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Single-Molecule Nanocatalysis of Pt Nanoparticles Xiaodong Liu, Tao Chen, Ping Song, Yuwei Zhang, and Weilin Xu J. Phys. Chem. C, Just Accepted Manuscript • Publication Date (Web): 02 Jan 2018 Downloaded from http://pubs.acs.org on January 2, 2018

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The Journal of Physical Chemistry

Single-Molecule Nanocatalysis of Pt Nanoparticles Xiaodong Liua,b, Tao Chena,c, Ping Songa, Yuwei Zhanga, Weilin Xua* a

State Key Laboratory of Electroanalytical Chemistry, and Jilin Province Key Laboratory of Low

Carbon Chemical Power, Changchun Institute of Applied Chemistry, Chinese Academy of Science, 5625 Renmin Street, Changchun 130022, China b

c

University of Science and Technology of China, Anhui 230026, China

University of Chinese Academy of Science, Beijing 100049, China

KEYWORDS: single-molecule, Pt nanoparticle, nanocatalysis, fluorescence, reduction

ABSTRACT: Due to the inhomogeneous structure of nanoparticles, many underlying catalytic details of these catalysts are hidden in the ensemble-averaged measurements. The single-molecule approach enables studying the catalytic behavior of nanoparticles at single-particle level in single-turnover resolution. Here, based on such method, we study the catalytic behaviors of individual Pt nanoparticles to reveal the catalytic properties of nanoparticles of the product formation and desorption process. It is found that the catalytic reaction on Pt nanoparticles follows competitive mechanism in product formation process, while the product desorption process shows no selectivity between the indirect and direct desorption pathways. Moreover, the dynamic heterogeneity of Pt nanoparticles in product formation and desorption process is revealed to be due to the catalysis-induced surface restructuring. Surprisingly, it is found both experimentally and theoretically that the tiny difference of substrate molecules could lead to huge difference of surface restructuring even on the same type of nanoparticles.

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Introduction Due to their ability to catalyze many chemical transformations and broad applications in industrial catalysts, metal nanoparticles have attracted considerable attention.1-3 Nanoparticles have superior catalytic activity because of their distinct properties, such as their increased surface-to-volume ratio and quantum confinement, compared with their corresponding bulk materials.4-19 Moreover, because of nanometer dimension and constantly adsorbate-surface interactions under reaction conditions, they are structurally dynamic,13,18-22 and the structural changes can cause temporal activity fluctuations. Although nanoparticles have been studied at the ensemble level to obtain the averaged catalytic properties, the method of characterization is inadequate because of structural dispersion and heterogeneous distribution of surface sites. 9,11,18,19,23-26

Therefore it still has challenge to understanding the catalytic behavior of metal

nanoparticles fundamentally; and it is necessary to study the catalysis of nanoparticles by an effective approach. To address the above questions, single molecule and single nanoparticle methods were proposed to study the nanocatalysis, such as Raman, scan electrochemical microscopy, single molecule fluorescence microscopy (SMFM).27-30 However, how heterogeneous the nanoparticle activities are and how individual nanoparticle behaves temporally remains to be elucidated. Recently, we have been working to study these problems toward the capabilities by SMFM.31,32 Such as, we have studied catalytic kinetic and dynamic of Pd nanocubes at single-molecule level,32 and the catalytic activity of different types of surface atoms on Pd nanocubes has been revealed at the sub-particle level.31 The single-molecule approach enables us to study the catalytic behaviors of individual catalyst in real time with single turnover resolution while individual metal nanoparticle differs in structure from one to another.11,24,33,34 The measurement offers us an


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effective approach to study the kinetics of the catalytic product formation and product desorption process, and to determine the underlying kinetic mechanism. Furthermore, the dynamic surface restructuring can be analyzed through the activity fluctuations of individual nanoparticles during the time scale. Such as, the size-dependent catalytic properties of Au nanoparticles35 and Pdnanocubes31 have been studied at single-molecule level. Pt has superior catalytic properties among noble metal catalysts so that it has attracted special attention and has extensive application in both oxidative and reductive reactions.20Chen et al36 had reported the different behaviors of Pt nanoparticles in catalyzing two different reactions (one is the oxidation of Amplex red by H2O2, the other is the reduction of Resazurin by N2H4) at single particle level. In this work, in order to reveal more catalytic properties of Pt in different reactions, we mainly study how Pt nanoparticles behave in the reduction of Resazurin by H2 in aqueous solution. Interestingly, by comparing with Pt-catalyzed reduction of Resazurin by N2H4, some new information was revealed at single particle level for a better understanding of the Pt nanoparticles in different catalytic process. Experimental Section Materials and Methods Synthesis and characterization of Pt nanoparticles. According to the literature,37 these colloidal Pt nanoparticles are prepared by reducing chloroplatinic acid (H2PtCl6•(H2O)6) with sodium borohydride (NaBH4) in aqueous solution that also contains citrate ions. 36 mL of a 0.2 wt.% solution of H2PtCl6·(H2O)6 was added into 464 mL boiling deionized water and kept boiling and stirring for 1 min. Then 11 mL of a solution containing 1 wt.% sodium citrate and 0.05 wt.% citric acid was added. After 30 s, 5.5 mL of 0.08 wt.% sodium borohydride solution containing 1 wt.% sodium citrate and 0.05 wt.% citric acid was quickly injected. The obtained


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solution was kept boiling and stirring for 10 min and then cooled down to room temperature.37 The obtained sample was washed several times by centrifugation for further application in ensemble and single-molecule nanocatalysis. Single-molecule experiments. Single-molecule fluorescence measurements were performed on a home-built prism-type total internal re-flection (TIR) fluorescence microscope based on an Olympus IX71 inverted microscope (Supplementary Information, SI). A continuous wave circularly polarized 532 nm laser beam was focused onto a small region on the sample. Then the fluorescence of resorufin was collected by a water-immersion objective, and projected onto a camera controlled by an Andor IQ software. The IDL program is used to analyze the movies.

Results and Discussion Firstly, we investigated the catalytic properties and dynamics of Pt nanoparticles. Figure 1A-C and Figure S1 show the typical TEM images of Pt-nanoparticles with averaged diameter of 4.9±0.9 nm and Pt(111) facet exposed. Then we tested the ability of the Pt-nanoparticles in catalyzing the reduction of resazurin to resorufin by H2 in ensemble measurements by using UVVis absorption spectroscope, which was carried out in aqueous solution at room temperature. We found that almost no reaction happened after mixing of resazurin and hydrogen in absence of Pt nanoparticles, and the similar result happened between resazurin and Pt nanoparticles without hydrogen. After Pt nanoparticles were added to the mixed solution of resazurin and hydrogen, the catalytic reaction could be monitored, and it turns out the color of solution changed from blue (resazurin) to red (resorufin) gradually. The absorption spectrum of the reaction system (Figure 1D) shows a decrease of the resazurin absorption at 602 nm and an increase of the resorufin at


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572 nm over time. The time profiles of the absorbance at 572 nm and 602 nm (Figure 1E) show the quantitative conversion from resazurin to resozufin catalyzed by Pt-nanoparticles.

Figure 1 (A) Typical TEM images of Pt nanoparticles, and the averaged diameter is 4.9±0.9nm. (B) High-resolution micrograph of Pt nanoparticles. (C) The corresponding FFT (fast Fourier transition) pattern from the square in (B). (D) The situ absorption spectrum of the resazurin reduction to resorufin by H2, catalyzed by Pt nanoparticles in aqueous solution with hydrogen saturated (0.8 mM), [resazurin]0 = 20µM, and [Pt nanoparticles] ≈40 nM. (E) The time profiles of absorbance at 572 and 602 nm from (D), respectively.

The single-molecule nanocatalysis of Pt nanoparticles is based on the same Pt-catalyzed fluorogenic reduction of resazurin to resorufin by hydrogen, as shown above. Firstly, Pt nanoparticles are immobilized sparsely on quartz at low density so that individual nanoparticles are separated spatially by micrometer scale. Then a microfluidic channel was fabricated on the quartz slide in order that the reaction can conduct there. The nonfluorescent substrate resazurin


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with different concentrations dispersed in aqueous solution which was saturated by H2, were kept at a flow state in the channel. Fluorescent product resorufin was produced on individual Pt nanoparticle surface, and fluorescent signal was detected before it dissociated from the nanoparticles because of its fast diffusion in solution.38 We followed the hydrogenation process of resazurin catalyzed by Pt nanoparticle at singleturnover resolution through the total internal reflection fluorescence microscope (Figure 2A). The fluorescence bursts were attributed to the formation of fluorescent product molecules. The turnover trajectory from a single Pt-nanoparticle contains stochastic fluorescence off-on signals which represent two waiting times: τoff and τon, which can be observed in the turnover trajectories (Figure 2B). And τoff shows the waiting time before the formation of each product molecule. Moreover, the duration of each burst (τon) represents the time that one product molecule spends before it dissociates from nanoparticle surface.38 The two waiting times enable us to probe the underlying kinetic mechanism of the reduction reaction. Their statistical properties, such as average values and distributions, can report the kinetic mechanism of the catalytic process despite the randomness of their individual values. Figure 2C shows the total simulated mechanism for a turnover process depending on the two-site Langmuir–Hinshelwood mechanism for one product molecule formation.38 Both substrate and product maintain a fast adsorption equilibrium on the surface of nanoparticles in the turnover (Figure 2C, reaction); and the number of adsorbed substrate molecules follow Langmuir adsorption isotherm. There are two parallel pathways (Figure 2C): a substrate-assisted (reactions II and III) and a direct dissociation pathway (reaction IV) for the product dissociation reaction. In Figure 2C, When [B]→0, the reaction process (Figure 2C (III)) becomes extremely slow,


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because it lacks substrate to drive the product desorption. Therefore, the direct desorption pathway dominate the product desorption process (Figure 2C (IV)). When [B]→∞, the substrates are abundant and they can occupy the sites all the time. The product desorption process dominantly takes the indirect desorption pathway (Figure 2C (II, III)).

Figure 2 (A) Experimental setup for total internal reflection fluorescence microscopy and the single-molecule catalysis based on the Pt-catalyzed reduction reaction of Resazurin to Resorufin by H2. (B) Part of a typical fluorescence intensity turnover trajectory of a single Pt nanoparticle under catalysis under saturated H2 at 100 ms time resolution. (C) Kinetic mechanism of the reaction catalyzed by Pt-nanoparticle. N, one Pt-nanoparticle; A, B, and P represent hydrogen, Resazurin, and Resorufin molecules; n, l, the number of H2 and Resazurin molecules adsorbed on one nanoparticle surface at equilibrium; γeff the effective rate constant for the product formation step; γi the rate constants for product dissociation steps; θA, θB, the fraction of catalytic sites occupied by hydrogen and Resazurin.

We know τoff contains the kinetic of the catalytic product formation and τon includes that of the product dissociation from Figure 2C. The statistical properties of τoff and τon are defined by the reaction kinetics so we analyze the kinetic mechanism of Pt nanocatalysis by resolving τoff and


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τon. We can quantify the catalytic activity of individual Pt nanoparticles in reaction and obtain their activity distributions by the kinetic mechanism. Here, we evaluate the probability density functions f(τ)39(SI) to analyze the distributions of τoff and τon from each turnover trajectory and then probe the activity heterogeneity. That is to say that foff (τ) is the probability density of τoff and fon (τ) is that of τon(SI). Moreover, at saturating substrate concentration where all surface catalytic sites are occupied by substrates, the rate constant for reactionⅠequals γeff and reaction Ⅲ controls the τon reaction process. And γeff can be obtained from the parameters in foff(τ) (SI).38,39 = − 1 Here, γeff is the effective rate constant for one resorufin molecule formation on surface of one nanoparticle. And the function is an exponential function of time. Figure 3A shows a typical distributions of τoff from one turnover trajectory at the saturating substrate concentration; the fitting with eq (1) gives the decay constant γeff = 0.060±0.002 s-1. By determining γeff for individual trajectories from its distribution of τoff, we obtained γeff of many individual nanoparticles and the distribution was shown in the inset of Figure 3A. The broad distribution of γeff indicates the activity heterogeneity among individual Pt nanoparticle. It may be because of the difference of structure among nanoparticles and the inhomogeneity of size. Moreover, for the product desorption process, at high substrate concentrations, the probability density function fon (τ) (SI) can be reduced to a single-exponential decay function: ≈ 2 The product dissociation rate constant of the substrate-assisted pathway, γ2, can be obtained. However, the statistic number of τon events is usually small so that accurate fitting from one


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trajectory of τon is not easy to obtain. Figure 3B shows the total distribution of τon from 100 trajectories at a high substrate concentration, and we can get γ2 by fitting with an exponential decay function. By determining γ2 for every nanoparticle from the distribution of τon, the distribution of γ2 among nanoparticles can be gotten (Figure 3B, inset). We found that the distribution of γ2 is also broad, which reflects the activity heterogeneity of the product desorption process on nanoparticles.

Figure 3 (A),(B) Distributions of τoff (A) and τon (B) from a single fluorescence turnover trajectory of a single Pt-nanoparticle at 10nM resazurin. Experiments were carried out in H2-saturated aqueous solution. The solid lines in (A) and (B) are singleexponential fits with γeff = 0.060±0.002 s-1 (A) and γ2 = 2.03±0.16 s-1 (B). Inset: (A), (B) are distributions of γeff and γ2 from about 100 trajectories; solid lines are Gaussian fits. (C), (D) Single-molecule catalytic kinetics of Pt nanoparticles. The product −

−

formation rate (〈τoff〉 1) (C) is dependent on resazurin concentration, and the product desorption rate (〈τon〉 1) (D) is independent. Each data is averaged over above 100 turnover trajectories from nanoparticles, with the error bar S.E. Solid lines is fitting with Equation (1) with γeff = 0.48 s-1, αA = 0.25mM-1, αB = 0.28nM-1.


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Based on the above analysis, we proposed the reaction mechanism depending on a bimolecular reaction model (Figure 2C). The statistical properties of τoff and τon, 〈τoff〉−1 and 〈τon〉−1, which represent the time-averaged single-particle rates of product formation and product desorption. Based on the above analysis, we proposed the mechanism depending on a bimolecular reaction model (Figure 2C). Depending on statistical analysis of single-molecule process (SI), we obtain the single-molecule rate equations of product formation and product desorption process:

〈 〉 =

〈 〉 =

1

1

=

=

!" #$%!& '() 3 1 + !" +$, + !& +(,

.|(| + 0 4 1 + .#(%

In the formulate, [A] and [B] represent the H2 and Resazurin concentrations; = γ34 is the effective rate constant per nanoparticle for the reduction reaction; αA, αB are the adsorption constants of H2 and Resazurin; γ2 and γ3 are the rate constants for the two desorption process of product; .= #$%⁄ + is a parameter, relating to both the substrate adsorption (γ1)/desorption (γ-1) and γ2 without clear physical meaning. When averaging a series of turnover trajectories from Pt nanoparticles, 〈τoff〉−1 is depended on the resazurin concentration (Figure 3C). However, 〈τon〉−1 shows resazurin independent kinetics (Figure 3D). For the product formation, 〈τoff〉−1 fundamentally increases with the increasing substrate concentration to a maximum, then decays with higher substrate concentrations. The decay tendency is because of the competitive adsorption model of resazurin and H2. In the reaction, resazurin and H2 adsorb onto the same type sites on Pt nanoparticles surface. When H2 is at saturation state and occupies the surface dominating site, which makes the resazurin


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unavailable, 〈τoff〉−1 would present a tendency of decay after a maximum.40 For the product dissociation process, the dissociation rate does not change with the substrate concentration; it may be that two product dissociation pathways play a part together in the reaction (Figure 2C reaction (Ⅲ) and (Ⅳ)), and the rate constant of direct dissociation (γ2) almost equals to that of indirect dissociation (γ3). From the turnover trajectory, the temporal variation rate of turnover can be further analyzed. The obvious temporal variations indicate dynamic activity fluctuations of one nanoparticle. Activity fluctuations are because of the change of reaction rate in the product formation reaction (τoff) and the product desorption reaction (τon). We separate τoff and τon reaction to analyze the activity fluctuations from a number of individual τoff and τon extracted from individual turnover trajectory with the autocorrelation function 6 7 = 〈∆0∆7〉⁄〈∆ 〉.41 Here, τ represents τoff or τon, m is the turnover index number from the sequence and ∆τ7 = 7 − 〈〉(< > denoting average). In the function, Cτ(m)≥0 and shows a decay behavior where the decay constant is the fluctuation correlation time. For a single Pt nanoparticle, Cτoff and Cτon present an exponential decay tendency, indicating the activity fluctuations in τoff and τon reactions, respectively. The decay constants of Cτ are moff = 2.9±0.8 turnovers and mon = 1.5±0.7 turnovers (Figure 4A, B, inset). Furthermore, the average turnover time of this turnover trajectory is ~59 s, and the fluctuation correlation times for the τoff and τon reactions are ~75s and 45s, respectively. However, the fluctuation timescales of the dynamic surface restructuring can be reflected by the two correlation times. Furthermore, we convert the turnover index m to time t to analyze Cτ(t) with the trajectory’s average turnover time over many nanoparticles.38 And the exponential decay behavior of Cτoff and Cτon is presented


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(Figure 4A and B). Moreover, the activity fluctuations could be attributed to small-scale dynamic surface restructuring on the surface sites of nanoparticles. That is to say the correlation times of fluctuation activity are the timescales of the surface restructuring dynamics. To further analyze the relevance between activity fluctuations and surface restructuring, we plotted the rates of activity fluctuation against the turnover rate at the various reactant concentrations for both τoff and τon reactions. And the fluctuation rates are the inverses of the correlation times. For Pt nanoparticles, the activity fluctuation rates increase approximately linearly with the turnover rates of τoff reaction and the τon reaction (Figure 4C). The tendency is consistent with that of Au nanoparticle;35 however, it is in sharp contrast to the behavior of Pdnanocubes,32 which was found that the rates of activity fluctuation are independent of 〈τoff〉−1 and 〈τon〉−1 at the various reactant concentrations. The activity fluctuation observed here reflects the underlying dynamic surface restructuring existing among Pt nanoparticles, and supports the catalysis-induced nature of the activity fluctuations.35 Moreover, owing to the dispersion of size of nanoparticle, the total number of surface site is different between nanoparticles; it also contributes to the dynamic surface restructuring during catalytic process. Furthermore, as shown in Figure 4C, when the fluctuation rates were extrapolated to zero, the positive intercepts can give the approximate rates (0.003±0.001and 0.002±0.001 s-1 for the product formation (τoff) and desorption (τon) processes, respectively)of spontaneous surface restructuring dynamic for nanoparticles,42 corresponding to a time scale of about 300-500 s of the spontaneous surface reconstruction. Compared with the catalysis-induced time scale of tens of seconds, the much longer time scale indicates that the dynamic surface restructuring of Pt nanoparticles is largely catalysis-induced effect under the reaction conditions while the spontaneous effect here is minimal. This result is different from the previous observation (no catalysis-induced surface


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restructuring) on Pd nanoparticles, which although catalyze the same reaction as here, 31 but similar to the observation on Au, which catalyze the reduction by Resazurin by NH2OH, 35 more interestingly, even on the same type of Pt nanoparticles, Chen et al had observed no catalysisinduced surface restructuring for both oxidation and reduction reactions.36 For instance, for the Pt-catalyzed reduction reaction of Resazurin by reductant N2H4, it was found that the catalysis did not induce any surface restructuring; 36 while with changing of reductant from N2H4 to H2, here, we observed a huge catalysis-induced surface restructuring as shown in Figure 4C. The differences observed in these two similar reduction reactions catalyzed by Pt probably could be due to the different interactions of different reductants with Pt surface. Interestingly, as shown in Table S1 in SI, density functional theory (DFT) results indeed show that the interaction energy (Eint= -3.684 eV)between H2 and Pt(111) facet was much larger than that (Eint= -1.530 eV) between N2H4 and Pt(111) facet. The larger Eint indicates a stronger interaction between H2 and Pt(111) surface than that between N2H4 and Pt.43 The strong interaction between H2 and Pt(111) facet can explain the observed huge catalysis-induced surface restructuring in this work with H2 as reductant. Such big differences observed in different reactions indicate the behavior of catalysis-induced surface restructuring is both reactant-and metal-dependent complicatedly.

Figure 4 (A), (B) shows exemplary autocorrelation function Cτ (t) of small-scale reaction time from turnover trajectories of individual Pt nanoparticles. The x-axis was correlation time converted from the turnover index using the average turnover time of


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each nanoparticle. The solid line is fit with a single-exponential for decay constants of (A) 73 ± 23s and (B) 46 ± 20s. Inset: autocorrelation function from turnover trajectory of a single Pt nanoparticle. The solid line is a single-exponential fit with decay constants of (A) 2.9±0.8 and (B) 1.5±0.7. (C) Dependences of the rates of activity fluctuation on the turnovers rates. Circles represent the τon reaction and Squares represent the τoff reaction. Solid lines are linear fits. Each data point is an average from >60 trajectories here.

In conclusion, we have studied catalytic behavior of single Pt nanoparticles in reduction reaction via single-molecule fluorescence approach. It was found that the catalytic kinetics of Pt nanoparticles in the reduction reaction follows two-site adsorption of kinetics model. Large activity heterogeneity exists in product formation process and product desorption process among nanoparticles. Moreover, the dynamic heterogeneity of Pt nanoparticles in product formation and desorption process is revealed to be due to the catalysis-induced surface restructuring. Surprisingly, it is found that the tiny difference of substrate molecules could lead to huge difference of surface restructuring even on the same type of nanoparticles. The approach enables us to study the catalytic behaviors of nanoparticle catalysts from fundamental insights at singleparticle level, which is complementary to ensemble-averaged method.

ASSOCIATED CONTENT AUTHOR INFORMATION Corresponding Author *Email: [email protected] Notes The authors declare no competing financial interest. Acknowledgment


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This work was supported by National Basic Research Program of China (973 Program, 2014CB932700), National Natural Science Foundation of China (U1601211, 21633008, 21433003, 21422307, and21503212) and the “Recruitment Program of Global youth Experts” of China. Science and Technology Innovation Foundation of Jilin Province for Talents Cultivation(20160519005JH) and Jilin Youth foundation (20160520137JH). Supporting Information Experimental details; Characterization (TEM images); single-molecule kinetic equations and DFT calculation details. This information is available free of charge via the Internet at http://pubs.acs.org.

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