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Characterization and prediction of the interactions between engineered nanoparticles (ENPs), proteins, and biological membranes is critical for advancing applications to nanomedicine and nanomanufacturing while mitigating nanotoxicological risks. In this work, we employ a coarse-grained dissipative particle dynamics (DPD) simulation to investigate the interactions among cytochrome c (CytC), lipid bilayers, and citrate-coated gold nanoparticles (AuNPs). We updated the DPD potential to accurately represent binding potentials between molecules, and validated the model relative to an all-atom representation. The DPD simulations successfully replicate experimental observations: CytC facilitates the binding of citrate-coated AuNPs to lipid bilayers composed of 90% dioleoylphosphatidylcholine (DOPC) mixed with 10% stearoylphosphatidylinositol (SAPI) or 10% tetraoleoyl cardiolipin (TOCL) but not to pure 100% DOPC bilayers. In addition, the simulations reveal nuanced differences in binding preferences between CytC, the lipid bilayers, and the ENP, at a scale that is not presently directly observable in experiments. Specifically, we found that the surface coating of the nanoparticles─viz variations in the CytC surface density─affects the protein-mediated binding with the bilayers. Such a molecular-sensitive result underscores the utility of DPD simulations in simulating complex biological systems. 

The development of devices that improve thermal energy management requires thermal regulation with efficiency comparable to the ratios R ∼ 105 in electric regu- lation. Unfortunately, current materials and devices in thermal regulators have only been reported to achieve R ∼ 10. We use atomistic simulations to demonstrate that Ferrocenyl (Fc) molecules under applied external electric fields can alter charge states and achieve high thermal switch ratios R = Gq/G0, where Gq and G0 are the high and low limiting conductances. When an electric field is applied, Fc molecules are positively charged and the SAM-Au interfacial interaction is strong, leading to high heat conductance Gq. On the other hand, with no electric field, the Fc molecules are charge neutral and the SAM-Au interfacial interaction is weak, leading to low heat conductance G0. We optimized various design parameters for the device performance, including the Au-to-Au gap distance L, the system operation temperature T, the net charge on Fc molecules q, the Au surface charge number Z, and the SAM number N. We find that Gq can be very large and increases with increasing q, Z, or N, while G0 is near 0 at L > 3.0 nm. As a result, R > 100 was achieved for selected parameter ranges reported here. 

We have pursued the use of polymer-networked engineered nanoparticles as a candidate material capable of retaining information or perhaps even processing information in some prescribed way. Such operations would be of use for the neuromorphic engineering of materials that can compute intrinsically—that is, that they are in no way subject to a von Neumann architecture—and they have been identified as autonomous computing materials. Using trajectories integrated to much longer time steps than previously observed, we can now confirm that the response of the polymer-networked engineered nanoparticle arrays are highly sensitive to external perturbations. That is, the specific internal connections around given nanopar- ticles can be assigned to states useful for information processing, and the variations in their physical properties can result in specific responses allowing the state to be read. Moreover, their resulting equilibrium properties also depend on such external driving, and hence are subject to control which is a minimal requirement for these materials to be candidates for autonomous computing. We also demonstrate that using long polymer chains can help regulate the networks structures by increasing the 1st nearest links and reducing other links.