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Not AvailableNanoparticle networks have potential applications in brain-like computing yet their ability to adopt different states remains unexplored. In this work, we reveal the dy namics of the attachment of polyelectrolytes onto gold nanoparticles (AuNPs), using a bottom-up two-bead-monomer DPD (TBM-DPD) model to show the heterogeneity of polymer coverage. We found that use of one polyelectrolyte homopolymer limits the complexity of the possible engineered nanoparticle networks (ENPNs) that can be built. In addressing this challenge, we first found the commensurability rules between the numbers of AuNPs and poly(allylamine hydrochloride)s (PAHs). This gives rise to a well-defined valency of a AuNP which is the maximum number of AuNPs that it can accommodate. We further use an engineered block copolymer, which has a conductive middle block to mediate the distance between a dimer of AuNP. We argue that by controlling the length of conductive block that is connecting the AuNPs and their respective topology, we can have ENPNs potentially adopt multiple states necessary for primitive neuromorphic computing.
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Emergence of Polymer-Networked Nanoparticle Structures as Primitive Neuromorphic Computing States
Polymer-networked nanoparticles are a promising alternative to silicon semiconductors for the realization of neuromorphic computing platforms. Variations in the interaction between gold nanoparticles (AuNPs) and polyelectrolyte linkers lead to the controlled formation of engineered nanoparticle network (ENPN) structures exhibiting a broad range of topologies and dynamics. Using dissipative particle dynamics (DPD) simulations, we designed tri-block copolymers with polyelectrolyte ends that can selectively attach to each of two AuNPs, and bridged them together through a middle polymer segment (or block). We leverage our earlier finding that AuNPs have well defined valencies—that is, an optimal number of polymers that can fit (or fill) their surface—for a specific choice of the outer blocks at a given polymer length. The precise selection of the AuNP valence allows for controlled binding between polymers and AuNPs. Meanwhile, the choice of the middle block enables control over inter-nanoparticle spacing and network topology. We found that ENPNs can achieve distinct and stable states, satisfying a necessary condition for primitive neuromorphic computing. By swapping the surface coating ligand from citrate to mercaptopropionic acid (MPA), the valence on a given sized nanoparticle is also increased. Thus, we found that the selection of the surface coating consequently affects the designed ENPN structures, allowing for more flexibility in searching for optimal components.
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- Award ID(s):
- 2102455
- PAR ID:
- 10652006
- Publisher / Repository:
- American Chemical Society
- Date Published:
- Journal Name:
- The Journal of Physical Chemistry A
- Volume:
- 129
- Issue:
- 36
- ISSN:
- 1089-5639
- Page Range / eLocation ID:
- 8432 to 8440
- Subject(s) / Keyword(s):
- DPD polymer-networked nanoparticle network topology neuromorphic computing
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Journal Article:
- https://doi.org/10.1021/acs.jpca.5c02941
