Multiscale simulation strategies and mesoscale modelling of gas and liquid flows

This paper was presented at the 2nd Micro and Nano Flows Conference (MNF2009), which was held at Brunel University, West London, UK. The conference was organised by Brunel University and supported by the Institution of Mechanical Engineers, IPEM, the Italian Union of Thermofluid dynamics, the Process Intensification Network, HEXAG - the Heat Exchange Action Group and the Institute of Mathematics and its Applications.This paper presents a review of multiscale simulation strategies for the modelling of micro- and nanoscale flows. These have been developed in the last two decades in an attempt to bridge the application gap between molecular and continuum simulation methods preventing the simulation of many micro- and nanofluidic devices. The paper is focused on hybrid molecular-continuum methods and reviews different coupling strategies, including geometrical decomposition in conjunction with state- and flux coupling, pointwise coupling, the heterogeneous multiscale method and the equation free approach. The different applications of these methods are briefly discussed

Review of the Synergies Between Computational Modeling and Experimental Characterization of Materials Across Length Scales

With the increasing interplay between experimental and computational approaches at multiple length scales, new research directions are emerging in materials science and computational mechanics. Such cooperative interactions find many applications in the development, characterization and design of complex material systems. This manuscript provides a broad and comprehensive overview of recent trends where predictive modeling capabilities are developed in conjunction with experiments and advanced characterization to gain a greater insight into structure-properties relationships and study various physical phenomena and mechanisms. The focus of this review is on the intersections of multiscale materials experiments and modeling relevant to the materials mechanics community. After a general discussion on the perspective from various communities, the article focuses on the latest experimental and theoretical opportunities. Emphasis is given to the role of experiments in multiscale models, including insights into how computations can be used as discovery tools for materials engineering, rather than to "simply" support experimental work. This is illustrated by examples from several application areas on structural materials. This manuscript ends with a discussion on some problems and open scientific questions that are being explored in order to advance this relatively new field of research.Comment: 25 pages, 11 figures, review article accepted for publication in J. Mater. Sc

Nanoinformatics knowledge infrastructures: bringing efficient information management to nanomedical research

Nanotechnology represents an area of particular promise and significant opportunity across multiple scientific disciplines. Ongoing nanotechnology research ranges from the characterization of nanoparticles and nanomaterials to the analysis and processing of experimental data seeking correlations between nanoparticles and their functionalities and side effects. Due to their special properties, nanoparticles are suitable for cellular-level diagnostics and therapy, offering numerous applications in medicine, e.g. development of biomedical devices, tissue repair, drug delivery systems and biosensors. In nanomedicine, recent studies are producing large amounts of structural and property data, highlighting the role for computational approaches in information management. While in vitro and in vivo assays are expensive, the cost of computing is falling. Furthermore, improvements in the accuracy of computational methods (e.g. data mining, knowledge discovery, modeling and simulation) have enabled effective tools to automate the extraction, management and storage of these vast data volumes. Since this information is widely distributed, one major issue is how to locate and access data where it resides (which also poses data-sharing limitations). The novel discipline of nanoinformatics addresses the information challenges related to nanotechnology research. In this paper, we summarize the needs and challenges in the field and present an overview of extant initiatives and efforts

Electron beam induced deposition (EBID) of carbon interface between carbon nanotube interconnect and metal electrode

Electron Beam Induced Deposition (EBID) is an emerging additive nanomanufacturing tool which enables growth of complex 3-D parts from a variety of materials with nanoscale resolution. Fundamentals of EBID and its application to making a robust, low-contact-resistance electromechanical junction between a Multiwall Carbon Nanotube (MWNT) and a metal electrode are investigated in this thesis research. MWNTs are promising candidates for next generation electrical and electronic devices, and one of the main challenges in MWNT utilization is a high intrinsic contact resistance of the MWNT-metal electrode junction interface. EBID of an amorphous carbon interface has previously been demonstrated to simultaneously lower the electrical contact resistance and to improve mechanical characteristics of the MWNT-electrode junction. In this work, factors contributing to the EBID formation of the carbon joint between a MWNT and an electrode are systematically explored via complimentary experimental and theoretical investigations. A comprehensive dynamic model of EBID using residual hydrocarbons as a precursor molecule is developed by coupling the precursor mass transport, electron transport and scattering, and surface deposition reaction. The model is validated by comparison with experiments and is used to identify different EBID growth regimes and the growth rates and shapes of EBID deposits for each regime. In addition, the impact of MWNT properties, the electron beam impingement location and energy on the EBID-made carbon joint between the MWNT and the metal electrode is critically evaluated. Lastly, the dominant factors contributing to the overall electrical resistance of the MWNT-based electrical interconnect and relative importance of the mechanical contact area of the EBID-made carbon joint to MWNT vs. that to the metal electrode are determined using carefully designed experiments.Ph.D.Committee Chair: Dr. Andrei G. Fedorov; Committee Member: Dr. Azad Naeemi; Committee Member: Dr. Suresh Sitaraman; Committee Member: Dr. Vladimir V. Tsukruk; Committee Member: Dr. Yogendra Josh

AtomAI: A Deep Learning Framework for Analysis of Image and Spectroscopy Data in (Scanning) Transmission Electron Microscopy and Beyond

AtomAI is an open-source software package bridging instrument-specific Python libraries, deep learning, and simulation tools into a single ecosystem. AtomAI allows direct applications of the deep convolutional neural networks for atomic and mesoscopic image segmentation converting image and spectroscopy data into class-based local descriptors for downstream tasks such as statistical and graph analysis. For atomically-resolved imaging data, the output is types and positions of atomic species, with an option for subsequent refinement. AtomAI further allows the implementation of a broad range of image and spectrum analysis functions, including invariant variational autoencoders (VAEs). The latter consists of VAEs with rotational and (optionally) translational invariance for unsupervised and class-conditioned disentanglement of categorical and continuous data representations. In addition, AtomAI provides utilities for mapping structure-property relationships via im2spec and spec2im type of encoder-decoder models. Finally, AtomAI allows seamless connection to the first principles modeling with a Python interface, including molecular dynamics and density functional theory calculations on the inferred atomic position. While the majority of applications to date were based on atomically resolved electron microscopy, the flexibility of AtomAI allows straightforward extension towards the analysis of mesoscopic imaging data once the labels and feature identification workflows are established/available. The source code and example notebooks are available at https://github.com/pycroscopy/atomai