The University of Southampton
University of Southampton Institutional Repository

Stateful self-assembly

Stateful self-assembly
Stateful self-assembly
Nature shows us many organised structures that form through interactions between their components with little external guidance. These self-assembling systems range from simple crystals to considerably more complex biological structures and organisms. Inspired by these systems, the development of programmable self assembling systems could lead to mass manufacturing processes that produce individually unique items. Current artificial self-assembling systems involve small numbers of centimetre-scale components, and have not resulted in structures anywhere near the complexity seen in natural systems. This thesis argues that to advance artificial self-assembling systems towards this complexity, the statistics of the interactions within self-assembling systems need to be empirically examined and understood. However, the pursuit of this involves the resolution of a variety of technical challenges. These are approached in this work through the development of a self-assembly toolkit that allows the collection of these statistics from a physical system with larger numbers of components than in previous works. A novel capacitive communication interface is developed for the components of this toolkit, which allows messaging between neighbouring components that are constrained to the surface of a plane. As self-assembling components reduce in size towards the microscale, the penalty for incorrect activation of a component’s binding mechanism is likely to increase. With this in mind, this capacitive communication interface is optimised to provide spatial alignment sensing, with the aim of allowing informed binding mechanism activation. The toolkit developed in this work uses components that are constrained to two degrees of freedom of motion. In pursuit of the development of programmable self-assembling components for 3D structures, a new design of alignment sensor for use in 3D is created. Simulation of this sensor, which is developed using an evolutionary algorithm, indicates that it is suited for detecting the alignment of components with three degrees of freedom. Approaches using computer vision are developed for the spatial tracking of the components of the toolkit, allowing the collection of empirical data regarding the interaction of components. The technical advances described within this work will allow the progression of data-driven self-assembly process design.
Spanton, Robert
ea49e593-454b-4106-a85e-85442d367221
Spanton, Robert
ea49e593-454b-4106-a85e-85442d367221
Zauner, Klaus-Peter
c8b22dbd-10e6-43d8-813b-0766f985cc97

Spanton, Robert (2013) Stateful self-assembly. University of Southampton, Faculty of Physical Sciences and Engineering, Doctoral Thesis, 135pp.

Record type: Thesis (Doctoral)

Abstract

Nature shows us many organised structures that form through interactions between their components with little external guidance. These self-assembling systems range from simple crystals to considerably more complex biological structures and organisms. Inspired by these systems, the development of programmable self assembling systems could lead to mass manufacturing processes that produce individually unique items. Current artificial self-assembling systems involve small numbers of centimetre-scale components, and have not resulted in structures anywhere near the complexity seen in natural systems. This thesis argues that to advance artificial self-assembling systems towards this complexity, the statistics of the interactions within self-assembling systems need to be empirically examined and understood. However, the pursuit of this involves the resolution of a variety of technical challenges. These are approached in this work through the development of a self-assembly toolkit that allows the collection of these statistics from a physical system with larger numbers of components than in previous works. A novel capacitive communication interface is developed for the components of this toolkit, which allows messaging between neighbouring components that are constrained to the surface of a plane. As self-assembling components reduce in size towards the microscale, the penalty for incorrect activation of a component’s binding mechanism is likely to increase. With this in mind, this capacitive communication interface is optimised to provide spatial alignment sensing, with the aim of allowing informed binding mechanism activation. The toolkit developed in this work uses components that are constrained to two degrees of freedom of motion. In pursuit of the development of programmable self-assembling components for 3D structures, a new design of alignment sensor for use in 3D is created. Simulation of this sensor, which is developed using an evolutionary algorithm, indicates that it is suited for detecting the alignment of components with three degrees of freedom. Approaches using computer vision are developed for the spatial tracking of the components of the toolkit, allowing the collection of empirical data regarding the interaction of components. The technical advances described within this work will allow the progression of data-driven self-assembly process design.

PDF
__soton.ac.uk_ude_PersonalFiles_Users_slb1_mydocuments_Spanton.pdf - Other
Restricted to Repository staff only

More information

Published date: July 2013
Organisations: University of Southampton, Electronics & Computer Science

Identifiers

Local EPrints ID: 355888
URI: https://eprints.soton.ac.uk/id/eprint/355888
PURE UUID: 85cd99df-830d-4696-826e-46224d975848

Catalogue record

Date deposited: 13 Jan 2014 14:14
Last modified: 18 Jul 2017 03:43

Export record

Contributors

Author: Robert Spanton
Thesis advisor: Klaus-Peter Zauner

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×