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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. 

DNA nanotechnology has broad applications in biomedical drug delivery and pro- grammable materials. Characterization of the self-assembly of DNA origami and quan- tum dots (QDs) is necessary for the development of new DNA-based nanostructures. We use computation and experiment to show that the self-assembly of 3D hierarchi- cal nanostructures can be controlled by programming the binding site number and their positions on DNA origami. Using biotinylated pentagonal pyramid wireframe DNA origamis and streptavidin capped QDs, we demonstrate that DNA origami with 1 binding site at the outer vertex can assemble multi-meric origamis with up to 6 DNA origamis on 1 QD, and DNA origami with 1 binding site at the inner center can only assemble monomeric and dimeric origamis. Meanwhile, the yield percentages of differ- ent multi-meric origamis are controlled by the QD:DNA-origami stoichiometric mixing ratio. DNA origamis with 2 binding sites at the αγ positions (of the pentagon) make larger nanostructures than those with binding sites at the αβ positions. In general, increasing the number of binding sites leads to increases in the nanostructure size. At high DNA origami concentration, the QD number in each cluster becomes the limiting factor for the growth of nanostructures. We find that reducing the QD size can also affect the self-assembly because of the reduced access to the binding sites from more densely packed origamis. 

Prediction of organismal viability upon exposure to a nanoparticle in varying environments─as fully specified at the molecular scale─has emerged as a useful figure of merit in the design of engineered nanoparticles. We build on our earlier finding that a bag of artificial neural networks (ANNs) can provide such a prediction when such machines are trained with a relatively small data set (with ca. 200 examples). Therein, viabilities were predicted by consensus using the weighted means of the predictions from the bags. Here, we confirm the accuracy and precision of the prediction of nanoparticle viabilities using an optimized bag of ANNs over sets of data examples that had not previously been used in the training and validation process. We also introduce the viability strip, rather than a single value, as the prediction and construct it from the viability probability distribution of an ensemble of ANNs compatible with the data set. Specifically, the ensemble consists of the ANNs arising from subsets of the data set corresponding to different splittings between training and validation, and the different bags (k-folds). A k−1k machine uses a single partition (or bag) of k – 1 ANNs each trained on 1/k of the data to obtain a consensus prediction, and a k-bag machine quorum samples the k possible k−1k machines available for a given partition. We find that with increasing k in the k-bag or k−1k machines, the viability strips become more normally distributed and their predictions become more precise. Benchmark comparisons between ensembles of 4-bag machines and 34 fraction machines suggest that the 34 fraction machine has similar accuracy while overcoming some of the challenges arising from divergent ANNs in the 4-bag machines. 

We address the challenge of representativity and dynamical consistency when un- bonded fine-grained particles are collected together into coarse-grained particles. We implement a hybrid procedure for identifying and tracking the underlying fine-grained particles—e.g., atoms or molecules—by exchanging them between the coarse-grained particles periodically at a characteristic time. The exchange involves a back-mapping of the coarse-grained particles into fine-grained particles, and a subsequent reassign- ment to coarse-grained particles conserving total mass and momentum. We find that an appropriate choice of the characteristic exchange time can lead to the correct effec- tive diffusion rate of the fine-grained particles when simulated in hybrid coarse-grained dynamics. In the compressed (supercritical) fluid regime, without the exchange term, fine-grained particles remain associated to a given coarse-grained particle, leading to substantially lower diffusion rates than seen in all-atom molecular dynamics of the fine- grained particles. Thus, this work confirms the need for addressing the representativity of fine-grained particles within coarse-grained particles, and offers a simple exchange mechanism so as to retain dynamical consistency between the fine- and coarse- grained scales. 

Polymer-networked nanoparticles are the basis for advanced materials useful wearable electron- ics, drug delivery, autonomous computing and other applications. To characterize and predict the physics and underlying mechanisms of the network connections in 2D and 3D engineered nanopar- ticle (ENP) arrays, we developed an analogous Potts model of 3-state sites. Together with dissipa- tive particle dynamics (DPD) simulations, we found that the network structures in polymer-linked nanoparticle assemblies are generally dominated by the number of nearest neighbors and not the topology of the lattice. When the E-field regulates the network connections, the links along the E-field direction always dominate the overall network structure. 

Limited data exist on how surface charge and morphology impact the effectiveness of nanoscale copper oxide (CuO) as an agricultural amendment under field conditions. This study investigated the impact of these factors on tomatoes and watermelons following foliar treatment with CuO nanosheets (NS-) or nanospikes (NP+ and NP-) exhibiting positive or negative surface charge. Results showed plant species-dependent benefits. Notably, tomatoes infected with Fusarium oxysporum had significantly reduced disease progression when treated with NS-. Watermelons benefited similarly from NP+. Although disease suppression was significant and trends indicated increased yield, the yield effects weren't statistically significant. However, several nanoscale treatments significantly enhanced the fruit's nutritional value, and this nano-enabled biofortification was a function of particle charge and morphology. Negatively charged nanospikes significantly increased the Fe content of healthy watermelon and tomato (20–28 %) and Ca in healthy tomato (66 %), compared to their positively charged counterpart. Negatively charged nanospikes also outperformed negatively charged nanosheets, leading to significant increases in the content of S and Mg in infected watermelon (37–38 %), Fe in healthy watermelon (58 %), and Ca (42 %) in healthy tomato. These findings highlight the potential of tuning nanoscale CuO chemistry for disease suppression and enhanced food quality under field conditions. 

The actin filament network is in part remodeled by the action of a family of filament severing proteins that are responsible for modulating the ratio between monomeric and filamentous actin. Recent work on the protein Actophorin from the amoeba Acanthamoeba castellani identified a series of site directed mutations that increase the thermal stability of the protein by 22 ◦C. Here, we expand this observation by showing that the mutant protein is also significantly stable to both equilibrium and kinetic chemical denaturation, and employ computer simulations to account for the increase in thermal or chemical stability through an accounting of atomic-level interactions. Specifically, the potential of mean force (PMF) can be obtained from steered molecular dynamics (SMD) simulations in which a protein is unfolded. However, SMD can be inefficient for large proteins as they require large solvent boxes, and computationally expensive as they require increasingly many SMD trajectories to converge the PMF. Adaptive steered molecular dynamics (ASMD) overcomes the second of these limitations by steering the particle in stages, which allows for convergence of the PMF using fewer trajectories compared to SMD. Use of the telescoping water scheme within ASMD partially overcomes the first of these limitations by reducing the number of waters at each stage to only those needed to solvate the structure within a given stage. In the PMFs obtained from ASMD, the work of unfolding Acto-2 was found to be higher than the Acto-WT by approximately 120 kCal/mol and reflects the increased stability seen in the chemical denaturation experiments. The evolution of the average number of hydrogen bonds andnumber of salt bridges during the pulling process provides a mechanistic view of the structural changes of the Actophorin protein as it is unfolded, and how it is affected by the mutation in concert with the energetics reported through the PMF.