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Regioselective surface encoding of nanoparticles for programmable self-assembly

A Publisher Correction to this article was published on 12 December 2018

This article has been updated

Abstract

Surface encoding of colloidal nanoparticles with DNA is fundamental for fields where recognition interaction is required, particularly controllable material self-assembly. However, regioselective surface encoding of nanoparticles is still challenging because of the difficulty associated with breaking the identical chemical environment on nanoparticle surfaces. Here we demonstrate the selective blocking of nanoparticle surfaces with a diblock copolymer (polystyrene-b-polyacrylic acid). By tuning the interfacial free energies of a ternary system involving the nanoparticles, solvent and copolymer, controllable accessibilities to the nanoparticles’ surfaces are obtained. Through the modification of the polymer-free surface region with single-stranded DNA, regioselective and programmable surface encoding is realized. The resultant interparticle binding potential is selective and directional, allowing for an increased degree of complexity of potential self-assemblies. The versatility of this regioselective surface encoding strategy is demonstrated on various nanoparticles of isotropic or anisotropic shape and a total of 24 distinct complex nanoassemblies are fabricated.

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Fig. 1: Directional and programmable encapsulated NPs.
Fig. 2: Fabrication of rseNPs.
Fig. 3: Control of the uncovered area.
Fig. 4: Programmable self-assemblies built from rseNPs.

Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Change history

  • 12 December 2018

    In the version of this Article originally published, the diblock copolymer structure in Fig. 2a showed a single bond between the carbon and the oxygen atoms; it should have been a double bond. This has been corrected in all versions of the Article.

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Acknowledgements

This work is supported by the University of Chicago and the NSF CAREER Award (DMR-1555361) to Y.W. D.L. acknowledges the Martha Ann and Joseph A. Chenicek Graduate Research Fund and HHMI International Student Research Fellowship. This research used resources of the Center for Functional Nanomaterials at Brookhaven National Laboratory, which is supported by US DOE Office of Science Facilities under Contract DE-SC0012704. O.G. gratefully acknowledges the support by the US Department of Energy, Office of Basic Energy Sciences, Division of Materials Sciences and Engineering under grant no. DE-SC0008772.

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Authors and Affiliations

Authors

Contributions

G.C. and Y.W. conceived the idea. G.C. and D.L. designed the experiments and developed the methodology. During the revision, H.C.R. and K.J.G. helped with the NP synthesis, K.J.G. performed the polymer encapsulation and self-assemblies, J.-H.L. helped with the microscopy. W.X., R.L. and H.L.X. performed the microscopy for the 3D tomograms. H.L.X. and O.G. analysed the data for the 3D reconstructions. Y.W. supervised the project. G.C., D.L., K.J.G., H.C.R. and Y.W. analysed the data and wrote the paper.

Corresponding author

Correspondence to Yossi Weizmann.

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The authors declare no competing interests.

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Supplementary information

Supplementary Information

Supplementary Video Legends 1–6, Supplementary Figures 1–37, Supplementary Tables 1 and 2 and Supplementary References

Supplementary Video 1

3D tomographic reconstruction of v-AuNC partially encapsulated nanostructure

Supplementary Video 2

Full rotation of 3D tomographic reconstruction of c-AuNC self-assembly with AuNS

Supplementary Video 3

Full rotation of 3D tomographic reconstruction of c-AuNC partially encapsulated nanostructure

Supplementary Video 4

Half rotation at slower speed of 3D tomographic reconstruction of c-AuNC partially encapsulated nanostructure

Supplementary Video 5

3D tomographic reconstruction showing detailed surface structure of the c-AuNC through a sliding partition plane

Supplementary Video 6

Full rotation of 3D tomographic reconstruction of c-AuNC self-assembly with AuNS

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Chen, G., Gibson, K.J., Liu, D. et al. Regioselective surface encoding of nanoparticles for programmable self-assembly. Nature Mater 18, 169–174 (2019). https://doi.org/10.1038/s41563-018-0231-1

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