How Does a Single Pt Nanocatalyst Behave in Two DifferentReactions? A Single-Molecule StudyKyu Sung Han,† Guokun Liu,† Xiaochun Zhou, Rita E. Medina, and Peng Chen*

Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States

*S Supporting Information

ABSTRACT: Using single-molecule microscopy of fluoro-genic reactions we studied Pt nanoparticle catalysis at single-particle, single-turnover resolution for two reactions: one anoxidative N-deacetylation and the other a reductive N-deoxygenation. These Pt nanoparticles show distinct catalytickinetics in these two reactions: one following noncompetitivereactant adsorption and the other following competitivereactant adsorption. In both reactions, single nanoparticlesexhibit temporal activity fluctuations attributable to domi-nantly spontaneous surface restructuring. Depending on thereaction sequence, single Pt nanoparticles may or may notshow activity correlations in catalyzing both reactions, reflecting the structure insensitivity of the N-deacetylation reaction and thestructure sensitivity of the N-deoxygenation reaction.

KEYWORDS: Single-particle catalysis, platinum nanoparticles, single-molecule imaging, deacetylation and deoxygenation,structure sensitivity

Metal nanoparticles are perhaps the most importantindustrial catalysts. They can catalyze many chemical

transformations and have applications ranging from chemicalsynthesis to pollutant removal and to energy production andstorage.1−9 Yet they are structurally inhomogeneous, even withthe state-of-the-art colloidal synthesis that is capable ofproducing single-crystal nanoparticles with well-definedshapes.4,10−13 Moreover, because of their nanometer dimen-sion, they are structurally dynamic, especially on their surfacesand under reaction conditions where the constantly changingadsorbate−surface interactions can further induce dynamicsurface restructuring.14−20 These structural inhomogeneitiesand dynamics present an inherent challenge to characterizingand understanding the catalytic activity of nanoparticlesfundamentally, as individual particles differ from one to anotherand from time to time; and it becomes necessary to study theircatalysis at the single-particle level in real time. Significantprogress has been made lately by several groups in studyingnanoparticle catalysis at the single-particle level using methodsincluding localized surface plasmon resonance spectrosco-py,21−24 single-molecule fluorescence microscopy,25−27 andelectrochemical detection.28−31 Our group has used single-molecule fluorescence microscopy to study Au nanoparticlecatalysis at single-particle, single-turnover resolution; thesestudies revealed intricate interplays between inhomogeneousreactivity, selectivity in parallel reaction pathways, dynamic-surface-restructuring coupled catalytic dynamics, and reactant-concentration-dependent surface switching in nanocataly-sis.26,32−37

Most metal nanoparticles can catalyze a multitude ofchemical transformations, for example, Pt nanoparticles cancatalyze both oxidative and reductive reactions.14 For aparticular type of nanoparticle, its activities in catalyzing thesedifferent reactions can be correlated to each other or have littlecorrelation, because different reactions may occur at differentsurface sites on the same nanoparticles. Considering theinherent inhomogeneity of nanoparticle catalysts, a fundamen-tal question then arises: how would the catalytic behavior of asingle nanoparticle be correlated between different reactions?An answer to this question will contribute to understanding thestructure−activity correlation of nanoparticle catalysts across avariety of chemical transformations. Along this line, here westudy the catalysis of individual Pt nanoparticles in two differentreactions, one an oxidative N-deacetylation reaction and theother a reductive N-deoxygenation reaction, using single-molecule microscopy of fluorogenic reactions (Figure 1A,B).We prepared colloidal Pt nanoparticles by reducing

chloroplatinic acid (H2PtCl6·(H2O)6) with sodium borohydride(NaBH4) in aqueous solution that also contains citrate ions,following reported procedures (see Supporting InformationSection 1 for details).38 The citrate ions here are weak ligands,helping stabilize the Pt nanoparticles. This preparation does notuse strong-binding capping ligands or polymers to stabilize Ptnanoparticles, thus alleviating ligand passivation of nanoparticlesurfaces for catalysis. The resulting Pt nanoparticles are 4.6 ±

Received: October 18, 2011Revised: January 21, 2012Published: January 25, 2012

Letter

pubs.acs.org/NanoLett

© 2012 American Chemical Society 1253 dx.doi.org/10.1021/nl203677b | Nano Lett. 2012, 12, 1253−1259

0.9 nm in diameter, having faceted shapes of cube, tetrahedron,or cuboctahedron (Figure 1C). These Pt nanoparticles cancatalyze two fluorogenic reactions (Figure 1B, and SupportingInformation Section 3, Figures S2 and S3): (1) the oxidative N-deacetylation of amplex red (AR), a nonfluorescent molecule,to resorufin, a highly fluorescent molecule, by hydrogenperoxide (H2O2), and (2) the reductive N-deoxygenation ofresazurin (RZ), another nonfluorescent molecule, to resorufinby hydrazine (N2H4). The strong laser-induced fluorescencesignal of the catalytic product resorufin in both reactions allowsits detection at the single-molecule level.To monitor single Pt nanoparticles catalyzing either of these

two reactions, we dispersed Pt nanoparticles on a quartz slideinside a microfluidic channel and flowed in the reactantsolution with various reactant concentrations (0.3 to 10 μMamplex red and 200 mM H2O2 for the oxidative N-deacetylation reaction; and 0.001 to 1 μM resazurin and0.025 to 8 mM N2H4 for the reductive N-deoxygenationreaction, Figure 1A). The Pt nanoparticles are immobile due tononspecific interactions with the quartz slide. Using a total-internal-reflection fluorescence microscope, an electric multi-plying charge coupled device camera operating at a 20 ms framerate, and a 532 nm laser to excite resorufin fluorescence (Figure1A), we recorded movies of fluorescence bursts on individual Ptnanoparticles; each fluorescence burst signals the generation ofa catalytic product molecule resorufin on a single Ptnanoparticle.

Figure 1D shows time trajectories of fluorescence intensitiesfrom two Pt nanoparticles, each undertaking one of the twocatalytic reactions. The trajectories contain many shortfluorescence intensity bursts, each burst marking the generationof a product molecule resorufin, that is, one catalytic turnover.For a single Pt nanoparticle, the time τ between the appearanceof a burst and that of the subsequent one is the microscopicreaction time for each product generation reaction; the lengthsof τ are stochastic, but its statistical properties, such as itsaverages and distributions, are well-defined by the underlyingreaction kinetics. From these fluorescence turnover trajectories,the turnover rate v of a single Pt nanoparticle can be obtainedeasily by counting the number of bursts per unit time; and valso equals ⟨τ⟩−1, where ⟨ ⟩ denotes averaging.With the capability of quantifying the turnover rates (v) of

individual Pt nanoparticles, we examined how v depends onreactant concentrations to probe the kinetic mechanisms of thetwo catalytic reactions. When averaged over many nano-particles, v shows a dependence on the reactant concentrationsin both catalytic reactions (Figure 2A,B). Yet, the two catalyticreactions show distinct differences in how v changes withreactant concentrations. For the oxidative N-deacetylationreaction, the single-particle turnover rate v exhibits saturationkinetics with increasing concentration of the reactant amplexred while the other reactant H2O2 is kept constant and at alarge excess (Figure 2A). In contrast, for the reductive N-deoxygenation reaction, when the concentration of the reactantresazurin is increased while that of N2H4 is kept constant, v

Figure 1. (A) Experimental scheme of single-molecule fluorescence measurements of catalysis by individual nanoparticles using total-internal-reflection fluorescence microscopy of fluorogenic reactions. (B) Pt-nanoparticle-catalyzed oxidative N-deacetylation of amplex red by H2O2 andreductive N-deoxygenation of resazurin by N2H4. Both reactions lead to formation of the fluorescent resorufin. (C) TEM image of Pt nanoparticles.Inset: diameter distribution of Pt nanoparticles; NPs = nanoparticles; Gaussian fit gives an average diameter of 4.6 ± 0.9 nm. (D) Exemplaryfluorescence intensity versus time trajectories of single Pt nanoparticles under catalysis for (1) the oxidative N-deacetylation reaction at 5 μM amplexred and 200 mM H2O2 and (2) the reductive N-deoxygenation reaction at 0.2 μM resazurin and 1 mM N2H4. Time resolution: 20 ms. τ is themicroscopic reaction time between sequential reaction events, which are manifested by the fluorescence intensity bursts.

Nano Letters Letter

dx.doi.org/10.1021/nl203677b | Nano Lett. 2012, 12, 1253−12591254

initially increases until it reaches a maximum before decaying athigher resazurin concentrations; similar behavior is observedwhen the N2H4 is increased while the resazurin concentration iskept constant (Figure 2B).The kinetics of the two catalytic reactions can both be

interpreted by the Langmuir−Hinshelwood mechanism forheterogeneous catalysis,39 but with key differences in how thetwo reactants of each catalytic reaction adsorb onto the surfacesites of Pt nanoparticles. For the oxidative N-deacetylationreaction, the adsorption of the two reactants, amplex red andH2O2, follows a noncompetitive model, in which they adsorbonto different types of surface sites on a Pt nanoparticle.Consequently, the turnover rate v follows saturation kineticswhen the concentration of one reactant is increased while theother is kept constant. This type of Langmuir−Hinshelwoodkinetics is described quantitatively by the following equation(see Supporting Information Section 2)39

=+ +

v kK K

K K[AR][O]

(1 [AR])(1 [O])AR effAR O

AR O (1a)

where vAR is the single-particle turnover rate for the oxidativeN-deacetylation reaction of amplex red; keff is the single-particlecatalytic rate constant, representing the reactivity of an entire Ptnanoparticle; AR stands for amplex red and O for H2O2; andKAR and KO are the respective reactant adsorption equilibriumconstants. Under saturating H2O2 concentrations, eq 1areduces to

=+

v kK

K[AR]

(1 [AR])AR effAR

AR (1b)

On the other hand, for the reductive N-deoxygenation reaction,the adsorption of the two reactants, resazurin and N2H4,follows a competitive model, in which they adsorb onto thesame type of Pt nanoparticle surface sites. Consequently, theturnover rate v decays when one reactant is at very highconcentration and dominates the surface site occupation,making the other reactant unavailable for reaction. This typeof Langmuir−Hinshelwood kinetics is described quantitativelyby the following equation (see Supporting Information Section2)39

=+ +

v kK KK K

[RZ][R]

(1 [RZ] [R])RZ effRZ R

RZ R2

(2)

where RZ stands for resazurin and R for N2H4; the restparameters have similar definitions as in eq 1a. Fitting the datain Figure 2A,B with eqs 1b and 2 gives keff = 0.047 ± 0.009s−1 particle−1 and KAR = 1.7 ± 1.2 μM−1 for the oxidative N-deacetylation reaction, and keff = 0.02 ± 0.03 s−1 particle−1, KRZ= 20 ± 11 μM−1, and KR = 0.0025 ± 0.0014 μM−1 for thereductive N-deoxygenation reaction. As the data in Figure 2A,Bare averaged over many particles, all the values here reflect theaveraged properties of many Pt nanoparticles. We could not dothe H2O2 concentration titration of the single-particle catalysisexperiment for the oxidative N-deacetylation reaction becauseat low concentrations H2O2 is unstable during the course ofexperiments (∼1 h), but ensemble kinetics show the expectedsaturation kinetics when H2O2 concentration is titrated(Supporting Information Figure S4B), as predicted by eq 1a.With the above kinetic mechanisms we can now quantify the

catalytic activity of individual Pt nanoparticles in both reactionsand determine their activity distributions. From the mechanism,we can derive the probability density functions f(τ) of themicroscopic reaction times τ for both the oxidative N-deacetylation reaction ( fAR(τ)) and the reductive N-deoxyge-nation reaction ( f RZ(τ)) (see Supporting Information Section2)

τ =+ +

−+ +

τ

= − τ→∞

→∞

⎛⎝⎜

⎞⎠⎟

f kK K

K K

kK K

K K

k k

( )[AR][O]

(1 [AR])(1 [O])

exp[AR][O]

(1 [AR])(1 [O])

exp( )

AR effAR O

AR O

effAR O

AR O

[O]

[AR]eff eff (3a)

τ =+ +

−+ +

τ

= − τ

⎛⎝⎜⎜

⎞⎠⎟⎟

f kK KK K

kK KK K

k k

( )[RZ][R]

(1 [RZ] [R])

exp[RZ][R]

(1 [RZ] [R])

exp( )

RZ effRZ R

RZ R2

effRZ R

RZ R2

app app (3b)

Figure 2. (A) Dependence of the single-particle turnover rate v on theamplex red concentration for the Pt-nanoparticle-catalyzed oxidativeN-deacetylation reaction. [H2O2] = 200 mM. (B) Dependence of v onthe resazurin and N2H4 concentrations for the reductive N-deoxygenation reaction. When resazurin concentration was titrated,N2H4 was kept at 1 mM; when N2H4 concentration was titrated,resazurin concentration was kept at 0.1 μM. Each data point in (A,B)is an average from the fluorescence turnover trajectories of >50nanoparticles, with sem as the error bar. Solid line in (A) is a fit witheq 1b with keff = 0.047 ± 0.009 s−1 particle−1, KAR = 1.7 ± 1.2 μM−1,and those in (B) are global fits with eq 2 with keff = 0.02 ± 0.03 s−1

particle−1, KRZ = 20 ± 11 μM−1, KR = 0.0025 ± 0.0014 μM−1. AR,amplex red; RZ, resazurin; R, N2H4. (C) Distribution of keff fromindividual Pt nanoparticles for the oxidative N-deacetylation reaction.Solid line is a Gaussian fit with center at 0.036 ± 0.002 s−1 particle−1

and full-width-at-half-maximum (fwhm) of 0.038 ± 0.005 s−1 particle−1.Inset: distribution of τ from a single fluorescence turnover trajectory at[AR] = 5 μM and [H2O2] = 200 mM; solid line is a single exponentialfit with a decay constant keff = 0.09 ± 0.01 s−1 particle−1. (D)Distribution of kapp from individual Pt nanoparticles at [RZ] = 0.2 μMand [N2H4] = 1 mM for the reductive N-deoxygenation reaction. Solidline is a Gaussian fit with center at 0.024 ± 0.001 s−1 particle−1 andfwhm of 0.018 ± 0.001 s−1 particle−1. Inset: distribution of τ from asingle fluorescence turnover trajectory; solid line is a single exponentialfit with a decay constant of kapp = 0.031 ± 0.003 s−1.

Nano Letters Letter

dx.doi.org/10.1021/nl203677b | Nano Lett. 2012, 12, 1253−12591255

In eq 3b kapp = keffKRZKR[RZ][R]/(1 + KRZ[RZ]+KR[R])2.

Both probability density functions are exponential distributionfunctions. Moreover, fAR(τ) reduces to a simple form when thereactant concentrations are saturating (eq 3a). It also followsthat ⟨τ⟩AR

−1 = 1/∫ 0∞τfAR(τ)dτ = νAR and ⟨τ⟩RZ

−1 = 1/∫ 0∞τf RZ(τ)dτ

= νRZ, as expected (see eqs 1a, , and 2, and SupportingInformation Section 2). Figure 2C inset shows the distributionof τ from the fluorescence turnover trajectory of a single Ptnanoparticle catalyzing the oxidative N-deacetylation reactionat a saturating reactant concentration. This distribution followsan exponential distribution with decay constant keff for this Ptnanoparticle, as shown by eq 3a. For the reductive N-deoxygenation reaction, the distribution of τ from thefluorescence turnover trajectory of a single Pt nanoparticlealso follows an exponential distribution (Figure 2D, inset); thedecay constant here is kapp as in eq 3b. By analyzing thedistributions of τ of single trajectories, we determined keff andkapp for many Pt nanoparticles and their distributions (Figure2C,D). Both keff and kapp are distributed over a broad range,indicating large activity heterogeneity among individual Ptnanoparticles in both reactions. We used a heterogeneity index(h, defined as the fwhm of the distribution divided by theaverage)33 as a measure of the relative spread of thedistribution. h for keff and kapp are 106 ± 15% and 75 ± 5%,respectively. This direct quantification of activity heterogeneityamong individual Pt nanoparticles is uniquely available fromsingle-particle measurements and is difficult to obtain fromensemble-averaged studies.Besides allowing for direct quantification of the activity

differences from one nanoparticle to another, the fluorescenceturnover trajectories also enable examining the activitydifferences from time to time for a single Pt nanoparticle, aunique capability of real-time single-particle catalysis measure-ments. Our previous studies32,34 have revealed temporal activityfluctuations of single Au nanoparticles of 6−14 nm in diameter,which are attributable to spontaneous and catalysis-induced

dynamic surface restructuring. The time scale of the activityfluctuations, which is also the time scale of the underlyingsurface restructuring, can be obtained from the autocorrelationfunction Cτ(m) of the microscopic reaction time τ: Cτ(m) =⟨Δτ(0)Δτ(m)⟩/⟨Δτ2⟩. Here m is the index of a catalyticturnover in a fluorescence turnover trajectory and Δτ(m) =τ(m) − ⟨τ⟩. In the presence of activity fluctuations, Cτ(m) ≥ 0and shows an exponential decay behavior; its decay timeconstant gives the activity fluctuation correlation time.The insets of Figure 3A,B show two such autocorrelation

functions Cτ(m), each from the fluorescence turnover trajectoryof a single Pt nanoparticle catalyzing either the oxidative N-deacetylation reaction or the reductive N-deoxygenationreaction at specified reactant concentrations (Figure 3 caption).Both Cτ(m) show exponential decay behaviors, manifesting thetemporal activity fluctuations of single Pt nanoparticles. Theexponential decay constants are 2.2 ± 0.6 turnovers for thenanoparticle in Figure 3A inset and 5 ± 2 turnovers for that inFigure 3B inset. Cτ(m) of each nanoparticle can then beconverted to Cτ(t) in which the turnover index m is convertedto real time t using the nanoparticle’s average turnover time.For both catalytic reactions, when Cτ(t) are averaged over manynanoparticles, their exponential decay behavior is preserved(Figure 3A,B). The two corresponding decay time constantsare 63 ± 18 and 39 ± 10 s, which are the activity fluctuationtime scales and also reflect the time scales of the underlyingdynamic surface restructuring at the respective active sites onthe Pt nanoparticles. Our observation of the surface-restructuring-coupled activity fluctuations of single Pt nano-particles is also consistent with the direct experimentaldemonstration that Pt-containing nanoparticles can reconstructdynamically during reactions.15

The inverse of activity fluctuation correlation times gives theactivity fluctuation rates. We determined the activity fluctuationrates of Pt nanoparticles at the various reactant concentrationsfor both catalytic reactions and plotted the fluctuation rates

Figure 3. (A) Autocorrelation function Cτ(t) of the microscopic reaction time τ from the fluorescence turnover trajectories of single Pt nanoparticlescatalyzing the oxidative N-deacetylation reaction at 5 μM amplex red and 200 mM H2O2. The x-axis was converted from the turnover index m to realtime using the average turnover time of each nanoparticle, and the data were averaged over >50 Pt nanoparticles. Solid line is a single exponential fitwith decay constant of 63 ± 18 s. Inset: autocorrelation function Cτ(m) from the fluorescence turnover trajectory of a single Pt nanoparticle; solidline is a single exponential fit with decay constant of 2.2 ± 0.6 turnovers. (B) Same as (A) but for the N-deoxygenation reaction at 0.2 μM resazurinand 1 mM N2H4. The decay constant of the exponential fit is 39 ± 10 s. Data averaged over >50 Pt nanoparticles. Inset: similar as that in (A). Thedecay constant of the exponential fit is 5 ± 2 turnovers. (C) Dependences of the activity fluctuation rates on the turnover rates of Pt and Aunanoparticles in catalysis. The activity fluctuation rates are the inverse of the activity fluctuation correlation times determined from theautocorrelation functions Cτ(t). Red and black lines are fits of horizontal lines at 0.017 ± 0.001 and 0.019 ± 0.006 s−1, corresponding to time scales of59 ± 4 and 53 ± 17 s, respectively. Blue line is for Au nanoparticles of ∼4.6 nm in diameter, extrapolated from the results on 6−14 nm Aunanoparticles in reference32 (see Supporting Information Section 4 for details). The linear dependence of the blue line on the turnover rate for Aunanoparticles reflects the catalysis-induced nature of the activity fluctuations, thus the underlying catalysis-induced surface restructuring; the y-intercept gives the time scale of the spontaneous surface restructuring of Au nanoparticles of ∼42 ± 9 s, corresponding to a spontaneous surfacerestructuring rate of 0.024 ± 0.005 s−1.

Nano Letters Letter

dx.doi.org/10.1021/nl203677b | Nano Lett. 2012, 12, 1253−12591256

against the corresponding turnover rates in Figure 3C. Theactivity fluctuation rates are essentially independent of theturnover rates. This reflects that the underlying dynamic surfacerestructuring of Pt nanoparticles, which causes the activityfluctuations, is independent of the turnover rate in bothcatalytic reactions. This is in sharp contrast to the behavior ofAu nanoparticles we studied previously:34 their activityfluctuation rate increases linearly with increasing turnover ratebecause of catalysis-induced dynamic surface restructuring(Figure 3C). Therefore, the dynamic surface restructuring ofPt nanoparticles is largely spontaneous (i.e., thermally driven)under our reaction conditions, and the catalysis-induced effecthere is minimal. Consistently, the observed activity fluctuationrates of Pt nanoparticles, which are equivalent to the underlyingsurface restructuring rates, are about the same in both theoxidative N-acetylation reaction and the reductive N-deoxyge-nation reaction (0.017 ± 0.001 and 0.019 ± 0.006 s−1,respectively; both are averages of the respective data pointsacross different turnover rates; Figure 3C), as spontaneousdynamic surface restructuring is intrinsic to the nanoparticleand should be independent of the type of the catalyticreactions. Furthermore, the spontaneous surface restructuringrate of Pt nanoparticles is slightly slower than that of Aunanoparticles of similar diameter (∼0.024 ± 0.005 s−1, Figure3C), consistent with that Pt surfaces are thermodynamicallymore stable than Au surfaces under similar conditions.40

With an understanding of the kinetic mechanisms for boththe oxidative N-deacetylation reaction and the reductive N-deoxygenation reaction and of how single Pt nanoparticlesdiffer individually and temporally in each reaction, we can nowexamine how a single Pt nanoparticle behaves in catalyzing both

reactions. To do so, we subjected the same set of Ptnanoparticles to the two catalytic reactions sequentially, eachreaction lasting for about an hour at fixed reactantconcentrations. The reactant concentrations were chosen tohave high turnover rates in both reactions based on the titrationkinetics in Figure 2A,B. We recorded the fluorescence turnovertrajectories for every Pt nanoparticle in both reactions. Figure4A plots the correlation between the turnover rates of every Ptnanoparticle in these two reactions, where each Pt nanoparticleunderwent the oxidative N-acetylation reaction first and thenthe reductive N-deoxygenation reaction. The turnover rate ofeach nanoparticle in each reaction is a time-averaged propertythroughout the entire period each reaction was run. ThePearson cross correlation coefficient ρ is merely 0.11 ± 0.05,that is, close to zero (the error bar here is the probable error ofthe correlation coefficient, given as 0.6745(1 − ρ2)/√N;41 N isthe number of nanoparticles). This small value of ρ indicateslittle correlation between the two turnover rates, that is, thecatalytic activity of a Pt nanoparticle in the later deoxygenationreaction have little bearing on its catalytic activity in the earlierdeacetylation reaction. In contrast, when the sequence of thetwo reactions was reversed, a significantly more positivecorrelation was observed between the two turnover rates ofindividual particles (ρ = 0.33 ± 0.04, Figure 4B). This positivecorrelation indicates that the catalytic activity of a Ptnanoparticle in the later deacetylation reaction now remem-bered more of its activity in the earlier deoxygenation reaction.As structure determines activity, the presence, or absence, of

correlation between the activities of individual Pt nanoparticlein catalyzing two different reactions must be related to theunderlying correlation, or the lack of it, between their surface

Figure 4. Correlation plots of the single-particle turnover rates v of individual Pt nanoparticles between two sequential reactions, each lasting for onehour. Each data point is from one Pt nanoparticle. The reaction sequences are (A) first is the oxidative N-deacetylation reaction and second is thereductive N-deoxygenation reaction, (B) first is N-deoxygenation and second is N-deacetylation, (C) first and second are both N-deoxygenation, and(D) first and second are both N-deacetylation. Histograms give the distributions of the single-particle turnover rates in each of the reactions and arefitted with Gaussian distributions (solid lines). The data points that lie outside the 95% confidence level (i.e., 1.96 standard deviations away from thecenter) of the Gaussian distributions were removed in all plots.

Nano Letters Letter

dx.doi.org/10.1021/nl203677b | Nano Lett. 2012, 12, 1253−12591257

structures under the two reaction conditions. Yet, the surface ofa Pt nanoparticle restructures dynamically during both of thesereactions, manifested by the temporal fluctuations of itscatalytic activity as discussed earlier. For these Pt nanoparticlesof ∼4.6 nm in diameter, the time scale of their dynamic surfacerestructuring is ∼50 s (Figure 3C caption), which is muchshorter than the time (∼1 h) we subjected them in catalyzingeach of the two reactions. Therefore, the surface structure of asingle Pt nanoparticle in the later reaction must be differentfrom that in the earlier reaction. As a nanoparticle always hasmany types of surface sites, this difference would be a differentcomposition of various sites on its surface, although the exactnature of the differences and the extent of these differences areunclear from our measurements. TEM showed slight roundingof the facetedness of these Pt nanoparticles after catalyzingthese reactions, but no large morphology was observed withinour reaction times (Supporting Information Figure S8).Past studies have shown that for a surface-catalyzed reaction,

depending on the nature of the chemical bond that is activatedin the rate-limiting step, the kinetics of this reaction can dependsensitively on the nanoparticle surface structure (i.e., a structuresensitive reaction) or be insensitive to the structural arrange-ments of surface atoms (i.e., a structure insensitivereaction).42,43

When catalyzing two reactions sequentially and during theearlier reaction the surface restructures (i.e., the composition ofvarious sites on the surface changes), this restructuring shouldcause a significant change in the activity of a nanoparticle incatalyzing a later structure-sensitive reaction. Consequently, theactivity of a nanoparticle in the later structure-sensitive reactionshould have little correlation with its activity in the earlierreaction. This scenario would rationalize the observed littlecorrelation (ρ ∼ 0.11) of the two turnover rates in Figure 4A,where the later N-deoxygenation reaction is presumably moreof a structure sensitive reaction. Alternatively, if the laterreaction is structure insensitive, the surface restructuringoccurred in the earlier reaction would affect less on the activityof a nanoparticle in the later reaction. Consequently, theactivity of a nanoparticle in the later structure-insensitivereaction should show some correlation with its activity in theearlier reaction. This alternative scenario would rationalize thesignificantly more correlation (ρ ∼ 0.33) in Figure 4B, wherethe later N-deacetylation reaction is presumably more of astructure insensitive one.If our hypothesis is correct that the N-deoxygenation

reaction is more of structure sensitive and the N-deacetylationreaction is more of structure insensitive, this hypothesis canpredict on the correlation between the two turnover rates ofindividual Pt nanoparticles if the two sequential reactions arethe same. If the two sequential reactions are both the structure-sensitive N-deoxygenation reaction, the correlation should stillbe weak because every Pt nanoparticle undergoes dynamicsurface restructuring during the reactions and individualnanoparticles restructure asynchronously and thus differently.If the two sequential reactions are both the structure-insensitiveN-deacetylation reaction, there should be a clear correlationbecause the surface restructuring of every nanoparticle has lesseffect on its activity. These two predictions were indeedobserved with ρ = 0.14 ± 0.05 and ρ = 0.43 ± 0.04,respectively, (Figure 4C,D), thus supporting our hypothesis.Our hypothesis is further supported by analyzing the activity

heterogeneity among the individual nanoparticles that under-went two sequential reactions. In each reaction, the turnover

rates of individual Pt nanoparticles follow a Gaussiandistribution; the heterogeneity index, h, defined as the fwhmof this distribution divided by the mean, is a quantitativemeasure of how the activities of individual particles differ fromone another. When a same set of Pt nanoparticles underwentthe N-deacetylation and N-deoxygenation reactions sequen-tially, and regardless of the order of the reactions, h for the N-deoxygenation reaction was always larger than that for the N-deacetylation reaction (Figure 5A). This is consistent with that

the N-deoxygenation reaction is more of structure sensitive andtherefore the structural differences among the individualnanoparticles translate more clearly into their differences inactivity, whereas the N-deacetylation is more of structuralinsensitive and therefore the structural differences among thesame set of nanoparticles translate less into the differences intheir activity. Consistently, when the two sequential reactionswere identical, no significant difference in their h values wasobserved for a same set of Pt nanoparticles (Figure 5B).Furthermore, our hypothesis that the reductive N-deoxyge-

nation of resazurin is more of structure sensitive is consistentwith our previous studies of 6−14 nm Au nanoparticlescatalyzing the same reaction (note the previous study usedNH2OH as the reductant instead of N2H4).

32,34 Withdecreasing Au nanoparticle size, its specific catalytic rateconstant increases significantly,34 following a class II structuresensitivity of surface reactions.42,44,45 Although more studies areneeded to elucidate the molecular mechanism of the nano-particle-catalyzed N-deoxygenation of resazurin studied here,previous computational studies have shown that this class IIstructure sensitivity is in general associated with σ-bondcleavage in the rate-limiting step.42

In summary, we have studied how single ∼4.6 nm Ptnanoparticles behave in catalyzing two different reactions in realtime at the single-turnover resolution using single-moleculemicroscopy of fluorogenic reaction: one an oxidative N-deacetylation of amplex red and the other a reductive N-deoxygenation of resazurin. We found that Pt nanoparticlesshow distinct catalytic kinetics in these two reactions: thereactants in the N-deacetylation reaction follow noncompetitiveadsorption while those in the N-deoxygenation reaction follow

Figure 5. Comparisons of the heterogeneity indices h of the turnoverrates among individual Pt nanoparticles in each of the reactions fromFigure 4, when (A) the two sequential reactions are different, one thereductive N-deoxygenation of resazurin and the other the oxidative N-deacetylation of amplex red, and (B) the two sequential reactions arethe same, being both N-deoxygenation or both N-deacetylation. The hvalues should be compared within each panel, not between differentpanels, as data in different panels are each obtained from a different setof Pt nanoparticles. In each panel, the labels on the x-axis designate thesequence of the reactions, that is, being the first or the second of thetwo sequential reactions.

Nano Letters Letter

dx.doi.org/10.1021/nl203677b | Nano Lett. 2012, 12, 1253−12591258

competitive adsorption within the model of Langmuir−Hinshelwood kinetics. Large activity heterogeneity is presentamong individual nanoparticles and the single-particle measure-ments provide direct quantification of this heterogeneity. Inboth reactions, individual Pt nanoparticles show temporalactivity fluctuations, which are independent of the turnover rateand attributable to dominantly spontaneous dynamic surfacerestructuring. The time scale of the underlying spontaneoussurface restructuring is about tens of seconds, slightly slowerthan that of Au nanoparticles of similar diameter, consistentwith Pt’s more stable surfaces. When catalyzing both reactionssequentially, and depending on the reaction sequence, single Ptnanoparticles may or may not show activity correlationsbetween these two reactions, reflecting that the N-deacetylationreaction is more of a structure insensitive surface reaction andthe N-deoxygenation reaction is more of a structure sensitivereaction. The knowledge from single-particle level studiesprovides fundamental insights into the catalytic behaviors ofnanoparticle catalysts, which are complementary to, and ofteninaccessible in, ensemble-averaged measurements.

■ ASSOCIATED CONTENT*S Supporting InformationMaterials and methods, additional results, and analyses. Thismaterial is available free of charge via the Internet at http://pubs.acs.org.

■ AUTHOR INFORMATIONCorresponding Author*E-mail: pc252@cornell.edu.Author Contributions†These authors contributed equally.NotesThe authors declare no competing financial interest.

■ ACKNOWLEDGMENTSThis research is funded mainly by the Chemical Sciences,Geosciences and Biosciences Division, Office of Basic EnergySciences, Office of Science, U.S. Department of Energy (DE-FG02-10ER16199), and in part by U.S. Army Research Office(W911NF0910232), the National Science Foundation (CBET-0851257), and an Alfred P. Sloan Research Fellowship (P.C.).Rita Medina was a REU student supported by the CornellCenter for Materials Research (CCMR), a NSF fundedMRSEC center. We also thank Ivan Keresztes for NMRanalysis and Nesha May Andoy for discussions. TEM facility atCCMR is supported by a NSF-MRSEC program (DMR-1120296).

■ REFERENCES(1) Somorjai, G. A.; Contreras, A. M.; Montano, M.; Rioux, R. M.Proc. Natl. Acad. Sci. U.S.A. 2006, 103, 10577.(2) Ertl, G.; Knozinger, H.; Weitkamp, J. Handbook of heterogeneouscatalysis; VCH: Weinheim, 1997.(3) Bell, A. T. Science 2003, 299, 1688.(4) Burda, C.; Chen, X.; Narayanan, R.; El-Sayed, M. A. Chem. Rev.2005, 105, 1025.(5) Chen, M.; Goodman, D. W. Acc. Chem. Res. 2006, 39, 739.(6) Crooks, R. M.; Zhao, M.; Sun, L.; Chechik, V.; Yeung, L. K. Acc.Chem. Res. 2001, 34, 181.(7) Lewis, L. N. Chem. Rev. 1993, 93, 2693.(8) Astruc, D.; Lu, F.; Aranzaes, J. R. Angew. Chem., Int. Ed. 2005, 44,7852.

(9) Daniel, M. C.; Astruc, D. Chem. Rev. 2004, 104, 293.(10) Tao, A. R.; Habas, S.; Yang, P. Small 2008, 4, 310.(11) Xia, Y.; Xiong, Y.; Lim, B.; Skrabalak, S. E. Angew. Chem., Int. Ed.2009, 48, 60.(12) Grzelczak, M.; Perez-Juste, J.; Mulvaney, P.; Liz-Marzan, L. M.Chem. Soc. Rev. 2008, 37, 1783.(13) Murphy, C. J.; Thompson, L. B.; Chernak, D. J.; Yang, J. A.;Sivapalan, S. T.; Boulos, S. P.; Huang, J.; Alkilany, A. M.; Sisco, P. N.Curr. Opin. Colloid Interface Sci. 2011, 16, 128.(14) Somorjai, G. A.; Li, Y. Introduction to Surface Chemistry andCatalysis, 2nd ed.; John Wiley & Sons: Hoboken, NJ, 2010.(15) Tao, F.; Grass, M. E.; Zhang, Y.; Butcher, D. R.; Renzas, J. R.;Liu, Z.; Chung, J. Y.; Mun, B. S.; Salmeron, M.; Somorjai, G. A. Science2008, 322, 932.(16) Wang, Z. L. Adv. Mater. 2003, 15, 1497.(17) Hansen, P. L.; Wagner, J. B.; Helveg, S.; Rostrup-Nielsen, J. R.;Clausen, B. S.; Topsoe, H. Science 2002, 295, 2053.(18) Newton, M. A.; Belver-Coldeira, C.; Martinez-Arias, A.;Fernandez-Garcia, M. Nat. Mater. 2007, 6, 528.(19) King, D. A.; Woodruff, D. P. Phase Transitions and AdsorbateRestructuring at Metal Surfaces; Elsevier Science: New York, 1994.(20) Imbihl, R.; Ertl, G. Chem. Rev. 1995, 95, 697.(21) Novo, C.; Funston, A. M.; Mulvaney, P. Nat. Nanotechnol. 2008,3, 598.(22) Liu, N.; Tang, M. L.; Hentschel, M.; Giessen, H.; Alivisatos, A.P. Nat. Mater. 2011, 10, 631.(23) Tang, M. L.; Liu, N.; Dionne, J. A.; Alivisatos, A. P. J. Am. Chem.Soc. 2011, 133, 13220.(24) Cheng, J.; Liu, Y.; Cheng, X.; He, Y.; Yeung, E. S. Anal. Chem.2010, 82, 8744.(25) Cremer, G. D.; Sels, B. F.; Vos, D. E. D.; Hofkens, J.; Roeffaers,M. B. J. Chem. Soc. Rev. 2010, 39, 4703.(26) Chen, P.; Zhou, X.; Shen, H.; Andoy, N. M.; Choudhary, E.;Han, K.-S.; Liu, G.; Meng, W. Chem. Soc. Rev. 2010, 39, 4560.(27) Tachikawa, T.; Majima, T. Chem. Soc. Rev. 2010, 39, 4802.(28) Xiao, X.; Pan, S.; Jang, J. S.; Fan, F.-R. F.; Bard, A. J. J. Phys.Chem. C 2009, 113, 14978.(29) Meier, J.; Friedrich, K. A.; Stimming, U. Faraday Discuss. 2002,121, 365.(30) Chen, S.; Kucernak, A. J. Phys. Chem. B 2004, 108, 13984.(31) Li, Y.; Cox, J. T.; Zhang, B. J. Am. Chem. Soc. 2010, 132, 3047.(32) Xu, W.; Kong, J. S.; Yeh, Y.-T. E.; Chen, P. Nat. Mater. 2008, 7,992.(33) Xu, W.; Kong, J. S.; Chen, P. Phys. Chem. Chem. Phys. 2009, 11,2767.(34) Zhou, X.; Xu, W.; Liu, G.; Panda, D.; Chen, P. J. Am. Chem. Soc.2010, 132, 138.(35) Chen, P.; Xu, W.; Zhou, X.; Panda, D.; Kalininskiy, A. Chem.Phys. Lett. 2009, 470, 151.(36) Xu, W.; Shen, H.; Liu, G.; Chen, P. Nano Res. 2009, 2, 911.(37) Xu, W.; Kong, J. S.; Chen, P. J. Phys. Chem. C 2009, 113, 2393.(38) Eychmuller, A.; Bigall, N. C.; Hartling, T.; Klose, M.; Simon, P.;Eng, L. M. Nano Lett. 2008, 8, 4588.(39) Satterfield, C. N. Heterogeneous Catalysis in Practice; McGraw-Hill Book Company: New York, 1980.(40) Foiles, S. M.; Baskes, M. I.; Daw, M. S. Phys. Rev. B 1986, 33,7983.(41) Eells, W. C. J. Am. Stat. Assoc. 1929, 24, 170.(42) Van Santen, R. A. Acc. Chem. Res. 2009, 42, 57.(43) Boudart, M.; Djega-Mariadassou, G. Kinetics of HeterogeneousCatalytic Reactions; Princeton University Press: Princeton, 1984.(44) Che, M.; Bennett, C. O. Adv. Catal. 1989, 36, 55.(45) Henry, C. C., C.; Giorgio, S.; Goyhenex, C. In Chemisorptionand Reactivity on Supported Clusters and Thin Films; Lambert, R. M.,Pacchioni, G., Eds.; Kluwer Academic: Dordrecht, The Netherlands,1997; p 117.

Nano Letters Letter

dx.doi.org/10.1021/nl203677b | Nano Lett. 2012, 12, 1253−12591259