Supporting Information

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1 Supporting Information to Single-Molecule Catalysis Mapping Quantifies Site-specific Activity and Uncovers Radial Activity Gradient on Single D Nanocrystals Nesha May Andoy, Xiaochun Zhou, Eric Choudhary, Hao Shen, Guokun Liu, Peng Chen* Department of Chemistry and Chemical Biology, Cornell University, Ithaca, NY Table of Content S1. Synthesis and characterization of mesoporous-silica-coated Au nanoplates (Au@mSiO nanoplates)... S. Ensemble and single Au@mSiO nanoplate catalysis of the reductive N-deoxygenation of resazurin to resorufin... 4 S3. SEM and fluorescence image correlation... 6 S4. Single-molecule super-resolution imaging of catalytic events on individual Au@mSiO nanoplates... 7 S5. Mapping of catalytic events on the nanoplate structure S6. Dividing a Au@mSiO nanoplate into different regions... 1 S7. Assigning catalytic events to different regions on the nanoplate and determining the region s specific activity S8. The fluorescent product molecule does not diffuse appreciably within the mesoporous shell before it diffuses away into the surrounding solution S9. Derivation of the quantitative model of radial activity gradient on the nanoplate flat facets based on the growth-rate-dependent defect density S10. The reaction kinetics on Au@mSiO nanoplates is not mass-transport limited S11. Correcting for the number of product molecules detected at different regions of the nanoplate using the on-time (τ on ) distribution... 1 S1

2 S1. Synthesis and characterization of mesoporous-silica-coated Au nanoplates nanoplates) Au nanoplates were synthesized by reduction of AuCl 4 with aqueous extract of lemon grass as described in the Experimental Section of the main text. After Au nanoplates were synthesized and throughout the mesoporous silica coating procedure, transmission electron microscopy (TEM) was used to characterize and monitor the morphology of the nanoplates at every step of the process. Figure S1A shows TEM images of Au nanoplates produced from lemon grass extract reduction of AuCl 4 ; the sample contains a mixture of nanoplates and pseudo-spherical particles, but they are easily distinguishable by TEM and also SEM. The thickness of the Au nanoplates is 13.7 ± 0.7 nm from atomic force microscopy (Figure 1B and Figure S). The Au nanoplates were then coated with mesoporous silica following established methods. 1-4 First, the nanoplates were coated with a thin layer of amorphous silica following the Ströber method. 3 In a typical experiment, ml of the Au nanoplate stock solution was diluted to 30 ml with H O and 7.5 µl of freshly prepared 0 mm 3-mercaptopropyltrimethoxysilane (MPTMS) in acetone was added while stirring vigorously. After 30 min, 1 ml of freshly prepared aqueous solution of 0.54% w/v Na SiO 3 (ph 10-11) was added drop wise and left at room temperature (RT) for 48 hours under continuous stirring. This procedure allowed the active silicate to polymerize onto the silanized surface of Au nanoplates. Afterwards, the reaction mixture was centrifuged at 1000 g-force for 0 min to precipitate the nanoplates (including the pseudospherical nanoparticles), which have a thin layer of silica, about -4 nm in thickness. 1, To grow a thicker silica shell, the pellet was re-suspended in 30 ml EtOH/H O mixture (.5:1 v/v). To this, 350 µl of 0.1 M NaOH was added followed by 30 µl of tetraethylorthosilicate (TEOS). The mixture was then stirred continuously for at least 4 hours at RT to allow the silica shell to grow. The resulting silica-coated nanoplates were further centrifuged at 1000 g-force for 10 min. Alternatively, allowing the solution to stand overnight also precipitated out the nanoplates/particles. Figure S1B shows representative images of amorphous silica-coated nanoplates where the Au nanoplate core is clearly distinguishable from the silica shell whose average thickness is about nm. In order to make the silica shell mesoporous, the silica-coated nanoplates were resuspended in 0 ml H O/EtOH mixture (4:1 v/v) saturated with cetyltrimethylammonium bromide (CTAB). 1 To this, 150 µl of 0.1 M NaOH was added and stirred at RT for 15 min. It was then transferred to an oil bath set to 90 o C for 1 hr. The mesoporous-silica-coated Au (Au@mSiO ) particles were recovered after centrifugation at 1000 g-force for 10 min then washed extensively, first with ethanol, then with deionized water. Figure S1C are representative TEM images of Au@mSiO nanoplates after washing. It is clear from these TEM images S

3 (compare with Figure S1B) that the silica shell has been transformed and has become mesoporous. To render the nanoparticles catalytically active, the organic ligands bound to the gold surface, including CTAB, were removed by UV/ozone treatment, following literature procedures. 5-7 The alternative method of calcination 4 to remove organic components was found to change the morphology of the Au nanoplate core (i.e., their corners became rounded). In this UV/ozone treatment, the washed Au@mSiO nanoplates were dispersed on a glass slide, dried, and placed under a W UV lamp, 10 cm below, in air for about 1 hours. Figure S1D are the TEM images of the Au@mSiO nanoplates after UV/ozone treatment, which show that after UV/ozone treatment the morphology of Au nanoplate cores, as well as the shell, of the Au@mSiO nanoplates is unchanged. As discussed in our previous paper 4, the mesoporous-silica-coating has the following advantages: (i) It stabilizes the nanoplate morphology during catalysis. (ii) It prevents aggregation of the nanoplates once the ligands are removed for catalysis. (iii) It can temporarily trap the fluorescent product, facilitating its single-molecule fluorescence detection; detecting in the shell also circumvents the possible fluorescence quenching by the Au surfaces. (iv) It mimics a silica support, which is often used in heterogeneous catalysis involving metal nanoparticles. A. B. C. D. 500 nm 500 nm 00 nm 00 nm 1000 nm 1000 nm 00 nm 500 nm Figure S1. TEM images of: (A) As-synthesized Au nanoplates from reduction of AuCl 4 by lemon grass extract. (B) Silica-coated nanoplates. (C) Au@mSiO nanoplates. (D) Au@mSiO nanoplates after UV/ozone treatment. S3

4 Figure S. AFM images of four as-synthesized Au nanoplates (insets) on silicon wafer and the corresponding line profiles showing the thickness of each nanoplate. The solid lines on the AFM images (insets) indicate where the line profiles were taken for thickness measurement using the software Gwyddion v.5. The thickness was calculated by subtracting the average baseline value from the average maximum value. For each nanoplate, the average of at least line profile measurements was used as its height. Figure 1B in the main text shows the distribution of the measured thicknesses of ~30 Au nanoplates. S. Ensemble and single nanoplate catalysis of the reductive N-deoxygenation of resazurin to resorufin After UV/ozone treatment of nanoplates, we checked for their activity in catalyzing the reductive N-deoxygenation of resazurin to resorufin by NH OH. 8 Figure S3A shows the time dependent absorption spectra of the conversion of 5 µm resazurin to resorufin by 1 mm NH OH in the presence of Au@mSiO nanoplates (note the sample is really a mixture of Au@mSiO nanoplates and pseudo-spherical nanoparticles). This is shown by the gradual decrease in absorbance at 598 nm, an absorption feature of resazurin, and the corresponding increase in absorbance at 568 nm, an absorption feature of resorufin (please note the small magnitude of the absorbance changes are due to the small amount of Au@mSiO nanoparticles used). S4

5 Ensemble measurements could not differentiate the contributions of the nanoplates to the observed catalytic activity from those of the pseudo-spherical nanoparticles. In order to verify that the nanoplates are catalytically active, analysis of the activity of individual nanoparticles and knowledge of their respective shape/structure are needed. As described in detail in the next section, this was done by doing a direct correlation between single-molecule fluorescence imaging of the catalysis on individual nanoparticles and SEM imaging of the same set of nanoparticles. Figure S3B shows the average single-particle turnover rate (= number of product molecules formed per second per particle) of 13 Au@mSiO nanoplates at different resazurin concentration in the presence of 1 mm NH OH, each undergoing catalysis for 1 hr. The concentration dependence shows an initial rise in the turnover rate with increasing resazurin concentration, reaches a maximum at ~50 nm resazurin, and eventually decays as the concentration is further increased. This suggests a competitive binding on the nanoplate surface between the two reactants: resazurin and NH OH, as described by the Langmuir-Hinshelwood mechanism in heterogeneous catalysis. 9,10 Ensemble measurements using as-synthesized Au nanoplates/nanoparticles in the presence of increasing amount of resazurin (Figure S3C) with 1 mm NH OH and increasing amount of NH OH (Figure S3D ) under constant resazurin concentration, 10 µm, also show a similar trend. In order to maximize the number of product molecules generated per particle, most of the single-molecule catalysis imaging experiments were done at 50 nm resazurin and 1 mm NH OH. A Absorbance (a.u.) C λ ( nm) B Turnover rate (particle -1 -s -1 ) D [Resazurin] (nm) Initial rate (a.u./min) 4x10-5 x10-5 1x10-5 Initial rate (a.u./min) 1.x x x [Resazurin] (µm) [NH OH] (mm) Figure S3. (A) Ensemble catalytic conversion of resazurin (5 µm) to resorufin with 1mM NH OH by UV/ozone treated Au@mSiO nanoplates. Each spectrum is taken at 30 min S5

6 interval. (B) Resazurin concentration dependence of the average turnover rate of 13 nanoplates obtained from single molecule experiments. (C) Resazurin and (D) NH OH concentration dependences of the ensemble catalysis by as-synthesized Au nanoplates. S3. SEM and fluorescence image correlation To correlate the structure of individual nanoplates to their catalytic activity, we took SEM images of the same set of nanoparticles whose catalysis was studied using singlemolecule fluorescence imaging. To facilitate image correlation, the quartz slides used in the single-molecule experiments have patterned marks, with sizes ranging from 3-15 µm, etched on their surface via photolithography (Cornell Nanofabrication Facility). These etched marks and the nanoplates (as well as the bigger spherical nanoparticles) strongly scatter the 53-nm laser, making them easily identifiable under optical microscopy. In addition, both the nanoplates and the pseudo-spherical nanoparticles are also emissive at wavelengths >550 nm. 4,11-13 We can therefore directly correlate the scattering image to that of the fluorescence image obtained during single-molecule catalysis imaging experiments. This allows us to identify the specific area, marked by the unique set of etched marks, where the fluorescence image was taken. These marks then allow us to find the same area during SEM imaging. In a typical experiment, overlapping SEM images of small-area sections ( 30 µm 0 µm with about 0-30 nm resolution) are obtained section-by-section, covering at least the whole area of the fluorescence image ( 10 µm 70 µm). By comparing the set of marks and the unique patterns of the nanoparticle emission with those obtained from SEM, a direct one-to-one correlation is obtained. Therefore, we can now identify individual nanoplates and differentiate them from the pseudo-spherical nanoparticles. Figure S4A shows the negative fluorescence image of the nanoparticle emissions, with the position of the etched mark (obtained from scattering image) outlined in red. This same area was imaged by SEM and can be directly correlated to the fluorescence image as shown in Figure S4B. S6

7 A. B. 5 µm Figure S4. (A) Negative fluorescence image of the nanoparticle emissions on a quartz slide. The etched mark (number 4, obtained from scattering image) is outlined in red. (B) SEM image of the area in the dashed box in A. S4. Single-molecule super-resolution imaging of catalytic events on individual Au@mSiO nanoplates Now that individual nanoplates are identified from the fluorescence image, the individual catalytic events on each nanoplate can be analyzed to localize their positions to nanometer precision. The details of the process is in our previous paper 4, but in this work, there is a slight difference in setting the fluorescence threshold for selecting catalytic events as described in step (1) below. The analysis process comprises the following major steps: (1) Fluorescence intensity trajectory extraction and selection of fluorescence bursts due to catalytic events. The fluorescence intensity trajectory of each nanoplate undergoing catalysis is obtained by integrating the EMCCD counts of a 7 7 pixel area enclosing the nanoplate, where each pixel is 67 nm. Each data point in the trajectory corresponds to this integrated intensity from each frame in the movie. Fig S5 shows a portion of such a trajectory of a nanoplate (shown in Figure 3A) undergoing catalysis. The background fluorescence signal comes from the emission of the nanoplate itself and each fluorescent burst on top of the background corresponds to the catalytic generation of one product molecule, resorufin, on the nanoplate. Previous studies have shown that mesoporous silica is transparent (>9% transmission in the visible wavelength range). 14 The Au nanoplate (~14 nm in thickness) is also transmissive to visible light, as shown by van Schrojenstein Lantman et al. who used visible light to excite through the Au nanoplate and collect visible light signal back through, 15 as well as because Au S7

8 films of nm thickness are optically transparent ( 55% transmission in the nm wavelength range) 16. Thus, we could excite and detect resorufin molecules generated on both sides of the nanoplate, i.e., both the side facing the quartz slide and the side away from it. Therefore, the total number of fluorescent bursts represents all the product molecules generated from each nanoplate. For our experiments using prism-type TIR excitation where the laser excitation is at the side opposite to that the fluorescence is collected (Figure A and B), if Au@mSiO nanoplates are not optically transparent (for either the excitation or emission wavelengths), it should then be impossible to detect any of the product molecules generated on the flat surface facets, which is clearly not the case. Moreover, we observed no significant differences in the fluorescence intensity per imaging frame of the resorufin molecules at different regions of the nanoplate, i.e., whether they are generated from the corners, or edges, or the surface facets. In selecting the fluorescence bursts generated from the formation of product molecules, a threshold was set manually using the first 1000 frames of the movie. The threshold value was selected such that it was higher than the intrinsic emission of the nanoplate but lower than the fluorescence intensity of the generated resorufin molecules (red line in Figure S5). The difference between the threshold value and that of the average background intensity was then calculated for the first 1000 frames. This difference was used as a fix value to set the threshold of every 1000 frames for the rest of the movie. All bursts higher than the threshold were selected as fluorescence coming from product molecules and their corresponding image frames from the movie were then selected for further analysis to localize the positions of product molecules. Fluorescence intensity (a.u.) 0k 15k 10k Time (s) Figure S5. A segment of the fluorescence intensity trajectory of the nanoplate shown in Figure 3A (main text), with threshold shown in red, with average on-time, τ on = 0.14 s. S8

9 () Localization of catalytic events on the nanoplate with nanometer precision To determine the location of catalytic reactions on the nanoparticle surface, superresolution imaging analysis, similar to those of STORM 17,18 and PALM 19-1, was used. The general idea is to use two-dimensional (D) Gaussian fitting of the point spread function (PSF) of the fluorescence image of each catalytic product molecule. This process is discussed in great detail in our previous paper 4 and exactly the same analysis was done here. An image area of pixels ( µm ) around a nanoplate was used for D Gaussian fitting. All image frames contributing to the fluorescent burst for each product detected were combined to form a single image. From this image was subtracted the emission signal of the nanoplate, which was taken from image frames right before the appearance of the fluorescence burst. The result is therefore the image of just the product molecule resorufin. This was then used for D Gaussian fitting to localize the center position of its PSF: x+δ x δ y+δ y δ I(x, y) = A + Bx + Cy + dx dyi o exp 1 x x o 1 y y o σ x σ y (S1) where I(x, y) is the intensity counts of the fluorescent molecule in the image at position (x, y), A+Bx+Cy to account for the inhomogeneity of illumination, δ is half of the pixel size (pixel size nm), and the exponential term is the D Gaussian function. Along x or y axis, the integration over each pixel is done numerically by dividing each pixel into 11 equal segments. Fitting then gives x 0 and y 0, which is the center location (coordinate) of the PSF. The accuracy of the center localization was calculated using:,3 Er i = σ i + a + 8πσ i 4 b N 1N a N Here Er i (i = x or y) is the x or y accuracy of the center location of the PSF, σ i is from the D Gaussian fitting of the emission PSF, N is the total number of fluorescence photons detected, a is the pixel size in the fluorescence image, and b is the standard deviation of the background. To obtain N, the number of photons detected for each molecule, the total integrated signal counts under the fitted D Gaussian function is converted to the photon counts, according to the formula provided by the camera supplier (Andor Technology): E v = (cts g) (S QE) 3.65 (S3) where E V is the total energy of photons, cts is the counts from the EMCCD camera (unitless), g is EM gain (unitless), S is CCD sensitivity (electrons per count) and available from the EMCCD camera specification, QE is the quantum efficiency (unitless) of the camera in the spectral region of the detected fluorescence, and 3.65 is a physical constant for electron creation in Silicon (ev per electron). The total number of the photons, N, equals the total energy of photons E V divided by the energy of a single photon, for which the center wavelength of the detected spectral region is used. To obtain b, the standard deviation of the background, we first obtain the background S9 (S)

10 image by subtracting the fitted D Gaussian function from the original data image. Then we convert the counts in the background image to the number of photons before calculating the standard deviation of the background signal, i.e., b. The detection and PSF localization of the resorufin fluorescence in the vicinity of the Au nanoplate can also be affected due to the scattering of the fluorescence signal by the nanoplate, and this scattering depends on the position and orientation of resorufin with respect to the nanoplate 4. This could, at times, make the PSF centroid position shifted slightly (by nanometers) from the true location of the resorufin molecule, as well as lead to a loss of some fluorescence, making our measured specific activity lower. 4 However, the scattering effect will not change the trends of specific activities since the average orientation of resorufin with respect to the nanoplate over the many catalytic events is about the same throughout the regions on a nanoplate and the position shift is slight relative to our spatial resolution (~40 nm, Section 3.3 in main text). (3) Sample drift correction During the course of the experiment, which lasted for at least 1 hour, the flow cell could drift significantly. It was therefore necessary to correct for this drifting by using the center positions of the emissive nanoparticles as position markers. As described above, the PSF of the nanoparticle emission is fitted with a D Gaussian and its center position is tracked throughout the course of the experiment. The fluorescence bursts due to product formation was excluded from this analysis. The average drifting of at least five of these markers was used to correct for sample drift. (4) Additional criteria to filter out noise contribution From the selected fluorescence bursts of product molecules, two additional filtering criteria were used to further filter out the contribution from random noise: (1) Er x and Er y, which determine the accuracy of the center position of the PSF fitting, and () σ x and σ y which define the widths of the fitted PSF. In the first criteria those fittings with either Er x or Er y > 1000 nm are filtered out. And for the second one, those fittings which have 100 nm < σ x or y < 500 nm are the only ones included in the analysis. S5. Mapping of catalytic events on the nanoplate structure To correlate the structure of the Au@mSiO nanoplate to its activity, the positions of the catalytic events on each nanoplate were mapped onto its structure from SEM imaging. The SEM image was always used as the reference image in all the overlaying steps outlined below: S10

11 (1) Overlaying of nanoparticle markers Using the etching on the quartz slide as a guide (as described in Section S3), at least 5 nanoparticles (pseudospherical Au@mSiO nanoparticles or nanoplates) per SEM image were chosen and used as markers in overlaying the two images obtained from fluorescence and SEM with high accuracy. The center positions of these markers were first determined from both the SEM image using its geometric center and the fluorescence image as described in Section S4. Using one marker as the reference point, its coordinates from SEM and fluorescence were perfectly matched. Figure S6A shows the overlaying of the fluorescence image coordinates (blue) to that of SEM coordinates (red) of 7 markers (from a single SEM image) using the upperleft most marker (inside black circle) as the reference point. The center coordinates of the other markers in the fluorescence image were then translated, rotated, and corrected for magnification differences to overlay them to the coordinates from SEM image (Figure S6B). After the transformation, the coordinates are still not perfectly matched but differ by merely a few nanometers. This imperfection of overlaying represents the error γ of SEM-fluorescence image overlaying; γ in Section 3.3 of the main text was the average γ obtained from overlays of many samples. A. B. Y coordinate ( 10 4 nm) Y coordinate ( 10 4 nm ) X Coordinate ( 10 4 nm) X Coordinate ( 10 4 nm) Figure S6. Mapping of fluorescence image onto SEM image using 7 nanoparticle markers by overlaying the coordinates of center of nanoparticle emission (red cross) and the geometric center of the SEM image of the nanoparticle (blue cross). (A) Coordinates of markers after aligning the coordinate of one marker (in black circle). (B) Aligned marker coordinates after transformation of the coordinates of all markers from the fluorescence image. () Individual nanoplate structure-activity correlation To map the positions of catalytic events on each nanoplate onto its SEM structure, the centroid of all catalytic product locations was used to overlay exactly with the geometric center of the nanoplate. The catalytic events centroid was used in overlaying rather than the emission S11

12 center of the nanoplate since many of the nanoplates have nearby particles that are also emissive, i.e. the emission center of the nanoplate obtained from PSF fitting is shifted. This is a valid approach because, for those nanoplates that are well isolated from other particles, the centroid position of all the catalytic events corresponds to the center of nanoplate emission well within the error bar of our analysis (Figure S7A). The positions of the catalytic events were then rotated with respect to this center point using the rotation transformation obtained from the markercoordinate transformations described in step (1) above. Figure S7B shows the catalytic events overlaid on top of the SEM image of several nanoplates after this transformation. A. 31.5k B. Y coordinate (nm) 31.0k 30.5k 43.k 43.6k 44.0k X coordinate (nm) 00 nm 00 nm 00 nm 00 nm Figure S7. Mapping of catalytic events on the SEM image of the nanoplate. (A) Locations of catalytic events (yellow) on a nanoplate in Figure 3A. The average of these localizations is shown as the blue asterisk which is only ~7 nm away from the center of the nanoplate emission, the red asterisk. (B) Four examples of catalytic events overlaid on top of the nanoplate SEM images with the nanoplate centroid shown as a red dot. S6. Dividing a Au@mSiO nanoplate into different regions Each nanoplate is divided into three different regions: corners, edges, and flat surface facets using the following steps: (1) The boundary of the msio shell was set. () The boundary of the Au nanoplate core was determined. (3) The core, along with the shell, was divided into different regions: corners, edges, surface facets. (4) The surface facet was divided into radial segments. From the SEM image, the contour of the 6 edges of the msio shell was determined (Figure S8A). The coordinates of the 6 corners of the msio shell coating were then obtained by fitting these edges with straight lines and getting their points of intersection. The fitted lines also S1

13 defined the boundaries of the msio shell (Figure S8B, black line). The boundary of the Au nanoplate core was then defined (Figure S8B & Figure S9, blue line) as 57 nm away from the shell edges. This includes the shell thickness of 43 nm determined from TEM (Figure 1D in the main text) plus a 17 nm correction factor to account for the difference in distance measurements obtained using SEM from that of TEM (i.e. 100-nm spherical gold nanoparticles: TEM = 97 ± 6 nm vs. SEM = 114 ± 8 nm). Next, the surface facet region (Figure S8B & Figure S9, red hexagon) was defined as the area enclosed by points ε (ε, spatial uncertainty; Section 3.3 of the main text) distance away from the corner points of the nanoplate core (Figure S9A). The remaining peripheral area (between the red and black lines in Figure S8B & Figure S9B) was then divided into corner and edge regions. A. B. Figure S8. (A) Contour that define the edge of the mesoporous silica shell. (B) Boundary of the shell (black), core nanoplate (blue) and surface facet (red). To divide the periphery area into two different regions (Figure S9B): corners (green dashed boundary) and edges (dark blue dashed boundary), the length, l, of the sides of the core nanoplate was first measured (Figure S9A). Then the following criteria were applied: (i) the side is considered as entirely corner regions if l 4ε (e.g., the two bottom corners in Figure S9B) (ii) divided into corners if 4ε l 5ε and (iii) divided into corners and edge region if l 5ε (e.g., 4 sides with dark blue dashed boundary in Figure S9B). The width of the corner region is ε away (Figure S9A) from the corner coordinates of the core nanoplate. The flat surface facet region of some nanoplates can be further subdivided into radial segments (Figure S9C black dashed boundary). That is, if the distance between the nanoplate s geometric center (Figure S9C, black dot) and the corner points of the surface facet boundary (Figure S9C red hexagon) is at least 3ε. The center most segment has boundary point ε away S13

14 from the geometric center. The succeeding segments have boundary points ε away from its inner segment. For the outer most radial segment (i.e. outside purple-dashed boundary) if its distance from the surface facet (red hexagon) is less than ε, it is incorporated as part of its inner segment (i.e. surface facet region in Figure S9C has 4 radial segments rather than 5). A. *ε B. C. l *ε Figure S9. Schematic of Au@mSiO nanoplate with the black hexagon as the mesoporous silica shell boundary and the blue hexagon as the core nanoplate. (A) Dividing the nanoplate into its surface facet region (enclosed in red hexagon), ε away from the corners of the core nanoplate, and peripheral region (area between red and black hexagon). (B) The peripheral region is subdivided into: corner regions (enclosed by green dashed-line) and edge regions (enclosed by dark blue dashed-line). The length of the side of the core nanoplate, l, and the spatial uncertainty of our analysis, ε, defines how the nanoplates are divided into different regions. (C) Surface facet region divided into 4 radial segments (black dashed-line). The purple-dashed line represents a boundary region whose distance from the surface facet boundary is less the ε. Thus, it is not considered as a radial segment. S7. Assigning catalytic events to different regions on the nanoplate and determining the region s specific activity Super-resolution imaging enabled us to obtain sub-nanoparticle resolution of the nanoplate s catalytic activity by dividing the catalytic events to different regions depending on where they occur. First, events localized outside the msio shell of the nanoplate are excluded (Figure S10A); these events are few and are probably due to adsorption of the product to the quartz surface. Then each catalytic event is assigned to a region (as described above) depending on where they are localized (Figure S10B): surface facet (red), edge (blue) and corners (cyan, green, and yellow). Figure 3B in the main text was generated the same way. For nanoplates that are large enough, the catalytic events occurring on the surface facet were further divided into radial regions as described above. Figure S10C shows the 7 radial divisions done on the catalytic events on the surface facet (red) in Figure S10B. Figure S11 shows more examples of this region assignment for other Au@mSiO nanoplates. S14

15 A. B. C. 00 nm 00 nm 00 nm Figure S10. (A) All catalytic events localized inside the nanoplate area are selected for further analysis. Each dot is one catalytic product. (B) Catalytic events assigned to different regions of the nanoplate: surface facet (red), edge (blue) and corners (cyan, green, yellow). (C) Surface facet catalytic events divided into different radial regions, represented by different colors. A. B. Figure S11. (A) More examples of catalytic events divided into different regions (similar to Figure S10B). (B) Assignment of catalytic events on the surface facet into different radial regions (similar to Figure S10C) for nanoplates in (A). Scale bars represent 00 nm for all panels. Once the events are subdivided, we calculated the specific activity by dividing the number of events by the total Au surface area in each region and by the time period. The surface area is calculated based on the model that the nanoplate is flat and its edges are perpendicular to the two surface facets with height of ~13.7 nm (Figure 1B in the main text) 5. S15

16 Figure 4A in the main text shows the size dependence of the specific activity of the different regions of the nanoplate as well as that of the whole nanoplate. As predicted by a thermodynamic model, based on the nanoparticles size-dependent chemical potential, we were able to obtain an empirical relationship (ν = ν exp a A ) between the nanoplate s size and its specific activity, 6-8 which we used to fit our data (Section 3.4 and Figure 4A in the main text). The parameters obtained from the fit, using the data for the whole nanoplate, were then used to normalize the specific activity based on the size of the nanoplate, i.e., (size) normalized specific activity = experimental specific activity predicted specific activity (ν). The normalized activity was then used to obtain the shape dependence (i.e. more triangular or hexagonal) as measured by the long vs. short edge ratio (Figure 4B in the main text). Figure S1 shows that for different regions of the nanoplates, there is no apparent specific activity dependence on the nanoplate shape either. A. Norm. specific activity Edge length ratio B Edge length ratio Norm. specific activity C. Norm. specific activity Edge length ratio Figure S1. The size-normalized specific activity of individual Au@mSiO nanoplates against the nanoplate s long-vs.-short edge length ratio, for the flat surface (A), edge region (B), and corner region (C). Each dot is for one nanoplate. The normalized specific activity of the whole nanoplate is presented in Figure 4B in the main text. S8. The fluorescent product molecule does not diffuse appreciably within the mesoporous shell before it diffuses away into the surrounding solution. When the fluorescent product molecules are detected, they are temporarily trapped in the mesoporous silica shell likely by adsorption to some sites in the mesopores. To examine the possible diffusion of the product resorufin within the mesoporous silica shell, we tracked its position frame by frame while it stays visible in the fluorescence movie, which usually lasts a few frames before it diffuses away in the surrounding solution. The mean square displacement is plotted in Figure S13 and is essentially independent of time. Therefore, the product molecule resorufin stays relatively immobile inside the mesoporous silica shell while its fluorescence is being imaged. S16

17 A B MSD (nm ) MSD (nm ) Figure S13. Mean square displacement (MSD) against frame index (i.e. duration of fluorescence burst) for product molecules on a single (A) and averaged over the data from many nanoplates (B). Each frame corresponds to ms. S9. Derivation of the quantitative model of radial activity gradient on the nanoplate flat facets based on the growth-rate-dependent defect density Au nanoplates are likely formed via D seeded growth out of a small nanoparticle seed. The center of the nanoplate is presumably where the growth started approximately and the perimeters are where the growth stopped. How the growth rate G(r) of a Au nanoplate depends on the growing radius r has not been studied, but should decrease with increasing r because the growth eventually comes to a halt (r is defined as the radial distance from the center of the nanoplate along the center-to-corner vector, Figure S14). Past studies 9 on the seeded growth of Au nanorods, which are 1-dimensional nanocrystals, showed that their growth rate decrease linearly with their growing length. Assuming the growth rate of Au nanoplates follows a similar behavior to Au nanorods, the growth rate of Au nanoplates should decrease approximately linearly with the square of increasing radius, because their growths are in D. We can then write: G r = α r R (S4) R() R( ) where α R is a proportionality parameter that depends on the particular nanoplate with an eventual radius R. When r R, G R (R) 0, i.e., the growth stops and the eventual radius of the nanoplate is achieved. If we make the following assumptions: (1) the catalytic activity, represented by the specific turnover rate, v, on the flat facet of Au nanoplates is linearly proportional to the surface density of defects (which are low-coordination metal sites), and () the defect density is linearly proportional to the nanoplate growth rate, then, the activity, reflected by the specific turnover rate v R (r), at a radial distance r away from the center of a nanoplate of radius R is: v () r = pg () r + v = pα ( r R ) + v (S5) R R 0 R 0 S17

18 Here v 0 is a parameter that represents the activity of the nanoplate flat facet when the growth rate approaches zero (note it is not the nanoplate edge/corner), and p is a proportionality constant. Let β R = pα R and v c,r = β R R + v 0, we have: v () r = β r + v (S6) R R c, R Equation (S6) is presented as Equation (1) in the main text. When r = 0, v R (0) = v c,r. Therefore, v c,r represents the catalytic activity at the center of a Au nanoplate s flat facet. β R is the radial gradient of activity from the center toward to the periphery, and reflects the underlying radial gradient of the defect density that comes from the radially decaying growth rate of the nanoplate. Figure S14 shows a schematic of the radial activity gradient along the nanoplate top flat facet represented by Equation (S6). ν R (Specific activity) ν c,r β R (Gradient slope) 0 ν o R r Gold nanoplate Figure S14. Schematic of the specific activity gradient from the center towards the edges of the surface facet of the nanoplate. Note the axis of v R is perpendicular to the nanoplate s flat facet in the schematic. S10. The reaction kinetics on Au@mSiO nanoplates is not mass-transport limited In our single-molecule experiment, to make sure that the kinetics is not limited by mass transport, we first confirmed that the amount of reactants available at any given time far exceeds the amount being consumed by the reaction. The reductant, NH OH, is always kept at 1 mm, a large excess compared with 50 nm of resazurin. With a flow rate of 0 µl-min 1, the limiting reactant (resazurin) is supplied at a rate of mol s 1. The average density of nanoparticles on the quartz slide in the flow chamber is m and the total area of the slide in the flow chamber is m. With average experimental turnover rate of 0.36 s 1 on a single nanoplate at 50 nm resazurin, the rate of reactant consumption is ~ mol S18

19 s 1, which is six orders magnitude smaller than the reactant supply rate. Thus, the amount of reactants does not limit the reaction in our single-molecule experiments. We next confirmed that the reaction kinetics is not limited by mass transport from the solution to the catalyst surface, i.e., not limited by diffusion. We used two simple diffusion layer geometries: planar geometry (Scheme S1) and hemi-spherical geometry (Scheme S) 30, for estimation. Scheme S1. Planar geometry of the diffusion layer a catalyst C 0 d C 1 a Parameter in the scheme: C 0, the bulk concentration of the reactant; C 1, the concentration of the reactant close to the solid-liquid interface where the catalyst particles are; d, the thickness of the diffusion layer. Planar geometry of diffusion layer. In the planar geometry of the diffusion layer (Scheme S1), we assume the diffusion layer is planar on top of the surface where the catalyst particles are immobilized and is an approximation for the cases when the average inter-particle distances are much smaller than the thickness of the diffusion layer (d). If the reaction is under diffusion control, C 1, the reactant concentration at the catalyst particles, is approximately zero. The flux of reactant from bulk solution to the catalytic surface follows Fick s first law: C 0 J = D d (S7) Here J is the flux. D is the diffusion coefficient of the reactant, which is in the range 10 8 to m s 1 for small molecules in aqueous solution. The diffusion coefficient for amplex red is not known, but that of the reaction product resorufin, whose structure is similar, is m s 1 in water 31. The thickness of the diffusion layer, d, is typically in the range of 0.1 ~ mm, depending on the extent of convection. Because of the mesoporous structure of the silica shell, the diffusion coefficient of a molecule needs to be corrected from that in water. The relation between the effective diffusion coefficient in a porous material and the porosity is 3,33 : 1.5 D e = Dε (S8) where D e is the effective diffusion coefficient in a porous material and ε is the porosity of the material. Losurdo et al. have measured the porosity to be ~0.5 for the mesoporous silica formed using CTAB as a template 34 ; D e is then about D , i.e., about 0.35D. Using D e = 0.35 ( m s 1 ) = m s 1, C 0 = 0.05 µm, the reactant concentration we used, d = 0.01 mm (which is about 00 times larger than the thickness of the msio shell on our catalyst particles), the flux J to the surface is mol m s 1. The surface area in our microfluidic reactor cell where the catalysts particles is dispersed is about 1 S19

20 10 4 m. Then the total mass transport of the reactant to the surface is ( mol m s 1 ) ( m ) = mol s 1 = s 1. The experimental total reaction rate by all the particles in the area is (average turnover rate per particle) (total number of particles) = (0.36 s 1 particle 1 ) particles = s 1, which is 5 orders of magnitude smaller than the mass-transport limited rate ( s 1 ). This corresponds to a Weisz-Prater parameter (= (actual reaction rate)/(diffusion-limited rate)) of , which is orders of magnitude smaller than 1. Therefore, using this planar diffusion-layer model, the catalytic reaction kinetics is not limited by mass transport or diffusion from the solution to the catalyst particle surface. Scheme S. Hemispherical geometry of the diffusion layer a C 1 C 0 a Parameter in the scheme: r 1 : radius of the catalyst particle; r 0, radius from the particle center to the boundary of the diffusion layer.; C 0, the bulk concentration of the reactant; C 0, the first derivative, i.e., the gradient of the concentration at position r 0 ; C 1, the concentration of the reactant at the solid-liquid interface where the catalyst particle is. Hemispherical geometry of diffusion layer. The second model we considered is a hemispherical model, where we approximate the catalyst as a hemispherical particle, surrounding which there is a hemispherical diffusion layer (Scheme S). In the geometry, the diffusion equation governing the mass transport is 30 : C( r, t) e C( r, t) C( r, t) = D ( + ) (S9) t r r r where C(r,t) is the concentration of the reactant in the diffusion layer at radius r at time t. Under C( r, t) steady-state conditions (which is true for our microscopy-based catalysis experiments), t = 0, then C( r, t) C( r, t) D e ( + ) = 0 (S10) r r r That is d C( r) dc( r) + = 0 (S11) Let dc() r dr dr r dr C, and solve for C using the boundary condition of C (r 0 ) = C 0, we have: r r 0 r 1 C 0 ' C = C0r0 (S1) Then solve for C using the boundary condition of C(r 0 ) = C 0, we have: ' 1 1 C = C0 C0r0 ( r r0 ) (S13) Under mass-transport limited reaction kinetics, C = 0 when r = r 1 (i.e., C 1 = 0 in Scheme S). We S0

21 thus have: C0 C = 1 ( (S14) r r ) ' 0 1 r0 1 0 Using C 0 = 0.05 µm, r 0 = 0.01 mm (which is about 100 times larger than the thickness of the msio shell on our catalyst), r 1 = 300 nm (which is about half of the average length of the ' long edge of the nanoplate), we have C 0 = mol m 3. Then the flux J at r 0 is: J = D e C ' 0 = mol m s 1. Total mass transport to the catalyst particle through the hemispherical diffusion layer is: J S = π r 0 = mol s 1 = s 1 where S is the hemisphere surface area at r 0. Compared with the experimental average singleparticle turnover rate of 0.36 s 1 particle 1, the mass-transport limited rate is about four orders of magnitude larger. This corresponds to a Weisz-Prater parameter (= (actual reaction rate)/(diffusion-limited rate)) of , which is about four orders of magnitude smaller than 1. Therefore, using this hemisphere diffusion-layer model, the catalytic reaction kinetics is not limited by mass transport or diffusion from the solution to the catalyst particle surface. Furthermore, mass-transport limited kinetics would be linearly dependent on the concentration of reactants. To the contrary, the experimentally observed kinetics is not, but rather show saturation with increasing reactant concentrations and eventually decreases at further higher reactant concentrations, explainable by a competitive binding model between the two reactants of the Langmuir-Hinshelwood mechanism 9,10 (see Figure S3B-D and the descriptions in Section S earlier). Considering all above estimates and discussions, we conclude that the catalytic kinetics in our system is not limited by mass transport. S11. Correcting for differential detection of the product molecules at different regions of the nanoplate using the on-time (τ on ) distribution In our region-specific analyses, we have shown that the specific activity of Au@mSiO nanoplates is highly site-specific, i.e., the corner, edge, and facet regions show different specific activities. Since the specific activity was calculated from the number of product molecules detected within each region, any region-dependent interactions of the product resorufin to the msio shell could affect the efficiency in detecting the product molecule and thus potentially influence the calculated specific activity. To directly measure any region-dependent resorufinmsio interactions, we compared the time the resorufin molecule spends within different regions of msio before they desorb into the surrounding solution: the on-time (τ on ) in the singlemolecule fluorescence trajectory (e.g. the duration of each fluorescence burst in Figure S5). Figure S15 shows that the average τ on, τ on, does depend slightly on where the product is generated: τ on corners < τ on edges < τ on flat facets. This means that those product molecules formed at S1

22 the corner regions are released slightly faster than those at the edges, which in turn desorb faster than those at the flat facets <τ on >, s center edges corners Region Figure S15. Average on-time, τ on, of the different regions of the nanoplate. Each point represents the mean of the region s τ on from 16 nanoplates. Error bars are s.d. Since the distribution of the individual τ on s follows a single exponential decay (Figure S16) and given the limited time resolution of our detection (i.e., ms), we expect to have detected relatively more products at the flat facet of the nanoplate compared with its edges and corners. Taking into account of this differential detection of products at different regions, we would still observe the same trend in specific activity (corners > edges > flat facet) but more pronounced. A B C # of events # of events # of events time (s) time (s) time (s) Figure S16. Distribution of on-time (τ on ) from different regions of the nanoplate in Figure 3A: (A) Flat facet (B) Edges (C) Corners. The red line is the single exponential fit, which gives the time constants: 70 ms, 47 ms, and 37 ms, respectively. S

23 These differences in τ on that affect the relative number of product molecules detected from different regions of the nanoplate can be corrected using the exponential function y = A exp( x t) that is used to fit distribution of τ on (Figure S16). The fraction of product detected, f, can then be calculated as: f = A exp( x t to ) dx A exp( x t) dx 0 = exp( t o t) (S15) where t is the exponential time constant and t o the time resolution of our detection. The number of detected product molecules in each region can then be corrected using f and the specific activity for each region can be corrected correspondingly. The corrected results corresponding to Figure 3D in the main text are plotted in Figure S17; the results are very similar to Figure 3D, except that the relative specific activity differences are more pronounced: corners are 13% more active than edges (vs 9%) and that edges in turn are 90% more active than the flat facet (vs 80%). 10 Specific Activity (x10 6 s 1 nm ) size Flat facet edge corner Regions Figure S17. Specific activities of different regions where the differential detection of the product molecules in different regions have been corrected. See Figure 3D for data before correction. In the same manner, the τ on of the radial segments within the flat facet were analyzed. Figure S18 A and B are representative plots of the τ on of these segments and the linear fit from the center of the flat facet towards the edges is the red line. The slopes of these linear fits were then used to generate Figure S18C, which shows that the differences in τ on for the radial segments are very subtle and does not show a clear trend, it neither increases nor decreases as it goes from center towards the edges of the flat facet. S3

24 A <τ on >, s C B <τ on >, s Segment slope # of segments on surface facet Segment Figure S18. (A) & (B) Average on-time of the events detected on the radial segments of the flat facet of nanoplates. The red line is a linear fit. (C) Slope of the linear fit plotted against the number of segments for the individual nanoplate ( ) and the average slope ( ) of nanoplates with the same number of segments (i.e., similar sizes). Still, we further corrected the specific activity of the radial segments of each nanoplate to account for potential differential detection of the product molecules in each radial segment using the τ on distribution as described above. Figure S19A is the corrected version of Figure 5B showing that the radial activity gradient within the flat facet is still prevalent. And the linear correlation between v c,r and β R R still holds (Figure S19B vs. Figure 5C). S4

25 A Specific activity (x10 6 s 1 nm ) k 60k r (nm ) B ν c (x10-6 s -1 nm - ) β A xr (x10-6 s -1 nm - ) Figure S19. (A) Specific activity of the radial segments of the nanoplate in Figure 5A where the differential detection of product molecules in each segment has been corrected for. See also Figure 5B in main text. (B) Re-plot of Figure 5C using the corrected specific activities. References 1 P. Botella, A. Corma & M. T. Navarro. Single gold nanoparticles encapsulated in monodispersed regular spheres of mesostructured silica produced by pseudomorphic transformation. Chem. Mater. 19, (007). L. M. Liz-Marzán, M. Giersig & P. Mulvaney. Synthesis of nanosized gold silica core shell particles. Langmuir 1, (1996). 3 W. Stöber, A. Fink & E. Bohn. Controlled growth of monodisperse silica spheres in the micron size range. J. Coll. Interf. Sci. 6, 6-69 (1968). 4 X. Zhou, N. M. Andoy, G. Liu, E. Choudhary, K.-S. Han, H. Shen & P. Chen. Quantitative super-resolution imaging uncovers reactivity patterns on single nanocatalysts. Nature Nanotech. 7, (01). 5 C. Aliaga, J. Y. Park, Y. Yamada, H. S. Lee, C.-K. Tsung, P. Yang & G. A. Somorjai. Sum frequency generation and catalytic reaction studies of the removal of organic capping agents from pt nanoparticles by uv ozone treatment. J. Phys. Chem. C 113, (009). 6 T. Clark, J. D. Ruiz, H. Fan, C. J. Brinker, B. I. Swanson & A. N. Parikh. A new application of uv ozone treatment in the preparation of substrate-supported, mesoporous thin films. Chem. Mater. 1, (000). 7 J. Vig. Uv/ozone cleaning of surfaces J. Vac. Sci. Tech. A 3, (1985). 8 W. Xu, J. S. Kong, Y.-T. E. Yeh & P. Chen. Single-molecule nanocatalysis reveals heterogeneous reaction pathways and catalytic dynamics. Nature Mater. 7, (008). 9 K. S. Han, G. Liu, X. Zhou, R. E. Medina & P. Chen. How does a single pt nanocatalyst behave in two different reactions? A single-molecule study. Nano. Lett. 1, (01). S5