Identification and Growth Mechanism of ZnS Nanoparticles with

J. Phys. Chem. C 2009, 113, 9681–9687

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Identification and Growth Mechanism of ZnS Nanoparticles with Mixed Cubic and Hexagonal Stacking Hengzhong Zhang* and Jillian F. Banfield Department of Earth and Planetary Science, UniVersity of California Berkeley, 307 McCone Hall, Berkeley, California 94720 ReceiVed: March 19, 2009

Nano-ZnS with mixed cubic and hexagonal stacking was synthesized via reaction of zinc acetate with thioacetamide in a weakly acidic solution, while nano-ZnS in primarily the sphalerite phase was synthesized in a basic solution. X-ray diffraction (XRD) and transmission electron microscopy were used to characterize the nanoparticle structures. The formation of nano-ZnS with mixed stacking was simulated numerically by generation of ZnS nanoparticles via random cubic and hexagonal stacking of the close packing layers. The numerically simulated stacking distribution is determined by the ratio of the probability of cubic vs hexagonal stacking. XRD patterns of ZnS nanoparticles with mixed stacking were simulated by using Debye function analysis. The wurtzite (102) peak is absent whereas the wurtzite (103) peak is present in nano-ZnS with mixed stacking, matching features of XRD results for synthesized ZnS. We conclude that crystallization of ZnS nanoparticles with mixed stacking occurs via a growth mechanism in which sequential addition of hexagonal vs. cubic stacked layers occurs with equal probability. The approach may be used for analysis of mixed stacking in other nanomaterials with related structures, such as CdS and CdSe. Introduction Zinc sulfide (ZnS) mineral is the major raw material for extractive metallurgy of zinc metal. ZnS crystal is also a photoluminescence material for electrooptical applications. At atmospheric pressure, the cubic sphalerite phase is thermodynamically stable up to ∼1020 °C, above which it converts to the hexagonal wurtzite phase.1 In fact, there exist ∼140 polytypes of ZnS,2 all of which are based on specific cubic close packing (ccp) and hexagonal close packing (hcp) arrangements.3 ZnS grown at high temperatures (e.g., 1600 °C) in vapor can include a significant number of polytypes.4 Since the differences in the energies of the polytypes are small, the occurrence of polytypes at high temperatures may be driven by the TS (temperature × entropy) term.5 In addition to these regular ZnS polytypes (including cubic sphalerite and hexagonal wurtzite), ZnS crystals with random cubic and hexagonal stacking were found in bulk minerals,5 nanominerals,6 and synthetic nanoparticles.7 Smith deduced a correlation between the ratio of integrated intensities of lowangle X-ray diffraction peaks of ZnS polytypes and the ratio of cubic-to-hexagonal stacking.5 Proportions of cubic and hexagonal stacking in natural minerals were estimated by using this relationship.5 However, due to significant overlap of diffraction peaks at small crystal sizes, it is difficult to apply this method to ZnS nanoparticles. Previously, we used a peak decomposition method to estimate the “wurtzite” component and the “sphalerite” component of nanoparticulate ZnS with mixed stacking.7 Vogel et al. synthesized very small (core size ∼1.4 nm) ZnS nanoparticles stabilized by mercaptoethanol.8 Using Debye function analysis (DFA), they found the ZnS nanoparticles were best described as a ZnS cluster with a stacking fault (i.e., ABCB). Mercaptoethanol-caped CdS (isostructural with ZnS) was described as a physical mixture of 2-3 nm wurtzite-type * To whom correspondence should be addressed. E-mail: heng@ eps.berkeley.edu.

and sphalerite-type nanoparticles in approximately the same proportions.9 In contrast to these studies, 2-3 nm thioglycerolstabilized ZnS nanoparticles existed in the sphalerite phase whereas ∼2 nm glutathione-stabilized CdS nanoparticles were best described by a structure with mixed cubic and hexagonal stacking (ABCAB′AB′).10-12 Using a modified Rietveld analysis, Gibson et al. found that CdS thin films produced by chemical bath deposition consisted of CdS polytypes with nearly random cubic and hexagonal stacking.13 These reports reveal diversity in the structures of small ZnS (and CdS) nanoparticles, and may indicate that the structures depend on the synthesis history. Although formation of bulk ZnS polytypes at high temperatures (>1000 °C) is likely entropy-driven, we found that the formation of ZnS nanoparticles with mixed stacking at low temperatures in a solution is kinetically controlled.7 In slightly acidic solution, ZnS precipitation is relatively slow, favoring mixed stacking, whereas in basic solution, ZnS precipitation is fast, favoring cubic stacking.7 However, questions about the growth of mixed layer stacking nanoparticles remain. In this work, we used a statistical approach to generate model structures for analysis of X-ray diffraction (XRD) data. We infer the growth mechanism based on the inference of a random probability of sequential addition of different layer types. Experimental Section Nanoparticulate ZnS was synthesized via reaction of zinc acetate (Zn(Ac)2) and thioacetamide (TAA) in a solution.7 The detailed procedure was reported previously.7 Briefly, 0.02 M Zn(Ac)2 and 0.02 M TAA were reacted at ∼70 °C in a solution for ∼1 h. The initial solution pH upon mixing of the two reagents was ∼6.5. For sample no. 1, the final pH was ∼5. For sample no. 2, the initial pH was adjusted to ∼12 by using a NaOH solution and the final pH was ∼9. The precipitated ZnS was centrifuged and washed repeatedly, and dried at ∼50 °C for further study.

10.1021/jp902499a CCC: $40.75  2009 American Chemical Society Published on Web 05/11/2009

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Zhang and Banfield cubic stacking (ABCABC...), i.e., they are primarily sphalerite. In contrast, TEM images in panels b-d of Figure 2 show that the nanoparticles synthesized in the weakly acidic condition (sample 1) consisted of mixed cubic and hexagonal stacking (e.g., ACBABCBCABCABA in Figure 2b). The style of the mixed stacking differs in each nanoparticle, suggesting complete stacking disorder. Structure Modeling and XRD Simulation a. Generation of Structure Models for Nanoparticulate ZnS with Mixed Stacking. Following formation of a ZnS cluster via reaction of Zn(Ac)2 and TAA, deposition of Zn/S atoms (or clusters) results in a nucleus that grows into a nanoparticle (Figure 3). Growth involves layer addition along a stacking direction equivalent to s [111] (or w [001]) and expansion of existing layers. Suppose the probability of addition of a layer representing cubic stacking (e.g., AB f ABC), c, is pc, and that of addition of a layer representing hexagonal stacking (e.g., AB f ABA), h, is ph. The ratio of pc to ph is k, thus

Figure 1. XRD patterns of (a) nano-ZnS with mixed stacking synthesized in a weakly acidic solution (pH 5.2) and (b) nanosphalerite synthesized in a basic solution (pH 9.0). Diffraction peaks from the ICDD database14 (vertical lines) for sphalerite and wurtzite are also shown for comparison. Note there is no w (102) peak in panel a.

The synthesized ZnS samples were examined by XRD and transmission electron microscopy (TEM). A Bruker D8 Discover GADDS diffractometer operated in the reflective geometry with Co KR radiation (λ ) 1.7903 Å) was used to measure the XRD patterns. A CCD camera was used to record the diffraction data, and angle-integrated image pixels generated 2D (intensity vs 2θ) diffraction patterns equivalent to 2θ scans in the range of ∼27-73° with a resolution of 0.02°. A JEOL ARM-800 kV electron microscopy operated at 800 kV was used for structural characterization. A TEM specimen was prepared by adding a droplet of ZnS nanoparticles ultrasonically dispersed in water onto a carbon-coated copper TEM grid, followed by drying in air. Experimental Results and Discussion Panels a and b of Figure 1 show XRD patterns of ZnS samples 1 and 2, respectively. Diffraction peaks from the ICDD database14 of bulk sphalerite (ref code 00-005-0566) and wurtzite (ref code 00-036-1450) are also shown for comparison. XRD peaks are broad, indicating that the ZnS is nanocrystalline. The XRD pattern of sample 1 contains overlapping, broad diffraction peaks of sphalerite and wurtzite, except that w (102) is missing. This discrepancy is an important indication that the particles have mixed cubic and hexagonal stacking (see below). Using numerical decomposition for separation of overlapped peaks, we estimated that the sample consisted of ∼47 wt % “wurtzite” and ∼53 wt % “sphalerite”, and that the particle size was ∼4 nm.7 The XRD pattern of sample 2 was attributed to primarily the sphalerite phase with a particle size of ∼3 nm.7 The TEM image in Figure 2a confirms that the nanoparticles synthesized in basic solution (sample 2) have predominantly

pc )

k 1+k

(1a)

ph )

1 1+k

(1b)

In the following, we numerically simulate the generation of ZnS nanoparticles with mixed stacking, assuming that k (and hence pc and ph) is a constant during crystal growth. The total number of nanoparticles is N. The probability that the initial stacking in each particle is ABC or ABA is pc and ph, respectively. For pcN particles chosen randomly, the next close packed layer has c stacking, and the rest (N - pcN ) phN particles) have h stacking. This was repeated until the total number of close packed layers reached 14 (∼4 nm particles). All nanoparticles are assumed spherical. Figure 4 shows the influence of N on the distribution of h stacking in the nanoparticles (i.e., the histogram showing the number of nanoparticles having the same number of h stacking versus the number of h stacking in a particle). Results show that the simulated stacking distribution converges at N g ∼104. Thus, in subsequent simulations, we set N ) 105. Figure 5 shows the influence of k on the distribution of h stacking in the nanoparticles. When k changes from 0.1 (Figure 5a) to 10 (Figure 5j), the amount of h stacking in the mostly populated category changes from 12 (Figure 5a) to 1 (Figure 5j). The “wurtzite” components (in layer number %, not wt %) for the distributions of ZnS nanoparticles in panels a-j of Figure 5 are respectively 91, 63, 56, 50, 45, 40, 36, 33, 20, and 9. Every nanoparticle has different stacking, consistent with TEM observations, and there is a distribution of particles with different c or h contents (Figure 5). Figure 6a illustrates an example of the structure of a ZnS nanoparticle with mixed stacking. Table 1 lists stacking sequences of 15 nanoparticles randomly sampled from the mostly populated distribution at k ) 1.0. At k ) 1.0, the layer number percentage of the “sphalerite” and the “wurtzite” components are all 50%. b. Simulation of XRD Patterns of Nanoparticulate ZnS with Mixed Stacking. To simulate the c or h stacking in nanoparticles of a real sample, one needs to include many nanoparticles (e.g., N ) 105; see Figure 4). XRD is an appropriate method for analysis of the average structure of many

ZnS Nanoparticles with Cubic and Hexagonal Stacking

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Figure 2. TEM images of nanosphalerite (a) and nano-ZnS with mixed stacking (b, c, and d). In panel b, circles are guides for the eyes on the lattice fringes. Stacking sequences are depicted by segments of lines in panels a, b, and c. In panel d, some areas with prominent mixed stacking are circled.

Figure 3. Scheme showing growth of a ZnS nanoparticle with mixed cubic and hexagonal stacking. The probability of making a c stacking in the growing front is k/(1 + k), and that of making a h stacking is 1/(1 + k).

nanoparticles. Thus, we simulated XRD patterns of ZnS nanoparticles with mixed stacking and predicted features characteristic of different stacking patterns. Positions of all Zn/S atoms in a ZnS nanoparticle are known in a simulated sample. Thus, we can use DFA15 to simulate the XRD pattern of a nanoparticle: M

I)

∑ fifj i*j

sin(2πbrij) 2πbrij

(2)

where I is the XRD intensity, fi is the X-ray scattering factor of atom i,16 b ) 2 sin(θ)/λ the length of the diffraction vector,

with θ being the Bragg angle and λ the X-ray wavelength, and rij is the distance between atoms i and j in the nanoparticle. The sum in eq 2 carries over all atoms (total number M) in the nanoparticle. Computationally, one would require ∼M2N = 20002 × 105 ) 4 × 1011 cycles for simulation of XRD patterns of all nanoparticles contained in a simulated sample (if the total number of atoms M ) 2000 in a nanoparticle). While possible, this calculation is not practical with most computers. To reduce the computational requirement, we assume that the XRD pattern for a given stacking distribution is approximated by the average of the XRD patterns of a number (n) of nanoparticles sampled

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Figure 4. Influence of N (simulated total number of particles) on the distribution of number of particles on the number of h stacking in a nanoparticle at k ) 1: (a) N ) 102, (b) N ) 103, (c) N ) 104, and (d) N ) 106. Shadowed areas indicate mostly populated distribution.

randomly from the mostly populated distribution (shaded areas in Figure 5). Suppose n ) 20, the cycles can be reduced to ∼M2n = 20002 × 20 ) 8 × 107, far less than the 4 × 1011 cycles required to consider all the nanoparticles. Figure 6b shows the simulated XRD patterns (Co KR radiation) of the 15 sampled nanoparticles listed in Table 1. The XRD patterns of individual nanoparticles that contribute to this pattern differ from one to another. Some are closer to that of sphalerite, without peaks at ∼60°; some are closer to that of wurtzite, with peaks at ∼60°; the rest are in between. However, as the result of averaging, the XRD pattern contained the following important features: the w (103) peak appears as a hump at ∼61°; however, the w (102) peak at ∼46° is missing. We tested the dependence of the average XRD pattern on n and found that the average XRD pattern converged if n g 15 (Figure 6c). The influence of k on the simulated average XRD pattern was studied (Figure 7). At k ) 0.1, w (102) at ∼46° and w (103) at ∼61° are all present due to the high “wurtzite” content (91% h stacking). At k between 0.6 and 1.2, w (103) and (102) peaks decrease in intensity as k increases. For k between 1.5 and 2.0, w (103) become less apparent as the “sphalerite” component increases. At k ) 4.0 and 10.0, the s (200) peak at ∼38° becomes obvious due to the high “sphalerite” component (80% and 90% c stacking, respectively). Thus, by comparing an experimental XRD pattern with the simulated XRD patterns (Figure 7), it is possible to estimate the stacking probability ratio k and the “sphalerite” and “wurtzite” contents. Comparing Figure 1a with Figure 7a, one sees that that the experimental XRD pattern resembles the simulated patterns at k ) 1.0 and 1.2. Figure 8a compares the experimental pattern with the average pattern of 15 randomly sampled nanoparticles (at k ) 1.0) after applying optimized scaling and baseline shifting. Although they do not match perfectly, the two important

features, the broad w (103) peak at ∼60° and the absence of the w (102) peak at ∼46°, are evident in both patterns. The influence of the randomness of mixed stacking on the simulated pattern was examined for nanoparticles with moreordered stacking selected from the 81 randomly generated particles (Figure 8a). In the first case, we simulated the patterns for a single particle with chhhhhhcccccc stacking (see Table 1) and another with hhhhhcccccchc stacking. The wurtzite (102) and sphalerite (200) peaks are apparent in the simulated patterns (Figure 8a), inconsistent with the experimental pattern. In the second case, we simulated the pattern from 15 nanoparticles in which the h- and c-layers are in blocks (four or more consecutive layers, see Table 1). In the simulated pattern, the broad peak at ∼61° disappears, distinguishing it from the pattern from 15 randomly sampled particles and the experimental one (Figure 8a). Thus, we conclude that the synthesized nanoparticles have essentially randomly mixed stacking. As noted above, there are discrepancies between the simulated and experimental patterns. These may arise due to surface effects, shape and size effects, and limited sampling: (1) Surface Effects. In XRD simulations, no surface relaxation was considered in the structure of a model nanoparticle. In real samples, surface relaxation occurs.17 To examine the influence of the surface relaxation on the XRD pattern (Figure 8b), the randomly sampled 15 nanoparticles were structurally relaxed by molecular dynamics (MD) simulations (see Figure 9b for an example of a relaxed structure). The simulations were carried for 5 ps at 300 K, using the SHELL-DYNAMO code18 and employing a core-shell ZnS interatomic potential set.19 Results (Figure 8b) show that after MD relaxation, the agreement between the simulated and the experimental patterns improves, as seen from the decrease in the intensity of the peak at ∼66.5° and the elimination of the minor hump at ∼51.5°.


Figure 5. Influence of k (ratio of probability of depositing c layer to that of depositing h layer) on the distribution of the number of particles (ordinate in relative magnitude) on the number of h stacking (abscissa) in a nanoparticle at N ) 105: (a) k ) 0.1, (b) k ) 0.6, (c) k ) 0.8, (d) k ) 1.0, (e) k ) 1.2, (f) k ) 1.5, (g) k ) 1.8, (h) k ) 2.0, (i) k ) 4.0, and (j) k ) 10.0. Shadowed areas indicate mostly populated distribution.

(2) Shape and Size Effects. In generation of ZnS structure models, all nanoparticles were assumed spherical and all were 4 nm in diameter. In fact, TEM images (Figure 2) show that ZnS nanoparticles with mixed stacking are not perfectly spherical and thus are not all 4 nm in all directions. To examine the shape effect on the simulated XRD pattern (Figure 8c), we calculated the XRD pattern of a 4 nm spherical nanoparticle (Figure 9a, stacking sequence chhccchcchchh) and that of an


Figure 6. (a) Structure of a sampled ZnS nanoparticle. The sacking sequence in the particle is (from top to bottom) ccchccchchhhh. (b) Simulated XRD patterns (thin color lines) of 15 ZnS nanoparticles randomly sampled from the mostly populated distribution at k ) 1.0. The thick red line is the average. (c) Influence of the number of randomly sampled nanoparticles on the average XRD pattern (k ) 1.0).

oval-shaped nanoparticle (Figure 9c, dimension 2a × 2b × 2c ) 4 × 4 × 8 nm3, stacking sequence chhccchcchchhcchhccchcchch). Results (Figure 8c) show that the agreement between the intensity of the simulated peak at ∼33° and the experimental data was greatly improved for the oval nanoparticle (the disagreement at other angles, particularly ∼60°, is due to the lack of averaging from a sufficient number of randomly sampled nanoparticles; see above). To examine the size effect on the simulated XRD patterns, we calculated patterns for 3 and 5 nm ZnS nanoparticles with mixed stacking (Figures S1 and S2, Supporting Information). Results show that even at the same stacking probability ratio,

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TABLE 1: Stacking Sequences in 15 ZnS Nanoparticles Randomly Sampled from the Mostly Populated Distribution at k ) 1.0 (Stacking Probability pc ) ph ) 0.5) particle no. 1 2 3 4a 5 6 7a 8 9a 10 11 12a 13 14a,b 15

stacking sequence h h c h h h c h h c c c c c h

c h h c h c h c h c h c h h c

h c c c c h c h h h h c h h c

c h c c h c c h h c c h h h c

c c h h c h h h c h c c c h h

c c c c c h c c c h h c c h c

h h h h h c h h c c c c c h h

h h h h c c h c c c c h h c h

h c h h c c h c c h h c h c h

c h c h c h h h h c h h c c c

c c h c h h c c c h c h h c c

h h h c h c h h h h h h c c h

h c c c c c c c h c c h c c c

a Nanoparticle with four or more consecutive h (or c) stacking layers (i.e., more h/c-packed nanoparticle). b Nanoparticle with five or more consecutive h and c stacking layers (i.e., highly h/c-packed nanoparticle).

Figure 7. Simulated XRD patterns of nano-ZnS (4 nm) with mixed cubic and hexagonal stacking: (a) k ) 0.1-1.2 and (b) k ) 1.5-10.0.

k, XRD features depend on size, especially at k e 1.2 and for smaller nanoparticles (e4.0 nm). For instance, at k ) 1.0, the number of small peaks at ∼61° increases from 0 to 1 to 2 as the particle size increases from 3 nm (Figure S1a, Supporting Information) to 4 nm (Figure 7a) and 5 nm (Figure S2a, Supporting Information).

Figure 8. Comparison between experimental and simulated (k ) 1.0) XRD patterns of ZnS nanoparticles (NPs) with mixed stacking. (a) Influence of randomness of nanoparticle sampling on simulated XRD pattern (see Table 1); (b) influence of surface relaxation on simulated XRD pattern; and (c) influence of particle shape and size on simulated XRD pattern.

(3) Limited Sampling. The simulated average XRD patterns were calculated for the mostly populated category of nanoparticles (Figure 5), and thus did not include effects due to less common and rare nanoparticle types. Despite small discrepancies, the agreement between the simulated XRD pattern at k ) 1.0 (Figure 8b) and the experimental data (Figure 1a) is reasonable, and we conclude that k = 1.0 for the synthetic nanoparticles. By using this and eq 1, the nano-ZnS sample synthesized in weakly acidic solution



Figure 9. Structures of ZnS nanoparticles (Zn: green; S: yellow): (a) an unrelaxed 4 nm spherical particle (the stacking sequence from top to bottom is chhccchcchchh); (b) particle in part a after surface relaxation, using molecular dynamics simulations; and (c) an unrelaxed oval particle with dimensions 2a × 2b × 2c ) 4 × 4 × 8 nm3 (the stacking sequence from top to bottom is chhccchcchchhcchhccchcchch).

was determined to contain ∼50% “wurtzite” and “sphalerite” components (h and c layer number percentage). This result is close to the weight percentages estimated from the XRD peak decomposition (above). The advantage of the XRD patternmatching analysis over simple peak decomposition is that the degree of stacking order can be deduced (and the absence of w (102) can be explained). The average size of the sample synthesized in basic solution is ∼3 nm, based on XRD peak broadening (above). The experimental XRD pattern (Figure 1b) most closely resembles the simulated patterns for 3 nm ZnS nanoparticles (Figure S1b, Supporting Information) with k g 3. Thus, we infer that it is dominated by c stacking (i.e., mainly in the sphalerite phase). The ZnS nanoparticle growth is controlled either by the nanoparticle structure (e.g., close packing in the surface region) or by the solution chemistry (e.g., speciation in the solution). In the former case, the stacking in nanoparticles should be more correlated (less random) and the structure should be insensitive to the solution pH. This is inconsistent with results from the DFA analysis (Figure 8a) and the experimental data (Figures 1 and 2). In the latter case, pH determines the forms and concentrations of Zn-S molecular clusters that favor approximately equal h and c stacking at a lower pH and c stacking at a higher pH.7 This agrees with DFA for random stacking with different k values. Thus, we suggest a growth mechanism in which nano-ZnS grows by random h (or c) stacking at the growing front, with the h to c deposition probability ratio determined by the solution chemistry. Conclusions Simulation of XRD patterns of ZnS nanoparticles confirmed the experimental observation that the w (102) peak is absent while the w (103) peak is present in nano-ZnS with mixed stacking. This is due to random stacking of h and c layers in the nanoparticles, which we infer reflects layer deposition controlled primarily by solution chemistry. The XRD pattern comparison approach reported here may be used to analyze

mixed stacking in isostructural nanomaterials, such as CdS, CdSe, SiC, and GaN. Acknowledgment. Financial support was provided by the U.S. Department of Energy (grant no. DE-FG03-01ER15218) and the National Science Foundation (Grant no. EAR-0123967). Transmission electron microscopy was performed at the National Center for Electron Microscopy at Lawrence Berkeley National Laboratory. Supporting Information Available: Simulated XRD patterns for 3 and 5 nm ZnS nanoparticles with mixed stacking. This material is available free of charge via the Internet at http:// pubs.acs.org. References and Notes (1) Zhang, H.; Huang, F.; Gilbert, B.; Banfield, J. F. J. Phys. Chem. B 2003, 107, 13051. (2) Trigunayat, G. C.; Chadha, G. K. Phys. Status Solidi A 1971, 4, 9. (3) Trigunayat, G. C. Solid State Ionics 1991, 48, 3. (4) Russell, G. J.; Woods, J. J. Cryst. Growth 1979, 47, 647. (5) Smith, F. G. Am. Mineral. 1955, 40, 658. (6) Moreau, J. W.; Webb, R. I.; Banfield, J. F. Am. Mineral. 2004, 89, 950. (7) Zhang, H.; Chen, B.; Gilbert, B.; Banfield, J. F. J. Mater. Chem. 2006, 16, 249. (8) Vogel, W. Langmuir 2000, 16, 2032. (9) Vogel, W.; Urban, J.; Kundu, M.; Kulkarni, S. K. Langmuir 1997, 13, 827. (10) Kumpf, C.; Neder, R. B.; Niederdraenk, F.; Luczak, P.; Stahl, A.; Scheuermann, M.; Joshi, S.; Kulkarni, S. K.; Barglik-Chory, C.; Heske, C.; Umbach, E. J. Chem. Phys. 2005, 123, 224707. (11) Kumpf, C. Appl. Phys. A: Mater. Sci. Process. 2006, 85, 337. (12) Niederdraenk, F.; Seufert, K.; Luczak, P.; Kulkarni, S. K.; Chory, C.; Neder, R. B.; Kumpf, C. Phys. Status Solidi C 2007, 4, 3234. (13) Gibson, P. N.; Ozsan, M. E.; Lincot, D.; Cowache, P.; Summa, D. Thin Solid Films 2000, 361-362, 34. (14) International Center for Diffraction Data, Powder Diffraction File ICDD-PDF2; Newton Square, PA, 2003. (15) Vogel, W.; Gnutzmann, V. J. Phys. Chem. 1990, 94, 4991. (16) Prince, E., Ed. International Tables for Crystallography, 3rd ed.; Kluwer Academic Publishers: Boston, MA, 2004; Vol. C. (17) Zhang, H.; Gilbert, B.; Huang, F.; Banfield, J. F. Nature 2003, 424, 1025. (18) Fincham, D. The SHELL-DYNAMO Reference Manual; Keele University: Keele, Staffordshire, U.K., 1996. (19) Wright, K.; Jackson, A. J. Mater. Chem. 1995, 5, 2037.

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Identification and Growth Mechanism of ZnS Nanoparticles with

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