
RSC = 0.28x + 1.5
(5)
RSC = 0.25x + 1.42
(6)
where x = (HpB,Z,) ,,/( HFB,I, I’ For most of the elements, a reasonably good agreement is observed between the predicted and experimental RSC values. However, in view of the experimental data, the discrepancy between the experimental and the predicted RSC values, particularly for the element antimony, is difficult to explain. The situation might be improved if a proper internal standard is chosen. It can be shown that when these elements are also included in the fitting, the regression coefficients remain practically unchanged. Moreover, the intervals for the regression coefficients at the 95% confidence level are reduced. This justifies the usefulness of the correlations studies for predictive purposes. It may be of interest to study regressional analyses for the other available data in the literature against the same element-sensitive properties used by us.
234 TABLE 5 Experimental Element
and predicted
Experimental RSC
relative sensitivity
Predicted
RSC
Eq. (4) Dev. .’ (%)
($)
of some elements
RSC from
Eq. (3) RSD
coefficients
RSC
Eq. (5) Dev.
RSC
(a,
-
Mean predicted
Eq. (6) Dev.
RSC
(%)
Dev. (%)
RSC *o 5.x * 0.5 3.0 * 0.2 2.1 kO.1 7.9 f 0.8 2.x *0.2 1.X +0.1 1.x *0.1 2.2 +0.1 2.9 * 0.2 2.7 * 0.2 2.0 +0.1 2.3 +0.1 2.6 * 0.2 2.7 kO.1
7
21.4
5.7
1X.6
5.2
25.1
6.4
X.6
5.x
17.1
Mg
3
22.5
3.2
6.6
2.9
3.3
3.2
6.1
2.9
3.3
Al
2.1
26.5
2.2
4.X
2.0
4.x
2.2
4.x
2.0
4.X
K
7
35.6
7.6
X.6
7
0.0
9
2X.6
x.1
15.7
Ca
2.7
27.9
3.0
9.6
2.7
0.0
3.1
14.x
2.9
1.4
Ti
2.0
11.4
1.9
5.0
1.7
15.0
1.x
10.0
1.7
15.0
V
1.7
19.0
1.9
11.X
1.1
0.0
1.x
5.9
1.7
0.0
cu
2.4
15.4
2.3
4.2
2.1
12.5
2.3
4.2
2.1
12.5
Sr
2
31.0
2.9
45
2.1
35
3.1
55
2.9
45
In
4
33.4
2.1
32.5
2.5
31.5
2.9
21.5
2.6
35
Sn
3
33.4
2.1
30
1.9
36.1
2.1
30
1.9
36.7
Sb
4
26.6
2.4
40
2.2
45
2.3
42.5
2.2
45
Ba
1.X
27.6
2.6
44.4
2.4
33
2.x
55.6
2.6
44.4
Bi
3.3
24.0
2.X
15.2
2.6
21.2
2.x
15.2
2.6
21.2
a Dev. is the relative percent
deviation
of the predicted
RSC from the experimental
Dev f%) 17.1 0.0 0.0 12.9 3.7 10.0 5.9 X.3 45 32.5 33.3 42.5 44.4 1X.2
value.
The trial fittings using the data by (a) Sasamoto et al. [21] for a glass-graphite matrix, (b) Konishi [22] for oxide-graphite or salt-graphite matrices and (c) Konishi [13] for Spex Mix. lOOO-graphite samples using 45 data points (excluding silicon which is present as the metal and thalium and mercury for which reliable bond strength data are not available) show that the plot of RSC vs. the inverse of the product of three parameters (B, Bs Ip or H~B,I,) of the element of interest relative to the reference yields reasonably good or better parameter values than those derived using the product of two parameters. Using the product (H~B,I,) J( H,?B,,Z,), the regression param-
235
eters obtained are: (a) m = 0.19 f (17.4%), c = 1.0 f (20.9%) and Rxy = 0.877 (b) m = 0.19 & (9.0%), c = 0.8 f (14.4%) and Rxy = 0.965 (c) m = 0.22 f (9.8%), c = 0.6 f (15.2%) and Rxy = 0.84 The slope for the data set (a), (b) and (c) are close to each other and conform with our data when calculated with reference to iron [slope (m) = 0.18 + (13.3%) for thirty three elements from photoplates]. The analysis thus shows that the RSC values for oxide-graphite matrices are explainable by a product of three specific elemental parameters. Secondly, the slope of the regression line reveals that in the plasma of these matrices, the variation of RSC with respect to element-sensitive properties is nearly the same. The data obtained by Vanderborght and Van Grieken [23] using a graphite matrix, when fitted against the product (U~Z,) J( HfZ,), gave m = 0.23 f (21%) c = 0.2 _ + (56.5%) and Rxy = 0.6. However, the data include RSC values having uncertainties as high as 100%. Irrespective of the chemical form (oxide or sulphate) of the traces present in the sample, if we assume that the bond strength of the IO species correlates well with the atom separation energy, then the product of three parameters (H:‘B,Z,) gives better parameter values [m = 0.21 + (11.5%), c = 0.27 + (26.8%) and Rxy = 0.821. This further points out the importance of the bond scission energy in the atomisation process. The slope of the regression line takes the same value as that of oxide-graphite matrices. This indicates that the plasma of the single element graphite as well as oxide-graphite matrices has a similar effect on the rate of evaporation and ionisation characteristics of the trace elements. The RSC values thus follow the same trend among the elements for the said matrices under optimum experimental conditions. Trial fittings were also studied with some of the data from the literature for conducting metal and alloy matrices. When the average RSC values reported by Sasamoto et al. [21] are plotted vs. (H~Z,),,/( H,?Z,),, the parameters take the values: m = 2.0 f (20.3%), c’ = -0.8 + (64%) and RXJ = 0.6. A similar study by Van Hoye et al. [24] gave data for an iron matrix: (a) with weighting: m = 3.3 & (23.3%) c = -2.3 5 (36%) and Rxy = 0.834; and (b) without weighting: m = 3.7 + (32.9%), c’ = -2.3 i (66.5%) and Rxy = 0.732. These results indicate that the RSC is much more sensitive to the element-sensitive properties for a metallic matrix than for the former (oxide-graphite or graphite matrix), although no general conclusion could be drawn from the data [24] reported for aluminium, copper and zinc matrices. CONCLUSIONS
The RX was studied in terms of element-sensitive properties. The studies show that the atomisation of elements in a spark plasma is better represented
236
by a product of bond scission and vaporisation parameters, while the ionization potential can be used to reflect the ionization process. Thus it has been possible to express RSC as a simple linear function of the product of the element-sensitive properties, and in particular, for oxide-graphite matrices the parameter turns out to be either the product of boiling point, bond strength and ionization potential or the product of heat of formation of gaseous atoms, bond strength and ionization potential. Statistical analysis was performed in support of this conclusion. It is shown that the rate of change of RSC with respect to element-sensitive parameters is, on the average, the same for oxide-graphite-type matrices. ACKNOWLEDGEMENTS
The authors are grateful to Dr. M.V. Ramaniah, Director, Radiological Group and Dr. P.R. Natarajan, Head of the Radiochemistry Division for their keen interest in this work. We also express our gratitude to other members of the mass spectrometric section for help in the experimentation on the standard samples used for comparison. REFERENCES 1 A.J. Ahearn (Ed.), Trace Analysis by Mass Spectrometry. Academic Press. New York. 1972. 2 S.R. Taylor, in K.A. Gschneidner, Jr. and L.R. Eyring (Eds.). Handbook on the Physics and Chemistry of Rare Earths, North-Holland, Amsterdam, 1979, Chap. 37B. 3 R.E. Honig, Adv. Mass Spectrom., 3 (1966) 101. 4 H.E. Beske, R. Gijbels, A. Hurrle and K.P. Jochum. Fresenius’ Z. Anal. Chem.. 309 (1981) 329. 5 F. Adams, Philos. Trans. R. Sot. London Ser. A. 305 (1982) 509. 6 G.I. Ramendik, Zh. Anal. Khim., 3X (1983) 2036. 7 G.I. Ramendik, 0.1. Kryuchkova. M.Sh. Kaviladze, T.R. Mchedlidze and D.A. Tyurin, Zh. Anal. Khim., 3X (1983) 1749. 8 G.1. Ramendik, 0.1. Kryuchkova, M.Sh. Kaviladze. T.R. Mchedlidze and D.A. Tyurin. Int. J. Mass Spectrom. Ion Processes, 63 (1985) 1. 9 B.P. Datta. V.A. Raman, V.L. Sant, P.A. Ramasubramanian, P.M. Shah, K.L. Ramakumar. V.D. Kavimandan, S.K. Aggarwal and H.C. Jain, Int. J. Mass Spectrom. Ion Processes. 64 (19X5) 139. 10 K.L. Ramakumar, B.P. Datta, V.D. Kavimandan, S.K. Aggarwal, P.M. Shah. V.A. Raman. V.L. Sant, P.M. Ramasubramanian and H.C. Jain. Fresenius’ Z. Anal. Chem.. 31X (1984) 12. 11 H. Farrar IV. in A.J. Ahearn (Ed.), Trace Analysis by Mass Spectrometry. Academic Press, New York, 1972. 12 J.M. McCrea, 16th Annu. Conf. Mass Spectrom. Allied Top.. Pittsburgh. 196X. 13 F. Konishi, Mass Specrosc., 1X (1970) X7X. 14 T. Sasamoto, H. Hara and T. Sata, Mass Spectrosc.. 24 (1976) 121.
237 15 S.R. Taylor and M.P. Gorton, Geochim. Cosmochim. Acta, 41 (1977) 1375. 16 M. Ito, S. Sato and K. Yanagihara. Anal. Chim. Acta. 120 (1980) 217. 17 I. Opauszky and I. Nyary, KFKI-1981-09, KFKI-Kozponti Fizikai Kutato Intezete, Budapest, Hungary. 18 E. Van Hoye, F. Adams and R. Gijbels, Int. J. Mass Spectrom. Ion Phys., 30 (1979) 75. 19 E. Van Hoye, F. Adams and R. Gijbels, Talanta, 26 (1979) 285. 20 J. Franzen, K.H. Maurer and K.D. Schuy, Z. Naturforsch. Teil A, 21 (1966) 37. 21 T. Sasamoto, Y. Itoh, H. Hara and T. Sata, Bull. Tokyo Inst. Technol.. 126 (1975) 91. 22 F. Konishi, Mass Spectrosc., 18 (1967) 878. 23 B. Vanderborght and R. Van Grieken, Talanta. 26 (1979) 461. 24 E. Van Hoye, R. Gijbels and F. Adams, Adv. Mass Spectrom., 8A (1980) 357. 25 JEOL Ltd., Tokyo, Japan, Model JMS-OlBM-2. 26 R.C. Weast and M.J. Astle (Eds.). Handbook of Chemistry and Physics, CRC Press, Boca Raton. FL, 63rd edn., 1982-1983.
219
CORRELATION OF SPARK SOURCE MASS SPECTROMETRIC SENSITIVITY CALIBRATION FACTORS WITH THE ELEMENTSENSITIVE PHYSICOCHEMICAL PROPERTIES
B.P. DAmA
and H.C. JAIN
Fuel Chemistry Division, Bhabha Atomic Research Centre, Bomhqv - 4000X5 (India) (First received
11 February
1985; in final form 15 July 1985)
ABSTRACT The sensitivity calibration factor commonly expressed as relative sensitivity coefficient (RSC) is used in quantitative spark source mass spectrometry. The RSC was studied from the viewpoint of element-sensitive physicochemical properties and a linear correlation was attempted. Statistical analysis of the experimental data was carried out to justify the linear correlation. This permits quantitative spark source mass spectrometric analysis of trace constituents whose relative sensitivity coefficients have not been experimentally determined. The variation of RSC with respect to specific element-sensitive properties was shown to be the same for certain oxide/graphite-type matrices.
INTRODUCTION
In the field of trace analysis, spark source mass spectrometry (SSMS) is a well-known multi-elemental technique and its characteristic features have been described by several authors [l-6]. However, the ion beam produced from a spark ion source which reaches the detector system does not truly represent the composition of the sample being sparked. This is due to the fact that production and transmission of the ion beam from a spark source depends upon the experimental conditions, the nature of the major or matrix element(s) and the trace constituents. The effect of the experimental conditions can be minimised by using controlled and optimum sparking parameters and keeping intact the geometry of ion production and transmission to the best possible reproducibility. The sensitivity of ion production, even for a particular matrix, is not the same for all the elements. This is because, irrespective of the actual mechanism of ion production and dispersal of the beam from the point of sparking, the element-sensitive volatilisation and ionization processes govern the beam composition in a spark ion source. Hence, to make a feasible accurate SSMS analysis it is necessary to establish
016%1176/86/$03.50
0 1986 Elsevier Science Publishers
B.V.
220
the element-sensitive RSC a priori under a fixed set of experimental conditions and using proper trace constituent standards. The RSC which relates the composition of the sample in atoms with the composition of the ion beam reaching the detector, is defined as [(RSC)&
= [ $1,
(1)
where [( RSC) t] M is the relative sensitivity coefficient of element A with reference to B in matrix M. The parameter ur$ is the experimentally measured concentration of element A with respect to B, and xt is the true concentration of A with respect to B. However, reliable multi-elemental trace constituent standards, in particular the standards containing the desired traces, are not readily available. In order to overcome this difficulty it is essential to have an alternative approach to arrive at the RSC. Ramendik et al. [7,8] have been trying to ascertain the conditions of spark discharge which are critical to produce an ion beam closely resembling the sample composition. They have shown that the degree of dependence of RSC on the matrix is dictated by the physicochemical properties of the matrix elements and the corresponding source parameters used. In this paper, we present the linear regressional analysis of RSC with respect to the element-sensitive properties and the correlation (for a given matrix and experimental conditions) is justified statistically. This alternative approach helps considerably in understanding the elemental parameter that could be responsible for the RSC. Our observations could be used for predictive purposes. A brief analysis of the RSC values reported from different laboratories is presented to illustrate the conformity of their results with our observations. This paper deals mainly with the experiences gained on the U,O,-graphite matrix using both the photoplate and electrical detection modes. Generally, U,O, is an important material in nuclear technology and spark source mass spectrometry is a highly sensitive.technique suitable for the quality control of nuclear-grade fuels. EXPERIMENTAL
The instrument used is a MattauchhHerzog-type double focusing RF-spark source mass spectrometer [25] equipped with both the photoplate and electrical detection systems. For the photoplate detection (PD) mode, the operating conditions and slit settings were: power amplifier anode voltage, 2.5 kV; pulse width, 20 psec; pulse repetition rate, 1000 set-‘; acceleration voltage, 30 kV; object slit width, 50 pm; (Yand /3 slits width, 0.8 mm. In the
221
case of the electrical detection (ED) system, the slit widths were increased to 200 pm for the object slit and 1.5 mm for both the divergence angle defining slit (a) and the energy defining slit (/3) to ensure a stable ion current and to increase the sensitivity. The acceleration voltage used for the electrical mode was 25 kV and the data were collected by the peak switching mode. Seven NBL-98-&O, impurity standards, procured from the National Brunswick Laboratory (NBL, U.S.A.), for general elements and four synthetically doped U,O, reference samples for rare earth elements were used for the determination of relative sensitivity coefficients. Further details of samples, experimentation, methodology and calculation procedure are all given elsewhere [9,10]. A BRIEF
ANALYSIS
Several workers in this field have devised different empirical equations containing the physicochemical parameters of the measured element and that of the reference or/and matrix to calculate the theoretical RSC [ll]. Several authors [12-171 have also made attempts to relate their experimental RSC to the various element-sensitive physicochemical properties by some sort of regressional means. But no generalised theme is available and further studies are necessary on this line. In this work, it is not intended to calculate the RSC (,Y) from the element-specific properties (x) using empirical means, because, this implies fitting of the type y = mx with slope (nz) equal to unity. It means that, if the reference element is fixed, RSC is independent of the experimental conditions and of the nature of the matrix. Moreover, even for a particular matrix, the RSC of an element with respect to a fixed reference element may significantly vary depending upon the sparking and other experimental settings [17,18]. This is further supported by the work of Van Hoye et al. [19] where it was shown that the ratios of the theoretical RSC and the experimental RSC values are not, in general, unity but deviate by more than 50%. In a spark ion source, the plasma features are governed by the properties of the major or the matrix element(s). The dependence of RSC on elementsensitive properties is highly sensitive to the plasma characteristics of the matrix through their relation to the rate of evaporation and ionization of the . trace elements. We may try an ansatz [ (RSC),] M = mx, + c
(2)
where [(RSC) ,lM stands for the RSC of the element (I) in the matrix (M), and x, relates the physicochemical property of the measured element (I). Both m and c’ are constants for a given matrix and fixed experimental conditions.
222
If it is at all possible to express the RSC in the form of Eq. (2) then the constant (c) defines the total contribution of the matrix as well as experimental conditions or/and instrumental bias factors towards the RSC, while the constant (m) defines the rate of change of RSC in the given matrix and experimental features. In the present work, we assume, under the controlled and optimum conditions of operation, the transmission coefficients of all the elemental ions of the same charge are nearly the same. With this presumption, the RSC values can be considered to represent the ion source yields in accordance with the specificities of the measured elements relative to that of the reference. The source yield and, subsequently, the RSC, irrespective of the actual ionization mechanism in the spark plasma, are a measure of the relative atomic volatility coupled with the relative ionization capability. By atomic volatility, is meant the formation of atomic species which may be formed by direct evaporation from the matrix (a process that probably represents the case from the metallic matrices) or by evaporation of some suitable species such as IO from the oxide matrices followed by rupture of the bond. The parameters such as boiling point (BP) or heat of formation of gaseous atoms at the standard state (Hr) are considered for the evaporation process. In an oxide matrix, depending on its method of preparation, an element may exist in different oxidation states and different bond rupture processes may occur depending upon the thermodynamic characteristics of the process. Again, the bond energy may be different depending upon the molecule considered for the calculation. Therefore, it is assumed that the bond strength (II,) of the IO species is a measure of the energy characteristics involved in the bond rupture process. The experimental RSC values used for correlation were determined for singly charged ions 193. So the first ionization potential ( IP) is used hereafter. There is no sufficient reason to believe that any elemental parameter should be raised to some exponent or should be weighted while studying the correlation. Our aim is not to establish the best fitted curve, but to check whether the experimental observations are compatible with certain linear relationships between RSC and elementspecific properties. As a simplifying assumption, some element-sensitive properties, such as the formation of polymerised species ions and the charge distribution, molecular ions, are ignored. Unless these properties are also well accounted in the calculation of RSC or considered for the fitting, the correlation is never expected to be perfect. However, the uncertainties observed on the RSC values are assumed to overshadow the element-sensitive variation in the charge distribution etc.
223 RESULTS
AND DISCUSSION
The relative sensitivity coefficients observed in the U,O,-graphite matrix for the rare earths and other elements relative to uranium using both the electrical and photoplate detection systems [9] are shown in Table 1. The RSC was calculated as the slope (m) of the line y = mx [cf. Eq. (l)], where y is the experimentally observed concentration and x is the certified concentration. For the evaluation of the photoplates, the following considerations were taken into account [9]: (1) the line width (IV,) of mass (m,) peak,
TABLE
1
Experimental relative sensitivity coefficients reference element a and their physicochemical Element
Photoplate detection system
Electrical detection system
RSC
RSC
RSD ’
Cd Ce Nd Sm EU Gd DY Er Lu W Pb
0.3 1.4 2 2.8 2.5 2.6 2.4 4 2.2 2.8 1.7 1.8 2.3 2.6 1.6 2.0 2.1 1.9 1.6 3.0
U
Reference
MO
50.0 39.0 53.1 27.3 28.6 31.4 30.5 29.0 15.3 13.8 6.0 18.7 19.4 25.5 11.0 18.6 24.1 14.6 10.0 16.0
Physicochemical
with respect
to uranium
data h
RSD
B,
(4%)
(“C)
B* (kcal mol-’
I, (ev)
HP (kcal mol-‘)
0.53 1.07 1.46 1.6 1.3 1.4 1.2
14.0 7.7 5.2 22.6 15.2 11.2 11.0
1.2 2.5 1.6 1.8 2.0 2.0 1.4 1.6 1.6 1.37 1.02 1.7
23.8 10.6 10.0 13.2 13.0 14.6 11.5 16.8 11.8 5.5 3.6 15.2
2550 280 2672 1962 2750 2870 2732 907 4612 765 3257 3127 1778 1597 3233 2335 2510 3315 5660 1740
192.7 142.6 102 96 97.7 88 93.6 67.9 145.1 88 190 168 148 133 171 146 146 166 156 90.3
8.298 10.486 6.766 7.435 7.87 7.86 7.635 9.394 7.099 8.993 5.47 5.49 5.63 5.67 6.14 5.93 6.10 5.246 7.98 7.416
134.5 75.2 94.8 67.1 99.5 101.5 102.7 31.245 157.3 26.77 101.0 78.3 49.4 41.9 95.0 69.4 75.8 102.2 203.0 46.6
3818
181.9
6.05
(S) B P Cr Mn Fe co Ni Zn
of some elements properties.
element
a Ref. 9. b The data were taken from ref. 26. ’ RSD is the relative standard deviation
at 68% confidence
limit.
128
as
224
2.60
2.10 : IY I .60
I
.oo
-0.30
I.36
I .41
I.75
2.13
2.50
2.66
3.25
3.63
2.52
3.63
4.74
5.66
6.97
6.06
CURVE(C) 3 .65
::
2.45
LL I ,25 0
Fig. 1. Plots of RSC obtained
from the photoplate
detection
system
against
(B,B,I,),,/
(qAI,)l.
corrected as (w, a WI~.~);(2) mass dependence of photoplate emulsion blackening, corrected by using the factor (wz;“~); and (3) background contribution on the line blackening employing the method of Franzen et al. [20]. For the ED mode no correction is applied. The absolute RSC values obtained from the two detection systems are not corrected for the effect of the different experimental conditions (namely, slit widths, path length, energy and mass response characteristics). However, the trend in the RSC values among the elements for the two detection modes is similar. This reflects the fact that the difference in the RSC values between the two detection systems is systematic. The plots of the experimental relative sensitivity coefficients vs. the inverse of the product of the boiling point ( Bp), bond strength (B,) and ionization potential ( lP) of the measured elements with respect to the reference element uranium are shown in Fig. 1. In Fig. 1, curve (A) relates to the rare earth elements, curve (B) refers to the elements other than rare earths, and the variation of the relative sensitivity coefficients for both the
225
1.10
I.85
2.23
2.60
2.98
3.35
3.73
2.41
3.47
4.53
5.59
6.64
7.70
2.41
3.47
4.53
5.59
6.64
7.70
(Bp.B,.Ip
ju
1.48
CURVE
(6)
2.28-
0.30
1.36
CURVE
(C)
I 0.30
Fig. 2. Plots
1.36
ABp.B,
of RSC obtained
from
‘Ip
)I
the electrical
detection
system
against
(B,B,I,),,/
(B,BI,),.
rare earths and other elements taken together are plotted in curve (C). The RSC values in Fig. 1 were obtained from the PD system (Table 1); the corresponding behaviours obtained for the ED system are exhibited in Fig. 2. In Figs. 1 and 2 the continuous line represents the regression line while the broken lines on either side of the fitted line show the standard error bars of the estimates. Regressional analysis with weighting (l/square of the standard deviation of the RSC as weighting factor) and without weighting are performed. The details of the fitted parameters are tabulated in Table 2 (blocks A,B,C are for the PD system and blocks D,E,F refer to the ED system). To avoid confusion in the figures, only the fitted curves without weights are shown. Nevertheless, every detailed feature of the weighted case is furnished in Table 2. An analysis of the figures and Table 2 reveals the following characteristic features of the correlations. (i) The regression coefficients (slopes and intercepts) are nearly the same for the two lines, namely, for the regression line with and without weighting. This indicates that the RSC values without the error bars, on the average,
226
follow the same trend among the elements as they would including the error limits. Hence, either of the regression equations can be used for predictive purposes. The errors on the slope and the intercept generally lie within the error bars of the experimental RSC values. However, at 95% confidence level, the intervais at which the regression coefficients lie are quite high. This is mainly because fewer points have been used for the correlation calculations. For a particular detection made, even for the different groups of elements (i.e. for the rare earths, other elements and all the elements taken together), there is no significant difference in the slopes and intercepts of the regression lines. This means the regression line obtained for the rare earth elements can still be used to predict the RSC of other elements and vice-versa. A notable difference is observed in the correlation of the RSC values of the rare earths alone in the PD mode. This is because too small a number of elements of very similar properties are involved in the correlation calculations. Thus a small change in the values can appear to significantly affect the regression coefficients. However, at the 95% confidence level the intervals within which the slope and intercept lie for this set of data for the rare earths easily include the regression coefficients obtained using other groups of elements. It is interesting to note that for the two detection systems the slopes of the regression lines remain practically unaltered. This implies that the trends in the RSC values among the elements for the two detection systems are very similar. That is to say, under the slightly different experimental conditions for the two detection modes the beam composition (i.e. charge and energy distributions of the measured elements with respect to the reference) remains nearly the same. It is noted that the difference in the experimenta bias factors for the two detection modes is reflected in the different intercept values. (ii) In any of the cases cited above, the evaluated correlation coefficient ( RXJI) is positive (see column 9) and it exceeds the appropriate critical value of Rxy {column 10) at the 95% confidence level. This gives an indication that the elemental RSC values and the inverse (x) of the product of the chosen physicochemical properties of the measured elements relative to the reference are significantly correlated. (iii) In general, the deviations of the experimental RSC values from the fitted line are low and within the uncertainty level observed on the RSC values. The highest deviation from the regression line is observed for neodymium in the correlation of the RSC values obtained, using the ED mode (Table 2, block F). A comparison of the RSC values (Table 1) of the rare earth elements between the two detection modes shows that the RSC of neodymium obtained from the ED system is somewhat higher than that expected from the general trend.
221
TABLE
2
Details of the regressional analysis of RSC against the inverse of the product measured elements with respect to the reference Block no.
A
Total no. of
8
Nature of
u
0.39 (10.7) 0.41 (14.9) 0.23 (24.6) 0.23 (23.6) 0.27 (16.1) 0.25 (17.3) 0.24 (29.1) 0.29 (33.5) 0.23
W
(7.1) 0.23
u w
B
10
u w
C
18
u w
D
8
U
w
E
F
9
17
Slope (m)
U W
(7.7) 0.20 (17.4) 0.26 (13.8)
Critical 95%h [N-Z
Intercept Y%RSD)
1.24
0.49 > M > 0.29 2.45 0.56 > m > 0.26
(7.0) 1.18
(BP&I,)
(c) Confidence interval
1.45>,>1.03 1.40 > c 3 0.96
0.35 > m 3 0.11
(7.6) 1.9 (11.5) 1.6
0.36 > m 2 0.18
(8.7) 1.6
1.9>.>1.3
(8.5) 1.45
1.6>c31.28
0.36 > m a 0.10 2.30
2.12 0.34 > m $ 0.16 0.41 > m 3 0.07 2.45 0.53 > m 3 0.05 0.27 > m > 0.19 2.36
(5.6) 1.2 (12.2) 1.0 (15.6) 0.89
of the
2.4hcal.4 1.9ac31.3
1.6 > c 3 0.84 1.43~20.62 1.01 3 c > 0.77
0.27>m>O.19
(5.8) 0.88
0.95 > c > 0.8
0.27 3 m + 0.13
(3.2) 1.1
1.3>cco.9
(8.7) 0.91
l.O>,,>O.S
2.13 0.34 3 m > 0.18
(6.1)
’ u and w indicate unweighted and weighted fittings. respectively. h I:?‘, is the critical r value at 95% confidence limit for (N - 2) degrees of freedam.
As one would expect, the deviations are further reduced if separate correlations for elements having more close physicochemical properties are considered (see column 11). (iv) The stand ar d errors of estimates (column 12) at 68% confidence level are relatively high (weighted cases). However, all the experimental points lie within plus or minus one standard error of the estimate values given by the regression equations. The error bars on the RSC values, of course, account well for the higher values of the standard error. This is supplemented by the fact that the standard errors of the estimates are less if one does not consider
228 TABLE 2 (continued) Block no.
Nature of fit a
Correlation coefficient (W
Evaluated RXJ
)
Max. absolute deviation w.r.t. the Signiffitted icance value level for Rxv t, (Relative)
A
U
0.967 0.707
B
w
0.937
u
0.821 0.632
c
w
0.832
u
0.841 0.468
D
w
0.823
u
0.815 0.707
E
w
0.773
U
0.983 0.666
F
w
0.980
U
0.829 0.482 0.882
w
Standard error of estimate at 68% confidence level
Null Test
F(P-l,,Y-
EvaIuated I for nl=O
Evaluated t for c=O
variate
P) with
p = 2c Evaluated F,I.!-2,
Critical F(I.‘\~-‘)
(%) 0.13
0.09
9.4
14.2
87.4
(6.5) 0.15
0.32
6.7
13.2
45
0.4
4.1
8.7
16.5
0.9
4.2
11.5
17.9
0.33
6.2
11.8
38.8
0.77
5.8
17.7
33.5
0.15
3.4
8.2
Il.8
0.91
3.0
6.4
0.09
14.1
17.4
199
0.60
13.0
31.7
170
0.21
5.7
Il.5
32.9
I.3
7.3
16.3
52.7
(7.4) 0.7 (19.6) 0.73 (22.4) 0.72 (20.4) 0.72 (21.8) 0.25 (16) 0.33 (22.7) 0.18 (13.1) 0.18 (13.1) 0.40 (28.4) 0.52 (40.2)
5.99
5.32
4.49
5.99 8.9
5.59
4.54
.’ u and w indicate unweighted and weighted fittings. respectively. h Critical value of the correlation coefficient (R-x;v) for I%’points at 95% confidence ’ Critical F value for 2 parameters and ( I%’- 2) degrees of freedom.
level.
the uncertainties of the RSC values for fitting. In that case, as mentioned earlier, the regression coefficients are not affected significantly. (v) The null tests for the slope and intercept show that the calculated t-values (columns 13 and 14) are always higher than the corresponding critical t-value at 95% confidence level (column 6). This rules out the possibility of either of the parameters being zero and this statement is made with 95% confidence. This means that there exists a relationship between the
229
measured RSC and the elemental parameters considered, and the experimental bias factor (c) is never zero. (vi) In any of the cases cited, the evaluated F( P - 1, N - P) variate (column 15) (where parameter P equals 2 and N is the total number of points or elements considered), which is the ratio of the mean square due to regression and the mean square of the regression, exceeds the appropriate critical F-value (column 16) with 95% confidence and hence statistically significant regression has been obtained. This implies that, if the experiments are repeated for the identical set of elements (and hence for the same x-values), the proportion of variation in the newly observed data is not likely to exceed, within the 95% confidence level, the variation observed in the data set used and which has been accounted for by the linear equation ( y = mx t-
Ce
1.0
10.6
Nd Sm ELI Cd DY Lu
1.03 1.2 1.20 0.95 1.05 0.97
1.3 13.3 6.X 2.55 4.9 9.73
d Ref. 9.
B P Cr Ml1 Fe co Zn MO Cd W Pb
0.1 0.7 1.0 1.1 1.1 1.1 1.7 0.8 1.x 0.67 1.4
33.3 1.4 17.1 13.2 15.4 15.4 2X.4 35.1 42.3 12.0 1x.3
0.80
1
I
I
0.50
0.70
0.90
I
I
I.10
I.30
(Bp.B,~Ip)Er
/
( BP’
I
I .50
I
I. 70
I I ,90
B,~Ip)l
2.09
” In lx
I .53
0.96
W 0.40 0 .I0
0.62 (BP
I
I
I
I.13
I.65
2.17
.Bs.QJNi
/
I 2.66
I
I
3.20
3.71
( t3p.B,.Ip)l
Fig. 3. Plots of RSC of rare earths with references to erbium reference to nickel as a function of the product (B,B,I,).
and of other
metals
with
erbium as an internal standard, and the RSC values of other elements with reference to nickel as the internal standard using the PD system [9]. A comparison of Table 3 with Table 1 shows that the precision of SSMS analysis is improved by a factor of two if the reference and the measured elements resemble each other closely with respect to mass and physicochemical properties. The regressional analysis (see Fig. 3 and Table 4, A for the rare earths and B for the other group) of these data would lead one to the same conclusions (wherever applicable) drawn from Table 2. These RSC values, as expected, yield an improved fitting. The correlation coefficients are very close to unity and the deviations are less. Trial fittings are also done replacing boiling point (BP) by the heat of formation of gaseous atoms in the standard state (H:‘), for all the cases stated above. Blocks C and D in Table 4 and curves (A) and (B) in Fig. 4 show the results of fitting for the PD and ED systems, respectively. after taking the RSC values, with reference to uranium, for all the elements together. It appears that the use of the formation energy of gaseous atoms is as good a measure as boiling point for volatility. It is noted that in replacing (BP) by (El,!‘), the RSC of phosphorus is well fitted for both the detection modes. This may account for the discrepancy in the case of phosphorus mentioned earlier.
231 TABLE 4 Parameters Block no.
of regressional
Total no. of elements /points
analysis
Nature Slope ( m ) of fitting a M Confidence (%RSD)
(N) A
I
U
w
B
9
U
w
C
19
U
w
D
18
u w
0.21 (14.3) 0.22 (11.8) 0.33 (9.8) 0.41 (12.3) 0.28 (16.5) 0.25 (16.3) 0.21 (13.6) 0.26 (11.5)
Critical
Intercept
(e)
45% h-2
c
Confidence interval
h
(%RSD)
interval WIf t=g N-Z%,
(’ f
t;?“zq
0.84
0.93 2 c 2 0.75
0.29 > m > 0.15
(4.0) 0.81
0.87 > c >, 0.75
0.41 > m > 0.25
(2.7) 0.70
0.84 > c > 0.56
0.53 > m > 0.29
(8.4) 0.60
0.72 > (’ 2, 0.48
0.38 > m > 0.18
(8.4) 1.5
1.8>c>1.2
0.29 > m > 0.13 2.57
2.36
2.11 0.34 > m > 0.16
(9.6) 1.42
0.27 > m > 0.15
(5.7) 1.03
1.2O>c>O.86
(7.9) 0.89
0.99 > c > 0.79
2.12 0.32 > m > 0.20
(5.2)
a u and w indicate unweighted and weighted fittings respectively. h ti5T2 is the critical t value at 95% confidence limit for (N - 2) degrees of freedom.
So far, the RSC has been explained in the light of the product of three elemental parameters. However, for metallic matrices and metal alloys, the atomic volatility is expected to be governed by the boiling point or the heat of formation of gaseous atoms alone. Whether a similar expectation is valid for oxide matrices may be checked by studying the correlations of the RSC against ( B,I,) ,J( B,I,), and ( &“I,) a/( H,!‘I,) ,. The RSC values obtained from the ED system can be described by the product of two parameters. But for the PD system the uncertainties in the regression coefficients are as high as 30% and the correlation coefficient is low ( = 0.6). Similar analyses using (B,I,) show that the product of (Z$‘B,) or (II,&) is more appropriate to use in plots against the atomic volatility than the bond strength or boiling point or heat of formation of gaseous atoms alone. Many authors [13,14,16,17] have shown that their experimental RSC values assume a good relation even with a single parameter such as ionization potential, melting point, boiling point or heat of formation of gaseous atoms, etc. However, most of these authors found it to be true for elements with similar properties, such as the halogens, the alkali metals etc. Much of the published data from different laboratories for different matrices as well
232 TABLE 4 (continued) Block no.
A
Nature of fitting ”
U
Correlation coefficient( Evaluated RX,V
Max.
Rxy ) absolute
Significance level for Rxy h
0.952 0.154
B
D
~~~~~ (relative)
W
0.829
u
0.879 0.468
W
0.909
Evaluated
Critical
F;,.,.
1)
0.41
8.5
36.8
71.7
0.1
10.2
11.9
105
0.53
8.1
11.9
65
0.36
6.1
10.4
36.9
0.74
6.1
17.5
37.5
0.18
1.4
12.6
54.2
1.10
8.7
19.2
75.9
(5.3) 0.15
0.456 0.81
uated t for c=O
48.8
0.968
0.827
rn = 0
(12 ) 0.27 (13.6) 0.60 (17.7) (25.4) 0.35 (24.2) 0.41 (29.1)
P)
“p”“;”
24.8
u
u
t for
F(P-l,NEva,_
7.0
0.967
0.950
Eva,_ uated
0.34
W
W
Null test
0.05 (4.0) 0.06
0.666
c
deviation w.r.t. the
Standard error of estimate at 68% confidence level
with
6.61
5.59
4.45
4.49
a u and w indicate unweighted and weighted fittings, respectively. h Critical value of the correlation coefficient (Rxy) for N points at 95% confidence ’ Critical F value for 2 parameters and (N - 2) degrees of freedom.
level.
by using any single as our results are not, in general, explainable physicochemical property of the elements. However, our data plotted vs. the heat of formation of gaseous atoms alone, gives a reasonable linear relationship. This means that the heat of formation of gaseous atoms is a very important parameter in determining the RSC, and thus supports the fact that a considerable fraction of ionization in a spark plasma occurs via the formation of neutral atoms [3,7,8]. However, one can achieve the same RSC for two or more elements with different II,? values. At this stage, one might hope to reproduce better fittings using the heat of formation of the gaseous ions instead of taking the product of the heat of formation of gaseous atoms and the ionization potential. Unfortunately, the results do not support this. The uncertainties on the regression coefficients are high. Finally, to judge the usefulness of the correlations arrived at in the present studies, we have selected some elements which are not used in the fittings. The RSC of these elements with reference to uranium are evaluated from the
233 4
70 CURVE
(A) 9”
0.30
2.62
1.39
2.47
3.56
4.64
5.73
6 81
7.90
r CURVE
(B
1
:: ix
0 .60 0.30
I.32
2.33 1 H;
.B;I&,
I
I
I
I
3.35
4.36
5.38
6 ,36
/_(
H;
7-41
‘B,‘Lp)I
Fig. 4. Plots of RSC against ( H~‘B,/,) [,/( H:‘B,/,) ,.
photoplates using NBL-98-U,O, reference materials. Table 5 shows the experimental RSC values and the RSC values predicted from the following two pairs of regression equations (according to block C in Tables 2 and 4) RSC = 0.27x + 1.6
(3)
RSC = 0.25x + 1.45
(4)
where x stands for (B, B,Z,),,/
BpBJrJl~
and
RSC = 0.28x + 1.5
(5)
RSC = 0.25x + 1.42
(6)
where x = (HpB,Z,) ,,/( HFB,I, I’ For most of the elements, a reasonably good agreement is observed between the predicted and experimental RSC values. However, in view of the experimental data, the discrepancy between the experimental and the predicted RSC values, particularly for the element antimony, is difficult to explain. The situation might be improved if a proper internal standard is chosen. It can be shown that when these elements are also included in the fitting, the regression coefficients remain practically unchanged. Moreover, the intervals for the regression coefficients at the 95% confidence level are reduced. This justifies the usefulness of the correlations studies for predictive purposes. It may be of interest to study regressional analyses for the other available data in the literature against the same element-sensitive properties used by us.
234 TABLE 5 Experimental Element
and predicted
Experimental RSC
relative sensitivity
Predicted
RSC
Eq. (4) Dev. .’ (%)
($)
of some elements
RSC from
Eq. (3) RSD
coefficients
RSC
Eq. (5) Dev.
RSC
(a,
-
Mean predicted
Eq. (6) Dev.
RSC
(%)
Dev. (%)
RSC *o 5.x * 0.5 3.0 * 0.2 2.1 kO.1 7.9 f 0.8 2.x *0.2 1.X +0.1 1.x *0.1 2.2 +0.1 2.9 * 0.2 2.7 * 0.2 2.0 +0.1 2.3 +0.1 2.6 * 0.2 2.7 kO.1
7
21.4
5.7
1X.6
5.2
25.1
6.4
X.6
5.x
17.1
Mg
3
22.5
3.2
6.6
2.9
3.3
3.2
6.1
2.9
3.3
Al
2.1
26.5
2.2
4.X
2.0
4.x
2.2
4.x
2.0
4.X
K
7
35.6
7.6
X.6
7
0.0
9
2X.6
x.1
15.7
Ca
2.7
27.9
3.0
9.6
2.7
0.0
3.1
14.x
2.9
1.4
Ti
2.0
11.4
1.9
5.0
1.7
15.0
1.x
10.0
1.7
15.0
V
1.7
19.0
1.9
11.X
1.1
0.0
1.x
5.9
1.7
0.0
cu
2.4
15.4
2.3
4.2
2.1
12.5
2.3
4.2
2.1
12.5
Sr
2
31.0
2.9
45
2.1
35
3.1
55
2.9
45
In
4
33.4
2.1
32.5
2.5
31.5
2.9
21.5
2.6
35
Sn
3
33.4
2.1
30
1.9
36.1
2.1
30
1.9
36.7
Sb
4
26.6
2.4
40
2.2
45
2.3
42.5
2.2
45
Ba
1.X
27.6
2.6
44.4
2.4
33
2.x
55.6
2.6
44.4
Bi
3.3
24.0
2.X
15.2
2.6
21.2
2.x
15.2
2.6
21.2
a Dev. is the relative percent
deviation
of the predicted
RSC from the experimental
Dev f%) 17.1 0.0 0.0 12.9 3.7 10.0 5.9 X.3 45 32.5 33.3 42.5 44.4 1X.2
value.
The trial fittings using the data by (a) Sasamoto et al. [21] for a glass-graphite matrix, (b) Konishi [22] for oxide-graphite or salt-graphite matrices and (c) Konishi [13] for Spex Mix. lOOO-graphite samples using 45 data points (excluding silicon which is present as the metal and thalium and mercury for which reliable bond strength data are not available) show that the plot of RSC vs. the inverse of the product of three parameters (B, Bs Ip or H~B,I,) of the element of interest relative to the reference yields reasonably good or better parameter values than those derived using the product of two parameters. Using the product (H~B,I,) J( H,?B,,Z,), the regression param-
235
eters obtained are: (a) m = 0.19 f (17.4%), c = 1.0 f (20.9%) and Rxy = 0.877 (b) m = 0.19 & (9.0%), c = 0.8 f (14.4%) and Rxy = 0.965 (c) m = 0.22 f (9.8%), c = 0.6 f (15.2%) and Rxy = 0.84 The slope for the data set (a), (b) and (c) are close to each other and conform with our data when calculated with reference to iron [slope (m) = 0.18 + (13.3%) for thirty three elements from photoplates]. The analysis thus shows that the RSC values for oxide-graphite matrices are explainable by a product of three specific elemental parameters. Secondly, the slope of the regression line reveals that in the plasma of these matrices, the variation of RSC with respect to element-sensitive properties is nearly the same. The data obtained by Vanderborght and Van Grieken [23] using a graphite matrix, when fitted against the product (U~Z,) J( HfZ,), gave m = 0.23 f (21%) c = 0.2 _ + (56.5%) and Rxy = 0.6. However, the data include RSC values having uncertainties as high as 100%. Irrespective of the chemical form (oxide or sulphate) of the traces present in the sample, if we assume that the bond strength of the IO species correlates well with the atom separation energy, then the product of three parameters (H:‘B,Z,) gives better parameter values [m = 0.21 + (11.5%), c = 0.27 + (26.8%) and Rxy = 0.821. This further points out the importance of the bond scission energy in the atomisation process. The slope of the regression line takes the same value as that of oxide-graphite matrices. This indicates that the plasma of the single element graphite as well as oxide-graphite matrices has a similar effect on the rate of evaporation and ionisation characteristics of the trace elements. The RSC values thus follow the same trend among the elements for the said matrices under optimum experimental conditions. Trial fittings were also studied with some of the data from the literature for conducting metal and alloy matrices. When the average RSC values reported by Sasamoto et al. [21] are plotted vs. (H~Z,),,/( H,?Z,),, the parameters take the values: m = 2.0 f (20.3%), c’ = -0.8 + (64%) and RXJ = 0.6. A similar study by Van Hoye et al. [24] gave data for an iron matrix: (a) with weighting: m = 3.3 & (23.3%) c = -2.3 5 (36%) and Rxy = 0.834; and (b) without weighting: m = 3.7 + (32.9%), c’ = -2.3 i (66.5%) and Rxy = 0.732. These results indicate that the RSC is much more sensitive to the element-sensitive properties for a metallic matrix than for the former (oxide-graphite or graphite matrix), although no general conclusion could be drawn from the data [24] reported for aluminium, copper and zinc matrices. CONCLUSIONS
The RX was studied in terms of element-sensitive properties. The studies show that the atomisation of elements in a spark plasma is better represented
236
by a product of bond scission and vaporisation parameters, while the ionization potential can be used to reflect the ionization process. Thus it has been possible to express RSC as a simple linear function of the product of the element-sensitive properties, and in particular, for oxide-graphite matrices the parameter turns out to be either the product of boiling point, bond strength and ionization potential or the product of heat of formation of gaseous atoms, bond strength and ionization potential. Statistical analysis was performed in support of this conclusion. It is shown that the rate of change of RSC with respect to element-sensitive parameters is, on the average, the same for oxide-graphite-type matrices. ACKNOWLEDGEMENTS
The authors are grateful to Dr. M.V. Ramaniah, Director, Radiological Group and Dr. P.R. Natarajan, Head of the Radiochemistry Division for their keen interest in this work. We also express our gratitude to other members of the mass spectrometric section for help in the experimentation on the standard samples used for comparison. REFERENCES 1 A.J. Ahearn (Ed.), Trace Analysis by Mass Spectrometry. Academic Press. New York. 1972. 2 S.R. Taylor, in K.A. Gschneidner, Jr. and L.R. Eyring (Eds.). Handbook on the Physics and Chemistry of Rare Earths, North-Holland, Amsterdam, 1979, Chap. 37B. 3 R.E. Honig, Adv. Mass Spectrom., 3 (1966) 101. 4 H.E. Beske, R. Gijbels, A. Hurrle and K.P. Jochum. Fresenius’ Z. Anal. Chem.. 309 (1981) 329. 5 F. Adams, Philos. Trans. R. Sot. London Ser. A. 305 (1982) 509. 6 G.I. Ramendik, Zh. Anal. Khim., 3X (1983) 2036. 7 G.I. Ramendik, 0.1. Kryuchkova. M.Sh. Kaviladze, T.R. Mchedlidze and D.A. Tyurin, Zh. Anal. Khim., 3X (1983) 1749. 8 G.1. Ramendik, 0.1. Kryuchkova, M.Sh. Kaviladze. T.R. Mchedlidze and D.A. Tyurin. Int. J. Mass Spectrom. Ion Processes, 63 (1985) 1. 9 B.P. Datta. V.A. Raman, V.L. Sant, P.A. Ramasubramanian, P.M. Shah, K.L. Ramakumar. V.D. Kavimandan, S.K. Aggarwal and H.C. Jain, Int. J. Mass Spectrom. Ion Processes. 64 (19X5) 139. 10 K.L. Ramakumar, B.P. Datta, V.D. Kavimandan, S.K. Aggarwal, P.M. Shah. V.A. Raman. V.L. Sant, P.M. Ramasubramanian and H.C. Jain. Fresenius’ Z. Anal. Chem.. 31X (1984) 12. 11 H. Farrar IV. in A.J. Ahearn (Ed.), Trace Analysis by Mass Spectrometry. Academic Press, New York, 1972. 12 J.M. McCrea, 16th Annu. Conf. Mass Spectrom. Allied Top.. Pittsburgh. 196X. 13 F. Konishi, Mass Specrosc., 1X (1970) X7X. 14 T. Sasamoto, H. Hara and T. Sata, Mass Spectrosc.. 24 (1976) 121.
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