We discuss the self-folding of patterned metallic sheets using both differential stress and surface forces. The advantageous characteristics of the technique include, (a) The creation of 3D patterned corrugated metamaterials with pattern resolution limited only by that of planar lithography. Since planar lithography is highly versatile, a variety of patterns with different sizes and shapes can be formed. (b) The hands-free and wire-free self-folding of these materials use two orthogonal forces derived from the release of residual stress and the minimization of surface tension. Hence, this process is highly parallel and scalable allowing such materials to be mass produced. (c) Finally, the edges of the materials self-align and seal due to capillary forces of the liquid hinges—this self-sealing enhances overall rigidity and strength of the materials.

Consequently, the self-folding of patterned and sealed “egg-crate” shaped metamaterials was realized. Patterns were incorporated in the form of “smart” patches on the walls of the egg-crates which can be selectively functionalized with biomolecules. Apart from the intellectual appeal of these hands-free, self-sealing materials, we envision applicability of these egg-crate like microstructures in lab-on-a-chip assays as functionalized microwells and as light weight mechanical metamaterials.

Corrected scanning transmission electron microscopy (STEM) was used to characterise a novel thin film displaying a complex three dimensional nanostructure. The film was prepared by plasma deposition in such a way that it self-organises into layers of silver islands (each with typical dimensions of a few nanometres) within an aluminium nitride matrix. Successful application of STEM imaging and subsequent analysis was able to determine critical information about the material structure, namely island size, shape and crystalline orientation and the detection of island – matrix intermixing. Such information is essential in being able to predict the properties of this material and the approach adopted here is applicable to any similarly structured material.

In the oil and gas industry, acoustic transducers have been found to provide valuable geological sonic information such as compressional wave velocity, shear wave velocity, and rock formation slowness. These data can be used to indicate lithology, determine porosity, detect over-pressured formation zones, and check well to well correlation. One category of such acoustic transducers is equipped with piezoelectric elements. Conventional piezoelectric transducers are packaged by epoxy resin. Because of the liquid nature of uncured epoxy resin, it is difficult to position the piezoelectric elements accurately. The introduction of polyether ether ketone (PEEK) as the packaging material solved this issue. Due to the ease of machining on solid form, architectures of the composite acoustic transducers can be devised with great flexibility and creativity. These designs can be modeled with finite element methods (FEM) and the best design for the oil drilling application can be finalized and fabricated.

COMSOL Multiphysics® solves problems in a programming environment that integrates relevant physics. In this case, it includes electrical circuit, solid mechanics, acoustics, and piezoelectricity. Here a compete model and procedure to study the performance of an architected composite acoustic transducer is provided. The displacement analysis gives insights into the resonance modes of the piezoelectric elements. The acoustics analysis gives the necessary information on the acoustic performance of the transducers, such as acoustic pressure spatial distribution, acoustic pressure frequency response, transmitting voltage response, and directivity. These are important criteria to judge the effectiveness of an architected transducer.

Nanostructures tend to comprise distinct and measurable forms, which can be referred to in this context as nanopatterns. Far from being random, these patterns reflect the order of well-understood chemical and physical laws. Under the aegis of said physical and chemical laws, atoms and molecules coalesce and form discrete and measurable geometric structures ranging from repeating lattices to complicated polygons. Rules from several areas of pure mathematics such as graph theory can be used to analyze and predict properties from these well-defined structures. Nanocarbons have several distinct allotropes that build upon the basic honeycomb lattice of graphene. Because these allotropes have clear commonalities with respect to geometric properties, this paper reviews some approaches to the use of graph theory to enumerate structures and potential properties of nanocarbons. Graph theoretic treatment of the honeycomb lattice that forms the foundation of graphene is completed, and parameters for further analysis of this structure are analyzed. Analogues for modelling graphene and potentially other carbon allotropes are presented.

Kinetic Monte Carlo (KMC) methods have been a successful technique for accelerating time scales and increasing system sizes beyond those achievable with fully atomistic simulations. However, a requirement for its success is a priori knowledge of all relevant reaction pathways and their rate coefficients. This can be difficult for systems with complex chemistry, such as shock-compressed materials at high temperatures and pressures or phenolic spacecraft heat shields undergoing pyrolysis, which can consist of hundreds of molecular species and thousands of distinct reactions. In this work, we develop a method for first estimating a KMC model composed of elementary reactions and rate coefficients by using large datasets derived from a few molecular dynamics (MD) simulations of shock compressed liquid methane, and then using L1 regularization to reduce the estimated chemical reaction network. We find that the full network of 2613 reactions can be reduced by 89% while incurring approximately 9% error in the dominant species (CH4) population. We find that the degree of sparsity achievable decreases when similar accuracy is required for additional populations of species.

TDDFT in real-space and real-time approach has been applied to study the optical property of poly(9,9'-dioctyl-fluorene) in the gas phase and in the solid state in order to clarify the effect of aggregation on the spectrum. We show that the spectrum of polymer chain is basically the same in linear and helical shape when it is in the gas phase, however, in solid state phase, the spectrum of aligned aggregate model is different from the spectrum sum of its constituent single chains, suggesting the existence of inter-chain interaction and its effect on the spectrum.

The presence of impurities in solids is a source of many interesting effects, particularly relevant in the conductivity, optical properties and specific heat. For instance, in nano-electronics these effects could be useful to develop molecular devices such as novel computer architectures, chemical and biomedical sensors. However, the inclusion of impurities breaks the translational symmetry, restricting the systems that can be addressed theoretically in an exact way to those of few atoms. In this work, we present an alternative way to study the electrical conductance in real-space by means of a renormalization plus convolution method applied on the Kubo-Greenwood formula for multidimensional systems of macroscopic size with site and bond impurities. The results show that the spectral average of conductivity depends strongly on the location of site and bond impurities in periodic chains. Particularly, when the distance between impurities follows the Fibonacci sequence, we find that the spectral average falls following a power law as the number of atoms in the system grows. Finally, we analyze the impurity effects on the conductance spectra of periodic core-shell nanowires with a macroscopic length and periodic and quasiperiodically located impurities.

The hydrogen solubility and the vacancy concentration in Ni single crystals at thermal equilibrium with a H2 gas have been determined from a combination of first principles calculations and statistical mechanics up to the melting point. We show that the H solubility increases and the vacancy formation is promoted at high PH2. The apparent solution enthalpy and entropy are extracted from the fit of the solubility with the Sieverts’s law. We show that our results are in good agreement with previous experimental data at PH2=1 bar. The vacancy concentration increases with PH2 whatever the temperature but the effect of H is more significant at low temperature. However, the vacancy concentration and the H solubility in single crystals remain small and a comparison with the experimental data on polycrystals indicates that the grain boundaries may play the most important source of superabundant vacancies and preferential sites for H incorporation.

A persistent challenge in multi-scale modeling of materials is the prediction of plastic materials behavior based on the evolution of the dislocation state. An important step towards a dislocation based continuum description was recently achieved with the so called continuum dislocation dynamics (CDD). CDD captures the kinematics of moving curved dislocations in flux-type evolution equations for dislocation density variables, coupled to the stress field via average dislocation velocity-laws based on the Peach-Koehler force. The lowest order closure of CDD employs three internal variables per slip system, namely the total dislocation density, the classical dislocation density tensor and a so called curvature density.

In the current work we present a three-dimensional implementation of the lowest order CDD theory as a materials sub-routine for Abaqus® in conjunction with the crystal plasticity framework DAMASK. We simulate bending of a micro-beam and qualitatively compare the plastic shear and the dislocation distribution on a given slip system to results from the literature. The CDD simulations reproduce a zone of reduced plastic shear close to the surfaces and dislocation pile-ups towards the center of the beam, which have been similarly observed in discrete dislocation simulations.

Using molecular statics calculations, sodium silicate glasses are expanded in an isotropic manner to analyze the composition dependence of the mechanical response.

Increasing the amount of sodium makes the systems more ductile. The tensile strength is reduced and the final load bearing strain limit is increased.

Hydrostatic strain hardening appears in the ductile samples. To explain this phenomena, the density is coarse-grained to identify microscopic defects. In samples containing a significant amount of sodium, a large amount of nano-voids appear before reaching the maximum load bearing capacity. In high sodium content silicates these cracks may cause the hardening observed in the pressure results.

In samples with low sodium content, the failure is abrupt and only a large crack is observed. Increasing the amount of long term but weaker Na-O interactions, instead of the short range Si-O ones could explain the observed transition.

We performed first-principles study of electronic properties of FeCrxSe (x=0.0, 0.01, 0.02, 0.04) alloys using the Green’s function-based Korringa-Kohn-Rostoker Atomic Sphere Approximation method within the coherent potential approximation (KKR-ASA-CPA). The KKR-ASA-CPA method is implemented with density function theory (DFT). We find that the excess of Cr into FeSe significantly affects the electronic structure with respect to the parent FeSe. The results have been analyzed in terms of changes in the density of states (DOS), partial DOS, band structures, Fermi surface, bare Sommerfeld constant and the superconducting transition temperature of FeCr0.01Se, FeCr0.02Se and FeCr0.04Se alloys respectively. Our calculations show that calculated Tc for these alloys are close to experimental values, given the nature of approximations used in our calculations.

This study uses ab initio quantum molecular dynamics (QMD) simulations to validate multimillion-atom reactive molecular dynamics (RMD) simulations, and predicts unexpected condensation of carbon atoms during high-temperature oxidation of silicon-carbide nanoparticles (nSiC). For the validation process, a small nSiC in oxygen environment is chosen to perform QMD simulation. The QMD results provide the number of Si-O and C-O bonds as a function of time. RMD simulation is then performed under the identical condition. The time evolutions of different bonds are compared between the QMD and RMD simulations. We observe the condensation of large number of C-cluster nuclei into larger C clusters in both simulations, thereby validating RMD. Furthermore, we use the QMD simulation results as an input to a multi-objective genetic algorithm to train the RMD force-field parameters. The resulting force field far better reproduces the ground-truth QMD simulation results.