The preparation of carbon fibres: a study of the physics and chemistry of the preparation of carbon fibres from acrylic precursors

High strength, high modulus carbon fibres are becoming increasingly important as high performance engineering materials. This thesis describes how they may be prepared by heat treatment from filaments spun from polyacrylonitrile and its copolymers. The chemistry of the first stages of heat treatment is very important in controlling the mechanical properties of the carbonised product. A cyclisation reaction has been found to be responsible for the relatively high thermal stability of pyrolysed polyacrylonitrile, but without oxidation the fibres degrade and fuse. An initial oxidation stage is, therefore, essential to the preparation of fibre of high orientation. The cyclised product of pyrolysis is probably a poly 1,4 dihydropiridine and oxidation converts this to aromatic structures, and cyclised structures containing carbonyl and other oxygenated groups. Oxidation is found to assist the carbon fibre preparation process, by producing a product which condenses at an earlier stage of heat treatment, before fusion can occur. Carbon fibre strength and modulus are dependent upon producing a highly oriented crystal structure. While oxidation of the polymer stabilises the fibre so as to prevent disorientation, further large increases in orientation, with a commensurate improvement in strength and modulus, can be obtained by stretching at temperatures above 1,700 °C. This process is analogous to the way fibre orientation is increased by the stretching of the precursor. A lamellar graphite structure can be created in high temperature fibre, by carefully controlling the degree of oxidation. This type of graphite can produce very high values of Young's modulus. More often, however, graphite fibre has a fibrillar fine structure, which is explicable in terms of continuous graphite ribbons. A ribbon model is the most satisfactory representation of the structure of carbon fibre, as it explains the mechanism of the development of long range order and the variation of Young's modulus with crystalline preferred orientation.

Signal processing for molecular and cellular biological physics:an emerging field

Recent advances in our ability to watch the molecular and cellular processes of life in action-such as atomic force microscopy, optical tweezers and Forster fluorescence resonance energy transfer-raise challenges for digital signal processing (DSP) of the resulting experimental data. This article explores the unique properties of such biophysical time series that set them apart from other signals, such as the prevalence of abrupt jumps and steps, multi-modal distributions and autocorrelated noise. It exposes the problems with classical linear DSP algorithms applied to this kind of data, and describes new nonlinear and non-Gaussian algorithms that are able to extract information that is of direct relevance to biological physicists. It is argued that these new methods applied in this context typify the nascent field of biophysical DSP. Practical experimental examples are supplied.

Networking - a statistical physics perspective

Networking encompasses a variety of tasks related to the communication of information on networks; it has a substantial economic and societal impact on a broad range of areas including transportation systems, wired and wireless communications and a range of Internet applications. As transportation and communication networks become increasingly more complex, the ever increasing demand for congestion control, higher traffic capacity, quality of service, robustness and reduced energy consumption requires new tools and methods to meet these conflicting requirements. The new methodology should serve for gaining better understanding of the properties of networking systems at the macroscopic level, as well as for the development of new principled optimization and management algorithms at the microscopic level. Methods of statistical physics seem best placed to provide new approaches as they have been developed specifically to deal with nonlinear large-scale systems. This review aims at presenting an overview of tools and methods that have been developed within the statistical physics community and that can be readily applied to address the emerging problems in networking. These include diffusion processes, methods from disordered systems and polymer physics, probabilistic inference, which have direct relevance to network routing, file and frequency distribution, the exploration of network structures and vulnerability, and various other practical networking applications. © 2013 IOP Publishing Ltd.

Coverage versus supply cost in facility location:physics of frustrated spin systems

A comprehensive coverage is crucial for communication, supply, and transportation networks, yet it is limited by the requirement of extensive infrastructure and heavy energy consumption. Here, we draw an analogy between spins in antiferromagnet and outlets in supply networks, and apply techniques from the studies of disordered systems to elucidate the effects of balancing the coverage and supply costs on the network behavior. A readily applicable, coverage optimization algorithm is derived. Simulation results show that magnetized and antiferromagnetic domains emerge and coexist to balance the need for coverage and energy saving. The scaling of parameters with system size agrees with the continuum approximation in two dimensions and the tree approximation in random graphs. Due to frustration caused by the competition between coverage and supply cost, a transition between easy and hard computation regimes is observed. We further suggest a local expansion approach to greatly simplify the message updates which shed light on simplifications in other problems. © 2014 American Physical Society.

Tracing the evolution of physics on the backbone of citation networks

Many innovations are inspired by past ideas in a nontrivial way. Tracing these origins and identifying scientific branches is crucial for research inspirations. In this paper, we use citation relations to identify the descendant chart, i.e., the family tree of research papers. Unlike other spanning trees that focus on cost or distance minimization, we make use of the nature of citations and identify the most important parent for each publication, leading to a treelike backbone of the citation network. Measures are introduced to validate the backbone as the descendant chart. We show that citation backbones can well characterize the hierarchical and fractal structure of scientific development, and lead to an accurate classification of fields and subfields. © 2011 American Physical Society.