values corresponding to log D = 0, or D = 1, is 6.15 while the means of these values calculated in Table I1 is 6.13 =t 0.08. Determination of Extraction Constants of Selected Metal 1-Pyrrolidinecarbodithioates. The extraction constants of the complexes of copper(II), cobalt(II), cadmium(Il), zinc(II), bismuth(III), and gallium(II1) for the chloroform-water system were determined by the spectrophotometric method employing a normalized absorbance scale (15). The ionic strength was adjusted to 0.1 for all extractions and a temperature of 24 =t1 "C was maintained. Figures 2-7 show
the continuous variations plots obtained experimentally. The extraction constants were calculated utilizing Equations 3 and 4, respectively. The values k and yma, are obtained experimentally. The exact experimental conditions, the a coefficients which were used, and the extraction constants determined are listed in Table 111. The experimental extraction constants have an average per cent relative deviation of * 3 x . RECEIVED for review December 2, 1970. Accepted May 6, 1971.
Instrumental4omputer System for Analysis of Multicomponent Organic Mixtures Ihor Lysyj and Peter R. Newton Rocketdyne, Division of North American Rockwell Corporation, Field Laboratory, P.O.Box 5220, Athens, Ga. 30604 William J. Taylor Environmental Protection Agency, Water Quality Ofice, Athens, Ga.
The direct qualitative and quantitative analysis of multicomponent organic compositions in a highly diluted aqueous solution is described. The analytical approach is based on pyrolysis of organic materials in the presence of a water matrix, separation of the resulting pyrolytic fragments using gas chromatography, detection of eluted fractions by means of hydrogen flame ionization detection, and application of the least squares solution to a series of linear equations. With this technique, the concentrations of individual organic components in a mixture are calculated by relating all peaks in the pyrogram to the concentration of each material.
THEMETHOD OF pyrographic analysis combines thermal fragmentation of organic molecules with the gas chromatographic separation of resulting products. The derivative composition, produced as a result of pyrolysis, reflects the composition of parent organic material, and has been used on many occasions for identification of the same. Greenwood, Knox, and Milne (1) characterized thermal decomposition products of carbohydrates by gas chromatography as early as 1961. Winter and Albro (2) demonstrated the possibility of differentiating amino acids by this method in 1964. Reiner (3) used pyrography for the identification of bacterial strains. A method for direct analysis of multicomponent organic mixtures in aqueous media using pyrolysis followed by gas chromatography was proposed in 1968 (4). Instrumentation
(1) C . T. Greenwood, J. H. Knox, and E. Milne, Cliern. Znd. ( k J H d O / ? ) , 1961, 1879-80. ( 2 ) L. N. Winter and P. W. Albro, J. Gas Chromatogr., 2, 1-6 (1964). (3) E. Reiner, Nature, 206, 1272-76 (1965). (4) I. Lysyj and K. H. Nelson, ANAL.CHEM., 40, 1365 (1968).
consisting of a pyrolysis tube contained in a combustion furnace and a conventional gas chromatograph were used at that time. Using this arrangement, different pyrographic prints were obtained for starch, gelatin, and heptanoic acid in aqueous solutions. It was postulated that with the application of least squares technique and the use of a computer, the possibility of interpreting complex pyrograms exists. Mathematical logic based on least squares solution of a series of linear equations was later developed and experimentally validated (5). With this technique, concentrations of individual organic components in a mixture were calculated by relating all peaks in the pyrogram to the concentration of each material. This mathematical treatment of data has the potential of providing rapid quantitative composition information about a multicomponent organic mixture, especially if computer procedures are used to carry out such computations. While both instrumental and mathematical components for pyrographic analytical methodology were developed and experimentally validated, a reliable instrumental hardware was needed to reduce this concept to a practical tool of analytical chemistry. The reproducibility of pyrolytic processes depends on the consistency of a number of parameters critical to pyrolysis. A practical analytical instrument must be capable of keeping such parameters constant. These include the temperature profile of thermal energy input, time control for the duration of sample exposure, rates of flow for the necessary carrier gases, and others. At that time, instrumentation capable of meeting such requirements was not commercially available, and for this reason the decision was made to design and fabri-
(5) I. Lysyj, K. H. Nelson, and S. R. Webb, Water Res., 4, 157 (1970).
ANALYTICAL CHEMISTRY, VOL. 43, NO. 10,AUGUST 1971
1277
o
INJECTOR VALVE (CHARGE POSITION1
INJECTOR VALVE (INJECT POSITION)
RELIEF VALVE RECORDER
ELECTROMETER
LOOP
U
COMPRESSED GAS
GAS
Figure 1. Diagram of pyrograph
cate a practical and reliable pyrographic instrument. The details of hardware design were reported (6). The instrumentation system which evolved from this effort consists of three principal components : analytical hardware, mathematical logic, and computer procedures. The operational methodology is based on the continuous mode of operation and involves injection of a n untreated water sample in the pyrolysis chamber, pyrolysis of the organic composition in the presence of water, and the separation of the resulting pyrolytic fragments using gas chromatography. The eluted fractions are detected by means of a hydrogen flame ionization detector. The generated analog signal is converted into digital form and recorded on punched paper tape, which is processed through a reader. A telephone receiver transmits the generated information to a remotely located time-sharing computer, where the results are computed. The results are then transmitted back and recorded on a teletype machine. EXPERIMENTAL
The flow diagram of the instrumental system is shown in Figure 1. A constant volume (0.15 ml) of water sample is introduced into a metering loop of the injection valve (Carle Instruments) from a syringe. By rotating the valve, the sample is injected into a flow of carrier gas, then pyrolyzed in a pyrolysis chamber. Pyrolytic fragments produced are separated on a gas chromatographic column and detected by a flame ionization detector. The instrument is operated under the following set of conditions. The temperatures employed were pyrolysis, 740 "C; GC column, 120 " C ; detector, 200 "C; and flow stabilization, 150 "C. (The actual temperature of pyrolysis is estimated from an external pyrometer reading. The external pyrometer is located between the heating wires and the skin of pyrolysis tube. The approximation of actual pyrolysis temperature was established by an experiment in which a number of thermocouples were placed inside the pyrolysis tube and simultaneous readings were taken on the external and internal thermocouples.) Gas flows were nitrogen, 25 cc/minute, inlet pressure 40 psig; hydrogen, 40 cc/minute, inlet pressure 40 psig; and oxygen, 180 cc/minute, inlet pressure 40 psig. The column (6) I. Lysyj, Proceedings of the National Symposium on Data and Instrumentation for Water Quality Management, University of Wisconsin, Madison, Wis., July 21-23, 1970. 1278
was 20 feet long by "16 inch diameter, packed with porous glass 30-50 mesh size (7). The signal produced by the flame ionization detector is amplified by a solid-state electrometer, Hewlett-Packard Model 5750B. It is recorded in the analog form on a Texas Instrument Company potentiometric recorder, and in the digital form by Hewlett-Packard integrator, Model 7600A. The digital integrator (H-P No. 7600A) is coupled to the Teletype (Model ASR-33). The system is equipped with the audio-data set (made by Specialized Communications, Inc.), which accepts a conventional telephone receiver providing a hook-up to any commercial time-sharing service. Three computer programs in BASIC language were developed for handling of pyrographic data and computation of results. They are DAY, CALIF, and BAJA. DAY Program. The DAY program inputs and edits the raw data. CALIF Program. The CALIF program reduces the data produced by a series of pyrographic runs on a given standard solution. The print-out lists retention times, average retention time, and standard deviation of retention time for each run. It also provides maximum and minimum retention times for each peak observed in a series of calibration runs. The maximum and minimum retention times form windows, which are used by the computer for the scanning and selecting of peaks in a subsequent analysis of mixed solutions. The CALIF print-out also provides the average area for the calibration peaks and standard deviation of the same. Finally, the area per unit concentration in ppm is calculated and printed-out. This is the proportionality constant used in subsequent calculation of the mixed solutions. CALIF also provides thresholding capabilities by rejecting peaks below certain predetermined intensity. There are two inputs into the CALIF program. Input DATD is edited data (in similar fashion to program DAY) received by the computer from a pyrograph through the telemetering system and is organized by assigning line numbers, The input DATD also carries information which is entered in table headings such as column, carrier gas, and sensitivity setting. Line 200 in CALIF, the second input, indicates the concentration of the calibrating solution, A flow diagram of the CALIF program is shown in Figure 2. Calibrations were run for glutamic acid, sucrose, and methyl isobutyl ketone, and the computed CALIFS are (7) I. Lysyj and P. R. Newton, ANAL.CHEM., 36, 2514 (1964).
ANALYTICAL CHEMISTRY, VOL. 43, NO. 10, AUGUST 1971
Table I. CALIF for Glutamic Acid Retention times,a Peaks 4 determinations 1 2 3 4
170, 175, 173, 175 227, 234, 232, 235 314, 326, 323, 328 467, 484, 482, 487
I - N=CONCENTRATION I I N P U T : DATD' U, 8 . C, D
Peaks 1
2
3
4 480
Average retention time 173 232 323 Standard deviation of re2 4 6 tention time 175 235 328 Maximum retention time Minimum retention time 170 227 314 Average areab 6839 29740 1141 Standard deviation of area 205 1939 86 Area/concentration 171 744 29 Retention time is in hundredths of a minute. Integrated peak area is expressed in pV-seconds.
9 487 467 112 17 3
-
Table 11. CALIF for Sucrose Retention times,n Peaks 4 determinations 1 2 3 4 5
I
174, 174, 172, 177 235. 234. 230. 239 322; 322; 317; 331 477, 482, 472, 496 543, 554, 543, 567
I
1
Peaks 1 174
2 235
3 323
Average retention time Standard deviation of retention time 2 4 6 Maximum retention time 177 239 331 Minimum retention time 172 230 317 Average area* 14615 10960 2941 Standard deviation of area 1221 1149 1196 Area/concentration 365 274 74 Retention time is in hundredths of a minute. b Integrated peak area is expressed in pV-seconds.
4 482
5 552
10 496 472 290 123 7
11 567 543 1097 193 27
1-1 AIV,41
I
AlY.51 AI;;61
-
.
m
AIY,41 t U4Z AIY.5) t E
Ar61
I
+ E42
Figure 2. Flow diagram of CALIF 1.0-
7
Q
u)
5
Table 111. CALIF for Methyl Isobutyl Ketone Retention times," Peaks 5 determinations 1 2 3 4 5 6
0.5-
i
172, 174, 171, 170, 171 239, 242, 237, 236, 235 335, 334, 324, 322, 323 541, 526, 509, 502, 508 617, 603, 583, 574, 581 793, 772, 753, 734, 754
0 3
Peaks 1 2 172 238
3 328
4 517
5 6 592 761
Average retention time Standard deviation of retention time 2 3 6 16 18 22 Maximum retention time 174 242 335 541 617 793 Minimum retention time 170 235 322 502 574 734 Average areab 19225 3971 20486 1634 4125 843 Standard deviation of area 1201 246 1141 246 412 20 Area/concentration 3845 794 4097 327 825 169 a Retention time is in hundredths of a minute. b Integrated peak area is expressed in pV-seconds. shown in Tables I, 11, and 111. One peak (retention time range 180-220) reproduced poorly and is omitted from CALIFS. The typical pyrograms for standard solutions are shown in Figures 3,4, and 5 . BAJA Program. Program BAJA (Figure 6) calculates the actual concentrations of individual organic components
5 7 MINUTES
9
1
1
Figure 3. Typical pyrogram for glutamic acid present in the aqueous solution. For this, a mathematical model is used which relates all pyrographic peaks to the concentration of each separate component. It then provides least squares coefficients t o a number of linear equations which are then solved. The mathematical technique used was described previously (5). There are four inputs into BAJA. Program DAY described above is edited data received by a computer from a pyrograph over the telemetering system. Input WINDOW gives the dimensions of the windows as determined by the CALIF program for extraction of valid pyrographic information. Each window consists of two numerals, representing a lower and higher limit for computation scanning. The numerals are expressed in hundredths of a minute.
ANALYTICAL CHEMISTRY, VOL. 43, NO. 10, AUGUST 1971
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1
t \PRINT HEADING/ t \ R E A D N,M / \
-
READ H(I.J)/
1 H(I,Jl * H(I.JI/TO I 4 IE(J.KI
* E ( J . K ) + H(J.D
-tZ x N
I J:
\
I
INPUT:
5
7
9
1
1
i
WINDOW: W(I)/
1
n 3
t
H(K.11
1
MINUTES
Figure 4. Typical pyrogram for sucrose
3 1 INPUT: DAY: 6 , C . 0
I = J * Z
7 SCRATCH. COE
I
I
A
I
RINT HEADINGS/
1 3 . 2 3 t V(J.1)
1
Figure 6. Flow diagram of BAJA program I
3
5 7 MINUTES
9
1
1
Table IV. Analysis of Mixed Solutions
Figure 5. Typical pyrogram for methyl isobutyl ketone
Standard deviaAverage, tion,
Present,
RESULTS
A number of multicomponent organic mixtures were prepared in various ratios, injected into the pyrograph, and analyzed. Two solutions containing methyl isobutyl ketone, glutamic acid, and sucrose in varying concentrations were analyzed as shown in Table IV. Each solution was analyzed five times. Average values and standard deviations were computed as shown in Table IV. Additional multicomponent synthetic mixtures, which were also analyzed pyrographically, included solutions of albumin, glucose, and oleic acid. These three materials belong to biologically important classes of proteins, carbohydrates, and lipids. 1280
Solution 1 Methyl isobutyl ketone Glutamicacid Sucrose Solution 2 Methyl isobutyl ketone Glutamic acid Sucrose
Found, ppm
PPm
PPm
1.8,1.9,1.6,1.9,1.8 1.8 5.6 5.2, 5.9, 4.8, 5.9, 6 . 0 1 1 . 3 , 1 1 . 0 , 1 2 . 3 , 8 . 8 , 10.8 10.6
0.39 0.54 1.28
2
1.9, 1.9, 1.8, 2.0, 2.0
1.9
0.27
10
9.8, 9.9, 9.9, 10.7, 10.6 6.8,6.7,8.0,5.7,6.7
10.2
0.43
6.8
0.82
PPm
Input DATA lists the number of the peaks to be used in computation, the number of components to be solved, and calibration constants for each peak of each component. Input PRINT provides identification of the analysis number and the names of the components analyzed as a table heading.
2 5 10
5
~~
CALIFS were obtained for each standard material, and then a number of mixed solutions of albumin, glucose, and oleic acid were prepared in various ratios and analyzed pyrographically. The analysis of each solution was performed three times. The results for six different solutions are shown in Table V. Average values and standard deviations are computed from triplicate analyses. The accuracy of
ANALYTICAL CHEMISTRY, VOL. 43, NO. 10, AUGUST 1971
Table V. Analysis of Mixed Solutions
Average found, ppm
Standard deviation, PPm
0
19.8 -0.1 0
0.92 0.52 0.12
10 10 0
8.0 9.3 0
0.51 0.20 0.06
20 20 0
18.2 19.2 0
2.17 2.13 0.12
20 10 0
19.0 8.7 -0.3
0.35 1.20 0.12
4 10 5
2.9 10.8 5.2
0.25 0.56 0
to
8.3 20.9 3.3
1.33 1.56 0.10
Present, ppm Solution 1 Albumin Glucose Oleic acid Solution 2
20 0
Albumin
Glucose Oleic acid Solution 3 Albumin Glucose Oleic acid Solution 4 Albumin Glucose Oleic acid Solution 5 Albumin
Glucose Oleic acid Solution 6 Albumin Glucose Oleic acid
20 3
analysis appears to be satisfactory considering the high order of dilution. CONCLUSIONS The results of the completed experiment indicated that direct pyrographic analysis of multicomponent organic mixtures can be carried out in highly diluted aqueous solutions. The method permits direct qualitative and quantitative analysis of high molecular weight-low vapor pressure organic materials in mixtures, such as are found in environmental and biological systems. Combining mathematical logic with analytical procedural operations through computerized means of data handling permits fast and efficient computation of results. Application of this technique in the field of water quality control work is being explored. ACKNOWLEDGMENT
We express our appreciation to Dr. H. Page Nicholson, FWQA Project Officer, for his guidance on this program. RECEIVED for review February 8, 1971. Accepted April 28, 1971. Mention of products and manufacturers is for identification only and does not’imply endorsement by the Federal Water Quality Administration, nor of the Environmental Protection Agency. The research upon which this paper is based was performed pursuant to Contract No. 14-12-802 with the Federal Water Quality Administration, Department of the Interior.
Computer Controlled Fourier Transform Nuclear Magnetic Resonance System for Carbon-13 and Phosphorus-31 Spectrometry R. J. Cushley, D. R . Anderson, and S. R. Lipsky Section of Physical Sciences, Yale University School of Medicine, New Haven, Conn. 06510
A high resolution NMR spectrometer has been modified for pulse-Fourier spectrometry (FFT-NMR). Data acquisition and data handling are accomplished by means of an IBM 1800 computer with 24K of 4 psec core storage and numerous peripheral devices. The NMR free induction decay signal (up to 8192 data points) can be digitized at rates up to 20 KHz. Spectra of lac,31P, and lH nuclei have been determined and time savings of 100-fold or sensitivity enhancement of 10- to 20-fold have been realized.
THECOMPLEX Fourier relationship : f(jw) =
J m -m
f(t)e-jUt x dt
(1)
and f(t) =
J m -m
f ( i ~ ) e i “X ~dw
(2)
shows that the frequency response function and the time response function form a Fourier transform pair. Application of the Fourier transform technique to proton NMR spectrometry was demonstrated in 1966 by Ernst and Anderson ( I ) . These authors outlined the theory of a spin system (1) R. R. Ernst and W. A. Anderson, Rev. Sei. Instrum., 31, 93
(1966).
subjected to a periodic sequence of pulses. In pulse NMR spectrometry, the time response function-the free induction decay (FID) signal-can be recorded in times on the order of Tz* (transverse relaxation time plus field inhomogeneity effects), thus making possible recording of spectra with a time saving of 100-fold, or, by use of a time-averaging computer, sensitivity enhancement on the order of 20-fold compared to continuous wave (cw) measurements. We wish to describe a versatile computer arrangement for fast Fourier transform NMR spectrometry (FFT-NMR). The experimental system consists of a high resolution spectrometer modified for pulse-Fourier and directly interfaced to an IBM 1800 computer with 24K of 4 psec core storage and numerous peripheral devices for data handling and data presentation. The importance of sampling with a computer of large memory capacity is determined by the bandwidth and resolution requirements of the spectrum being measured. The sampling theorem ( 2 ) states simply that, the observed spectral bandwidth .is limited to one half the digitizing frequency. Thus, for nuclei exhibiting large chemical shifts (e.g., 13Cand 31P)digitizing rates of 10 KHz or greater are needed. Since spectral resolution varies inversely with total observation (2) H. S . Black, “Modulation Theory,” D. Van Nostrand, Princeton, N. J., 1953, Chapter 4.
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