Design of functional materials from molecular building blocks

This dissertation is a summary of my research developing the synthesis and assembly of functional materials from nanoscale building blocks and studying their emergent properties. Chapter 1 introduces superatoms as exciting atomically precise supramolecular building blocks for materials design. Bottom-up assembly of these superatoms into materials with increased dimensionality (0D, 1D, 2D, and 3D) offers exciting opportunities to create novel solid-state compounds with tailored functions for widespread technological applications. I review recent advances to assemble superatomic materials and focus on assemblies from metal chalcogenide clusters and fullerenes. In subsequent chapters, I employ several of these nanoscale superatoms as the precursors to functional materials. Chapter 2 describes the synthesis and structural characterization of a hybrid solid-state compound assembled from two building blocks: a nickel telluride superatom and an endohedral fullerene. Although a varied library of binary superatomic solids has been assembled from fullerenes, this is the first demonstration of a superatomic assembly using an endohedral fullerene as a building block. Lu3N@C80 fullerenes are dimerized in this new solid-state compound with an unpreceded orientation of the encapsulated metal nitride cluster. I explore the structural characterization of this material supported with computational evidence to explain the dimerization and orientation of the endohedral fullerenes. In Chapter 3 I begin to detail my exploration into assembling superatoms at micro and meso-scales –which will be the focus of Chapters 3-5. Polymers offer attractive mechanical and self-assembly properties that when combined with the attractive redox, optical, and magnetic properties of molecular clusters, these materials chart new paths to developing advanced materials and technologies. Chapter 3 describes charge transfer interactions between perylene diimide and cobalt telluride superatoms that drive the assembly of a solid-state compound from these two building blocks and inspired the design of a diblock copolymer template. Chapters 4 and 5 detail the synthesis and characterization of a polymer with functionalized cobalt selenide side units. I describe a cationic homopolymer in Chapter 4 and diblock copolymer in Chapter 5 synthesized from ring opening polymerization of norbornene-derived monomers. Chapter 4 describes potential applications of the homopolymer system such as thin film fabrication. Chapter 5 discusses the self-assembly of the redox-active diblock copolymer into cross-linkable vesicle structures that can encapsulate molecular cargo. Finally, in Chapter 6 I introduce a new molecular building block to form gold metal surface bonds. Bisaminocyclopropenylidenes (BACs) are a class of carbenes that, much like N-heterocyclic carbenes, have been widely employed for catalysis but have yet to be explored for materials design. This chapter describes the structure and binding orientation of a BAC on an Au(111) surface. Each of these chapters illustrates how the synthetic flexibility of molecular building blocks enables the design of functional materials with tunable properties

Effect of Substrate Support on Dynamic Graphene/Metal Electrical Contacts.

Recent advances in graphene and other two-dimensional (2D) material synthesis and characterization have led to their use in emerging technologies, including flexible electronics. However, a major challenge is electrical contact stability, especially under mechanical straining or dynamic loading, which can be important for 2D material use in microelectromechanical systems. In this letter, we investigate the stability of dynamic electrical contacts at a graphene/metal interface using atomic force microscopy (AFM), under static conditions with variable normal loads and under sliding conditions with variable speeds. Our results demonstrate that contact resistance depends on the nature of the graphene support, specifically whether the graphene is free-standing or supported by a substrate, as well as on the contact load and sliding velocity. The results of the dynamic AFM experiments are corroborated by simulations, which show that the presence of a stiff substrate, increased load, and reduced sliding velocity lead to a more stable low-resistance contact

Chameleon Coatings: Adaptive Surfaces to Reduce Friction and Wear in Extreme Environments

Adaptive nanocomposite coating materials that automatically and reversibly adjust their surface composition and morphology via multiple mechanisms are a promising development for the reduction of friction and wear over broad ranges of ambient conditions encountered in aerospace applications, such as cycling of temperature and atmospheric composition. Materials selection for these composites is based on extensive study of interactions occurring between solid lubricants and their surroundings, especially with novel in situ surface characterization techniques used to identify adaptive behavior on size scales ranging from 10−10 to 10−4 m. Recent insights on operative solid-lubricant mechanisms and their dependency upon the ambient environment are reviewed as a basis for a discussion of the state of the art in solid-lubricant materials

Survey on software tools that implement deep learning algorithms on intel/x86 and IBM/Power8/Power9 platforms

Neural networks are becoming more and more popular in scientific field and in the industry. It is mostly because new solutions using neural networks show state-of-the-art results in the domains previously occupied by traditional methods, eg. computer vision, speech recognition etc. But to get these results neural networks become progressively more complex, thus needing a lot more training. The training of neural networks today can take weeks. This problems can be solved by parallelization of the neural networks training and using modern clusters and supercomputers, which can significantly reduce the learning time. Today, a faster training for data scientist is essential, because it allows to get the results faster to make the next decision. In this paper we provide an overview of distributed learning provided by the popular modern deep learning frameworks, both in terms of provided functionality and performance. We consider multiple hardware choices: training on multiple GPUs and multiple computing nodes. © The Authors 2019.Council on grants of the President of the Russian Federation: MK-2330.2019.9You can use a special version of Caffe, NVCaffe, which is supported by NVidia. This version was created specifically for the use of several GPUs. User instructions can be found in [35].For NVidia, MXNet is supported by Nvidia Cloud. MXNet also has support for CUDA and CuDNN.The results described in this paper were obtained with the financial support of the grant from the Russian Federation President Fund (MK-2330.2019.9)

Radial Strains of Double-layer Cylinders in Hydraulic Props of Powered Supports

At present a lot of efforts are made to use double-layer power cylinders in hydraulic props of powered supports. To study the response of these cylinders to loads a special finite-element model has been developed and used for investigations into tension effect and double-layer cylinder thickness – radial strain relation under pressure of hydraulic liquid 50 MPa. It has been revealed that double-layer cylinders are distinguished by much lower radial strains in the zone of cup-like sealing elements as if compared with one-layer cylinders, as well as equivalent stresses are lower, and safety factor is higher. The data of the study can be recommended to calculate appropriate geometrical parameters of hydraulic props with respect to lower radial strains of a hydraulic cylinder, which improve its leak-tightness and functioning of cup-like sealing elements. The obtained results can be useful for design and construction of powered supports

Multifrequency dial sensing of the atmospheric gaseous constituents using the first and second harmonics of a tunable CO2 laser radiation

The results of field measurements of concentration of some gaseous components of the atmosphere along the paths, in Sofia, Bulgaria, using a gas analyzer based on the use of a CO2 laser radiation frequency-doubled with ZnGeP2 monocrystals are presented. The gas analyzer is a traditional long path absorption meter. Radiation from the tunable CO2 laser of low pressure and from an additional He-Ne laser is directed to a colliminating hundredfold Gregori telescope with a 300 mm diameter of the principal mirror. The dimensions of the mirrors of a retroreflector 500 x 500 mm and a receiving telescope allow one to totally intercept the beam passed through the atmospheric layer under study and back

Photo-Sensitivity of Large Area Physical Vapor Deposited Mono and Bilayer MoS2

We present photosensitivity in large area physical vapour deposited mono and bi-layer MoS2 films. Photo-voltaic effect was observed in single layer MoS2 without any apparent rectifying junctions, making device fabrication straightforward. For bi-layers, no such effect was present, suggesting strong size effect in light-matter interaction. The photo-voltaic effect was observed to highly direction dependent in the film plane, which suggests that the oblique deposition configuration plays a key role in developing the rectifying potential gradient. To the best of our knowledge, this is the first report of any large area and transfer free MoS2 photo device with performance comparable to their exfoliated counterparts