2024 AIChE Annual Meeting

(18i) Hybrid Active Learning for Thermally-Responsive Solid-Solid Structural Transitions in Self-Assembled DNA-Functionalized Nanoparticle Crystals

Authors

McKeen, D., Columbia University
Thart-Chiang, H., University of Washington
Pozzo, L., University of Washington
Gang, O., Columbia University
Ferguson, A., University of Chicago
DNA-functionalized nanoparticles (DFPs) represent a class of self-assembling crystalline materials with readily tunable structural and thermodynamic properties which have found broad applications ranging from plasmonics1 and molecular sensing2 to biological catalysis3 and medical therapeutics4. Engineering structural transformations in the crystal packing and lattice spacing of these systems is of particular interest and while significant development has been made in designing switching mechanisms for DFP crystals to-date, previous work has generally relied on external intervention through strand displacement5 and entropic packing effects of binary systems6. In this work we advance the structural control over DFP solid-solid crystal transitions through a novel switching mechanism that utilizes the interplay between local and global thermodynamic driving forces to actuate thermally-responsive and reversible modulation of the crystal lattice spacing. Here we employ a hybrid computational/experimental active learning cycle and leverage multi-fidelity Bayesian optimization over high-throughput simulations and high-fidelity experiments to identify top-performing DFP systems and learn general design rules. The results of this work have immediate applications in creating thermally-responsive metamaterials for sensors and probes with bespoke structural and plasmonic properties.

References

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(6) Mao, R.; Pretti, E.; Mittal, J. Temperature-Controlled Reconfigurable Nanoparticle Binary Superlattices. ACS Nano 2021, 15 (5), 8466–8473. https://doi.org/10.1021/acsnano.0c10874.