Letter | Published:

Self-assembly of highly symmetrical, ultrasmall inorganic cages directed by surfactant micelles

Naturevolume 558pages577580 (2018) | Download Citation

Abstract

Nanometre-sized objects with highly symmetrical, cage-like polyhedral shapes, often with icosahedral symmetry, have recently been assembled from DNA1,2,3, RNA4 or proteins5,6 for applications in biology and medicine. These achievements relied on advances in the development of programmable self-assembling biological materials7,8,9,10, and on rapidly developing techniques for generating three-dimensional (3D) reconstructions from cryo-electron microscopy images of single particles, which provide high-resolution structural characterization of biological complexes11,12,13. Such single-particle 3D reconstruction approaches have not yet been successfully applied to the identification of synthetic inorganic nanomaterials with highly symmetrical cage-like shapes. Here, however, using a combination of cryo-electron microscopy and single-particle 3D reconstruction, we suggest the existence of isolated ultrasmall (less than 10 nm) silica cages (‘silicages’) with dodecahedral structure. We propose that such highly symmetrical, self-assembled cages form through the arrangement of primary silica clusters in aqueous solutions on the surface of oppositely charged surfactant micelles. This discovery paves the way for nanoscale cages made from silica and other inorganic materials to be used as building blocks for a wide range of advanced functional-materials applications.

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Change history

  • Correction 10 July 2018

    Change history: In Fig. 3b of this Letter, the labels for the outer (11.8 nm) and inner (7.4 nm) diameters of the structure were inadvertently omitted. Fig. 3 has been corrected online.

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Acknowledgements

This project was supported by the National Cancer Institute of the National Institutes of Health under award number U54CA199081. Y.G. and P.C.D. acknowledge financial support from the National Science Foundation (NSF) under grant number 1217867, and Y.G. acknowledges financial support from a 2017 Google PhD Fellowship in Machine Learning. T.A. acknowledges financial support from the Ghent University Special Research Fund (BOF14/PDO/007) and the European Union’s Horizon 2020 research and innovation program (MSCA-IF-2015-702300 and MSCA-RISE-691185). M.Z.T. acknowledges fellowship support from the Ministry of National Education of the Republic of Turkey. This work used shared facilities of the Cornell Center for Materials Research, with funding from the NSF Materials Research Science and Engineering Center program (DMR-1719875), as well as the Nanobiotechnology Center’s shared research facilities at Cornell. The authors thank V. Elser, Y. Jiang and D. Zhang for helpful discussions.

Author information

Affiliations

  1. Department of Materials Science and Engineering, Cornell University, Ithaca, NY, USA

    • Kai Ma
    • , Tangi Aubert
    • , Melik Z. Turker
    • , Teresa Kao
    •  & Ulrich Wiesner
  2. School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA

    • Yunye Gong
    •  & Peter C. Doerschuk
  3. Department of Chemistry, Ghent University, Ghent, Belgium

    • Tangi Aubert
  4. Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY, USA

    • Peter C. Doerschuk

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Contributions

K.M., T.A. and U.W. designed the experimental work. Y.G. and P.C.D. performed the reconstructions. K.M. synthesized the silica-based materials. T.A. synthesized the metal-based and transition-metal-oxide-based materials. K.M. and T.A. performed TEM and cryo-EM characterization. K.M., T.A., M.Z.T. and T.K. processed the images for reconstructions. K.M., T.A. and U.W. discussed the experimental work. U.W. wrote the manuscript with input from all co-authors. U.W. supervised the work.

Competing interests

The authors declare that they have submitted a patent disclosure based on this study through Cornell University.

Corresponding author

Correspondence to Ulrich Wiesner.

Extended data figures and tables

  1. Extended Data Fig. 1 PEGylated silicages after cleaning, and nitrogen sorption measurements on calcined cages.

    a, b, Representative dry-state TEM images, at different magnifications, of PEGylated silicages after the removal of surfactant (CTAB) and TMB (see Methods). The insets in a (black arrows) and b reveal cage-like structures, suggesting structure preservation after the removal of CTAB and TMB. c, Nitrogen adsorption and desorption isotherms of calcined silicages. After CTAB and TMB were removed, particles were calcined at 550 °C for 6 hours in air before nitrogen sorption measurements were taken. A particle synthesis yield of 67% was estimated from the weight of the calcined powder. The surface area of calcined silicages, as assessed by the Brunauer−Emmett−Teller (BET) method, was 570 m2 g−1, consistent with theoretical estimations (Methods). Scale bars in the insets in b represent 5 nm.

  2. Extended Data Fig. 2 Cluster averages of two-dimensional images of silicages.

    a, Around 19,000 single-particle cryo-EM images were sorted into 100 clusters20. b, Some of the projections (examples highlighted in a) exhibited features similar to those of simulated projections of dodecahedral cage structure. Also shown are projection models. Scale bars represent 10 nm.

  3. Extended Data Fig. 3 Reconstruction of a silicage using the RELION 2.1 system.

    a, Reconstructed dodecahedral silicage24. b, Its three most unique projections, along the two-fold, three-fold and five-fold symmetry axes. The reconstruction was obtained from a single-class calculation run by RELION 2.1, using the same set of single-particle images as for the dodecahedral cage in Fig. 3a. Visualization is by UCSF Chimera23.

  4. Extended Data Fig. 4 A typical contrast transfer function, and determination of reconstruction resolution.

    We used CTFFIND4.1.8 (ref. 25) to estimate defocus for individual micrographs or a set of micrographs, with results consistent with nominal defocus values of 1–2 μm. a, Contrast transfer function (CTF) for a defocus of 1.98 μm. Given that the first zero-crossing of the CTF occurs at 0.44 nm−1, the CTF has little effect on reconstructions unless the resolution is greater than 1/(0.44 nm−1), that is 2.27 nm. b, A Fourier shell correlation (FSC)25 computed by a standard package20 for two Hetero reconstructions that are independent, starting at the level of separate sets of images each containing 2,000 images (that is, ‘gold standard’ FSC). The resolution implied by the FSC curve (the inverse of the value of spatial frequency where the FSC curve first crosses 0.5) is 1/(0.99 nm−1), that is 1.01 nm. c, Energy function for the same pair of reconstructions as in panel b. The energy is the spherical average of the squared magnitude of the reciprocal-space electron-scattering intensity, where the denominator of FSC is the square root of a product of two energy functions, one for each reconstruction. The observation that energy has dropped by more than 10−3 times its peak value, and that the character of the curve has become oscillatory and more slowly decreasing—both by 0.44 nm−1—indicates that the resolution implied by the FSC curve is exaggerated22 and that a more conservative resolution is 1/(0.44 nm−1), that is 2.27 nm. d, FSC computed by a standard package20 for two RELION 2.1 reconstructions computed from the same images as those in panel b, from which the resolution (at 0.5 threshold) is estimated to be around 1/(0.50 nm−1), that is 2.00 nm.

  5. Extended Data Fig. 5 Probability analysis of silicage projections.

    a, Orientation dependence of silicage projections. Right panel, the nine different silicage projections identified by 3D reconstruction (Fig. 3) are shown. Left panel, these orientations are manually mapped onto the surface of a dodecahedron. The orientations corresponding to different projections are assigned different colours. b, Probability analysis for different silicage projections. The probability of imaging a particular projection by electron microscopy is estimated by dividing that subset of the surface area of a sphere that contains the orientations corresponding to a specific projection, by the total surface area of the sphere. c, Experimental probability of imaging different silicage projections. The probability of imaging each projection is calculated by dividing the number of single-particle images assigned to a specific silicage projection via 3D reconstruction, by the overall number of silicage single-particle images. The error bars in c are standard deviations calculated from three projection distributions, which were obtained from three independent reconstruction runs using different sets of single-particle images.

  6. Extended Data Fig. 6 Size analysis of silica clusters at an early stage of cage formation.

    Particle-size distribution for primary silica clusters at an early stage of cage formation, obtained by manually analysing 450 silica clusters using a set of TEM images. The measured silica clusters were randomly split into three groups, each containing 150 particles. A cluster-size distribution was then obtained for each of the three groups, and the results were averaged. The error bars are standard deviations calculated from the three cluster-size distributions. A representative TEM image is included at the right. In order to quench the very early stages of cage formation, PEG-silane was added into the synthesis mixture about three minutes after the addition of TMOS, thereby PEGylating early silica structures. TEM sample preparation and characterization were as described in the Methods. Primary silica clusters with diameters of around 2 nm were identified, consistent with the proposed cage-formation mechanism.

  7. Extended Data Fig. 7 Role of TMB in cage formation.

    a, b, TEM images, at different magnifications, of silica nanoparticles synthesized with (a) and without (b) TMB. Nanoparticles synthesized without TMB (b) show stronger contrast at the particle centres than do the nanocages (a), suggesting that these nanoparticles do not exhibit a hollow cage-like structure but instead are conventional mesoporous silica nanoparticles with relatively small particle sizes.

  8. Extended Data Fig. 8 Optical characterization of gold- and silver-based synthesis solutions.

    a, Survey of the gold-based synthesis, showing the absorption profile of solutions after the successive addition of HAuCl4 (orange) and THPC (blue) and then one day after the addition of K2CO3 (red); also shown is the absorption profile when the same concentration of HAuCl4 is added to the equivalent water/ethanol solution but without any CTAB or TMB (green). b, Absorption profile of a solution obtained from the silver synthesis 6 hours after the addition of K2CO3.

  9. Extended Data Fig. 9 High-resolution TEM image of a single cage-like gold nanoparticle.

    This gold particle exhibits lattice fringes with a spacing of 2.3 Å, consistent with the known lattice spacing between (111) planes of gold (Joint Committee on Powder Diffraction Standards no. 04-0784, http://www.icdd.com/). Scale bar represents 5 nm.

Supplementary information

  1. Video 1: Reconstruction of silicage using Hetero algorithm

    . The reconstruction was obtained from a single-class calculation by the Hetero algorithm using the same set of single particle images as was used in the class of the dodecahedral cage shown in Figure 3a.

  2. Video 2: Reconstruction of silicage using RELION 2.1 system.

    The reconstruction was obtained from a single-class calculation by the RELION 2.1 system using the same set of single particle images as was used in the class of the dodecahedral cage shown in Figure 3a.

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DOI

https://doi.org/10.1038/s41586-018-0221-0

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