Heat Capacity Studies of Surface Water Confined on Cassiterite

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Heat Capacity Studies of Surface Water Confined on Cassiterite (SnO2) Nanoparticles Quan Shi,† Juliana Boerio-Goates,† Kellie Woodfield,† Mckay Rytting,† Katie Pulsipher,† Elinor C. Spencer,‡ Nancy L. Ross,‡ Alexandra Navrotsky,§ and Brian F. Woodfield*,† †

Department of Chemistry and Biochemistry, Brigham Young University, Provo, Utah 84602, United States Department of Geosciences, Virginia Polytechnic Institute and State University, Blacksburg, Virginia 24061, United States § Peter A. Rock Thermochemistry Laboratory and NEAT ORU, University of California at Davis, Davis, California 95616, United States ‡

S Supporting Information *

ABSTRACT: Heat capacities have been measured on a series of 2, 6, 11, and 20 nm SnO2 nanoparticles with varying amounts of surface water as well as on a bulk parent material, in the temperature range from 2 to 300 K. By subtracting the heat capacity values for 2 nm SnO2 samples with different water contents, we calculated the heat capacity contribution of the anhydrous lattice and found that the lattice heat capacity of the nanoparticle is the same as that of the bulk material within experimental error. This is further confirmation that, for several systems, once one accounts properly for the heat capacity of adsorbed water there is no measurable excess lattice heat capacity related to particle size. Using this result, we have calculated the heat capacities of confined water on the surfaces of the various SnO2 nanoparticles and found the water behavior to be generally similar to that of bulk ice, although with some differences in detail. The heat capacity of confined water on these same SnO2 nanoparticles calculated from inelastic neutron scattering spectra and those determined calorimetrically agree within experimental error at temperatures below 200 K.

1. INTRODUCTION It is well-known that, in comparison to their bulk counterparts, nanomaterials exhibit unique properties reflecting their small grain size, large surface area, and high porosity, which enables these particles to be used in diverse applications such as coatings,1 catalysts,2 batteries and fuel cells,3 ceramics,4 biological and medicinal uses,5 and photoelectronics and solar energy.6 Water adsorbed on the surface of nanomaterials is nearly ubiquitous,7 especially for metal-oxide nanoparticles,8 and surface water exhibits unique properties relative to bulk liquid water, such as a reported fragile-to-strong liquid−liquid glass transition between 150 and 250 K for water confined on the surface of mesoporous silica,9 DNA,10 protein,11 CeO2,12 carbon nanotubes,13 and rutile.8 A number of reports have also pointed out that the heat capacities of nanomaterials could be significantly larger than those of their parent bulk materials,14−21 and the origin for that has been mostly attributed to size effects, sample purity, sample density, surface composition, surface morphologies, and thermal expansion.14,22 However, in a work published several years ago, we have shown that surface water, rather than any effect associated with the nanoparticle itself, is the dominant cause of the reported excess heat capacity.14 This was shown by measuring the heat capacity of a series of 7 nm TiO2 rutile and anatase nanoparticles with varying water contents. By perform© 2012 American Chemical Society

ing successive subtractions, the heat capacity of adsorbed water could be calculated. It was found that the heat capacity of the surface water could be divided into outer and inner layer water contributions, with each behaving differently. The heat capacity of the outer water lies below that of hexagonal ice at low temperatures, and the outer water appears to melt or soften above 150 K, achieving a significant amount of mobility on the particle surface by room temperature. The heat capacity of the inner layer water, in contrast, stays significantly below that of hexagonal ice at all temperatures and shows no transition. Subtracting the water contribution, we calculated the lattice heat capacities of anhydrous (water free) rutile and anatase nanoparticles and concluded that the nanoparticle heat capacity is the same as that for the bulk materials within experimental error.14 Surface water can also significantly affect the stability and growth of nanoparticles during synthesis. In a previous work, we prepared high purity anatase23 and rutile24 TiO2 nanocrystals and studied the grain growth kinetics and surface hydration chemistry and have shown that the surface hydration plays a dominate role in limiting grain growth; that is, removal of Received: September 14, 2011 Revised: January 14, 2012 Published: January 18, 2012 3910

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together with a mortar and pestle with about 25 mL of milli-Q deionized water added. The resulting product was washed with milli-Q water via filtration using a Corning 250 mL selfcontained vacuum filtration apparatus until Cl− ions could no longer be detected by the addition of a concentrated AgNO3 solution. The final product (sample 1A) was obtained by drying the sample in a vacuum oven at room temperature with a vacuum of 10 mTorr. The second 2 nm SnO2 sample (1B) was prepared by heating sample 1A for about 20 h in air at 373 K. Heating at or below this temperature prevents grain growth, but there is always a minimum amount of water that could not be removed from the surface of nanoparticles.33,23,24 The third 2 nm SnO2 sample (1C) was obtained by dehydrating sample 1B in high vacuum (less than 10−5 Torr, in the PPMS chamber) at room temperature for about 20 h. The 6 nm SnO2 sample (2) was synthesized by calcining a portion of the 2 nm SnO2 sample (1A) at 773 K for 2 h. The 11 nm (3), 20 nm (4), and bulk (5) SnO2 samples were purchased from Acros Organics (powders, 99.995% pure), Sigma-Aldrich (powders, 99.9% pure), and Cerac Incoporated (3−12 mm pieces, 99.9% pure), respectively, and used as received. The phase purity and particle size of the samples were determined using a Scintag X-ray diffractometer with a scanning rate of 0.2 2θ·min−1 and equipped with a Cu Kα monochromated radiation source powered to 15 kV. The water content of the samples was measured by a Netzsch 409 Thermogravimetric Analyzer, where the samples were placed in corundum pans and heated from room temperature to 1173 K with a rate of 10 K·min−1 in a He atmosphere. The results of XRD and TG measurements are listed in Table 1. Inductively coupled plasma optical emission spectrometry (ICP-OES) measurements performed on a Perkin-Elmer ICP-OES Optima 4300 DV system showed that the sodium and chloride contents of the 2 nm SnO2 sample (1A) were less than 100 ppm. The TEM image acquired with a Tecnai F30 TEM system clearly indicated that the 2 nm SnO2 sample (1A) had a high degree of particle uniformity with a spherical morphology. The details of sample characterization, including TEM images and XRD patterns, have been published elsewhere.34 The inelastic neutron scattering measurements on 2, 6, and 20 nm SnO2 samples were performed at (11 ± 2) K on the Thermal Original Spectrometer with Cylindrical Analyzers (TOSCA) at the ISIS Facility (STFC Rutherford Appleton Laboratory, UK).27 ISIS is a pulsed neutron spallation source, and TOSCA is the thermal neutron time-of-flight (TOF) spectrometer with excellent resolution (ΔE/E ≈ 2%) at low energy transfers ( C1B > C1C, while sample 3 (11 nm) has a larger heat capacity than that of sample 2 (6 nm) even though sample 2 has a smaller particle size. In our previous heat capacity study of surface water confined on 7 nm rutile and anatase nanoparticles,14 we were able to estimate the surface water heat capacity by making a series of successive subtractions with samples containing different water contents. Here we use the same assumptions to investigate the three 2 nm SnO2 nanoparticles, sample 1A, sample 1B, and sample 1C with a water content of 1.26, 0.73, and 0.57 mol, respectively. Since sample 1B and sample 1C were obtained by successively dehydrating sample 1A, we assume that any difference in the heat capacities among the samples must be due to the removed surface water. We can thus further use the surface water heat capacity to carry out an estimate of the heat capacity of the 2 nm anhydrous SnO2 nanoparticle. We presume that the differences in the molar heat capacity of sample 1B from sample 1A give a determination of the heat capacity contribution from the outermost layers of water (outer water), while the differences of sample 1C from sample 1B describe water that is closer to the actual particle surface (inner water). Shown in Figure 3 is the calculated heat capacity of outer water and inner water on the 2 nm SnO2 particles compared with that of hexagonal ice,45,46 liquid H2O,47 as well as zeolitic H2O, which was obtained from Paukov et al.48 To better analyze and interpret our results, we have applied error bars on each data point to represent an estimate of the total uncertainties in the measurement and calculation of the surface water heat capacity on the SnO2 nanoparticles, as well as the calculation of anhydrous SnO2 heat capacity discussed below. An interesting result we can see in Figure 3 is that the heat capacity of outer water on the 2 nm SnO2 particles is much smaller and does not soften when compared to the outer water of the 7 nm rutile and anatase particles.14 This notable difference may be attributed to the surface properties of different nanoparticles. It is clear from the TiO2 results14 that the surface water is, in general, bound more tightly than for SnO2 since the

Figure 1. Measured heat capacity of SnO2 bulk and nanoparticles.

can be seen, there are no phase transitions or other thermal anomalies below 250 K, while there is a noticeable “bump” above 250 K for sample 1. This bump, however, is not due to any intrinsic characteristics of the 2 nm SnO2 samples but is caused by the irreproducible heat capacity of the Apiezon N grease,36−38 which is used to pot the sample to increase the thermal conductivity of insulating samples during the PPMS measurement sequence.35 Details on correcting for the “grease problem” have been discussed at length and can be found elsewhere.35−38 We have already eliminated the grease problem by means of applying small copper strips instead of using Apiezon N grease, details of which can also be found elsewhere.39,40 Fortunately, the grease problem is not significant in the present study and can be alleviated by fitting the experimental data in the temperature range from 50 to 250 K using Debye and Einstein functions41,42 and extrapolating the data through the grease transition as shown in Figure 2. The experimental data below 50 K were fitted to orthogonal polynomial functions.43,44 The fitting deviations from the experimental data are within ±5% and ±1% in the temperature range below 10 K and above 10 K, respectively. From both Figures 1 and 2, we can see again that the heat capacity of nanoparticles, for the formula SnO2·nH2O, is larger than that of bulk SnO2. In general, the observed heat capacity 3912

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sample 1C behaves as inner layer water. These two models can be represented by Cp(anhydrous) = Cp(1A) − n*Cp(IW) − m*Cp(OW) (Model 1)

Cp(anhydrous) = Cp(1B) − q*Cp(IW) − r *Cp(OW) (Model 2)

where n, m, q, and r represent the number of moles of water for each layer of water, the sum of which must add up to the number of moles of water on sample 1A and sample 1B, respectively. In the calculation, the actual number of moles used is n = 0.73, m = 0.53, q = 0.57, and r = 0.16. Shown in Figure 4 is the anhydrous heat capacity for the 2 nm SnO2 nanoparticles calculated using both models compared with Figure 3. Heat capacities of outer water (C1A − C1B) and inner water (C1B − C1C) on 2 nm SnO2 nanoparticles compared to liquid H2O, hexagonal ice, zeolitic H2O, and outer water on 7 nm TiO2 nanoparticles.14

surface water heat capacity for the TiO2 particles is significantly lower than that of hexagonal ice. Whereas the outer water melts for TiO2 anatase, it is the inner water that appears to melt or soften for the SnO2 nanoparticles. One plausible explanation is that the surface energy is lower for SnO2,49 and thus the inner water is bound more loosely. The outer water, however, does not see the SnO2 surface but the inner layer water instead, which likely provides a stronger confinement and thus prevents the outer water from melting or softening. Additionally, for SnO2 nanoparticles, the heat capacities of outer and inner water show a similar trend with temperatures below about 180 K lying below that of hexagonal ice and zeolitic water. Above 180 K, however, the outer water heat capacity increases to follow the ice curve, while that of inner water rises rapidly and agrees well with the heat capacity of zeolitic water. The rapid increase of both water heat capacities is likely due to the transition of ice melting into liquid water, but here the temperature evolution of the heat capacity in both curves resembles a glass transition rather than a sharp first-order melting. This was only observed in the outer water heat capacity of rutile and anatase nanoparticles,14 but it is much more noticeable for the SnO2 nanoparticles. As was mentioned previously, a similar glass transition has already been observed in the surface water absorbed on other nanoparticle systems8−13 and interpreted as a fragile-to-strong liquid−liquid transition.9 Although some reports point out that there was no fragile-tostrong transition in the confined water on SnO2 nanoparticles in quasielastic neutron scattering experiments,8 we have observed a glass-transition-like feature in the heat capacity measurements on our 2 nm SnO2 nanoparticles. As we concluded in the TiO2 work,14 we should treat the outer and inner layers of water differently to correct the experimental heat capacity for the surface water contribution. We can apply two different models to take into account the water effects on the nanoparticles. The first model assumes that the water removed by the first dehydration (sample 1A to sample 1B) behaves as outer-type water (OW) and that all of the remaining water on sample 1B behaves like inner water (IW). The second model assumes that the water dehydrated from sample 1B to sample 1C behaves like outer layer water, and the remaining water on

Figure 4. Heat capacity of anhydrous SnO2 nanoparticles calculated by Model 1 and Model 2 compared to SnO2 bulk.

the bulk heat capacity. The error bars in the heat capacity curve show an estimate of the combined errors in the data collection and subsequent data analysis. It can be seen that the anhydrous heat capacity of 2 nm SnO2 nanoparticles agrees with the bulk heat capacity within experimental error. Also, these two models have produced nearly the same result, suggesting that there is no definite boundary between the two types of surface water. While there appears to be a peak in the anhydrous heat capacity, further analysis suggests that this is a temperature scale artifact and is not intrinsic. Consequently, this is an important result as it is a confirmation for our conclusion in the TiO2 study14 that the nanoparticle lattice behaves like the bulk lattice. On the basis of this conclusion that the nanoparticle lattice is the same as bulk, we have carried out a series of calculations to further study the behavior of surface bound water where we have subtracted the SnO2 bulk heat capacity from each of the nanoparticle heat capacities to extract the behavior of the surface water. The results are shown in Figure 5 and compared with the heat capacity of hexagonal ice, liquid H2O, and zeolitic H2O. Figure 5 shows that in general the surface water heat capacities agree with those of hexagonal ice or zeolitic water within the experimental uncertainties. For samples 1B and 1C, their surface water behaves like zeolitic water confined on paranatrolite.48 Sample 1A, which has much more water than samples 1B and 1C, has a heat capacity closer to that of zeolitic water above 200 K. but below 200 K it exhibits a behavior closer to that of 3913

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alternatively, values for hexagonal ice must be used. However, the limitations of this approximation should be appreciated; the water on the surface of the nanoparticles does not have the structure of hexagonal ice, as evidenced by the noticeable differences in the heat capacity of the confined water and hexagonal ice, extensive quasi-elastic neutron scattering experiments, and complex theoretical modeling.8,50,51 Furthermore, the disparity between the hydration layer structure and that of hexagonal ice is certainly significant for nanoparticle samples with low water contents. The application of this approximation leads to further complications; the values of the constants Vm, α, and β for hexagonal ice are known to vary over the temperature range from 0 to 273 K, and due to the solid−liquid transition at 273 K, they are undefined above this temperature. Moreover, there is little agreement in the literature as to how these constants vary as a function of temperature.52−56 This correction also does not take into account the anharmonic contribution to the conversion factor that is believed to be important in the case of hexagonal ice.28,57 Due to these limitations, and as the conversion term is estimated to be negligible (about 0.025 J·mol−1·K−1 at 100 K),58 we will assume that for our INS data CV ≈ Cp. Shown in Figure 6 are heat capacity curves determined by both INS and calorimetry, for water confined on the surface of 2, 6, and 20 nm SnO2 nanoparticles with different water contents. Also shown in this figure are the curves for hexagonal ice for reference. There is good agreement between Cp data determined by these two techniques. It can be seen that below about 200 K the water heat capacities of sample 1A and sample 4 agree well with the INS results within experimental error, while at higher temperatures the deviations are relatively large. Sample 1B, sample 1C, and sample 2 have much larger water heat capacities than those of INS measurements over the entire experimental temperature range. Moreover, in the INS heat capacity data there was no evident softening or melting as was observed in the calorimetric measurements. The INS vibrational density of states (VDOS) from which the CV curves were calculated are recorded at low temperatures (4−11 K), and when performing these calculations, it is necessary to assume that the spectra will not change significantly over the 0−300 K temperature range. This assumption is required since obtaining variable-temperature INS data is presently not possible due to thermal (Debye−Waller) effects reducing the resolution of the spectra such that they are no longer suitable for evaluating the thermodynamic properties of water on the surface of metal oxide nanoparticles. It is therefore not surprising that we could not see these thermal anomalies at high temperatures in the INS results since the samples were measured at very low temperatures. To better understand the cause of the difference between INS and calorimetry measurements, we have also performed INS measurements on hexagonal ice and compared the results with those from calorimetry in Figure 6(d). It can be seen from the two heat capacity curves that the INS results deviate from the calorimetric curve, and the deviation increases with increasing temperature especially above 150 K. We know from DFT calculations that assuming the VDOS is constant up to 300 K is valid for hydrated nanoparticle systems, but we have no evidence that this is true for hexagonal ice.51 Indeed, it is possible that this assumption is not appropriate in the case of hexagonal ice due to the expected variation in the vibrational spectra of this material over the entire temperature range from 0 to 300 K. The consequence of this is that the difference between the heat capacity curves for hexagonal ice as

Figure 5. Heat capacities for H2O on all SnO2 nanoparticles compared to liquid H2O, hexagonal ice, and zeolitic H2O.

hexagonal ice. This suggests that the surface water on sample 1A is more tightly confined than that on samples 1B and 1C. Sample 2 (6 nm) and sample 4 (20 nm) have less water than the other samples and consequently show much larger error bars for the calculated water heat capacity, but both samples show behavior different from the other samples. Even given the large error bars, sample 4 exhibits an upturn in the heat capacity above 250 K which is very similar to what we observed in the TiO2 data.14 While the adsorbed water in sample 2 has no upturn like in sample 4, its heat capacity is significantly higher than any of the other samples. Sample 4 has the largest particle size (20 nm) of all the SnO2 nanoparticles, implying that the number of sites for strongly bound water is the smallest of the samples. This may give rise to overall weak confinement of the surface water. Sample 3 has more surface water than samples 2 and 4, and the calculated water heat capacity is in good agreement with that of hexagonal ice. In summary, while the heat capacity of the surface water for each of these SnO2 nanoparticles exhibits similar behavior with heat capacities near that of hexagonal ice or zeolitic water, there are interesting differences that suggest that the water behaves differently with different coverage amounts. To further study the confined water on the SnO2 nanoparticles, we have performed INS measurements on the same 2, 6, and 20 nm SnO2 nanoparticles measured in the calorimetry study and calculated the heat capacity of surface water on these samples from the VDOS. The details of INS experimental results and discussions are presented elsewhere.34 In this study, we only show the heat capacity results from the INS results compared with that of calorimetric measurements. In the INS measurement, the heat capacity of the nanoparticle surface water calculated directly from the VDOS is the isochoric heat capacity (CV), which is necessarily converted to the isobaric heat capacity (Cp) for comparing the INS results with calorimetric data. The following expression relates the isobaric (Cp) and isochoric (CV) heat capacities Cp = TVm α2 /β + CV

where T is the temperature (K); Vm is the molar volume (m3·mol−1); α is the coefficient of thermal expansion (K−1); and β is the isothermal compressibility (Pa−1). However, none of the constants required for this conversion are available for water confined on the surface of metal oxide nanoparticles, and so, 3914

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Figure 6. Heat capacity curves determined by INS and calorimetry (CAL) for water confined on the surface of (a) 2 nm, (b) 6 nm, and (c) 20 nm SnO2 nanoparticles. Also shown are curves (d) of the reference material hexagonal ice.

determined by INS and calorimetric techniques results from a combination of both the CV to Cp correction term and the breakdown of the assumption that the hexagonal ice INS spectra will be the same at high and low temperatures. In view of the above limitations, it is reasonable that our calorimetric heat capacities of surface water on SnO2 nanoparticles should be larger than the INS results at higher temperatures. On the other hand, it might be interesting to use the difference between the INS and calorimetric hexagonal ice data to estimate the anharmonic difference between Cp and CV. Klug et al. have carried out this correction in their study28 on the heat capacities of crystalline and amorphous H2O ice determined by neutron scattering and used these differences as an anharmonic contribution in the conversion of CV to Cp: Cp − CV = TVmα2/β + ΔCVanh where ΔCVanh is the anharmonic heat capacity at constant volume. They found that the anharmonic heat capacity at constant volume could increase from 0.47 to 2.08 J·K−1·mol−1 over the temperature range from 100 to 200 K.28 In our case, we can also take the difference of the hexagonal ice heat capacity between INS and calorimetry as the anharmonic contribution to the INS CV conversion to Cp. Shown in Figure 7 is the result after this INS CV correction, demonstrating that the INS data agree very well with the calorimetric heat capacity within experimental error. However, even if we can assume that the difference between the INS and calorimetric hexagonal ice data is predominately

Figure 7. Heat capacity curves determined by INS and calorimetry (CAL) for water confined on the surface of (a) 2 nm, (b) 6 nm, and (c) 20 nm SnO2 nanoparticles.

due to the CV to Cp conversion term and that the contribution from temperature-dependent variations in the hexagonal ice INS VDOS is negligible, then an additional, and possibly more problematic, issue arises. By assuming that the water on the nanoparticles conforms to the structure of hexagonal ice despite evidence to the contrary, and therefore applying the anharmonic 3915

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(3) Arico, A. S.; Bruce, P.; Scrosati, B.; Tarascon, J. M.; Van Schalkwijk, W. Nat. Mater. 2005, 4, 366. (4) Mukhopadhyay, A.; Basu, B. Int. Mater. Rev. 2007, 52, 257. (5) Gao, J.; Xu, B. Nano Today 2009, 4, 37. (6) Prashant, V. K. J. Phys. Chem. C 2007, 111, 2834. (7) Rasaiah, J. C.; Garde, S.; Hummer, G. Annu. Rev. Phys. Chem. 2008, 59, 713. (8) Mamontov, E.; Vlcek, L.; Wesolowski, D. J.; Cummings, P. T.; Wang, W.; Anovitz, L. M.; Rosenqvist, J.; Brown, C. M.; Sakai, V. G. J. Phys. Chem. C 2007, 111, 4328. (9) Faraone, A.; Liu, L.; Mou, C. Y.; Yen, C. W.; Chen, S. H. J. Chem. Phys. 2004, 121, 10843. (10) Chen, S. H.; Liu, L.; Chu, X.; Zhang, Y.; Fratini, E.; Baglioni, P.; Faraone, A.; Mamontov, E. J. Chem. Phys. 2006, 125, 171103. (11) Chen, S. H.; Liu, L.; Fratini, E.; Baglioni, P.; Faraone, A.; Mamontov, E. Proc. Natl. Acad. Sci. 2006, 103, 9012. (12) Mamontov, E. J. Chem. Phys. 2005, 123, 171101. (13) Mamontov, E.; Burnham, C. J.; Chen, S. H.; Moravsky, A. P.; Loong, C. K.; de Souza, N. R.; Kolesnikov, A. I. J. Chem. Phys. 2006, 124, 194703. (14) Boerio-Goates, J.; Li, G; Li, L.; Walker, T. F.; Parry, T.; Woodfield, B. F. Nano Lett. 2006, 6, 750. (15) Volokitin, Y.; Sinzig, J.; de Jongh, L. J.; Schmid, G.; Vargaftik, M. N.; Moiseev, I. I. Nature 1996, 384, 621. (16) Chen, Y. Y.; Yao, Y. D.; Hsaio, S. S.; Jen, S. U.; Lin, B. T.; Lin, H. M.; Tung, C. Y. Phys. Rev. B 1995, 52, 9364. (17) Sun, N. X.; Lu, K. Phys. Rev. B 1996, 54, 6058. (18) Barron, T. H. K.; Berg, W. T.; Morrison, J. A. Proc. R. Soc. (London) 1959, A250, 70. (19) Terwilliger, C. D.; Chiang, Y. M. J. Am. Ceram. Soc. 1995, 78, 2045. (20) Zhang, H.; Banfield, J. F. Nanostruct. Mater. 1998, 10, 185. (21) Wolf, D.; Wang, J.; Gleiter, H. Phys. Rev. Lett. 1995, 74, 4686. (22) Tan, Z. C.; Wang, L.; Shi, Q. Pure Appl. Chem. 2009, 81, 1871. (23) Li, G.; Li, L.; Boerio-Goates, J.; Woodfield, B. F. J. Am. Chem. Soc. 2005, 127, 8659. (24) Li, G.; Li, L.; Boerio-Goates, J.; Woodfield, B. F. J. Mater. Res. 2003, 18, 2664. (25) Loong, C. K.; Iton, L. E.; Ozawa, M. Phys. B 1995, 213, 640. (26) Loong, C. K.; Richardson, J. W.; Ozawa, M. J. Catal. 1995, 157, 636. (27) Mitchell, P. C. H.; Parker, S. F.; Ramirez-Cuesta, A. J.; Tomkinson, J. Vibrational Spectroscopy with Neutrons with Applications in Chemistry, Biology, Materials Science and Catalysis; World Scientific Publishing Co. Pet. Ltd.: River Edge, NJ, 2005. (28) Klug, D. D.; Whalley, E.; Svensson, E. C.; Root, J. H.; Sears, V. F. Phys. Rev. B 1991, 44 (2), 841. (29) Ross, N. L.; Spencer, E. C.; Levchenko, A. A.; Kolesnikov, A. I.; Wesolowski, D. J.; Cole, D. R.; Mamontov, E.; Vlcek, K. Neutron Scattering Studies of Surface Water on Metal Oxide Nanoparticles. In Neutron applications in Earth, Energy, and Environmental Sciences; Liang, L., Rinaldi, R., Schober, H., Eds.; Springer: New York, 2008. (30) Levchenko, A. A.; Kolesnikov, A. I.; Ross, N. L.; Boerio-Goates, J.; Woodfield, B. F.; Li, G.; Navrotsky, A. J. Phys. Chem. A 2007, 111, 12584. (31) Spencer, E. C.; Levchenko, A. A.; Ross, N. L.; Kolesnikov, A. I.; Boerio-Goates, J.; Woodfield, B. F.; Navrotsky, A.; Li, G. J. Phys. Chem. A 2009, 113, 2796. (32) Liu, S.; Liu, Q.; Boerio-Goates, J.; Woodfield, B. F. J. Adv. Mater. 2007, 39, 18. (33) Li, G.; Boerio-Goates, J.; Woodfield, B. F.; Li, L. Appl. Phys. Lett. 2004, 85, 2059. (34) Spencer, E. C.; Ross, N. L.; Parker, S. F.; Kolesnikov, A. I.; Woodfield, B. F.; Woodfield, K.; Rytting, M.; Boerio-Goates, J.; Navrotksy, A. J. Phys. Chem. C 2011, 115, 21105. (35) Shi, Q.; Snow, C. L.; Boerio-Goates, J.; Woodfield, B. F. J. Chem. Thermodyn. 2010, 42, 1107. (36) Moriya, Y.; Kawaji, H.; Atake, T.; Fukuhara, M.; Kimura, H.; Inoue, A. Cryogenics 2009, 49, 185.

hexagonal ice CV to Cp correction to the INS data, there is a significant risk that genuine differences between the calorimetric and INS curves will be eliminated erroneously. We cannot assume that the heat capacity curves for the nanoparticle hydration layers as determined by these two techniques should be the same due to the different types of data supplied by these two methods. As the INS heat capacity data are based only on low-temperature data, no temperature-dependent state transitions associated with the confined water will be observed, which is not an issue for calorimetrically determined heat capacity data. Using the above correction is therefore not an accurate means of converting the INS CV data to Cp.

4. CONCLUSION In summary, the heat capacity of surface water confined on a series of SnO2 nanoparticles has been studied using a Quantum Design physical property measurement system and inelastic neutron scattering techniques. In the calorimetric study, the heat capacity of anhydrous SnO2 nanoparticles has been calculated using two models assuming two different layers of water on the nanoparticles. The results indicate that the heat capacity of the anhydrous lattice nanoparticles agrees well with the bulk within experimental error. This is an important result as it is further confirmation that the nanoparticle lattice is the same as the bulk. Using this conclusion, the heat capacities of surface water on SnO2 nanoparticles were obtained by subtracting the bulk heat capacity from that of nanoparticle samples, and it suggests that the surface behaves as either hexagonal ice at lower temperatures and closer to zeolitic or even liquid water at higher temperatures. The heat capacities of surface water on the SnO2 nanoparticles determined using INS techniques show reasonable agreement with the calorimetry results. Finally, the conversion of INS CV data to Cp using the hexagonal ice heat capacity difference between the INS and calorimetry measurements is not applicable to the water on the surface of nanoparticles due to the difference in data collection methodologies in the different experimental temperature ranges using these two techniques.

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ASSOCIATED CONTENT

S Supporting Information *

The heat capacity of SnO2 nanoparticles organized into two different tables according to PPMS and INS measurements including an overall temperature range from 0 to 300 K. This material is available free of charge via the Internet at http:// pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected] (B. F. Woodfield). Phone: +1 801 422 2093. Fax: +1 801 422 0153. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS B. F. Woodfield, J. Boerio-Goates, N. L. Ross, and A. Navrotsky acknowledge support from the U.S. Department of Energy, Office of Basic Energy Sciences (DOE−BES), grant DE-FG0205ER15666.

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REFERENCES

(1) Saji, V. S.; Thomas, J. Curr. Sci. 2007, 92, 51. (2) Serp, P.; Castillejos, E. ChemCatChem 2010, 2, 41. 3916

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dx.doi.org/10.1021/jp2088862 | J. Phys. Chem. C 2012, 116, 3910−3917