Polypeptide Folding-Mediated Tuning of the Optical and Structural Properties of Gold Nanoparticle Assemblies

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1 Supporting information Polypeptide Folding-Mediated Tuning of the Optical and Structural Properties of Gold Nanoparticle Assemblies Daniel Aili, 1,2,4 Piotr Gryko, 1,2 Borja Sepulveda, 5 John A. G. Dick, 1,2 Nigel Kirby, 6 Richard Heenan, 7 Lars Baltzer, 8 Bo Liedberg, 4,9 Mary P. Ryan, 1* and Molly M. Stevens 1,2,3* 1 Department of Materials, 2 Institute for Biomedical Engineering, 3 Department of Bioengineering, Imperial College London, Exhibition Road, SW7 2AZ London, UK, 4 Centre for Biomimetic Sensor Science, School of Materials Science and Engineering, Nanyang Technological University, Research Techno Plaza, 6th storey XFrontiers block, 50 Nanyang Avenue, Singapore, 5 Research Center on Nanoscience and Nanotechnology (CIN2) CSIC, Campus UAB Edificio Q 3rd floor, Bellaterra, Barcelona, Spain, 6 Australian Synchrotron, Clayton, Vic. 3168, Australia, 7 ISIS, Rutherford Appleton Laboratory, Oxfordshire, UK, 8 Department of Biochemistry and Organic Chemistry, Uppsala University, BMC, Box 576, Uppsala, Sweden, 9 Division of Molecular Physics, Department of Physics, Chemistry and Biology, Linköping University, Linköping, Sweden. Calculation of A/D ratio The A/D ratio is defined as the ratio of the integrals from 550 to 700 nm and 490 to 540 nm. This ratio reflects subtle changes in the UV-Vis spectrum caused by particle aggregation, and in contrast to λ max also show the magnitude of peak broadening. S1

2 Figure S1. Schematic description of the A/D ratio and the influence of particle aggregation on the A/D ratio. The area in red is denoted D (dispersed) and corresponds to the integral from 490 to 540 nm. The area in blue is denoted A (aggregated) and corresponds to the integral from 550 to 700 nm. The spectra in a) and b) show the change in the A/D ratio for dispersed and aggregated particles, respectively. UV-Vis Spectra UV-Vis spectra of 10, 20, and 40 nm Au NPs modified with JR2EC 2 before and after addition of JR2KC 2 at different concentrations are listed in Figures S2-S4. Spectra were recorded with 2 minute intervals for 30 minutes. JR2KC 2 was added after 6 minutes Figure S2. UV-vis spectra of 10 nm Au NPs modified with JR2EC before and after addition of JR2KC 2 at different concentrations. S2

3 Figure S3. UV-Vis spectra of 20 nm Au NPs modified with JR2EC before and after addition of JR2KC 2 at different concentrations. Figure S4. UV-Vis spectra of 40 nm Au NPs modified with JR2EC before and after addition of JR2KC 2 at different concentrations. S3

4 SAXS scattering profiles Scattered intensities of JR2EC 2 modified 10, 20 and 40 nm Au NPs, 15 minutes after addition of JR2KC 2 are presented in Figures S5-S7. The concentrations of JR2KC 2 use the following colour scheme: (black) 25 μm, (red) 10 μm, (orange) 2.5 μm, (yellow) 0.5 μm, (green) 0.1 μm, (blue) 25 nm, and (purple) 5 nm Figure S5. Scattered intensities of JR2EC 2 modified 10 nm Au NPs, 15 minutes after addition of JR2KC 2. Figure S6. Scattered intensities of JR2EC 2 modified 20 nm Au NPs, 15 minutes after addition of JR2KC 2. S4

5 Figure S7. Scattered intensities of JR2EC 2 modified 40 nm Au NPs, 15 minutes after addition of JR2KC 2. Fits using spherical formfactor The scattering patterns of un-aggregated 10, 20 and 40 nm Au NPs were fitted with the Irena Macros 1 using a spherical formfactor assuming a log normal particle size distribution. We find obtain an excellent agreement between the model and experimental data (Figure S8). Figure S8. Example of spherical form factor fit. Scattering profile of 20 nm Au NPs (black) fitted with a model spherical form factor (red) with a size of 18.3±2 nm. S5

6 Calculation of fractal dimension (Porod fits) Logarithmic scattering (also called power law scattering) is commonly observed in SAXS scattering from particulate systems and networked polymers. Fitting the slope of the linear regions on a log (I) log(q) plot (also called a Porod fit) provides an estimate of the fractal dimension D f of an aggregate, (Figure S9). Figure S9. Example of Porod fit. Scattered intensity (green) of JR2EC 2 modified 20 nm Au NPs, with 0.1 μm JR2KC 2. The gradient of the logarithmic scattering at low q is fitted yielding a fractal dimension of D f = 2.4. The fractal dimension D f describes the aggregate mass (M) scaling with aggregate size (R g ) i.e. ( ). Fractal dimensions (D f ) 2-4 with values between 1<D f <3 indicate the formation of mass fractals (Figure S10a), these typically demonstrate a branched structure which becomes more dense with higher values of D f. Values of 3<D f <4 indicate the presence of surface fractals, with rough surfaces exhibiting D f ~ 3 and smooth surfaces higher a D f ~ 4 (Figure S10b). These concepts are described in detail by Meakin 5, Glatter 6 and Martin 4. Park et al provides a visual 3D models of mass fractals formed by DNA aggregates 7. S6

7 Figure S10. Schematic illustration of mass and surface fractals Extraction of structure factors The structure factors S(q) were obtained by dividing the scattering from aggregates by the scattering from dilute particles. The structure factor S(q) which is purely determined by inter-particle correlations providing a clearer picture of short-range inter-particle structure. Structure factor for 10, 20 and 40 nm Au NPs modified with JR2EC in the presence of JR2KC 2 are shown in Figures S JR2KC 2 concentrations are color coded (black) 25 μm, (red) 10 μm, (orange) 2.5 μm, (yellow) 0.5 μm, (green) 0.1 μm, (blue) 25 nm, and (purple) 5 nm. Figure S11. Structure factor for 10 nm Au NPs modified with JR2EC in the presence of 0.1 µm -25 µm JR2KC 2. S7

8 Figure S12. Structure factor for 20 nm Au NPs at a) 5 nm to 0.1 μm of JR2KC 2 b) 0.1 μm to 25 μm JR2KC 2. The red arrows in a) illustrate the effect of increasing aggregation in S(q) Figure S13. Structure factor for 40nm Au NPs at a) 5 μm, 25 μm and 0.1 μm JR2KC 2 b) 0.1 μm to 0.5 μm JR2KC 2. Fitting structure factor using sticky hard sphere The sticky hard sphere model first introduced by Baxter 8, uses a Percus-Yevick approximation of hard spheres with a square well potential, allowing for the mapping of interactions between colloidal particles 9,10. Adhesive hard spheres are assumed to interact through a pair interaction potential, described by a square well of infinitesimal width and infinite depth, which is superimposed on a hard-core repulsion. This hard-core repulsion is introduced once the distance between particles r becomes less than or equal to twice the hard sphere radius ( ). The interaction potential has the form Where is the hard sphere radius of the colloid, Δ is the width of the square well potential and τ is the stickiness parameter. The particle stickiness (τ), is a dimensionless quasi-temperature describing S8

9 the change from hard sphere behavior (large τ) to sticky sphere behavior (small τ) 11. The reciprocal 1/τ provides an estimate of the adhesive potential between particles. Structure factors were fitted directly using a least square fit; whilst the same model code is implemented in both the Irena 1 and NIST SANS 12 macros, the fitting process was found to converge more smoothly with the NIST SANS marcos. The fitting parameters were particle volume fraction (φ), particle stickiness (τ), hard sphere radius ( ) and perturbation parameter (ε). The perturbation parameter ( ) describes the ratio of well (Δ) width to hard sphere diameter ( and is valid for 0.01<ε<0.1. The model was fitted using a least squares fit, with a variation of 5% of a parameter easily detectable. For fitting the NIST model documentation 13 recommends to initially keep ε fixed and to allow other parameters to settle toward a fit. For the initial fitting process, it was found that fits tended to settle stably near. Hence ε was fixed at 0.05 and φ, τ and were allowed to converge towards a fit. Once a good fit was obtained, all the parameters were allowed float. Under this fitting process, ε was found to remain around 0.05± However, the volume fraction (φ) and particle stickiness (τ) are not completely independent as an increase in attractive potential can be partially compensated by a decrease in the aggregate volume. Additionally φ and ε both have a well-known dependence on τ 14, making it difficult to find a unique independent solution for both φ and ε. Several systems proposed in the literature demonstrate different model potentials derived from structure factors and thermodynamical properties 10. Upon fitting, it was found that a change in φ could be compensated by a change in τ, yielding multiple solutions, each fitting the experimental structure factor closely. To simplify the fitting process τ was set to a fixed value; under free fitting, τ was found to vary τ=0.1±0.02. Hence τ was set to 0.1. Under these conditions, φ and were fitted to yield a unique result; hence an increase in φ describes both a positive change in volume fraction and particle interaction potential. Figure S14. Schematic illustration of pair-potential implemented in the sticky hard sphere model, where, and Δ are the well depth, hard sphere radius and the width of the square well potential respectively. Crystalline fitting S9

10 Structure factors reveal secondary peaks, whose positions (i.e. ratios) relative to the primary peak can be compared with common crystal lattices 15. A clearer representation can be achieved by scaling the q axis relative to the primary peak. Structure factors, scaled to the primary peak position, for 10, 20 and 40 nm Au NPs modified with JR2EC in the presence of JR2KC 2 are shown in Figures S JR2KC 2 concentrations are color coded (black) 25 μm, (red) 10 μm, (orange) 2.5 μm, (yellow) 0.5 μm, (green) 0.1 μm, (blue) 25 nm, and (purple) 5 nm. The vertical dashed lines in Figures S15-17 represent the theoretical peak positions for HCP structures. Table S1: Diffraction planes and relative lattice positions of lattice peaks for FCC, HCP and BCC lattices 15. FCC BCC HCP Diffraction plane (HKL) Relative position Diffraction plane (HKL) Relative position Diffraction plane (HKL) Relative position (111) 1.00 (110) 1.00 (100) 1.00 (200) 1.15 (200) 1.41 (002) 1.06 (220) 1.63 (211) 1.73 (101) 1.13 (311) 1.91 (220) 2.00 (102) 1.46 (222) 2.00 (310) 2.24 (110) 1.73 (400) 2.31 (222) 2.45 (103) 1.88 Figure S15. Structure factor of 10 nm Au NPs, with the q axis scaled to q n / q 0. The vertical dashed lines indicate the expected positions of an ideal HCP structure. S10

11 Figure S16. Structure factor of 20 nm Au NPs, with the q axis scaled to q n / q 0. The vertical dashed lines indicate the expected positions of an ideal HCP structure. Figure S17. Structure factor of 40 nm Au NPs, with the q axis scaled to q n / q 0. The vertical dashed lines indicate the expected positions of an ideal HCP (black) and FCC (red) structures. S11

12 References 1 Ilavsky, J., Jemian, P. J. Appl. Crystallog. 2009, 42, Mandelbrot, B. The fractal geometry of nature. W. H. Freeman and co.: San Francisco, Schaefer, D., Martin, J., Wiltzius, P. & Cannell, D. Phys. Rev. Let. 1984, 52, Martin, J. E. & Hurd, A. J. Appl. Crystallog. 1987, 20, Meakin, P. Annu. Rev. Phys. Chem. 1988, 39, Glatter, O., Kratky, O. Small angle X-ray scattering. Vol. 69, Academic Press: London, Park, S., Lee, J., Georganopoulou, D., Mirkin, C., Schatz, G.. J. Phys. Chem. B 2006, 110, Baxter, R. J. Chem. Phys. 1968, 49, Menon, S., Manohar, C., Rao, K. A., J. Chem. Phys. 1991, 95, Regnaut, C., Ravey, J. J. Chem. Phys. 1989, 91, Vavrin, R., Kohlbrecher J, Wilk A, Ratajczyk M, Lettinga MP, Buitenhuis J, Meier G., J. Chem. Phys. 2009, 130, Kline, S. R., J. Appl. Crystallog. 2006, 39, Kline, S. R, SANS Model Function Documentation, ftp://webster.ncnr.nist.gov/pub/sans/ kline/download/sans_model_docs_v4.00.pdf (accessed March 21, 2011). 14 Kruif de, C.G., Rouw, P.W., Briels, W.J., Duits, M.H.G., Vrij, A., May, R.P. Langmuir 1989, 5, Huang, Y., Chen, H., Hashimoto, T. Macromolecules 2003, 36, S12