Inverse design of a pyrochlore lattice of DNA origami through model-driven experiments
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866916250822639616 |
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| author | Liu, Hao Matthies, Michael Russo, John Rovigatti, Lorenzo Narayanan, Raghu Pradeep Diep, Thong McKeen, Daniel Gang, Oleg Stephanopoulos, Nicholas Sciortino, Francesco Yan, Hao Romano, Flavio Šulc, Petr |
| author_facet | Liu, Hao Matthies, Michael Russo, John Rovigatti, Lorenzo Narayanan, Raghu Pradeep Diep, Thong McKeen, Daniel Gang, Oleg Stephanopoulos, Nicholas Sciortino, Francesco Yan, Hao Romano, Flavio Šulc, Petr |
| contents | Sophisticated statistical mechanics approaches and human intuition have demonstrated the possibility to self-assemble complex lattices or finite size constructs, but have mostly only been successful in silico. The proposed strategies quite often fail in experiment due to unpredicted traps associated to kinetic slowing down (gelation, glass transition), as well as to competing ordered structures. An additional challenge that theoretical predictions face is the difficulty to encode the desired inter-particle interaction potential with the currently available library of nano- and micron-sized particles. To overcome these issues, we conjugate here SAT-assembly -- a patchy-particle interaction design algorithm based on constrained optimization solvers -- with coarse-grained simulations of DNA nanotechnology to experimentally realize trap-free self-assembly pathways. As a proof of concept we investigate the assembly of the pyrochlore (also known as tetrastack) lattice, a highly coveted 3D crystal lattice due to its promise in construction of optical metamaterials. We confirm the successful assembly with two different patchy DNA origami designs via SAXS as well as SEM visualization of the silica-coated lattice. Our approach offers a versatile modeling pipeline that starts from patchy particles designed in silico and ends with wireframe DNA origami that self-assemble into the desired structure. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2310_10995 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Inverse design of a pyrochlore lattice of DNA origami through model-driven experiments Liu, Hao Matthies, Michael Russo, John Rovigatti, Lorenzo Narayanan, Raghu Pradeep Diep, Thong McKeen, Daniel Gang, Oleg Stephanopoulos, Nicholas Sciortino, Francesco Yan, Hao Romano, Flavio Šulc, Petr Soft Condensed Matter Mesoscale and Nanoscale Physics Materials Science Statistical Mechanics Computational Physics Sophisticated statistical mechanics approaches and human intuition have demonstrated the possibility to self-assemble complex lattices or finite size constructs, but have mostly only been successful in silico. The proposed strategies quite often fail in experiment due to unpredicted traps associated to kinetic slowing down (gelation, glass transition), as well as to competing ordered structures. An additional challenge that theoretical predictions face is the difficulty to encode the desired inter-particle interaction potential with the currently available library of nano- and micron-sized particles. To overcome these issues, we conjugate here SAT-assembly -- a patchy-particle interaction design algorithm based on constrained optimization solvers -- with coarse-grained simulations of DNA nanotechnology to experimentally realize trap-free self-assembly pathways. As a proof of concept we investigate the assembly of the pyrochlore (also known as tetrastack) lattice, a highly coveted 3D crystal lattice due to its promise in construction of optical metamaterials. We confirm the successful assembly with two different patchy DNA origami designs via SAXS as well as SEM visualization of the silica-coated lattice. Our approach offers a versatile modeling pipeline that starts from patchy particles designed in silico and ends with wireframe DNA origami that self-assemble into the desired structure. |
| title | Inverse design of a pyrochlore lattice of DNA origami through model-driven experiments |
| topic | Soft Condensed Matter Mesoscale and Nanoscale Physics Materials Science Statistical Mechanics Computational Physics |
| url | https://arxiv.org/abs/2310.10995 |