Anal. Chem. 1986, 58, 1173-1178
1173
Quantitative Thermogravimetry on Peat. A Multivariate Approach Jan-Ake Persson,*l Erik Johansson: and Christer Albano National Swedish Laboratory for Agricultural Chemistry, Box 5097, S-900 05 UmeP, Sweden
Thermogravlmetry (TO) In combination with a statistical method, partlal least-squares modeling of latent Variables (PLS), has been proved to be useful for quantltatlve determlnatlons of chemlcal and physical properties In complex samples, e.g., peat. Samples representing the major peat types In Sweden were used and the sample properties determined were calorific value, carbon content, hydrolyzable substance ( R value), and volume weight. A comparison between PLS and stepwise multlple regression for this klnd of data evaluatlon Is made.
Due to the increased price of fossil fuels and the environmental concern about combustion exhausts, there exists a need for analytical methods that can be used for characterization of fuels. Among them thermal analysis has always had a prominent place, especially in studies concerning the burning characteristics of a fuel. In recent years the overall use of thermal methods has increased both in quality control and in product development as well as in basic research ( I ) . Among the numerous thermal methods thermogravimetry (TG), recording of the weight loss of a sample subjected to an increase in temperature, has been widely used for the characterization of materials by their thermal stability or their oxidizability. TG has mainly been used for qualitative work in which the thermal behavior of a compound has been compared to that of a known standard substance. In this way a substance can be classified as similar or not to a certain group of compounds. An example of this technique is the detection and identification of adhesion agents in bitumen (2). Due to the complexity of the obtained TG curves and lack of adequate evaluation methods, TG has not been widely used for quantitative work. The method has been applied only where it has been possible to utilize a specific point on the TG curve for the actual determination. In coal analysis such techniques have been used for the simultaneous determination of moisture, total volatiles, fixed carbon, and ash content (3). By determination of the fixed carbon content and volatile matter in bituminous coals and by use of the empirical relationship described by Goutal ( 4 ) ,calorific values could also be determined. Other applications include the determination of the water of hydration and carbonate content in various inorganic substances ( I ) . All these applications are based on steady-state measurements; a single value is used from a point on the TG curve where constant weight has been obtained. Otherwise, quantitative analysis of properties in complex organic samples using TG has proved to be difficult because of the wide variety of possible thermal reactions at elevated temperatures. Variations in the chemical composition of a sample can have a significant influence on the shape of the TG curve without necessarily changing the actual end point value. A simple direct relationship between steady-state values Present address: Boliden Metal1 AB, S-932 00 Skelleftehamn, Sweden. Present address: AB Hassle, S-43183 Molndal, Sweden. 0003-2700/86/0358-1173$01.50/0
and the property to be analyzed cannot be found. Possible solutions should, therefore, in some way use the actual curve shape for the evaluation. Today new possibilities in the field of TG exist, due to the rapid development of small computers. Problems that involve numerically demanding solutions can be handled without time-consuming labor. The location and resolution of signals in TG have thus been tackled with the use of high-order derivatives in search for inflection points and maxima/minima on the curves. However, to obtain stable solutions, only good quality measurements with a low degree of noise can be used. Smoothing of data must frequently be used. The widest use of computers has been in nonisothermal kinetics, which can be used for determining the activation energy, the rate of reaction, and the mechanism for a certain reaction. These calculations are based on the so-called p ( x ) function, for which either uncertain assumptions about the process have to be made or terms have to be neglected in order to obtain a solution (5). This limits the usefulness of the method, since such mathematical modifications introduce an alteration from the actual measurement to the end result. This means that results often have a high precision, but the accuracy can be low, and validation of results often has to be made by comparisons with other experimental techniques (5). Attempts to use numerical methods to quantitatively correlate sample properties in heterogeneous samples in a more direct way with the TG curve shape have to our knowledge not been reported. However, in other fields of analytical chemistry where multivariate measurements can be made instead of univariate ones, such methods have been successfully applied. In near-infrared spectroscopy, stepwise multiple regression has been integrated into commercial equipment (6) that is used for determining quality parameters in agricultural products. In chromatography the use of partial least-squares modeling of latent variables (PLS) for resolving and quantifying overlapping amino acid patterns has been demonstrated (7). PLS has also been used in fluorescence spectroscopy for resolving severely overlapping spectra (8);these spectra were similar in shape to TG curves. In TG the signal obtained is the result of the combined thermal processes acting on the sample, a situation which is similar to that with overlapping spectra. The aim of this work was to evaluate the possibilities of TG, in conjunction with PLS evaluation, for quantifying chemical constituents and physical properties in biological samples. Highly decomposed plant material, peat, from different origins was used for the study.
EXPERIMENTAL SECTION Apparatus. All TG measurements were made with a Stanton Redcroft STA 780 simultaneous thermal analyzer. The equipment was used with a STA 785 DSC/TGA furnace. A data acquisition system, DAPS 2, connected to a microcomputer, PET Commodore CBM 4032, allowed collection of the measurement data in digital form on disk media. The collected raw data were transferred via a RS 232 interface (SCIP) to an IBM PC compatible computer, and then the SIMCA-3B BASIC program package was used for data evaluation. A LECO AC-200 calorimeter was used for all de0 I986 Amerlcan Chemical Society
1174
ANALYTICAL CHEMISTRY, VOL. 58, NO. 0, MAY 1986
Table I. Description of the Peat Used for Calibration and Evaluation type
of peat
carex sphagnum sphagnum + carex carex + brownmoss polytrichum + sphagnum eriophorum + sphagnum equisetum + carex eriophorum + carex
humification,
ash
calorific
2-8 2-8 4-7 3-4 6-7
3.4-12.1 0.9-6.3 1.8-3.3 3.1-4.2 3.1-4.2
22.7-24.0 19.6-24.1 23.2-24.6 22.5-23.4 22.5-23.4
5-6
1.7-2.0
23.1-23.3
7 7
3.8-7.4 2.6
23.3-25.0 24.7-25.0
von Post scale content, % value, MJ/kg
-
terminations of calorific heat values. Carbon content was determined with a LECO CHN-600 elementary analyzer. A KAMAS Slagy 200 B hammer mill was used for milling the samples. Samples. Most of the samples were collected from bogs within 200 km frola Umea, Sweden. A description of the peat samples used is given in Table I. The samples were collected during the summer and stored in aluminum foil inside a plastic bag at 4 OC until preparation. The samples represent the major peat types in Sweden. Peat samples were dried at 105 OC to complete dryness. All samples were milled through a 1-mm sieve before being measured. Detailed information on all samples used and raw data from the TG measurements can be obtained from the National Swedish Laboratory for Agricultural Chemistry, Box 5097, S-90005 UmeB, Sweden. Measurements. Before the actual thermogravimetric measurement, the milled samples were again dried at 105 O C to ensure dryness. Samples were then weighed on a balance, Mettler HK 60, and placed in the thermobalance. Sample weights were kept between 11.5 and 12.9 mg. The balance was heated to 115 "C, and the system was equilibrated for 15 min. A t the end of this period the weight setting on the thermobalance was adjusted to 95% of full scale deflection. TG curves were then recorded from 115 to 600 OC using a heating rate of 15 "C/min. From these curves, weights at 40 equidistant temperatures were sampled forming the X matrix (Figure 1). This matrix X together with the results from the nonthermal methods in a matrix Y could then be subjected to data analysis. All measurements were made in random order so that a reduction of experimental errors caused by drift was obtained. Calorific values were determined according to Swedish national standard SS 18 71 72, which is similar to IS0 1928-1976. R values were determined by using German national standard DIN 11 542 and volume weight on the milled and dried peat according to Sarasto (9). Botanical compositions were determined according to Heikurainen (10). Data Programs. The S I M C A - 3 ~BASIC program package is available from Sepanova AB, Ostrandsvtigen 14,S-12243Enskede, Sweden, or from Principal Data Components, 2505 Shepard Blvd., Columbia, MO. Stepwise multiple regression was made on a Control Data main frame computer using the Minitab 82 program package. This package is available from Minitab Project, Statistics Department, 215 Pond Laboratory, The Pennsylvania State University, University Park, PA.
MATHEMATICAL METHODS The possibilities of using conventional univariate calibration methods for quantification of sample properties are limited, when TG is used for analysis of biological samples. This is mainly due to the nonspecific nature of the TG curve itself; i.e., no single region of a curve can be related to a particular property of a sample. The measurement is cumulative in its nature so that the late parts of the curve are highly dependent on the earlier parts. In a multivariate approach a calibration set of n samples with known composition (4)are analyzed to give a number of signals (p) for each sample. In this way a matrix is obtained where one block, Y (n X q ) , contains the known compositions of the samples and one block, X (n X p ) , contains the digitized
-
m LY B
X
Pl
Y
ql
92
PZ
x
-
P
l*P t TSP t E
y
-
0
l*g
+ Ut0 t F
U-TSBth
Flgure 1. PLS model in matrix form. The matrices T and P model the X block (TG measurement) similarly to an ordinary PC model, while the matrices U and 0 model the Y block (calibration properties). The connection between the blocks is modeled as a relation between the U and T matrices by means of a diagonal matrix B. The block residual matrices are E and F, and the vector of residuals for the inner relations is h.
TG curves for the samples as shown in Figure 1. This matrix can then be subjected to a number of analytical data techniques for relating X to Y, and the model obtained can be used for predicting the composition of future unknown samples. The most common multivariate method used is multiple regression (MR). A prerequisite in MR is that each x variable is sampled independently without being correlated to the others. This limits its application in TG, as this condition is not fulfilled. A reduced data set with less correlated variables can be obtained by using stepwise multiple regression (11).
Another way of reducing the dimensionality of the data is to apply principal components (PC) analysis to X. PC analysis produces a set of new uncorrelated variables that can be used in conjunction with multiple regression to give principal components regression (PCR) (12). The partial least-squares method in latent variables (13) is similar to PCR, but this method when modeling the X matrix at the same time correlates the signals with the known properties of each sample. The elements in the Y block can then be predicted from X through the latent variables t and u as stated in the equations in Figure 1. When a TG curve is digitized to create an X block and then used in a PLS calibration the shape of the curve is incorporated into the model. Further samples are then quantified both with respect to their TG curves and to their relation with the constituents of the calibration samples. We repeat, the entire curve is used in the calibration and not any "specific" signal. With PLS, as with all other calibration methods, the substances used for calibration should be evenly distributed throughout the calibration space in order to obtain a stable and reliable calibration model. This means that both the properties of interest and other properties that possibly affect the measurements should vary according to what can be expected in future real samples. In this way unexpected interactions, chemical or physical, within samples will be accounted for in future predictions. However, if any changes are introduced into the analytical procedure, reliability of the method has to be checked. All the measurements made for calibration can be made with a certain precision. However, a t times highly erroneous measurements do occur: random errors caused by operator personnel or by malfunctioning instruments. When included
ANALYTICAL CHEMISTRY, VOL. 58, NO. 6, MAY 1986
1175
MJ / kg I 0 0 '1
ih
eo 70 60 50 40 30 20 90
csv
10-
0
RSD 1
I
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I
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3
.
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I
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6
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7
I
.
9
I
.
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PLS OlMENSlONS / No.
Figure 3. Residual standard deviation of the calibration set (RSD), the prediction set (SEP), and the cross validation term (CSV) as a function of the number of PLS components used. It should be noted that CSV
Is a unltless number.
2. The problem is to resolve the curves so that adequate information on the chemical and physical properties are obtained, The curves shown represent the outer limits in this investigation both with regard to chemical content and TG curve shape. It can be seen that all of these curves are similar in both magnitude and shape; furthermore no single section with specific information that can be used for quantification can be found. Hence, traditional univariate methods of calibration cannot extract the information inherent in the curves. The TG measurements are highly reproducible, which means that they are controlled by specific factors, i.e., the chemical content of the samples. Below, use of the partial least-squares model in latent variables (PLS) for quantification of different properties of peat samples, using these curves, is demonstrated. Evaluation of t h e P L S Algorithms for Resolving TG Curves. The use of PLS for resolving TG curves was validated by dividing the samples used into three parts. In sequence, each part was then used for calibration of the model, and the remaining parts were used for prediction. The sample property used in the investigation was the calorific value of the peat. In order to account for matrix effects, each calibration set was chosen so that the whole range for the calorific values as well as all the peat types was included. A critical aspect of multivariate calibration is the determination of the optimal number of regression factors, PLS dimensions. There will be an overfitting to the calibration data if too many are used, and if too few are used the modeling of interactions within the actual measurements cannot be accounted for. In both cases prediction errors increase from their optimal value. In Figure 3, the mean value of the cross validation term (CSV), the residual standard deviation for the calibration set (RSD), and the standard error of prediction (SEP) are shown as a function of the number of PLS dimensions used. The mean is presented, as no significant difference between the three sets could be detected. In this example the optimum number of PLS dimensions is six, for which the SEP is 0.334 MJ/kg. In contrast to the error of prediction, the RSD of the calibration set continues to decrease with the number of components used. Instead of using a calibration and a prediction set, the magnitude of the cross validation term can be used for estimating the number of significant dimensions in the model. When CSV is below 0.95, the component can be included in the model. The use of SEP or CSV is normally equivalent to each other. CSV can be seen as an estimation that in most situations, including the present, will be satisfactory. Comparison of Stepwise Multiple Regression with PLS. One of the subsets chosen above was further investigated by making a comparison between the PLS method and
,1176
ANALYTICAL CHEMISTRY, VOL. 58, NO. 6 , MAY 1986 M J / kg
PREDICTED CALORIFIC VALUE
A
25
+4. 24
-
23
-
22
-
A A A
RSD
0.0, 0
. , . , . , . , . , . , . , 1
3
4
6
7
9
10
VARIABLES INCLUOED / No.
Flgure 4. Residual standard deviation of the calibration sat (RSD) and the prediction set (SEP) as a function of the number of variables
included in the multiple regression. stepwise multiple regression. The results from these calculations are shown in Figure 4. As with the PLS method it can be seen that the RSD of the calibration set decreases as more variables are included in the regression model. But this does not give any improvement in the predictive ability of the model when more than four variables are used. If more variables are included, warnings are given by the program due to high intercorrelations between variables. The optimal value of the SEP is 0.348 MJ/kg for the stepwise multiple regression, which can be compared with 0.321 MJ/kg, obtained with the PLS method on the same set. An advantage with the PLS method is that all data can be used without any sort of selection of predictive variables; Le., no information is lost.
PREDICTIVE ABILITY OF THE PLS ALGORITHM Below, use of the partial least-squares model in latent variables is demonstrated for the quantification of both chemical and physical properties of peat samples. Altogether 78 samples were used in the investigation; out of these, 28 representative samples were chosen for calibration of the PLS models. In this way the 50 omitted samples could be used for validating the obtained PLS model. The same set of samples was used for all calibrations, and it should be noted that no assumptions about the model nor any exclusions of any class of peat samples had to be made. In the figures, samples used for calibration are marked with crosses and those for prediction with triangles. Calorific Value. The main use of peat is for agricultural purposes, but as a consequence of the energy crisis an increased interest in peat as a fuel resource can be noted. Consequently, determination of the heat of combustion is a primary concern in peat analysis. Figure 5 shows the calorific values predicted from the T G curves as a function of those obtained with the traditional bomb calorimetric method. In routine analysis the bomb method was found to give a standard deviation of 0.2 MJ/kg for samples repeatedly analyzed during 4 days. The magnitude of this error is mainly caused by the inhomogeneous nature of the peat itself and to a lesser extent to instrumental instability and human errors. The calorific values used are based on organic matter only, due to the fact that the ash content of peat varies in a nonpredictable way with local environmental influences, for example with sand coming in from flooding water. This means that calorific values based on dry weight will vary not only with the botanical origin and the degree of humification of the peat but also with the ash content. As shown in the figure predicted calorific values agree well with those obtained with the bomb method. The SEP was 0.32 MJ/kg, which is comparable with the long-term stability of the bomb method, 0.2 MJ/kg. The maximum prediction errors were 0.88 and -0.75
2o 1919
20
21
22
CALORIFIC VALUE
-
23
24
25
26
MJ/Kg DRY ASHFREE PEAT
Figure 5. Calorific values as a function of results obtained with a standard bomb calorimetrlc procedure. Crosses denote samples used in the calibration set, and triangles denote samples in the prediction set. PREDICTED CARBON CONTENT 64
-
62
-
60
-
58
-
56
-
541
+A 4
A
A+A
+A +A A
52 52
+A
54
56
CARBON CONTENT
-
58
60
62
64
X OF DRY ASHFREE PEAT
Figure 6. Carbon content as a function of results obtained with an elementary analyzator. Crosses denote samples used In the calibration set, and triangles denote samples in the prediction set.
MJ/kg, using an optimal number of six PLS dimensions. The total range in the calorific values of the samples used was 19.6-25.0 MJ/kg. Carbon Content. Analysis of elementary composition has earlier been used for the indirect determination of calorific values in coal (14). Therefore, and as calorific values could be successfully determined by using TG, the possibilities of obtaining information about carbon contents simultaneously was also investigated. The results of this investigation are shown in Figure 6. The carbon contents were based on organic matter only due to reasons discussed above. Figure 6 shows that a good agreement between predicted and analyzed values has been obtained. The stability of the carbon determinations, expressed as the long-term standard deviation, was 0.6% carbon. To obtain optimal results, four PLS dimensions had to be used. This gave a standard error of prediction of 0.80% carbon with maximum errors of 2.15% and -1.85%. This is to be compared with the total range in carbon content, 52.5-63.1 %. These results do not equal those for the calorific value with regard to the predictive ability obtained; the reason for this
ANALYTICAL CHEMISTRY, VOL. 58, NO. 6, MAY 1986 PREDICTED R
- VALUE
1177
PREDICTED VOLUME WEIGHT
1
A
h
+
0.5
0.4 -
0.3-
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.
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.
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- VALUE
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50 / %
.
I
60
.
I
70
*
I
80
IN DRY PEAT
Figure 7. R values (hydrolyzable content) as a function of results obtained by a manual standard procedure. Crosses denote samples used in the calibration set, and triangles denote samples in the prediction set.
is mainly the poorer analytical precision of the elementary analysis in comparison with the calorimetric analysis. This means that the predictive ability of the PLS model will be degraded due to lack of precision in the Y block used for calibration. R Value. One of the more important factors in peat investigations is the degree of humification, a factor reflecting the state of decomposition of the peat. The determination of this factor using objective chemical methods has not been successfully solved. A number of methods have been proposed (15-18), among them a chemical method based on determination of the residue of a peat sample subjected to hydrolysis in concentrated sulfuric acid (18). The hydrolyzable part in peat, mainly polysaccharide$,decreases with increasing degree of humification, and hence the amount of residue increases. It can be seen from the results in Figure 7 that a predictive ability of the same quality as for carbon content has been obtained. The standard error of prediction is 3.33% (R units) with maximum errors of 12.0% and -8.6%. To obtain these results, three PLS dimensions were necessary. This is to be compared with the total range in R values, 25.7-74.7%. Volume Weight. Methods based on physical measurements have also been used for the determination of the degree of humification (9, 19, 20). As the humification of a peat proceeds an increase in the volume weight is also obtained. This parameter is cheap and simple to determine (9) in comparison with most of the chemical methods for humification studies, and hence it has been extensively used. Volume weights predicted from the TG weight loss curves are shown in Figure 8. It can be seen that the accuracy of these predictions is less than for the other properties discussed, but useful results can still be obtained. The standard error of predictiofi was 0.05 g/mL with maximum differences 0.17 and -0.11 g/mL using an optimal number of four PLS dimensions. This is to be compared with the range in volume weights from 0.14 to 0.53 g/mL. As the volume weight is a physical property and not a chemical one, this parameter cannot be supposed to influence the weight loss curve in the same way as the calorific value, carbon content, and the l-2 value. The chemical parameters directly reflect the nature of the sample and hence also affect the shape of the TG curve. The reason that volume weight also can be predicted from the TG curves is the strong common links (latent variables) to which all these properties are more or less correlated, the botanical origin and the amount
0.14 0.1
;
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I
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0.3
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VOLUME WEIGHT OF DRY PEAT
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I
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Figure 8. Volume weights as a function of a manual standard procedure. Crosses denote samples used in the calibration set, and triangles denote samples in the prediction set.
of degradation. This means that volume weight is indirectly predicted from the TG curves due to its covariance with the chemical properties. Simultaneous Determination of All Four Properties. Finally an attempt was made to calibrate the PLS model with all four properties at the same time. The result was still of the same quality as when calibrations for single properties were made. The standard error of prediction was 0.05 g/mL for volume weight, 3.39% ( R units) for the R value, 0.36 MJ/kg for the calorific value, and 0.81% carbon for the carbon content. The optimum number of PLS dimensions was found to be five. CONCLUSION The PLS algorithm can be used for extracting information from complex TG curves. This information can then be used for quantification of both chemical and physical properties. The approach is attractive, since no assumptions about reaction processes have to be made. An attractive feature is the possibility of calibrating for diffuse sample properties, such as a value on a scale from good to bad in the quality control of a manufactured product. In such situations PLS could prove to be a valuable tool, eliminating the need for personal judgement. The only requirements are that the TG measurements are reproducible and that the sample property in some way, directly or indirectly, is correlated to the chemical composition of the sample. This means that there exists a clear and easy way to determine if TG is a possible solution for a stated problem. ACKNOWLEDGMENT We wish to thank Christina Rockner, Asa Naslund, and Elisabeth Bohlin for their skillful experimental help throughout this work. LITERATURE CITED (1) Wendlandt, Wesley W. Anal. Chem. 1984, 56, 250R-261R. (2) Donbavand, J. Thermocbim Acta 1984, 7 9 , 161-169 (3) Earnest, Charles, M.; Fyans, Richard L Perkin-Elmer Therm. Anal. Appl. Study 1981, No. 32. (4) Goutai, M. C. R . Hebd. Seances Acad. Sci 1902, 135, 477 (5) Sesta'k, J "Comprehensive Analytical Chemistry Vol 12-Thermal Analysis Part D"; Elsevier: Amsterdam, 1984 (6) Wetzel, D. L. Anal. Cbem. 1983, 55, 1165A-1176A (7) Lindberg, W. Thesis, Department of Analytical Chemistry, University of Umea, S-90187 UmeA, Sweden, 1985. (8) Lindberg, W.; Persson, J.-A.; Wold, S. Anal Cbem 1983, 55, 643-648. (9) Sarasto, J. Acta For. fenn 1961, 71 (2), 1-16. (IO) Heikurainen, L. "Skogsdiknlng"; Norstedt & Soner, Stockholm, 1973.
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(11) Draper, N. R.; Smkh, H. "Applied Reqesslon Analysis", 2nd ed.: Wiley: New York, 1981. (12) Ho, C. N.; Chrlstian, G. D.; DavMson, E. R . Anal. Chem. 1980, 5 2 , 1071. (13) Wokl, S. et al. "NATO Advanced Study InstRute on Chemometrics": Kowalski, B. R., Ed.; Reidel Publishing Co.: Dordrecht, Holland, 1984. (14) Culmo. R. F. Perkin-€/mer €/em. Anal. Appl. Study 2 . (15) Melin, E.; Ode'n, S. Sver. Geol. Unders., Ser. C 1918, C278. (16) Kaila, A. Mastaloust. Alkak. 1858. 2 8 , 18-30. (17) Keppeler, G. Angew. Chem. 1832, 2 9 , 473-476.
(18) German National Standard DIN 11 542, 1978. (19) Levesque, M.; Dinel, H. Can. J. Sol/ Scl. 1877, 5 7 , 187-195. (20) von Post, L. Sven. Mosskui?ud&en. TMskr. 1822, 1 , 1-27.
RECEIVED for review August 20, 1985. Accepted December 10, 1985. This work was supported by the Swedish Energy Administration (STEV).
Mass Spectrometric Determination of Lanthanum in Nuclear Fuels Nancy L. Elliot, Lawrence W. Green,* Bernadette M. Recoskie, and Richard M. Cassidy
General Chemistry Branch, Chalk River Nuclear Laboratories, Atomic Energy of Canada Limited, Chalk River, Ontario, Canada KOJ 1JO
A sensitive, precise technique was developed for determina-
tkn of ianthanun in nudear fuel samples by thennai lonlzatlon mass spectrometry. Interference from barium was sup pressed by measurement of Lao' at b w ftlament temperatwe and by eilmination of filament loadlng reagents. Nanogram quantities of lanthanum were used for analysis; the overall precision for fuel samples was 0.2%. Good agreement (0.5 %) was observed between resuHs obtained by this technique and those obtained by HPLC on a series of sampies. Slgnificant change in isotopic fractionation was not observed during the fkst 3 h of analysls. The SenSnMty of the observed Isotope ratio to filament temperature was studied, and the optimal range for measurement was 1200-1230 'C.
The 139 isotope of lanthanum is a stable, high-yield fission product that has most of the properties required for a fission monitor in nuclear fuel studies (I, 2). Although it was used in this manner by some laboratories (2-6), the fact that it never gained widespread use was probably due to difficulties associated with its determination by isotope dilution mass spectrometry (IDMS). The most serious difficulty is interference from 138Ba;138Lais the only isotope of lanthanum available for spiking, and it is not available in high enrichment. Barium, which has an ionization potential of 5.21 eV, is an easily ionized ubiquitous contaminant, especially in the rhenium filaments used in thermal ionization mass spectrometry, and its most abundant isotope is 138 (71.7%). Some laboratories have used tantalum filaments to reduce this problem (7),but our experience showed that this technique yielded poorer sensitivity and did not eliminate barium interference. Others (8) have used an addition of borax to the rhenium filament to promote emission of Lao+ a t low temperatures. This technique reportedly suppressed barium interference, since the filament temperature was lower and barium ionized preferentially as the metal ion. Recently, (1,9), a high-performance liquid chromatographic (HPLC) method has been developed for determination of '%a in fuel samples; this method takes advantage of the fact that 139Lais the only stable or long-lived isotope of La produced in fBsion, and thus its content may be determined by chemical means. The method uses dynamically coated ion exchangers on high-performance reversed-phase columns to effect the separations and analyses, and has provided large cost and time 0003-2700/86/0358-1178$01.50/0
savings in burnup analyses. In order to cross-check the HPLC-La results and establish the accuracy of the method, analysis of some of the samples by IDMS was required. The borax addition technique described above was used initially, but contamination problems were sufficiently severe that it was abandoned. Consequently, a reagent-free loading procedure was developed, and this paper reports a study of the accuracy and precision of this new procedure and gives detaiIs of the loading and analysis parameters that were essential to obtain good signals and consistent results.
EXPERIMENTAL SECTION Reagents and Materials. Nitric acid solutions were prepared from subboiling distilled nitric acid (Seastar Chemicals, Sidney, B.C.) diluted with deionized water. A 2.8 m g / d borax solution was prepared from Anachemia (Anachemia Chemicals, Ltd., Montreal, Quebec) Na2B,0r10H,0 dissolved in deionized water. HPLC reagents, a-hydroxyisobutyric acid (HIBA) and n-octanesulfonate, were passed through a strong cation-exchange resin for purification (9). The lanthanum primary standard was prepared from Specpure La203(Johnson & Matthey & Co., Ltd., London, U.K.), which was ignited to constant weight at 900"C. The calculated concentration agreed with titrimetric standardization within 0.170. The lanthanum spike solution was prepared from ORNL (Oak Ridge National Laboratories, Oak Ridge, TN) ls8Laenriched (6.7%) La203 and was calibrated by IDMS using the above primary standard as a spike. The results of five replicate calibration determinations gave a '%La concentration of (2.579 f 0.013) X lO-'mol/kg. The primary standard was also used for all standard curves prepared for the HPLC analyses. Apparatus. Fuel solutions were sampled in a hot cellglovebox facility; the hot cell contained a Mettler PC 2000 (Mettler Instr., AG, Switzerland) electronic balance for precise weighing of sample and spike aliquots, and the glovebox contained an HPLC for separation of lanthanum from the fuel solution. Used with the HPLC was a 15 cm X 4.6 mm i.d. analytical column packed with 5Mm Supelcosil LC-18 reversed phase. Postcolumn reaction with Arsenazo I11 was used for detection, and fractions were collected in 1-mL polyethylene microvials. Full details of the HPLC are given elsewhere (9). The mass spectrometer was a Nuclide (Nuclide Corp., State College, PA) 90" magnetic sector instrument equipped with Cathodeon type 553 triple filament assemblies, a Vacumetrics 0 1986 American Chemical Society
