Comment on “Mass Accommodation Coefficient of Water: Molecular

14742

J. Phys. Chem. B 2005, 109, 14742-14746

COMMENTS Comment on “Mass Accommodation Coefficient of Water: Molecular Dynamics Simulation and Revised Analysis of Droplet Train/Flow Reactor Experiment” P. Davidovits* Chemistry Department, Merkert Chemistry Center, Boston College, Chestnut Hill, Massachusetts 02167-3809

D. R. Worsnop, L. R. Williams, and C. E. Kolb Center for Aerosol and Cloud Chemistry, Aerodyne Research, Inc., Billerica, Massachusetts 01821-3976

M. Gershenzon Combustion Research Facility, Sandia National Laboratories, LiVermore, California 94551-0969 ReceiVed: NoVember 2, 2004 In a recent article, Morita et al.1 describe two simulation studies of the mass accommodation coefficient (R) for H2O(g) into liquid water. One study employed molecular dynamics (MD) simulation, and the other employed computational fluid dynamics simulation of the droplet train/flow reactor experiments performed by the Aerodyne Research Inc./Boston College (ARI/BC) group.2 The MD simulation predicts R to be 1. The fluid dynamics simulation suggests that the ARI/BC measurements obtained with the droplet apparatus are consistent with values of R for water at 273 K between 0.2 and 1. These results lead Morita et al.1 to conclude that our experimental results for the uptake of H2O(g) into liquid water are consistent with a value of R ) 1. We disagree with their conclusion. In our (ARI/BC) studies, we showed that R for H2O(g) into liquid water has a negative temperature dependence, with the magnitude ranging from 0.17 ( 0.03 at 280 K to 0.32 ( 0.04 at 258 K. At 273 K, R ) 0.23 ( 0.02.2 We stand by these results. In this Comment, we point out the following: (1) There is reason for questioning the results of the MD simulations because they do not necessarily model the full mass accommodation process and may not be an adequate representation of the gas/ water interface. (2) In the presentation of their fluid dynamic simulation results of our experimental technique, Morita et al.1 ignore key experimental results that support the values of R for H2O(g) as quoted in our publication. (3) Analysis of our experimental accuracy precludes a value of R ) 1 when the measurements yield R ) 0.23. Molecular Dynamics Simulation of the Mass Accommodation Process In addition to the work of Morita et al.,1 two other recently published molecular dynamics simulations of R for H2O(g) into liquid water also yield a value of R ) 1.3,4 This is not surprising, * To whom correspondence [email protected].

should

be

addressed.

E-mail:

since the methods used in all these simulations are similar. The simulations are done with periodic cells each initially containing about 1000 molecules. The pairwise intermolecular potentials used are likewise similar; two of the molecular dynamics studies rely primarily on the extended simple point charge model (SPC/ E).1,3 At this point, there is no compelling evidence that such simulations can yield reliable values for kinetic parameters such as R. For example, there are a number of recently published studies that indicate the currently available water interaction potentials may not fully capture liquid water properties, particularly at the liquid/vapor interface. Kathmann et al.5 showed that the widely used Dang-Chang6 and TIP4R7 potentials do an inadequate job of reproducing the properties of small water clusters that can be characterized as “nearly all surface”. Similarly, comparisons of Monte Carlo simulations of the critical cluster sizes and homogeneous water vapor nucleation rates with accurate experimental nucleation rate measurements showed that three popular water interaction potentials all did a poor job of reproducing experimental nucleation rates, which depend on accurately modeling the structures of small water clusters.8 The unpolarizable SPC/E model results, the potential used in the MD simulations by Morita et al.9 and Tsuruta and Nagayama,3 were the most disappointing, badly underpredicting sizes of the critical clusters at various temperatures and water vapor densities which results in overpredicting nucleation rates by many orders of magnitude.8 In their concluding section, Merikanto et al.8 write, “The water models generally do not reproduce saturation vapor pressure behavior of real water correctly.” Furthermore, there is recent evidence that both the SPC and more realistic intermolecular potential models may not provide a fully accurate simulation of the bonding in bulk water. In a recent experiment, Wernet et al.10 used X-ray absorption spectroscopy and X-ray Raman scattering to study the first hydration shell of a water molecule in liquid water. They report, “Serious discrepancies with structures based on current molecular dynamics simulations are observed.” This comment applies to all three MD simulation models tested by Wernet et al.,10 who note that the simulation using the SPC model potential fit the data more poorly than the other two tested. In a “Perspectives” commenting on the Wernet et al.10 work in the same issue of Science, Zubavicus and Grunze11 write, “What is disturbing, and probably of major consequence for all molecular dynamics simulations of water performed with established and widely used computer program packages, is their disagreement with the water structure ‘snapshots’ measured by Wernet et al.”10 If molecular dynamics simulations based on SPC/E and similar model intermolecular potentials fail to adequately reproduce bonding in either bulk water or water clusters, it is unlikely that they adequately reproduce the relaxed and disordered hydrogen bonded network known to exist at the liquid water/vapor interface12,13 with a high level of both single donor and acceptor only surface species.14 This fact has recently lead Kuo and Mundy15 to utilize ab initio (Car-Parrinello) molecular dynamics in an attempt to obtain adequate simulations of the

10.1021/jp0449915 CCC: $30.25 © 2005 American Chemical Society Published on Web 07/09/2005

Comments molecular state of water at the liquid/vapor interface. The ab initio calculations seem to provide a representation more consistent with some of the experimental observations. Before MD calculations of interfacial uptake coefficients can be considered accurate, it must be demonstrated that such MD calculations can reliably reproduce both interface densities and the types and abundances of surface hydrogen bonding species specified by nonlinear surface vibrational spectroscopy16,17 and X-ray absorption spectroscopy12-14,18 experiments. Using methods pioneered by Morita and Hynes,19 an initial attempt to compare water surface vibrational spectra with molecular dynamics predictions has been published by Raymond et al.,17 who note that the comparison is subject to “the approximate nature of the H2O-H2O interactions” provided by the SPC/E model. Regardless of the accuracy of the currently available MD simulations of water vapor interacting with a liquid water/vapor interface, there is another issue that casts doubt on the supposition of Morita et al.,1 Tsuruta and Nagayama3, and Vieceli et al.4 that their simulations have captured the full water vapor mass accommodation process, that is, the time and distance scales of the simulations. Current MD simulations of mass accommodation are necessarily restricted to simulating spatial scales of the order 10 nm and time scales of less than a nanosecond. Important processes that may control the mass accommodation of vapors to macroscopic liquid surfaces take place on much larger spatial and temporal scales. It is possible that the MD simulations are capturing thermal accommodation of vapor molecules, which our measurements for deuterated water vapor indicate is near unity, without fully simulating mass accommodation. Here we suggest two possible mechanisms that may not be properly treated by MD simulations. The improper treatment may result in an overestimate of uptake. At equilibrium, the surface region of water reconstructs (exchanging vapor and “liquid” molecules) on time scales of microseconds, a time factor at least a 1000 larger than accommodated by current MD simulations. If the molecules “accommodated” in MD simulations are still in the near surface region on a microsecond time scale, microscopic reversibility would indicate that they have a high probability of evaporating, erasing their apparent uptake. Further, Phillips20 suggests that interfacial mass transport for macroscopic gas/liquid surfaces is governed by thermally excited capillary waves. Because the capillary wave description “must break down on distance scales at which the discrete molecular surface is important”,21 they are not present in the MD calculations. Their absence in MD simulations may neglect a major process that returns initially accommodated molecules to the outer surface and then to the gas phase. Thus, while the MD simulations of vapor/liquid interactions published by Morita et al.,1 Tsuruta and Nagayama,3 and Vieceli et al.4 are useful contributions, it is not yet clear that they adequately represent the full physics of water vapor mass accommodation to liquid water surfaces. Computational Fluid Dynamics Simulation of the Droplet Train/Flow Reactor Studies The computational fluid dynamics simulation by Morita et al.1 of the ARI/BC droplet train/flow reactor experiments is an important contribution to our understanding of gas phase diffusive transport to a train of moving droplets. As was already pointed out in their previous publication9 and again in the subject publication,1 these simulations confirm our key experimental findings. Namely, for droplets of differing diameters produced

J. Phys. Chem. B, Vol. 109, No. 30, 2005 14743 by changing the vibration frequency of a given dropletgenerating orifice, the diffusion transport resistance does not depend on the droplet diameter. Rather, as we have shown experimentally, the diffusion transport resistance is characterized by the diameter of the droplet-forming orifice. Both our experimental results and the Morita et al.1,9 simulation conclude that the effect of gas phase diffusion can be accounted for by using the Fuchs-Sutugin equation with an effective diameter (df) that is related to the droplet-generating orifice diameter (do) by df ) Cdo, where C is a constant. Further, the calculations of Morita et al.1,9 show that the diffusion transport resistance is only moderately dependent on the droplet velocity. This again is in accord with our experiments that show diffusive transport to be unaffected within experimental accuracy by variations in droplet velocity over a relatively wide range. In the Morita et al.1 simulations, uptake coefficients (γ) are calculated as a function of the effective Knudsen number (Kneff) with R ) 0.2 and R ) 1 and are compared to our measured γ. Their calculated values of γ for the two values of R bracket our measured values and in the range of Kneff accessible to simulation are close to each other. It seems that the simulations underestimate the rate of gas phase diffusion to the droplet train. This underestimate reduces gas uptake (i.e., γ) for R ) 1, resulting in the small separation of the uptake coefficients for the two values of R. The quantitative results of the simulation lead Morita et al.1 to the following erroneous conclusion: “Comparing the calculated γ values with the experimental ones, a range in R 0.2 ∼ 1 is reasonable. This is a significant increase in the uncertainty of the extracted R, and it is primarily due to the consideration of the many uncertainties in both the experiment and the calculations.” The simulations certainly contain several approximations and uncertainties as discussed in the subject article as well as in our Comment on a previous Morita et al.9 article. As is evident in Figure 7 of the Morita et al. article,1 the simulations do not yield a convincing separation for uptake with R ) 0.2 and R ) 1. However, as will be shown, unlike the simulations, our experimental γ measurements can clearly distinguish between R ) 0.2 and R ) 1. Morita et al.1 recognize this point but dispense with it incorrectly. In the concluding section of the article, they state, “A remaining problem between experiment and the present calculations for the water accommodation process is the uptake of D2O. The droplet train/flow reactor experimental result for the uptake coefficient of D2O is significantly larger than that of H217O, and the deuterated experiment is well described by the empirical Fuchs-Sutugin formula assuming R ) 1. An alternative explanation has been recently provided by Hanson et al.22 who argued that the interpretation of an enhanced uptake that is thought to be due to isotope exchange on the droplet surface can be complicated by facile H-D exchange on the reactor wall. Further work from both experimental and theoretical sides is needed to fully elucidate the uptake kinetics due to isotope exchange on surfaces.” Two points must be made in connection with the above statement. First, as has been clearly stated in our previous publications, in the D2O experiments, there is no measurable wall loss (i.e., H-D exchange on the reactor wall).23,24 Second, our ability to clearly distinguish measured uptake with R ) 0.2 and R ) 1 has been conclusively demonstrated for several species in experiments where there was no measurable wall loss and the gas phase diffusive transport (i.e., Kneff) was a well controlled and widely varied parameter.24,25 Wall Loss. In all of our uptake experiments, we routinely measure wall loss by turning off the droplets (all other conditions

14744 J. Phys. Chem. B, Vol. 109, No. 30, 2005

Comments

Figure 1. Plot of measured uptake coefficient (γmeas) as a function of Kneff. The asymptote at large Kneff is the uptake coefficient without gas phase diffusion limitation (γ0). In the absence of surface reactions, γ0 ) R. The figure shows ARI/BC experimental data for γ0 ) 1 and γ0 ) 0.23. The solid lines are Fuchs-Sutugin plots used in the ARI/BC data analysis. The measured uptake coefficients (γmeas) in the figure were obtained from the following uptake studies: H2O and D2O from ref 2; CD3COOD and CH3COOH from ref 26; NH3 from ref 51; HCl from ref 33; HBr from ref 54. The standard deviation of the experimental γmeas data on the γ0 ) 1 line is (0.05, and that on the γ0 ) 0.23 line is (0.02. The sample error bars shown in the figure are typical of the data.

remaining constant) and testing for any change in the trace gas concentration with changing gas-wall interaction length. The interaction length is selected by using one of the three loop injectors positioned along the flow tube. First, we note that, of all the uptake studies we have performed, significant wall loss was observed only in uptake studies for gas phase deuterated acetic acid and deuterated ethanol.26 The loss parameter for d-ethanol increases significantly for a wall contaminated by splattered acidified or basic droplets. Detailed wall loss studies were conducted with d-ethanol where the wall loss coefficient (kw) was purposely increased by contaminating the flow tube walls. The wall loss coefficient (kw) is defined as n(z)/n(0) ) exp(-kwz). In the range of kw encountered, the measured gas uptake by the droplets was not affected by kw. In our earlier manuscripts, we did not report wall loss studies when wall loss was not measurable. However, in a more recent article on our studies of H2O(g) and D2O(g) uptake into aqueous sulfuric acid droplets, we discuss D2O(g) wall loss as follows:23 “In the D2O experiments, we measured the wall loss by turning off the droplets and testing for any change in the D2O concentration when changing loop injectors. No change was observed. The uncertainty in the D2O concentration measurement ( 1) regime. The asymptote (γ0) at large Kneff is the uptake coefficient without gas phase diffusion limitation. In the absence of surface reactions, γ0 ) R.

Comments The experimental data in Figure 1 were obtained as follows. For a given trace gas and droplet composition, the uptake was measured with the maximum attainable Kneff. The Knudsen number was then varied (i.e., reduced) by increasing the orifice diameter, which increases the droplet size, and the type of carrier gas and its pressure, which changes the gas phase diffusion coefficient for the trace gas. The solid lines are Fuchs-Sutugin plots as used in our data analysis with γ0 set at 1 and 0.23. The figure shows our experimental data for several species with γ0 ) 1 and for species with γ0 ) 0.23 (∼ (10%). The plotted measured uptake coefficients (γmeas) were obtained from several uptake studies, as identified in the figure. The figure includes results from uptake studies with D2O and CD3COOD that undergo facile isotopic exchange surface reactions. Our formulation of the gas phase transport matches the measurements over the full range from the continuum (Kneff < 0.1) to the near free molecular regime (Kneff > 1). Similar agreement was obtained for other values of R and other chemical systems.25 The ARI/BC measurements follow the uptake coefficient over the range of Kn to a point where the curvature toward the diffusion-free asymptote is evident, showing that our value of γ0 ) 1 is indeed just that, and the value we determined to be R ) 0.23 is not unity but is in fact 0.23 within the experimental accuracy. Data sets for some of the other γ0 values likewise show that the asymptote is almost reached,25 confirming the validity of the ARI/BC uptake formulation, including treatment of gas phase diffusive transport to the droplet train. In recent experiments, we studied the uptake of gas phase hydrocarbons on 1-octanol27 and on 1-methylnaphthalene28 droplets. These studies further contradict the central point in the critique of Morita et al.1 that “a significant uncertainty is presumably in the calculation of the gas phase resistance in the flow tube.” In these experiments, we were able to measure independently gas phase diffusion through water vapor and show that our standard analysis method accounts for gas phase diffusion through significant levels of water vapor. Our usual treatment of gas phase resistance in the flow tube recovered the same uptake coefficients (∼0.1-0.35) for γ-terpinene, R-pinene, and p-cymene in a pure helium carrier flow and in a He flow with ∼2-5 Torr of water vapor present. Although these studies were not designed to connect with the H2O(g) uptake experiments, the range of uptake coefficients and water vapor pressures spans a significant portion of the conditions in our H2O(g) uptake studies. Accuracy of the Droplet Train/Flow Reactor Measurements Typical experimentally established precision of the γmeas values obtained with the droplet train/flow reactor is (8% (1σ). This is determined from the reproducibility of multiple uptake measurements (usually more than 20) for a given gas phase species at a set temperature over a range of Kneff numbers.23,25,29 This value is consistent with an error analysis that takes into account uncertainties in the following parameters: diameter of the droplet-generating orifice (i.e., droplet surface area), measurement of gas flow rates, average thermal velocity, trace gas density, and gas and liquid phase diffusion coefficients.29 The uncertainty in γ0 calculated by propagating the (8% uncertainty through the resistor formulation (eq 1 in Morita et al.1), including effects of gas phase diffusion transport and Henry’s law solubility, does not exceed (20% (1σ). Several observations support this estimate and also test the measurements for systematic errors. In our very first paper on

J. Phys. Chem. B, Vol. 109, No. 30, 2005 14745 the droplet technique, we measured the uptake of SO2 by gas phase loss and compared it to the S(IV) content in the collected liquid.30 The uptake of SO2 measured by these two methods was nearly the same, well within the quoted precision. In cases where solubility affects the gas uptake, the product HDl1/2 (H ) Henry’s law constant; Dl ) liquid phase diffusion coefficient) can be obtained from the time dependence of γmeas. Using calculated values of Dl, values of H for acetone were obtained as a function of temperature.31 These were in agreement to within about 20% with H obtained by direct measurements of gas and liquid phase densities.32 Studies of time-dependent HCl uptake by sulfuric acid solutions yielded values of H*Dl1/2 (H* is the effective Henry’s law constant).33 Our measurements of H*Dl1/2 over a range of conditions (T ) 230-264 K; H2SO4 concentration ) 49-59 wt %) are in agreement with Dl measurements of Klassen et al.34 and the H* model of Carslaw et al.35 within an absolute uncertainty of ∼20%. For several species, our measurements of uptake coefficients can be compared to those obtained in other laboratories using a variety of techniques. The Schurath group measured R for formic acid, acetic acid, and NH3 using a coaxial jet technique.36-38 Within experimental accuracy, their data are in agreement with our results. We measured uptake coefficients for ClONO2 and N2O5 on sulfuric acid solutions.39 We compared our results for ClONO2 to those obtained using a wetted wall flow tube,40,41 a Knudsen cell,42 and an aerosol flow tube.41,43,44 The agreement for ClONO2 is excellent. Our N2O5 studies can be compared to those performed with wetted wall flow tubes40,45 and aerosol flow tubes.46-48 As discussed by Robinson et al.,39 in all of these studies, the scatter in the uptake coefficients measured for N2O5 is greater than that in the ClONO2 data. However, our results obtained with the droplet apparatus are firmly within the range of measurements made with the other methods. In two cases, our measured uptake coefficients do not agree with those obtained in the Hanson laboratory. On sulfuric acid solutions at low acid concentrations, Hanson and colleagues measure uptake coefficients for both HCl49 and NH350 close to unity. Our measurements yield values significantly lower, in both cases, about 0.3 at 20 wt % sulfuric acid.33,51 The reason for the discrepancy between the two sets of results is at this point not evident. The uptake of H2O(g) presents a special case. Because of its importance, there have been more than 40 measurements of R for H2O(g) on water, yielding values ranging from 0.001 to 1. Recently, our ARI/BC group2 and the University of Vienna/ University of Helsinki (UV/UH) group52 remeasured the mass accommodation coefficients of gas phase H2O on liquid water employing two different experimental methods. The ARI/BC group employed the droplet train apparatus where uptake is measured under near equilibrium water vapor pressure conditions. In the UV/UH method, the mass accommodation coefficient is calculated from the measured growth rate of monodisperse liquid droplets under controlled vapor supersaturation conditions (saturation ratio 1.3-1.45). The mass accommodation coefficient of H2O(g) on water as measured by the ARI/BC group has a negative temperature dependence, with the magnitude ranging from 0.17 ( 0.03 at 280 K to 0.32 ( 0.04 at 258 K. On the other hand, the UV/UH group measured a mass accommodation coefficient that within experimental error was unity and excluded values below 0.4 for temperature in the range 251-290 K.

14746 J. Phys. Chem. B, Vol. 109, No. 30, 2005 In an article cowritten by our group and colleagues from UV/ UH and York University,53 we suggest that the mass accommodation process measured by the two experiments may not be the same. A two-step mechanism for mass accommodation, discussed in the article, is a possible description of the uptake of a gas molecule into the bulk liquid under equilibrium water vapor conditions of the ARI/BC experiments. In contrast, the growth of droplets at saturation ratios of 1.3-1.5, as in the UV/ UH experiments, may be effectively a single-step process, governed by the surface (thermal) accommodation coefficient of H2O(g) on liquid water. Under the fast droplet growth conditions of the UV/UH experiments, surface accommodation of water vapor molecules might be followed by very efficient mass accommodation as the newly arriving flux promotes their incorporation into the bulk liquid. Summary In our ARI/BC studies, we showed that R for H2O(g) entering liquid water has a negative temperature dependence, with the magnitude ranging from 0.17 ( 0.03 at 280 K to 0.32 ( 0.04 at 258 K.2 On the basis of a molecular dynamics simulation and a computational fluid dynamics simulation of the droplet train/flow reactor experiments, Morita et al.1 suggest that the ARI/BC measurements of R for H2O(g) entering liquid water obtained with the droplet apparatus are consistent with a value of R )1. In this Comment, we showed that there is reason to question the MD simulations yielding a value of R ) 1 and that, in the presentation of the simulation results, Morita et al.1 ignore key experimental results that strongly support the values of R for H2O(g) as quoted in our publication. Acknowledgment. Helpful discussions with Bruce Garrett, Pavel Jungwirth, and John Vieceli on the nature of vapor/liquid mass accommodation and molecular dynamics simulation of liquid water/vapor surfaces are gratefully acknowledged. Funding from the Atmospheric Chemistry and Chemistry Programs of the National Science Foundation (grants ATM-0212464 and CH-0089147) and the Department of Energy (grant DE-FG0298ER62581) is appreciated. References and Notes (1) Morita, A.; Sugiyama, M.; Kameda, H.; Koda, S.; Hanson, D. R. J. Phys. Chem. B 2004, 108, 9111. (2) Li, Y. Q.; Davidovits, P.; Shi, Q.; Jayne, J. T.; Kolb, C. E.; Worsnop, D. R. J. Phys. Chem. A 2001, 105, 10627. (3) Tsuruta, T.; Nagayama, G. J. Phys. Chem. B 2004, 108, 1736. (4) Vieceli, J.; Roeselova, M.; Tobias, D. J. Chem. Phys. Lett. 2004, 393, 249. (5) Kathmann, S. M.; Schenter, G. K.; Garrett, B. C. J. Chem. Phys. 2002, 116, 5046. (6) Dang, L. X.; Chang, T. M. J. Chem. Phys. 1997, 106, 8149. (7) Jorgensen, W. L.; Chandrasekhar, J.; Madura, J. D.; Impey, R. W.; Klein, M. L. J. Chem. Phys. 1983, 79, 926. (8) Merikanto, J.; Vehkamaki, H.; Zapadinsky, E. J. Chem. Phys. 2004, 121, 914. (9) Morita, A.; Sugiyama, M.; Koda, S. J. Phys. Chem. A 2003, 107, 1749. Preliminary results of this work were first published in Chem. Phys. Lett. 2002, 362, 56. (10) Wernet, P.; Nordlund, D.; Bergmann, U.; Cavalleri, M.; Odelius, M.; Ogasawara, H.; Naslund, L. A.; Hirsch, T. K.; Ojamae, L.; Glatzel, P.; Pettersson, L. G. M.; Nilsson, A. Science 2004, 304, 995. (11) Zubavicus, Y.; Grunze, M. Science 2004, 304, 974. (12) Wilson, K. R.; Rude, B. S.; Catalano, T.; Schaller, R. D.; Tobin, J. G.; Co, D. T.; Saykally, R. J. J. Phys. Chem. B 2001, 105, 3346.

Comments (13) Wilson, K. R.; Cavalleri, M.; Rude, B. S.; Schaller, R. D.; Nilsson, A.; Pettersson, L. G. M.; Goldman, N.; Catalano, T.; Bozek, J. D.; Saykally, R. J. J. Phys.: Condens. Matter 2002, 14, L221. (14) Wilson, K. R.; Schaller, R. D.; Co, D. T.; Saykally, R. J.; Rude, B. S.; Catalano, T.; Bozek, J. D. J. Chem. Phys. 2002, 117, 7738. (15) Kuo, I.-F. W.; Mundy, C. J. Science 2004, 303, 658. (16) Du, Q.; Freysz, E.; Shen, Y. R. Science 1994, 264, 826. (17) Raymond, E. A.; Tarbuck, T. L.; Brown, M. G.; Richmond, G. L. J. Phys. Chem. B 2003, 107, 546. (18) Wilson, K. R.; Rude, B. S.; Smith, J.; Cappa, C.; Co, D. T.; Schaller, R. D.; Larsson, M.; Catalano, T.; Saykally, R. J. ReV. Sci. Instrum. 2004, 75, 725. (19) Morita, A.; Hynes, J. T. J. Phys. Chem. B 2002, 106, 673. (20) Phillips, L. F. J. Phys. Chem. B 2000, 104, 2534. (21) Phillips, L. F. J. Phys. Chem. B 2004, 108, 1986. (22) Hanson, D. R.; Sugiyama, M.; Morita, A. J. Phys. Chem. A 2004, 108, 3739. (23) Gershenzon, M.; Davidovits, P.; Williams, L. R.; Shi, Q.; Jayne, J. T.; Kolb, C. E.; Worsnop, D. R. J. Phys. Chem. A 2004, 108, 1567. (24) Worsnop, D. R.; Williams, L. R.; Kolb, C. E.; Mozurkewich, M.; Gershenzon, M.; Davidovits, P. J. Phys. Chem. A 2004, 108, 8546. (25) Worsnop, D. R.; Shi, Q.; Jayne, J. T.; Kolb, C. E.; Swartz, E.; Davidovits, P. J. Aerosol Sci. 2001, 32, 877. (26) Shi, Q.; Li, Y. Q.; Davidovits, P.; Jayne, J. T.; Worsnop, D. R.; Mozurkewich, M.; Kolb, C. E. J. Phys. Chem. B 1999, 103, 2417. (27) Zhang, H. Z.; Li, Y. Q.; Xia, J.-R.; Davidovits, P.; Williams, L. R.; Jayne, J. T.; Kolb, C. E.; Worsnop, D. R. J. Phys. Chem. A 2003, 107, 6388. (28) Zhang, H. Z.; Davidovits, P.; Williams, L. R.; Kolb, C. E.; Worsnop, D. R. J. Phys. Chem. A 2005, 109, 3941. (29) Gershenzon, M. Thesis UMI number 3103291, Boston College, 2003. (30) Gardner, J. A.; Watson, L. R.; Adewuyi, Y. G.; Davidovits, P.; Zahniser, M. S.; Worsnop, D. R.; Kolb, C. E. J. Geophys. Res. 1987, 92, 10887. (31) Duan, S. X.; Jayne, J. T.; Davidovits, P.; Zahniser, M. S.; Worsnop, D. R.; Kolb, C. E. J. Phys. Chem. 1993, 97, 2284. (32) Zhou, X.; Mopper, K. EnViron. Sci. Technol. 1990, 24, 1482. (33) Robinson, G. N.; Worsnop, D. R.; Jayne, J. T.; Kolb, C. E.; Swartz, E.; Davidovits, P. J. Geophys. Res. 1998, 103, 25371. (34) Klassen, J. K.; Hu, Z.; Williams, L. R. J. Geophys. Res. 1998, 103, 16. (35) Carslaw, K. S.; Clegg, S. L.; Brimblecombe, P. J. Phys. Chem. B 1995, 99, 11. (36) Bongartz, A.; Schurath, U. EEC Air Poll. Res. Report 1993, 45, 29. (37) Bongartz, A.; Schweighoefer, S.; Roose, C.; Schurath, U. J. Atmos. Chem. 1995, 20, 35. (38) Carstens, T.; Wunderlich, C.; Schurath, U. In Proceedings of EUROTRAC Symposium ‘96; Borrell, P. M., Borrell, P., Cvitas, T., Kelly, K., Seiler, W., Eds.; Computational Mechanics: Southampton, 1996; p 345 (39) Robinson, G. N.; Worsnop, D. R.; Jayne, J. T.; Kolb, C. E.; Davidovits, P. J. Geophys. Res. 1997, 102, 3583. (40) Hanson, D. R.; Ravishankara, A. R. J. Geophys. Res. 1991, 96, 17. (41) Hanson, D. R.; Lovejoy, E. R. Science 1995, 267, 1326. (42) Williams, L. R.; Manion, J. A.; Golden, D. M.; Tolbert, M. A. J. Appl. Meteorol. 1994, 33, 785. (43) Ball, S. M.; Fried, A.; Henry, B. E.; Mozurkewich, M. Geophys. Res. Lett. 1998, 25, 3339. (44) Hanson, D. R. J. Phys. Chem. A 1998, 102, 4794. (45) Zhang, R.; Leu, M.-T.; Keyser, L. F. Geophys. Res. Lett. 1995, 22, 1501. (46) Hanson, D. R.; Lovejoy, E. R. Geophys. Res. Lett. 1994, 21, 2401. (47) Fried, A.; Henry, B. E.; Calvert, J. G.; Mozurkewich, M. J. Geophys. Res. 1994, 99, 3517. (48) Hu, J. H.; Abbatt, J. P. D. J. Phys. Chem. A 1997, 101, 871. (49) Hanson, D. R.; Lovejoy, E. R. J. Phys. Chem. 1996, 100, 6397. (50) Hanson, D. R.; Kosciuch, E. J. Phys. Chem. A 2003, 107, 2199. (51) Swartz, E.; Shi, Q.; Davidovits, P.; Jayne, J. T.; Worsnop, D. R.; Kolb, C. E. J. Phys. Chem. A 1999, 103, 8824. (52) Winkler, P. M.; Vrtala, A.; Wagner, P. E.; Kulmala, M.; Lehtinen, K. E. J.; Vesala, T. Phys. ReV. Lett. 2004, 93, DOI: 10.1103. (53) Davidovits, P.; Worsnop, D. R.; Jayne, J. T.; Kolb, C. E.; Winkler, P.; Vrtala, A.; Wagner, P. E.; Kulmala, M.; Lehtinen, K. E. J.; Vesala, T.; Mozurkewich, M. Geophys. Res. Lett. 2004, 31, L22111. (54) Li, Y. Q.; Zhang, H. Z.; Davidovits, P.; Jayne, J. T.; Kolb, C. E.; Worsnop, D. R. J. Phys. Chem. A 2002, 106, 1220.