In this talk, Material Intelligence (MI) will be introduced as a novel concept and key enabling technology for insect-scale robotics for new engineering applications. MI is defined here to be the science, methodology and application of materials with the abilities to sense and respond to stimuli, and adapt to/learn from their environments for robotic applications to accomplish desired tasks. With a delocalized suite of functions MI enables intelligent robotic systems to be constructed at the insect scale where conventional sensors and actuators (such as electromagnetic, pneumatic or hydraulic motors) are too bulky to be employed. Through the discovery of new materials exhibiting stimuli-induced chemo/physio-mechanical reactions or phase transformations, and development of methods for their integration to achieve compact material systems with intelligent capabilities, MI enables robotic devices to be built at the insect scale. MI will be illustrated in this talk using visible-light-driven, dual-responsive materials such as manganese-based oxides, which exhibit high actuation performance and electrical resistivity changes under light illumination. Utilizing these properties, compact micro-robotic devices capable of self-sensing and responding to visible light to perform complex motions along multi-selectable configurational pathways are fabricated. Intelligent robotic functions including self-adapting load lifting, object sorting, and on-demand structural stiffening are demonstrated in these devices. This talk will also present novel enabling techniques including direct printing of robots using open-electrodeposition and key chemo-mechanics principles for analyzing robotic performances. The concepts demonstrated here lay down a solid foundation for creating robotic intelligence using multi-stimuli-responsive materials.
In 1921 Griffith published his seminal paper, basically describing the theory of Linear Elastic Fracture Mechanics. Today, 100 years later, this theory shows new generalizations and implications that we will discuss in this Keynote. Understanding fracture mechanics in several disciplines, from nano- to earthquake- engineering including medicine (e.g. bone fracture), is indeed vital and is currently limiting our technologies and lives.
Many important properties of crystalline materials are controlled by the dislocation core. There have been many attempts to remove the elastic field singularities at the dislocation core. Three of the most common methods for regularizing the elastic fields are: (1) considering a cutoff parameter, (2) spreading the Burgers vector in all directions as proposed by Cai et al., (2006. A non-singular continuum theory of dislocations. J. Mech. Phys. Solids, 54, 561–587), and (3) using gradient elasticity. Each of these methods requires an extra parameter with the dimension of length. We show that these characteristic length parameters can significantly affect the results of the discrete dislocation simulations. By comparing with the results of atomistic simulations, we show how the core energy should be included if an arbitrary constant is chosen for the characteristic parameters for each of these three nonsingular theories of dislocations.
This lecture will showcase how computational approaches can support the understanding of certain aspects regarding infectious diseases. On the one hand, it will focus on computationally modelling the spatio-temporal spread of COVID-19 based on either ordinary differential equations or integro-differential equations coupled to mobility networks. It will demonstrate these approaches for the example of the first two waves of infections in Germany. On the other hand, it will examine how computational fluid dynamics simulations of particle-laden flow in the human lung can help assessing and understanding the risk of severe infections across different age groups and different levels of cardiovascular activities.
Keywords:Materials failure has for decades been considered one of the paradigmatic multiscale phenomena, involving processes from the atomic to the systems scale. On the continuum level, a well established approach is provided by the laws of fracture mechanics established by Griffith's seminal work exactly a century ago. However, the relationship between key concepts of fracture mechanics such as fracture toughness on the one hand, and parameters characterizing the microstructure of materials from the atomic to the grain scale on the other hand, remains poorly understood. The same is true for the transition from diffuse accumulation of damage to the formation and propagation of a macroscopic crack. Data analytic approaches may offer new pathways towards closing the gap between discrete and continuous descriptions of material microstructures undergoing failure under load. We illustrate this on a range of examples from the atomic to the geo-scale. On the atomic level, we show how machine learning methods can be used to identify local atomic configurations prone to irreversible change under load, and how continuum mechanics concepts can provide essential 'domain knowledge' in approaching this task. On the mesoscale, we demonstrate how network theoretical concepts can be used to identify potential failure locations in load-carrying structures that can be mapped onto networks transmitting linear momentum. Finally, on the macroscale, we discuss how macroscopic monitoring data can be used to predict imminent failure under load.
The development of materials with enhanced mechanical properties and ionic conductivity constitutes a major challenge in the area of solid polymer electrolytes (SPEs) for lithium batteries. We utilize high functionality star polymers as nanostructured additives to liquid electrolytes for the development of SPEs that simultaneously exhibit high modulus and ionic conductivity. We discuss two different cases of multiarm stars used. When high functionality PMMA stars are dispersed in low molecular weight PEO, the SPEs exhibit two orders of magnitude higher conductivity and one order of magnitude higher mechanical modulus compared to the linear PMMA analogues due to the formation of a highly interconnected network of pure liquid electrolyte that leads to high conductivity. When mikto-arm star copolymers are introduced (with PS and PEO arms), SPEs are obtained with high modulus and high ionic conductivity (close to those for practical use) due to their self-assembled morphology of highly interconnected structures formed within the PEO host. The intramolecular nanostructuring of the mikto-arm star particles and their self-assembly within a homopolymer matrix are studied by molecular dynamics simulations as well. The functionality and the arm lengths lead to an intramolecular nanostructure of the stars, which influences the overall morphology. These miktoarm stars form percolated interconnected assemblies within the PEO host as opposed to simple cylindrical micelles formed when linear diblock copolymers of equivalent characteristics are introduced into the same host.
* In collaboration with E. Glynos, P. Petropoulou, G. Nikolakakou, D. Chatzogiannakis, L. Papoutsakis, E. Mygiakis, A. D. Nega, G. Sakellariou, W. Pan, E. P. Giannelis, P. Bačová and V. Harmandaris
# Acknowledgements: This research has been co-financed by EU and Greek national funds (Action RESEARCH – CREATE - INNOVATE).
We studied the uniaxial compression behavior of micro- and nanoparticles of several elemental metals (Au [1], Ni [2], Ag [3], Mo [4]) and alloys (Ni-Fe, Ni-Co [5], Au-Ag). The particles were obtained by solid state dewetting of thin metal films and multilayers deposited on sapphire substrates. The high homological temperatures employed in the dewetting process ensure the low concentration of dislocations and their sources in the particles. The particles compressed with a flat diamond punch exhibit purely elastic behavior up to very high values of strain approaching 10%, followed by a catastrophic plastic collapse. The uniaxial yield strength of the particles defined as an engineering stress at the point of catastrophic collapse reached the astonishing values of 34 GPa and 46 GPa for the smallest faceted particles of Ni and Mo, respectively. The atomistic molecular dynamic simulations of the particle compression demonstrated that the catastrophic plastic yielding of the particles is associated with the multiple nucleation of dislocations at the facet corners or inside the particles. The latter, homogeneous nucleation mode resulted in higher particle strength. The size effect in compression was observed both in the experiments and in atomistic simulations, with smaller particles exhibiting higher compressive strength. In contrast with the solute hardening observed in bulk alloys, alloying the pure metal nanoparticles with a second component resulted in significant decrease of their strength. Finally, we produced Au-Ag core-shell nanoparticles by coating the single crystalline Ag nanoparticles with a polycrystalline Au shell. The core-shell nanoparticles exhibited much lower strength than their single crystalline pure Ag counterparts. We related this decrease in strength with the activity of grain boundaries in the polycrystalline Au shell.
Keywords:The talk ventures to describe a high-risk proposal to extend classical laws of mechanics and physics by enhancing them with a Laplacian term accounting for nonlocality and underlying heterogeneity effects. The approach is motivated by a robust gradient model of the classical theory of elasticity which in the last two decades has been shown very useful in eliminating undesirable singularities and interpreting size effects. Implications to a variety of unsettled questions across scales and disciplines are outlined.
The talk ventures to describe a high-risk proposal to extend classical laws of mechanics and physics by enhancing them with a Laplacian term accounting for nonlocality and underlying heterogeneity effects. The approach is motivated by a robust gradient model of the classical theory of elasticity which in the last two decades has been shown very useful in eliminating undesirable singularities and interpreting size effects. Implications to a variety of unsettled questions across scales and disciplines are outlined.
Keywords:Obtaining predictive dynamical equations from data lies at the heart of science and engineering modeling, and is the linchpin of our technology. In mathematical modeling one typically progresses from observations of the world (and some serious thinking!) first to equations for a model, and then to the analysis of the model to make predictions.
Good mathematical models give good predictions (and inaccurate ones do not) - but the computational tools for analyzing them are the same: algorithms that are typically based on closed form equations.
While the skeleton of the process remains the same, today we witness the development of mathematical techniques that operate directly on observations -data-, and appear to circumvent the serious thinking that goes into selecting variables and parameters and deriving accurate equations. The process then may appear to the user a little like making predictions by "looking in a crystal ball". Yet the "serious thinking" is still there and uses the same -and some new- mathematics: it goes into building algorithms that jump directly from data to the analysis of the model (which is now not available in closed form) so as to make predictions. Our work here presents a couple of efforts that illustrate this ``new” path from data to predictions. It really is the same old path, but it is travelled by new means.
The key features will be presented of the Standard Model (SM) [1] and of the Rotating Lepton Model (RLM) [2] of composite particles. They both seek to describe the nature and structure of matter, i.e. of quarks, baryons, mesons and bosons, at the subatomic level. They differ in the number of elementary particles (17 in the SM vs 5 in the RLM), in the number of forces (four in the SM, vs only two in the RLM) and in the number of unknown parameters (26 in the SM vs none in the RLM). The RLM is a Bohr-type model which combines gravity with special relativity [3] and with the de Broglie equation of quantum mechanics [4] to compute the relativistic masses of extremely fast gravitational confined neutrinos rotating on fm size circular orbits. The relativistic masses of these very fast neutrinos reach the masses of quarks [5,6] and this allows for the computation of composite particle masses (e.g. of hadrons and bosons) which are found to be in excellent agreement with experiment (within 2%) without any adjustable parameters.
Graphene, despite its many unique properties, is neither intrinsically polar due to inversion symmetry nor magnetic. However, based on density functional theory, we find that Mn, one of the transition metals, embedded in single or double vacancy (Mn@SV and Mn@DV) in a graphene monolayer induces a dipole moment perpendicular to the sheet, which can be switched from up to down by Mn penetration through the graphene. Such switching could be realized by an external stimulus introduced through the tip of a scanning probe microscope, as already utilized in the studies of molecular switches. We estimate the energy barriers for dipole switching, which are found to be 2.60 eV and 0.28 eV for Mn@SV and Mn@DV, respectively. However, we propose a mechanism for tuning the barrier by applying biaxial tensile strain. We find that 10% biaxial tensile strain, already experimentally achievable in graphene-like two-dimensional materials, can significantly reduce the barrier to 0.16 eV in Mn@SV. Moreover, in agreement with previous studies, we find a high magnetic moment of 3 μB for both Mn@SV and Mn@DV, promising the potential of these structures in spintronics and nanoscale electro-mechanical or memory devices.
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