The Kinetics of Ni/Al Reactive Intermetallic Composites

Molecular dynamics (MD) simulations have been used to study the underlying physics and atomistic mechanisms of the reaction progression in Ni/Al reactive intermetallic composites. Preparation of these composites, either through deposition techniques or through the process of mechanical ball milling, gives rise to a periodic ordered, nanolaminated structure and in the first part of this thesis, the effects of this laminate period, ignition temperature and volumetric defects are studied. The presence of defects not only speeds up the reaction by as much as 5 times, but changes the nature of mass transport from diffusive to partly ballistic. Subsequently, the feasibility of using amorphous energetic materials is studied. The use of amorphous precursors is found to speed up the reaction as well as increase the heat of reaction, starting as it does from a higher energy state. Amorphous Ni recrystallizes at elevated temperatures and this process has been investigated (both thermal and shock induced recrystallization). The results presented herein, hint at the possibility of nanostructural tiling and the building of hierarchal nanostructures, starting from amorphous rather than liquid or chemical precursors

Shock response of granular Ni/Al nanocomposites

Intermolecular reactive composites find diverse applications in defense, microelectronics and medicine, where strong, localized sources of heat are required. However, fundamental questions of the initiation and propagation mechanisms on the nanoscale remain to be addressed, which is a roadblock to their widespread application. The performance and response of these materials is predominantly influenced by their nanostructure, and the complex interplay of mechanical, thermal, and chemical processes that occur at very short time scales. Motivated by experimental work which has shown that high-energy ball milling (which leads to the formation of granular composites of Ni/Al) can significantly improve the reactivity as well as the ease of ignition of Ni/Al intermetallic composites, we present large scale (~41 million atom) molecular dynamics simulations of the shock response of granular Ni/Al composites, which are designed to mimic the microstructure that is obtained post mechanical milling. The shock response of granular composite materials is not well understood, and much less so for reactive nano-composites. Fully atomistic simulations such as these provide a unique insight into the subgrain response of granular media. Shock propagation in these porous, lamellar materials is observed to be extremely diffuse at low impact velocities, leading to large inhomogeneity in the local stress states of the material; whereas at higher impact velocities, the shock front is observed to be much sharper. We relate this transition in the nature of the shock, to the mechanism of void collapse, with plastic deformation dominant at slow impacts but jetting into the voids dominant at higher impact velocities

Effects of grain size on the martensitic phase transformation of nanocrystalline Ni/Al shape memory alloys

Shape memory alloys (SMAs) owe their distinct properties to a diffusion less martensitic phase transformation from a high temperature, high symmetry phase (austenite) to a low temperature (martensite) phase upon cooling or strain. Their shape memory and pseudoelastic properties make SMAs useful as active components in microdevices, medical implants and for vibrational damping. Despite their widespread application, the miniaturization limit of SMAs is not known. In this study, we use large-scale molecular dynamics simulations (up to ~40 million atoms) to characterize the martensitic transformation in nanocrystalline Ni/Al disordered alloys. We quantify how mechanical constraints affect both the transformation temperature and the resulting martensitic domain structure. We find that decreasing the grain size makes the transformation more difficult, and this results in a reduction of the transformed volume fraction at a given temperature. Interestingly, we find a minimum in the transformed fraction as a function of decreasing grain size, with extremely fine-grained samples showing a greater tendency to transform

Designing Meaningful Molecular Dynamics (MD) Simulations: The Lithiation of Silicon

Molecular dynamics (MD) is used to understand the properties of materials by following the time evolution of the system and exploring the interactions between its constituent atoms. MD simulation allows making reliable predictions of various properties of materials; however, designing useful computer experiments is a complex task that requires the appropriate selection of interatomic interactions (force fields) and other conditions. In this work we discuss some aspects of molecular dynamics that would help the inexperienced users design reliable simulations. The simulation of the lithiation process of silicon is taken as an example for better understanding

Real-time sparse-sampled Ptychographic imaging through deep neural networks

Ptychography has rapidly grown in the fields of X-ray and electron imaging for its unprecedented ability to achieve nano or atomic scale resolution while simultaneously retrieving chemical or magnetic information from a sample. A ptychographic reconstruction is achieved by means of solving a complex inverse problem that imposes constraints both on the acquisition and on the analysis of the data, which typically precludes real-time imaging due to computational cost involved in solving this inverse problem. In this work we propose PtychoNN, a novel approach to solve the ptychography reconstruction problem based on deep convolutional neural networks. We demonstrate how the proposed method can be used to predict real-space structure and phase at each scan point solely from the corresponding far-field diffraction data. The presented results demonstrate how PtychoNN can effectively be used on experimental data, being able to generate high quality reconstructions of a sample up to hundreds of times faster than state-of-the-art ptychography reconstruction solutions once trained. By surpassing the typical constraints of iterative model-based methods, we can significantly relax the data acquisition sampling conditions and produce equally satisfactory reconstructions. Besides drastically accelerating acquisition and analysis, this capability can enable new imaging scenarios that were not possible before, in cases of dose sensitive, dynamic and extremely voluminous samples