ARTICLE pubs.acs.org/Langmuir
Development of Catalytically Active Silver Colloid Nanoparticles Stabilized by Dextran Renato Eising,† Aline M. Signori,† Sebastien Fort,‡ and Josiel B. Domingos*,† †
LaCBio - Laboratory of Biomimetic Catalysis, Chemistry Department, Federal University of Santa Catarina, Campus Trindade, Florianopolis - Santa Catarina 88040-900, Brazil ‡ Centre de Recherches sur les Macromolecules Vegetales, 601 rue de la Chimie, BP 53X 38041 Grenoble, cedex 09, France
bS Supporting Information ABSTRACT: Colloidal silver nanoparticles (Ag-NPs) with a mean diameter of 6.1 nm and a narrow size distribution were prepared by reduction of the correspondent metal salt with injection of NaBH4, in the presence of dextran, and characterized by UVvis, TEM, and DLS. The concentration of all reactants involved in the formation of the nanoparticles was optimized with the use of a new multivariate method, which revealed a significant reduction in the number of experiments when compared with the vast majority of univariate methods described in the literature. The Ag-NPsdextran composite was able to efficiently catalyze the p-nitrophenol reduction in water by NaBH4 with a rate constant normalized to the surface area of the nanoparticles per unit volume (k1) of 1.41 s1 m2 L, which is higher than values ever reported for Ag-NPs catalytic systems.
’ INTRODUCTION Metal nanoparticle (M-NP) catalyst systems have attracted great interest in academic research and industry due to their exceptional properties, such as selectivity, efficiency, and recyclability, modern requirements for new green catalysts.14 Moreover, M-NPs present other properties (electronic, magnetic, optical, etc.) which are very different from their bulk properties,58 playing important roles in many research fields including, but not limited to, environmental science, chemistry, and medical applications.8,9 These exceptional properties of metal nanoparticles are greatly dependent on their size, shape, composition, crystallinity, and structure. In principle, one could control any of these parameters to fine-tune the properties of M-NPs. Nevertheless, the challenge of synthetically controlling such parameters has met limited success and is the subject of several studies in different research areas.1014 The most common method for the preparation of metal nanoparticle colloids involves wet chemical “bottom-up procedures”, particularly the chemical reduction of metal salts. The chemical reduction of transition metal salts is normally performed in the presence of stabilizing agents to generate zerovalent metal colloids in aqueous or organic media. Due to small interparticle distances, aggregation of the NPs by van der Waals forces is virtually unavoidable, and stabilizers are employed to provide stability to the NPs when they are formed. The presence of a stabilizer helps to control the kinetics of the precursor transformation, the most common being the use of small molecules as surfactants or a polymer matrix. The great majority of the r 2011 American Chemical Society
proposed mechanisms for M-NP formation are, in essence, based on the nucleation, growth, and agglomeration mechanism, first described by Turkevich.15 Detailed kinetic and thermodynamic studies show that the nucleation and growth of M-NPs, and consequently the control of their size, shape, composition, and structure, are dependent on a large number of experimental parameters and on an adequate selection of these variables: primarily the concentration of all reactants, the metal salt precursor, the reducing agent, and the stabilizer, as well the temperature and pH.10,16,17 Optimization of such parameters has been traditionally carried out using a univariate method, i.e., changing each factor at a time over the investigated range while the others are held constant at a selected level. However, the univariate approach involves time-consuming and labor-intensive procedures, requiring several experiments to be performed, leading to an expressive waste of material, and also neglecting any possible interactions between the variables.18,19 This paper describes the formation and catalytic properties of aqueous silver colloid nanoparticles (Ag-NPs) stabilized by the natural polymer dextran and, for the first time, the application of a multivariate method to optimize the concentration of all reactants involved in the formation of the nanoparticles. The evaluation of the multivariate experiments was performed in situ simply by following the formation of Ag-NPs through the Received: July 27, 2011 Revised: August 18, 2011 Published: August 24, 2011 11860
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Langmuir appearance of their UVvis surface plasmon resonance (SPR) band. Indeed, analysis of the SPR bands resulting from the formation of metal nanoparticles has proven very efficient approach to optimize the preparation of M-NPs in term of efficiency of the reduction, as well as size and polydispersity control.20 Finally, the catalytic activity of Ag-NPsdextran composites in the reduction reaction of p-nitrophenol (Nip), using NaBH4 as the reducing agent, was verified and its kinetics fully analyzed. The reduction of Nip to p-aminophenol (Amp) is a model reaction21 which has been widely used for the quantification and comparison of the catalytic activity of different metal nanoparticles immobilized in a variety of carrier systems.20,2225
’ EXPERIMENTAL SECTION The sodium borohydride (Sigma-Aldrich, 98%), silver nitrate (Cennabras, 98%), p-nitrophenol (Riedel, 99%), and dextran T500 (Pharmacia) was used as received without any further purification. Ultrapure water (resistivity of 18.2 mΩ.cm), degassed by ultrasonic treatment, was used in all experiments. All glassware was washed with concentrated nitric acid and rinsed copiously with deionized water prior use. Multivariate Optimization and Synthesis of Ag-NPs. The optimization step of the variables (i.e., concentrations of dextran, silver nitrate, and sodium borohydride) in the formation of silver nanoparticles was carried out using a two-level full factorial design. This design involved fourteen basic experiments plus four central points. The values, corresponding to the high (+), low (), and central (0) points for each factor, are shown in Table 1 and the experimental designs in Table S1 (Supporting Information). The NaBH4 concentration-level range was chosen to have a minimum ratio of NaBH4/AgNO3 equal to 3; at smaller ratios, the effect on the response was somewhat insignificant. The concentration-level range of dextran was defined observing that at concentrations above 0.3 mol L1 the dextran solution became highly viscous. The evaluation of the results of the factorial design was carried out using analysis of variance (ANOVA, Table S3, Supporting Information) at the 95% confidence level. These experiments were carried out in transparent 96-well plates (NUNC) and acquiring in situ the UVvis spectra at 300 to 800 nm with a microtiter plate reader (Molecular Devices Spectramax Plus 384). Typically, to 180.0 μL of an aqueous dextran solution, 10.0 μL of AgNO3 was added and the solution incubated for 10 min before the addition of the reducing agent (10.0 μL of NaBH4), to give a total volume of 200 μL. The final concentrations of dextran, AgNO3, and NaBH4 for each multivariate experiment are shown in Table S2 of the Supporting Information. All of the experiments were carried out at least in duplicate. Experimental data were processed using the Statistica 8.0 computer program. Characterization. Transmission electron microscopy (TEM) images were recorded on Kodak SO163 film using a CM200 Philips microscope operating at 18 kV. A typical method for preparing TEM samples was as follows: one drop of reaction mixture was deposited on a 200-mesh Formvar/carbon-coated copper grid, and excess solution was removed by wicking with filter paper to avoid particle aggregation. The particle size analysis was conducted by determining the diameter of at least 150 particles. Dynamic light scattering measurements were performed using an ALV laser goniometer, which consists of 35 mW HeNe linear polarized laser operating at a wavelength of 632.8 nm, an ALV-5004 multiple τ digital correlator with 125 ns initial sampling time, and a temperature controller. The measurements were recorded at 90°. The aqueous solutions of AgNPsdextran were filtered directly into the glass cells through a 0.22 μm Millipore Millex PES hydrophilic filter. Data were collected using digital ALV Correlator Control software. The relaxation time distributions— A(t)—were obtained using the Contin analysis applied on the autocorrelation functions C(q,t). The zeta potential (ζ) measurements were
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Table 1. Factor Levels in the Experimental Design [Dextran]
[AgNO3]
[NaBH4]
103 mol L1
103 mol L1
103 mol L1
2
1.0
0.08
1.4
1
75.75
0.185
2.3 3.2
level
0
150.5
0.29
+1
225.25
0.395
4.1
+2
300.0
0.5
5.0
performed in a Malvern Zetasizer ZS with a HeNe laser at wavelength 633 nm and a detection angle of 173° at 25 °C. Catalytic Activity Assays. The catalytic activities of Ag-NPs dextran composites were evaluated in the reduction reaction of p-nitrophenol (Nip) to p-aminophenol (Amp) in 96-well plates (Nunc) with a final volume of 200 μL at 25 °C. First, the reducer concentration (NaBH4) was varied from 8.8 103 to 0.264 mol L1, while keeping the Nip and Ag-NPs (based on amount of silver atoms) concentrations at 8.8 105 mol L1 and 2.5 106 mol L1, respectively. Second, the catalyst concentration (Ag-NPs) was varied from 2.5 106 to 20 106 mol L1 keeping the Nip and NaBH4 concentrations at 8.8 105 mol L1 and 0.176 mol L1, respectively. Reactions were started with the addition of Nip and the kinetics monitored through the decrease in absorbance at 400 nm on a microtiter plate reader (Molecular Devices Spectramax Plus 384). All experimental procedures were carried out in triplicate and reproducibly gave catalysts with kinetic properties within 15% of the values described.
’ RESULTS AND DISCUSSION Synthesis and Characterization of Ag-NPs. Despite the large number of papers dedicated to the preparation of metal colloid nanoparticles through wet chemical synthesis with the reduction of a metal salt in the presence of a stabilizer, it is not absolutely clear how the variables of the system (stabilizer, metal, and reducing agent) influence the structure of the final material. However, it is clear that each component has an important role in the formation of metal nanoparticles and the synergism between them is quite important.26,27 The methodologies used to optimize the M-NP preparation described in the literature generally employ univariate methods, which do not take into account possible synergism between the variables. To address this issue, in this study we used a multivariate approach. The quantitative optimization design chosen for this work was the so-called response surface model: a two-level factorial design expanded further to a central composite design. When the number of independent variables is small, then overlying the response surfaces and choosing the optimum conditions constitutes a simple and, in most cases, highly effective method. The analytical response (ψ) used for the plot of the response surfaces was estimated from eq 1
ψ¼
Amax λmax FWHH
ð1Þ
which combines the maximum absorbance (Amax), which reflects the yield of the Ag-NPs formed,28,29 the wavelength at Amax (λmax), which is related to the size of the Ag-NPs,2932 and the full width at half-height (FWHH), which is associated with the size polydispersity of the Ag-NPs.28,29 In this equation, the best response (ψ) would be obtained when the Amax value is maximized and the λmax and FWHH values are minimized, indicating the formation of small narrow Ag-NPs. 11861
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At a confidence level of 95%, the results considering the analysis of variance (ANOVA, Table S3 of Supporting Information) demonstrate that all three factors and the interactions [dextran] [AgNO3] and [dextran] [NaBH4] are statistically significant. The equation below, indicating regression coefficients and their standard errors, illustrates the relationship of the three variables (x1, x2, and x3) and the analytical response (ψ). ψ ¼ 6:3ð ( 1:0Þ þ 18:5ð ( 4:0Þx1 4:1ð ( 6:3Þx 1 2 þ 32:4ð ( 3:0Þx2 11:8ð ( 3:2Þx2 2 þ 1:6ð ( 0:4Þx3 0:08ð ( 0:04Þx3 2 47:3ð ( 7:5Þx1 x2 2:8ð ( 0:9Þx1 x3 1:3ð ( 0:6Þx2 x3 where x1, x2, and x3 are dextran, AgNO3, and NaBH4 concentrations, respectively. This equation has a mean square lack of fit to mean square pure error ratio of 6.22, smaller than the 95% significant F5,3,95% value of 9.01 and a determination coefficient (R2) of 0.98, indicating that the results obtained are reliable and the model does not suffer from lack of fit. The response surfaces governed by this equation are shown in Figure 1. Each 3-D graph shows the normalized response as a function of two variables and the shape reflects the interactions and curvatures (or not) for the variables. From Figure 1a, one can observe a maximum point at high silver nitrate concentration and low dextran concentrations. On the other hand, the maximum response is observed in Figure 1b when the dextran concentration is varied, but little effect in the response is observed with the variation in sodium borohydride concentration. However, in Figure 1c a strong effect of silver nitrate concentration was observed with a weak effect of sodium borohydride concentration, but no maximum was observed in this experiment, which may be related with the detection limit of the spectrophotometer. To verify the linearity of the response under the experimental conditions, an absorbance correlation with the Ag-NP concentrations was performed (Figure S1 of Supporting Information). It was observed that Amax varied linearly with respect to the Ag-NPs concentration range studied. The best conditions for the Ag-NP formation (called the optimal point), taking into account the combined analysis of the three graphs, were 6.3 103, 0.5 103, and 3.0 103 mol L1 of dextran, silver nitrate, and sodium borohydride, respectively. The UVvis spectrum of this experimental condition is shown in Figure 2. The TEM analysis of this optimal point, presented in Figure 3, revealed nanoparticles with spherical geometry and low polydispersity (small FWHH), as expected from Ag-NPs with a sharp UVvis band and λmax around 400 nm,28 validating the analytical response represented in eq 1. Through the Gaussian fits of the size distribution histogram, nanoparticles with mean diameters of 6.1 ( 1.3 nm were estimated. The theoretical specific surface area of the Ag-NPs was estimated from the TEM analysis and the density of bulk silver (F = 10.5 g cm3). Additionally, the Ag-NPsdextran composite was characterized by dynamic light scattering (DLS) as shown in Figure 4. Three populations of particles can be observed with average sizes of 2Rh = 4.9, 27.3, and 95.0 nm, respectively. The smallest particles can be reasonably attributed to Ag-NPs since their size is consistent with the TEM observations. The particles with an average diameter of 27.3 nm may result from the presence of free dextran chains given that a similar relaxation-time distribution can be observed in Figure 4b for a solution of dextran alone. The third population can be attributed to aggregates containing
Figure 1. Response surfaces for the variables: (a) dextran and silver nitrate concentrations (indicate sodium borohydride level), (b) dextran and sodium borohydride concentrations (indicate silver nitrate level), and (c) silver nitrate and sodium borohydride concentrations (indicate dextran level).
Ag-NPs embedded in a dextran matrix. It should be noted here that the distribution presented in Figure 4 is, by nature, a 11862
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Figure 2. UVvis spectrum of the optimal point. [dextran] = 6.3 103 mol L1, [AgNO3] = 0.5 103 mol L1, and [NaBH4] = 3.0 103 mol L1.
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Figure 4. Dynamic light scattering autocorrelation function (squares) and relaxation-time distribution (solid line) for (A) Ag-NPs-dextran at [dextran] = 6.3 103 mol L1, [AgNO3] = 0.5 103 mol L1, and [NaBH4] = 3.0 103 mol L1 and (B) dextran at 6.3 103 mol L1.
Figure 5. Variation in UVvisible absorption spectra for the Nip reduction reaction at 25 °C in the presence of Ag-NPsdextran ([Nip] = 6.0 105 mol L1, [Ag-NPs] = 2.5 106 mol L1, [NaBH4] = 6.0 105 mol L1).
Figure 3. TEM micrograph and size distribution histogram for AgNPdextran composite. [dextran] = 6.3 103 mol L1, [AgNO3] = 0.5 103 mol L1, and [NaBH4] = 3.0 103 mol L1.
mass-weighted distribution. Consequently, although the peaks corresponding to the large-scale structures are the most intense, the number of aggregates is very low, as confirmed by their negligible presence in the TEM image. Catalytic Activity. The reduction of p-nitrophenol (Nip) to p-aminophenol (Amp) by NaBH4 was used as a model reaction
to evaluate the catalytic activity of Ag-NPs stabilized by dextran. This reaction was monitored spectrophotometrically by measuring the disappearance of Nip (Figure 5), which shows a distinct spectral profile with an absorption maximum at 317 nm in water, but with a shift to 400 nm in the presence of NaBH4 due to the formation of the p-nitrophenolate ion.33,34 In order to obtain a full set of kinetic data, the influence of all reagents (Nip, AgNPsdextran and NaBH4) on the reaction was verified. The effect of the NaBH4 concentration is shown in Figure 6. In a typical set of experiments, the concentrations of Nip and catalyst were kept constant and the NaBH4 concentration was varied. At least a 100-fold excess of NaBH4 over the concentration of Nip was used assuring pseudo-first-order conditions. 11863
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Table 2. Zeta Potential (ζ) at Different NaOH Concentrations of Ag-NPdextran in Water Solutions in the Presence of NaBH4a [NaOH] 103, mol L1
0
2
10
20
ζ, mV
7.21
18.4
14.2
5.05
[NaBH 4] = 8.8 10 2 mol L1, [Ag-NPs] = 2.5 10 6 mol L1 at 25 °C. a
Figure 6. kapp as a function of NaBH4 concentration at [Nip] = 8.8 105 mol L1, [Ag-NPs] = 2.5 106 mol L1 and 25 °C. The solid line is a guide for the eyes.
Figure 8. kapp as a function of Nip concentration at [NaBH4] = 8.8 102 mol L1, [Ag-NPs] = 2.5 106 mol L1, and 25 °C. The solid line is a guide for the eyes.
Figure 7. kapp as a function of NaOH concentration, at [Nip] = 8.8 105 mol L1, [NaBH4] = 8.8 102 mol L1, [Ag-NPs] = 2.5 106 mol L1 at 25 °C. The solid line is a guide for the eyes.
Figure 6 shows that kapp increases with an increase in NaBH4 concentration until it levels off, indicating that after a certain value the reaction proceeds independently of the NaBH4 concentration, i.e., it becomes zero-order. During these experiments, we verified that the sodium hydroxide, used to improve the stabilization of borohydride, had a major influence on the apparent rate constant. Many studies reported in the literature use sodium hydroxide to adjust the NaBH4 stock solution pH to between 10 and 11 in order to improve its stability in water.24,35 However, none of them verified the influence of NaOH on the results of the kinetic experiments (i.e., over kapp), which is to be expected considering possible competition between HO and BH4 for the nanoparticle surfaces. Although not very prominent, NaOH indeed shows an intrinsic influence over kapp, as shown in Figure 7. At low NaOH concentrations, kapp increases reaching a maximum at 1 103 mol L1 (pH ∼10). After this point, kapp decreases quickly until it levels off at around 5 103 mol L1 of NaOH.
Competition for Ag-NP surfaces has previously been noted by Bell et al.,36 who demonstrated that many different anions can bind to the Ag-NP surface, in many cases by displacement of other anions. In order to gather more information on the influence of NaOH, we performed Zeta potential (ζ) measurements of Ag-NPdextran in water solutions in the presence of NaBH4 and at different NaOH concentrations (Table 2). The results show that the magnitude of ζ is highest at 2 103 mol L1 of NaOH, which coincides with a maximum in the apparent rate constant as shown in Figure 7. Higher concentrations of NaOH (10 103 and 20 103 mol L1) lead to a sharp decrease in the ζ magnitude, probably by decreasing the system stability and also by lowering the ion mobility due to the increase in the ionic strength. On the basis of these results, we decided to use the stock solution of NaBH4 in water (free of NaOH). In the following experiments, the NaBH4 concentration was kept at a specific value where the reaction is guaranteed to be zero-order with respect to NaBH4. The Nip influence over kapp is shown in Figure 8. In this profile, a decrease in kapp was observed with an increase in the Nip concentration. This can be attributed to competition between the two reactants for the reactive sites of the Ag-NP surface. The increase in Nip concentration would induce extensive coverage of the Ag-NP surface, which in turn should decrease the electron supply at the metal surface by NaBH4. This behavior was explained by Wunder et al. in terms of the LangmuirHinshelwood model, which assumes the adsorption of both reactants on the surface of the catalyst in a reversible manner.22 The rate-determining step would consist of the reduction of Nip by the surface hydrogen species. Since under microheterogeneous conditions it is well-accepted that the apparent kinetic rate constant is proportional to the total 11864
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to also play an important role in the catalytic activity of such systems, particularly the nature of the stabilizer.23,26,41,42 When the interaction between the M-NP and stabilizer backbones, normally presenting sulfur or nitrogen atoms, is too strong, the size of the NPs became smaller while the catalytic activity is diminished. In the case of Ag-NPsdextran (entry 1, Table 3), the moderate interaction between Ag-NPs and oxygen atoms in dextran backbones might allow an efficient equilibrium between NPs stabilization and catalysis. Moreover, dextran as a polysaccharide, presenting both hydrophobic and hydrophilic regions, which can form multiple intermolecular interactions in aqueous systems.43 These hydrophobic domains in the polymer backbone may increment the hydrophobicity surrounding the reaction microenvironment, which would be responsible for drawing in substrates from aqueous medium increasing the local substrate concentration, and consequently, accelerating the reaction rate. These results suggest that the catalytic performance of Ag-NPs depends on both the surface functional groups and the structures of the stabilizer/support. Figure 9. kapp as a function of Ag-NP total surface area (S), at [NaBH4] = 8.8 102 mol L1, [Nip] = 8.8 105 mol L1, and 25 °C.
Table 3. Comparison between Catalytic Activity of Silver Nanoparticle Systems in p-Nitrophenol Reduction entry
Dm, nmb
k1, (s1 m2 L)c
6.1 ( 1.3
1.41
24.5 ( 4.1 3.0 ( 1.2
0.57 0.078
stabilizer
1
dextran T500a
2 3
derivatized PEI20 anionic polyelectrolyte brush38
4
chitosan33
5
aminosilicate39
∼3.6
0.188
6
carboxymethyl chitosan microgel40
2.81 ( 0.62
0.124
7
carboxymethyl chitosan microgel40
3.45 ( 0.65
0.196
∼3
0.15
a
This study. b Diameter of metal nanoparticles. c Rate constant normalized to surface area of metal nanoparticles per volume unit.
surface area of all metal nanoparticles,8,21,23,35,37 associated with the fact that at certain conditions the reaction is zero-order with respect to NaBH4, the rate of the reduction reaction of Nip can be expressed as
d½Nip ¼ k1 S½Nip ¼ kapp ½Nip dt
ð2Þ
where S is the surface area of the metal nanoparticles normalized to the unit volume of the reaction system and k1 is the rate constant normalized to S. The plot of kapp as a function of the surface area S is shown in Figure 9. It can be observed that the rate constant kapp is indeed proportional to the total surface area of the nanoparticles in the system; hence, it can be concluded that catalysis takes place on the surface of the nanoparticles. From the angular coefficient for the plot in Figure 9, the rate constant k1 was determined to be 1.41 ( 0.07 s1 m2 L, an extraordinarily high catalytic efficiency when compared with other silver nanoparticle catalysts reported in the literature (Table 3). In all cases, Ag-NPs were used for the reduction of Nip with a large excess of NaBH4. The comparison and rationalization of the values listed in Table 3 is rather difficult, and attribution of these catalytic effects to reasons other than the high surface area presented by metal nanoparticles is speculation. Indeed, many variables are believe
’ CONCLUSIONS With the multivariate method used in this study, the formation of small Ag-NPs with a narrow size distribution was successfully optimized. The significant reduction in the number of experiments represents a more economical approach compared with the vast majority of univariate methods reported in the literature. Moreover, the Ag-NPsdextran composite was able to efficiently catalyze the p-nitrophenol reduction in water with a rate constant normalized to the surface area of the nanoparticles per unit volume (k1) of 1.41 s1 m2 L, which is higher than values reported in the literature for Ag-NPs catalytic systems. This catalytic effect is most probably due to the moderate interaction of the Ag-NPs and the oxygen atoms present in dextran, which allow an efficient equilibrium between NP stabilization and catalysis. Moreover, the presence of hydrophobic domains in dextran may concentrate the substrate at the local reaction microenvironment by drawing it from aqueous medium and, consequently, accelerating the reaction rate. ’ ASSOCIATED CONTENT
bS
Supporting Information. Multivariate optimization experimental designs, concentrations data in each optimization experiment and ANOVA table. This material is available free of charge via the Internet at http://pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*E-mail address:
[email protected].
’ ACKNOWLEDGMENT We are grateful to CNPq for financial support and to CAPES for the scholarship from the Colegio Doutoral Franco-Brasileiro granted to Renato Eising. We would also like to thank Prof. Roy Bruns, Prof. Eduardo Carasek, and Dr. Edmar Martendal for their help with the multivariate experiments, the Central Laboratory of Electron Microscopy (LCME) at UFSC for the TEM analysis, Isabelle Pignot-Paintrand for the TEM, and Christophe Travelet for DLS analysis at CERMAV. 11865
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