Anal. Chem. 1997, 69, 478-484
Particulate Material Analysis by a Laser Ionization Fast Conductivity Method. Water Content Effects Vladimir V. Gridin, Valery Bulatov, Alona Korol, and Israel Schechter*
Department of Chemistry, Technion-Israel Institute of Technology, Haifa 32000, Israel
Combined laser multiphoton ionization and fast-conductivity methods have been applied to probe organic contamination on wet particulate samples. This is a first attempt of testing such a technique for this purpose. A special emphasis has been made on establishing correlation between sample water content and detection of pyrene, which has been used as a probe contaminant in this study. Environmental (soil) and artificial samples (silica gel) have been studied. The experimental setup includes a pulsed N2 laser and a fast-conductivity detection system, operated in an opened chamber under ambient conditions. The correlation between the observed photoionization signals and water content has been investigated in a slow-drying mode, where water has been gradually evaporated. Surface contamination of wet samples has been more efficient than moist ones. This was evident, in the case of soil samples, from the earlier appearance (at lower pyrene concentrations) of saturationtype features of the detected photoionization signals. Assumptions of a contamination model has enabled certain compensation for sample and matrix effects. Soil contamination by organic polycyclic aromatic hydrocarbons (PAHs), as well as by other chemicals, is considered an important environmental issue. Special attention is given when agricultural soils and playground sands are concerned. There are several possible processes responsible for soil contamination by organic compounds. These can be divided according to the way the contaminant reaches the soil surface: external deposition (aerosols, rain), spills and water flows (including irrigation by wastewater), and osmotic or capillary drifts (e.g., proximity to industrial sites). The contamination mechanism is based in most cases on surface adsorption of the organic compounds. Therefore, a surface analytical method (sensitive to surface concentrations) has an advantage in these cases, as compared to bulk ones. One such surface method, which has the potential of in situ fast analysis, is based on the combined fast-conductivity and laser multiphoton ionization (MPI) techniques. Analytical methods based on laser MPI are known as very sensitive and selective when the resonant mode is applied.1-5 In this approach, a short laser pulse is focused onto the examined (1) Letokhov, V. S. Laser Analytical Spectrochemistry; Adam Hilger: Bristol, PA, 1986. (2) Lambropoulos, P., Smith, S. J., Eds. Multiphoton Ionization; Proceedings of the 3rd International Conference, Iraklion, Crete, Greece, Sept 1984; SpringVerlag: Berlin, 1984. (3) Laser Applications to Chemical Analysis; 1990 Technical Digest Series; Optical Society of America: Washington, DC, 1990; Vol. 2. (4) Ogawa, T. Anal. Sci. 1991, 7, 1475.
478 Analytical Chemistry, Vol. 69, No. 3, February 1, 1997
substrate. Several photons are absorbed simultaneously to ionize the molecules. Once ionization is performed, analysis can be carried out in several ways: either by ion detection and identification using known MS techniques6,7 or by simple and low-cost detection systems, usually based on charge counting.8-12 A different detection system based on measurement of time resolved mirror charges has been recently proposed and applied to online analysis of aromatic compounds in ambient air.13-16 The fast-conductivity technique applied here has been extensively employed, for instance, to analysis of trace organic compounds in nonpolar solvents,17-26 on metal,27-29 and on water surface.30,31 Two-color MPI,32-34 as well as single crystals35 and aerosol particles,36 have also been investigated. The main conclusion has been that this versatile method can be extended to (5) Hurst, G. S.; Payne, M. G.; Kramer, S. D.; Young, J. P. Rev. Mod. Phys. 1979, 51, 767-819. (6) Burlingame, A. L.; Millington, D. S.; Norwood, D. L.; Russel, D. H. Anal. Chem. 1990, 62, 268R. (7) Stuke, M. Appl. Phys. Lett. 1984, 45, 1175-1177. (8) Brophy, J. H.; Rettner, C. T. Opt. Lett. 1979, 4, 337-339. (9) Voigtman, E.; Winefordner, J. D. Anal. Chem. 1982, 54, 1834-1839. (10) Yamada, S.; Hino, K.; Kano, K.; Ogawa, T. Anal. Chem. 1983, 55, 19141917. (11) Yamada, S. Anal. Chem. 1991, 63, 1894-1897. (12) Judge, J. W.; McGuffin, V. L. Anal. Chem. 1991, 63, 2564-2570. (13) Schechter, I.; Schro ¨der, H.; Kompa, K. L. Anal. Chem. 1992, 26, 27872796. (14) Schechter, I.; Schro ¨der, H.; Kompa, K. L. Anal. Chem. 1993, 65, 19281931. (15) Schechter, I.; Schro¨der, H.; Kompa, K. L. Proc. SPIE-Int. Soc. Opt. Eng. 1994, 2092, 186-195. (16) Schechter, I. Proc. SPIE-Int. Soc. Opt. Eng. 1994, 2366, 21-31. (17) Yamada, S.; Yoshida, S.; Kawazumi, H.; Nagamura, T.; Ogawa, T. Chem. Phys. Lett. 1985, 122, 391-394. (18) Yamada, S.; Ogawa, T. Anal. Chim. Acta 1986, 183, 251-256. (19) Yamada, S.; Sato, S.; Kawazumi, H.; Ogawa, T. Anal. Chem. 1987, 59, 27192721. (20) Yamada, S. Anal. Chem. 1988, 60, 1975-1977. (21) Nakashima, K.; Kise, M.; Ogawa, T. Chem. Lett. 1992, 837-838. (22) Kawazumi, H.; Isoda, Y.; Ogawa, T. Chem. Lett. 1992, 123-124. (23) Ogawa, T.; Kise, K.; Yasuda, T.; Kawazumi, H.; Yamada, S. Anal. Chem. 1992, 64, 1217-1220. (24) Kawazumi, H.; Isoda, Y.; Ogawa, T. J. Phys. Chem. 1994, 98, 170-173. (25) Chen, H.; Inoue, T.; Ogawa, T. Anal. Chem. 1994, 66, 4150-4153. (26) Ogawa, T.; Sato, M.; Tachibana, M.; Ideta, K.; Inoue, T.; Nakashima, K. Anal. Chim. Acta 1995, 299, 355-360. (27) Ogawa, T.; Yasuda, T.; Kawazumi, H. Anal. Sci. 1992, 8, 81-82. (28) Ogawa, T.; Yasuda, T.; Kawazumi, H. Anal. Chem. 1992, 64, 2615-2617. (29) Kawazumi, H.; Yasuda, T.; Ogawa, T. Anal. Chim. Acta 1993, 282, 111114. (30) Sander, M. U.; Luter, K.; Troe, J. Ber. Bunsen-Ges. Phys. Chem. 1993, 97, 953-961. (31) Inoue, T.; Masuda, K.; Nakashima, K.; Ogawa, T. Anal. Chem. 1994, 66, 1012-1014. (32) Yamada, S. Anal. Chem. 1989, 61, 612-615. (33) Yamada, S. J. Photochem. Photobiol. A 1991, 62, 45-52. (34) Nakashima, K.; Kise, M.; Ogawa, T.; Kawazumi, H.; Yamada, S. Chem. Phys. Lett. 1994, 231, 81-85. (35) Katoh, R.; Kotani, M. Chem. Phys. Lett. 1990, 166, 258-262. S0003-2700(96)00511-2 CCC: $14.00
© 1997 American Chemical Society
Figure 1. Schematic experimental setup. The sample holder is described in the inset.
numerous applications, and detection limits in the sup-ppb or even ppt levels can be achieved.25,31 Therefore, such an analytical tool should be given serious consideration whenever high sensitivity, as well as on-line and in situ capabilities, is required. In this regard it seems quite promising to extend the fastconductivity MPI method to other fields of research interest. Our study is concerned, therefore, with applications of this method to environmental analysis in general and trace soil contamination in particular. One of the main problems in these applications is the complexity of the environmental systems; thus, each of the involved effects has to be studied. Among others, due to its signifiance for fast-conductivity data acquisition, the effect of water content in particulate materials is addressed and focused on in this paper. Having selected the PAH analysis as our target, in most cases pyrene has been applied as a probe contaminant. This is due to its high photoabsorption at 337.1 nm (N2 laser output). Concentrations of PAH compounds in contaminated soils are in the range of 0.85-125 µg/g.37-41 The fast-conductivity MPI method has the potential to detect these molecules at the required concentrations, provided that the interfering effects, such as water content, are canceled out. In this work, a special emphasis has been given to the establishment of a correlation between soil water content and detection of externally deposited organic compounds. We first establish a contamination procedure and then present several observations related to soils’ water content and its correlation with the detected photocurrent signals and dc conductivity of the samples. Then we introduce certain useful relations and notations which are later applied for quantitative analysis of the results as well as for a special data treatment that is shown to compensate for sample matrix effects associated with different sources of origin of the studied soils. EXPERIMENTAL SECTION Process Modeling and Environmental Conditions. First, an external contamination source was singled out. As a methodical (36) Zhan, Q.; Voumard, P.; Zenobi, R. Rapid Commun. Mass Spectrom. 1995, 9, 119-127. (37) Pace, C. M.; Betowski, L. D. J. Am. Soc. Mass Spectrom. 1995, 6, 597-607. (38) Lipniak, M.; Zastepa, P.; Gawlik, M. Rocz. Panstw. Zakl. Hig. 1994, 45, 97106. (39) Pathirana, S.; Connell, D. W.; Vowles, P. D. Ecotoxicol. Environ. Sci. 1994, 28, 256-269. (40) Wetzel, A.; Werner, D. Environ. Toxicol. Water Qual. 1995, 10, 127-133. (41) Kirso, U.; Tanner, R.; Irha, N. Eesti Tead. Akad. Toim. Keem. 1995, 44, 13-20.
(and one of the simplest) way of simulating deposition of organic contamination we decided upon dripping small fixed amounts of pyrene/hexane solutions, which allows a wide range of pyrene concentrations to be tested for. Upon evaporation of hexane, pyrene is deposited onto sand or soil particles. Contaminated soils were produced in this way. Second, a possible influence of geometrical effects associated with the particle size of the as-collected soil probes was minimized by grinding the original samples to supermicrometer powders of a typical particle size ranging from 1 to 20 µm. The background water content of the samples, M*, was due to the equilibrium adsorption of air moisture by soil particles. (As described in the following, this background moisture is necessary for our measurements, since it supplies an electrical conductivity medium for the signal detection technique.) Last, but not least, an open-chamber setup provided laboratoryspecific, yet stable and accountable for, environmental conditions, namely: room temperature (20 ( 2 °C), ambient air pressure (750 ( 5 Torr) and composition, and ambient moisture level (65 ( 10%). We have found that upon establishing thermal equilibrium with the experimental surroundings (i.e., at the above-specified typical laboratory conditions), reproducible measurements on the samples became possible. Experimental Chamber and Hardware. A schematic diagram of the experimental chamber and measuring devices is shown in Figure 1. The main purpose of the design of the sample holder is to allow measurements of moist and wet soils. The experimental setup and hardware were engineered to be similar to those used by Ogawa and co-workers in their conductivity study of laser multiphoton ionization of molecules adsorbed on a metal surface.27,28 A stainless steel disk electrode was located 10 mm above the upper surface of soil samples. The samples were held in a Teflon insert (hereafter referred as sample holder), fixed in a stainless steel container. The container also served as a negative electrode and was directly connected to the current preamplifier. A drawing of this container, together with relevant dimensions are given at the inset of Figure 1. The main optic and electronic components of the setup are as follows: nitrogen laser (PL 2300, Photon Technology International; 10 Hz, 1.5 mJ, 600 ps); digital oscilloscope (LeCroy 9300) triggered with a fast-responding photodiode (rise time of 20 ns); high-voltage power supply (PS325, Stanford Research System); current amplifier (428, Keithly), used at a typical gain level of 107-108 and in Analytical Chemistry, Vol. 69, No. 3, February 1, 1997
479
the filter-off mode of the instrument. Laser energy was measured by a power meter (Nova, Ophir Ltd.) equipped with a piroelectric detector. The pulsed laser radiation at 337.1 nm was focused onto the sample surface by a pair of cylindrical quartz lenses and a final quartz lens (f ) 100 mm) to a spot of ∼100 µm2. Typical laser energy used was at the level of 120 ( 6 µJ/pulse, measured slightly off focus at the sample surface. The incident angle to the samples was set at 60°. A positive high-voltage bias for charge separation was applied, in the 2500 V/0 V on/off measurement mode. Each photoionization signal (fed from the current preamplifier) was integrated over 50 laser pulses by the digital oscilloscope. The data presented in the following section are given in a form that eliminates zero-bias background, i.e., for instance I(t) ≡ I(t,2500V) - I(t,0V). Sample Preparation. Special attention was devoted to the sample preparation routine. This procedure was closely followed for all the samples studied. Particulate samples were gravitationally packed into the sample holder by sedimentation from a muddy mixture of the powder and triply deionized water. This was followed by a 4-h stove (at 70 ( 5 °C) and a further 1-h room temperature stay for equilibrating sample water content with laboratory moisture level. The wetting steps of the sample preparation procedure were performed with the sample holder fixed in its place inside the (air-opened) experimental chamber. In what follows, the term moist is referred to the samples with residual (ambient) water content, Mm due to moisture adsorption from air. The so-called, wet samples were produced simply by adding a fixed amount, ∆M ) 150 ( 1.5 µL, of triply deionized water. This procedure raised the total water content, M, to Mw ≡ ∆M + Mm. In the above, the subscripts m and w stand, respectively, for the moist and wet samples. This amount of ∆M was deliberately chosen, since according to our estimates it was intended to occupy nearly the entire free volume of the studied samples. Such extra watering has to be done from below, so that surface wetting is due to capillary effects, minimizing thus, possible washing down of pollutants and their partial removal from the laser-irradiated layer. Several data points were also collected for M < Mm. Reduction of the ambient water content below Mm was obtained upon in situ (inside the chamber) fan-drying of moist samples (at ∼50 °C) for 120 min. The same drying procedure was followed through using analytical scales, in order to specify the corresponding values of M. Contamination of the sample in its moist condition was carried out by dripping freshly prepared pyrene/hexane solutions directly onto the sample (from ∼1 mm height above its surface). In this case, the deposition of pyrene/hexane solution onto the sample surface was followed by evaporation of hexane (fan-dried at 50 °C for 5 min). This procedure resulted in stable and reproducible conductivity signals. Contamination of wet samples was performed immediately after their wetting step. In this case, hexane was allowed to evaporate from the sample for at least 30 min. After the evaporation period, the diffusive losses of water content were carefully compensated for, by making use of the associated water content calibration curve. Such curves are presented later. Materials. Pyrene-contaminated hexane was prepared from n-hexane (analytical grade, Frutarom Ltd.) and pyrene (99%, 480 Analytical Chemistry, Vol. 69, No. 3, February 1, 1997
Table 1 soil sample
wt, g
M m, mg
particle size, µm
SA
1.0042
8.6
1-20
SB
0.8987
9.0
1-5
remarks of origin chalk stone soils nearby organic lab silica, 99%; Sigma Chemical Co.
Aldrich) without further purification. These solutions were deposited in two aliquots of 50 µL each ((0.3 µL), by means of a 50-µL syringe, (Hamilton Co.). Pyrene concentration was varied in the range from 1 ng/mL to 1 mg/mL. The dry weights of the samples were in the range from 0.9 to 1.1 g. Several specimens of each soil batch were used. Here we present our mean findings for each particular batch, hereafter referred as samples SA and SB. The representative values of Mm and other relevant physical characteristics of these samples, including their affiliation with a specified environmental or model source, are listed in Table 1. RESULTS AND DISCUSSION Soil probe water content has been the only environmental parameter that we have voluntarily altered during this study. Hence, each freshly prepared soil probe was characterized by means of its water release calibration curve. Starting with M ) Mm, we brought each sample to M ) Mw ≡ ∆M + Mm and recorded its diffusely evaporated mass loss from Mw all the way down to Mm. A similar water release procedure was applied to the soil probes when we obtained their respective time-dependent dc conductivity data, σ ) σ(t). Measured calibration curves for the time-dependent sample water content, M ) M(t), are shown in Figure 2a. The data sets are given in terms of normalized values, M(t)/Mm and σ(t)/σm. The solid line in Figure 2a represents a typical σ(t)/σm (σm corresponds to M ) Mm). A close similarity between the time dependencies of M(t)/Mm and σ(t)/σm is self-evident. Neither of these two sample characteristics was significantly influenced by either the soil probe affiliation or its respective background (or voluntarily deposited) contamination. A semilogarithmic plot of M(t)/Mm vs time (Figure 2b) shows a linear dependence. It means that M(t)/Mm ∼ exp(-t/τ). Such behavior might be expected for an open-chamber diffusive evaporation. The characteristic evaporation times in our systems, τ, were calculated by least-squares fitting and estimated as τ ) 104 ( 103 s. Having established sample-independent calibration curves, M(t)/Mm and σ(t)/σm, we now discuss the fast-conductivity results obtained in photoionization measurements on differently moisturized soil probes. A typical water content influence on photoionization current time profile, I(t), is illustrated in Figure 3 for Mw and Mm. Panels a and b of Figure 3 respectively correspond to the lowest (x ) 0) and highest (x ) 1 mg/mL) concentrations of pyrene used. The photoionization charge, Q, in each case corresponds to the area under the relevant I(t) curve. The photoionization charges due to contaminated samples are ∼10 times higher than the charges due to the as-collected samples. In all cases, the contaminated wet samples produced higher photoionization charges as compared to moist ones. We now introduce several useful relations and relevant terminology. Let an incoming monochromatic photon flux of density J, release a single electron per any successful ionization
Figure 2. (a) Time-dependent normalized water content for SA and SB samples. The solid line is a typical time-dependent variation of the dc conductivity of soil samples, when they lose their water content from Mw to Mm. (b) A semilog plot of M/Mm vs evaporation time, t.
event, in an off-resonance photoionization process of kth order. This means that k photons of energy hνlaser each, must be “simultaneously” absorbed by the substrate, in order to make contribution to the detected photocharge, Q. The latter is related to J as Q ∼ Jk. More explicitly, let Γk be a quantum-mechanical cross-section for a single pyrene molecule to undergo a photoionization event of order k (k ) 2 for pyrene at 337.1 nm). The above-defined Γk is clearly sample and pyrene concentration independent. Furthermore, we assume N ) N(x), where N is the number of pyrene neutrals present at the soil sample surface and exposed to excitation by incoming photons. Neither pyrene nor hexane is readily dissolved in water. Hence, N might be affected by the procedural order of the sample contamination and moisturizing steps. Since in the former case, the deposition of pyrene occurs under ambient water content conditions, such N is hereafter referred as Nm. Following similar reasoning, Nw stands for the number of pyrene neutrals obtained by the second sample preparation route, namely, contamination of wet samples. Such procedural order dependence could be anticipated for, because of the water table action (i.e., rejection of both pyrene and hexane by the increased amount of water just beneath the soil surface) that tends to effectively increase the number of pyrene neutrals present nearby the sample surface. Nevertheless, the samples produced reproducible photoionization signals for several repeated drying/watering steps. It is worth to note that at each particular x value the deposited amount of pyrene/hexane solution was fixed and equal to 50 µL in either of the two routes of contamination. To the contrary, Nm and Nw are the corresponding amounts of pyrene molecules that have been actually adsorped by the soil surface where the MPI
Figure 3. Photoionization current, I ) I(t), for SB soil samples, (moist and wet) as recorded by the digital oscilloscope: (a) x ) 0 (no additional contamination); (b) x ) 1 mg/mL (high contamination level).
processes have taken place. Hence, in accordance with the abovementioned water table effects, these Nm and Nw are expected to differ from each other at any fixed x value. In order to account for variation of experimental conditions as a result of sample replacement or because of alteration of its water content, an implicit instrumental function, fs, is introduced and assumed to be such that fs ) Asf(M); here f(M) is sample independent. As, however, represents such geometrical and chemical characteristics that correspond to the ability of each particular soil to absorb pyrene contamination (in the abovespecified contamination procedure). Thus, the released photocharge, qs(x,M,J), for a photoionization event involving pyrene molecules only, reads
qs(x,M,J) ) pΓkJkNm/w(x)f(M)As
(1)
In the above s indicates sample-dependent properties; m/w stands for one of the above-mentioned procedural orders (contamination of moist or of wet samples). The prefactor p takes care of matching physical units on both sides of eq 1. Let Qs(x,M,J) stand for the total photoionization charge detected for a certain soil sample. With the sample-dependent background photocharge denoted as Qs(0,M,J), we obtain
Qs(x,M,J) ) Qs(0,M,J) + qs(x,M,J)
(2)
Surely, with x ) 0 (since N ) 0 for x ) 0) the second term of eq 2 drops out and the signals are due to the photoionization of Analytical Chemistry, Vol. 69, No. 3, February 1, 1997
481
Table 2 qs(x,M;J*)/fC soil sample
x)
SA:moist SB:moist SA:wet SB:wet a
0a
x ) 1 mg/mLb
2.7 0.9 26.0 9.8
51.8 54.1 117.1 107.4
Values (8%. bValues (2%.
Figure 4. Plot of G ) G(M) (see eqs 3 and 3a) vs normalized water content. Observe the close similarity of the results obtained for different soils.
the soil substrate and correspond to its internal composition (organic materials and original contaminations). These signals, in general, would be associated with photoionization processes that have very little to do with the pyrene related ones (i.e., different cross section, ionization order, etc.). Our purpose is to single out such data features that are merely due to the elevated water content and are expected, by and large, to be sample independent. As is specified by eqs 1 and 2, there are three variables, x, M, and J, that enter our description. They affect the photoionization detection of external contamination of soil samples by pyrene. In what follows, we split the discussion into three subsections according to the designated pair of variables that are kept fixed in each case. The main purpose of the forthcoming treatment is to produce a certain description of the contamination process that is sample and matrix independent. Fixed x and J; M Varied. By means of eqs 1 and 2 we define a variable G that is expected to be M dependent only
G(M) ≡ qs(x,M,J)/qs(x,Mm,J)
(3)
The above qs(x,M,J) and qs(x,Mm,J) are given in terms of our measured data qs(x,M,J) ) Qs(x,M,J) - Qs(0,M,J) and qs(x,Mm,J) ) Qs(x,Mm,J) - Qs(0,Mm,J), measured at a series of M values. From eq 1, one observes that all the sample matrix-dependent properties are canceled out in eq 3 (we can assume that N(x) is constant, since the surface contamination is not affected by water evaporation), so that
G ) f(M)/f(Mm)
(3a)
A typical behavior of G ) G(M) is presented in Figure 4. Different materials show quite similar behavior, indicating that the sample-dependent properties have been canceled out and the water content has been properly accounted for. These results have been obtained upon measuring of Qs(x,M,J) at fixed x and J, while M was gradually varied from Mw to Mm. The existence of a sample matrix-independent interrelation between the amount of water content and the detected photocharge is self-evident in this figure. An in situ fan-drying (at ∼50 °C for 120 min) has allowed addition of a few more data points for M < Mn to Figure 4. The photoionization charge (calculated as the integral of the photo482
Analytical Chemistry, Vol. 69, No. 3, February 1, 1997
Figure 5. Plot of H ) H(J) (see eq 4) vs normalized laser intensity; x ) 0 (no added contamination) and M ) Mm. Note the slopes () ionization orders) that characterize these samples.
current over time) falls off to zero when the sample’s water content vanishes. Such behavior is in accord with the open circuit characteristics that appear in the setup when M f 0. Upon measurement of Qs(x,M,J) we also found that the detected photocharge is closely related to the time-dependent water content of the samples. It is noteworthy that there is a striking similarity in the results obtained from different soil samples in three series of mutually independent measurements, namely M(t), Q(M), and σ(t) (refer also to Figure 2). Fixed x and M; J Varied. We now illustrate the possibility of establishing a process order of the surface photoionization, which is valid for the above contamination processes. Using eqs 1 and 2 for fixed M and x, one observes that the log-log plot of qs(x,M,J) ) Qs(x,M,J) - Qs(0,M,J) vs the incoming laser beam intensity, J, should be linear. The slope of this plot provides the effective (i.e., a statistical mean value) order of the photoionization process involved. A sample matrix-independent variable for this case is defined as
H(J) ) qs(x,M,J)/qs(x,M,J*)
(4)
where J* is the maximum laser intensity. The values of qs(x,M;J*) for the relevant x and M are given in Table 2. An overall reproducibility in repeated measurements on the studied samples is expressed in percentage of the specified figure in this table. Figure 5 presents the matrix-independent function, H(J), for samples SA and SB. These measurements have been carried out with x ) 0 and M ) Mm. In the absence of pyrene contamination, the artificial soils have shown a four-photon ionization process. These samples have been free of previous organic contamination, and most likely the observed signals are due to ionization of water (four photons at 337.1 nm are required to ionize water molecules). To the contrary, the data obtained from environmental samples, SA, that were gathered near an organic chemistry laboratory,
Figure 6. Plot of H ) H(J) as in Figure 5, with x ) 1 mg/mL (substantial added contamination). The global ionization order of 2 indicates pyrene coverage saturation.
indicate nearly a two-photon process, even for x ) 0. Such an order is expected for many organic contaminants irradiated by a N2 laser (∼3.7 eV/per single photon). The preexisting contamination of these samples already provided a second-order ionization signal, regardless of the new contamination, x. A plot of H(J), similar to that of Figure 5 but with x ) 1 mg/ mL, is presented in Figure 6, for samples SA and SB. Both samples underwent the same photoionization order (k = 2). These results indicate that two-photon ionization of pyrene is the dominant process. The exciting photon flux is primarily absorbed by the outer pyrene layer and the k values are not related to the inner matrix. Fixed M and J; x Varied. To this point we have successfully applied eqs 1 and 2 in order to obtain sample-independent derivations for the effects of M and of J. The former was illustrated for J and x being fixed, while the latter treatment fixed x and M. Let us now vary x, while keeping M and J unchanged. Then once again using eqs 1 and 2 and introducing new sample matrixindependent variables: Ψm(x) ≡ qs(x,Mm,J)/qs(x*,Mm,J) and Ψw(x) ≡ qs(x,Mw,J)/qs(x*,Mw,J) we arrive at
Ψm(x) ) Nm(x)/Nm(x*)
(5)
Ψw(x) ) Nw(x)/Nw(x*)
(6)
and
with x* ) xmax ) 1 mg/mL. In fact, any x value in the experimental range could be used as a fixed point. This choice does not affect the main results and only produces an insignificant scaling factor. Here too, the entries of Table 2 are used. Equations 5 and 6 suggest a quantitative presentation of the above-mentioned water table effect, which has been detailed regarding the meanings of Nm and Nw. Hence, each of thus defined Ψ(x) and Ψw(x) represents a sample matrix-independent property of the pyrene deposition process modeled here. In the former situation, the increasing concentrations of contaminant are deposited under ambient (moist soil) conditions, while in the latter case, this is performed after substantially elevating the sample water content (wet soil). In Figure 7 we present a log-log plot of both Ψm(x) and Ψw(x) for the studied samples. The overlapping of the points of
Figure 7. Plot of Ψ ) Ψ(x) for all examined soils. The overlapped plots, over 6 orders of concentration, indicate that geometrical and matrix effects have been eliminated. The straight line of slope 1 is used for the LOD estimates. Note that the plots of the wet samples (right axis) have been shifted by 0.5 unit relative to the moist samples (left axis) for clarity reasons.
different samples indicates the sample matrix-independent nature of the resulting curves. The straight line in this figure has a slope of 1 and corresponds to the situation for which the number of neutral pyrene molecules contributing to the detected photoionization charge is a linear function of the concentration of the deposited pyrene/hexane solution. The extent of the interaction volume for the photoionization events, mainly governed by the focused laser spot, imposes certain restrictions on N(x). In particular, it reflects itself in a tendency to reach saturation values when contamination is carried out by ever increasing concentrations. Above a certain concentration a multilayer deposition is expected to occur. The laser beam, however, probes the outer contamination only. This saturation stems, therefore, from the very nature of this surface method. Such a saturation trend is evident in figure 7 for log x > -5. In fact, since neither hexane nor pyrene is readily mixed or dissolved in the water table of the soil sample, the appearance of a linear region at lower values of x for the wet samples, as compared with the moist ones (see Figure 7), is in accord with the above picture of the contamination process modeled here. In the former case such an onset occurs around log x ) -8 and in the latter one near log x ) -7. In terms of soil contaminations this corresponds to 3.5 and 35 ng/g, respectively (taking a typical soil density of ∼2.9 g/mL). The target PAH concentrations for contaminated soils are in the range from 0.85 to 125 µg/g.37-41 CONCLUDING REMARKS This study has been focused on the effects of water content of MPI analysis of contaminated soils and particulate materials. Analysis has been based on fast photocurrent detection in ambient conditions. The main purpose has been to establish sample matrix-independent procedures and data analysis. A special emphasize has been placed on finding correlations between the detected photocharges and the monitored water content of the soils. Environmental soil samples, as well as commercially available silica gel, were studied here, under a wide range of contamination. The observed photoionization signals were found to be strongly correlated with the time-dependent water content of the samples, which in turn is closely related to the dc conductivity of the investigated soils. Analytical Chemistry, Vol. 69, No. 3, February 1, 1997
483
We also observed that surface contamination of wet samples has been more efficient than the moist ones. Our model of external deposition of contaminants, where the soil is substantially watered and neither the solvent nor the impurity are readily mixed or dissolved in the water table of the soil, is of current environmental interest. An extensive (6 orders of magnitude wide) range of externally deposited contamination has been covered for different soil samples. Despite quite significant differences in background photoionization properties of our samples, a correct order of ionization process involving pyrene molecules only could be successfully deduced from the data. Moreover, our data treatment allows us to separate geometrical effects from the pure MPI process. As a result, a remarkable compensation for matrix effects has been obtained. Our investigation of external contamination, where impurities are deposited onto the soil surface by contaminated solvents, suggests that the detection ability of the applied method could be influenced by the permeability of the soil surface with respect to the solvent.
484
Analytical Chemistry, Vol. 69, No. 3, February 1, 1997
Since the method detects such contamination levels that are within the target PAH concentrations for soils, further investigation of the application of this technique to analysis of various organic contaminants in environmental samples is worthwhile. Further investigation of other effects related to MPI analysis of environmental samples is planned. ACKNOWLEDGMENT This research was supported, in part by the Israeli Ministry of the Environment and by the James-Franck Program for Laser Matter Interaction. V.V.G. and V.B. are grateful for partial financial support provided by the Israeli Ministry of Science and Technology and by the Ministry of Absorbtion to the scientists regarded as return citizens and/or new immigrants. Received for review May 22, 1996. Accepted October 24, 1996.X AC960511X X
Abstract published in Advance ACS Abstracts, December 15, 1996.
