Programming Nanotechnology: Learning from Nature

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Abstract

For many decades, nanotechnology has been developed with cooperation from researchers in several fields of studies including physics, chemistry, biology, material science, engineering, and computer science. In this chapter, we explore the nanotechnology development community and identify the needs and opportunities of computer science research in nanotechnology. In particular we look at methods for programming future nanotechnology, examining the capabilities offered by simulations and intelligent systems. This chapter is intended to benefit computer scientists who are keen to contribute their works to the field of nanotechnology and also nanotechnologists from other fields by making them aware of the opportunities from computer science. It is hoped that this may lead to the realisation of our visions.

Introduction

In 1959, Richard Feynman, a future Nobel Laureate, gave a visionary talk entitled “There's Plenty of Room at the Bottom”1 on miniaturisation to nanometre-scales. Later, the work of Drexler [1], [2] also gave futuristic visions of nanotechnology. Feynman and Drexler's visions inspired many researchers in physics, material science, chemistry, biology and engineering to become nanotechnologists. Their visions were fundamental: since our ancestors made flint axes, we have been improving our technology to bring convenience into our everyday life. Today a computer can be carried with one hand—40 years ago a computer (hundreds of times slower) was the size of a room. Miniaturisation of microprocessors is currently in process at nanometre-scales [3]. Yet, the style of our modern technology is still the same as ancient technology that constructed a refined product from bulk materials. This style is referred to as bulk or top–down technology[1]. As conventional methods to miniaturise the size of transistors in silicon microprocessor chips will soon reach its limit2 and the modification of today's top–down technology to produce nanoscale structures is difficult and expensive [3], a new generation of computer components will be required. Feynman and Drexler proposed a new style of technology, which assembles individual atoms or molecules into a refined product [1]. This Drexler terms molecular technology or bottom–up technology[1]. This bottom–up technology could be the answer for the computer industry. Though top–down technology currently remains the choice for constructing mass-produced devices, nanotechnologists are having increasing success in developing bottom–up technology [3].

There are some concerns regarding emergent bottom–up technology. First, the laws of physics do not always apply at nanometre-scales [4]. The properties of matter at nanometre-scales are governed by a complex combination of classical physics and quantum mechanics [4]. Nevertheless, bottom–up fabrication methods have been successfully used to make nanotubes and quantum dots [3]. These methods are not yet suitable for building complex electronic devices such as computer processors, not to mention nanoassemblers that can make copies of themselves and work together at a task. Furthermore, and significantly, once knowledge of nanotechnology is advanced and real-world nanoassemblers are realised, they must be properly controllable to prevent any threats to our world.

More recently computer science has become involved in nanotechnology. Such research is wide ranging and includes: software engineering, networking, Internet security, image processing, virtual reality, human–machine interface, artificial intelligence, and intelligent systems. Most work focuses on the development of research tools. For example, computer graphics and image processing have been used in nanomanipulators that provide researchers an interactive system interface to scanning-probe microscopes, which allow us to investigate and manipulate the surface at atomic scales3[5], [6]. In addition, genetic algorithms have been used as a method in automatic system design for molecular nanotechnology [7].

Computer science offers more opportunities for nanotechnology. Soft Computing techniques such as swarm intelligence, genetic algorithms and cellular automata can enable systems with desirable emergent properties, for example growth, self-repair, and complex networks.4 Many researchers have successfully applied such techniques to real-world problems including complex control systems in manufacturing plants and air traffic control.4 With some modifications towards nanotechnology characteristics, these techniques can be applied to control a swarm of a trillion nanoassemblers or nanorobots (once realised). It is anticipated that soft computing methods such as these will overcome concerns about implications of nanotechnology, and prevent the notorious scenario of self-replicating nanorobots multiplying uncontrollably.

This chapter reviews nanotechnology from different points of view in different research areas. We discuss the development of the field at the present time, and examine some concerns regarding the field. We then focus on the needs and benefits of computer science for nanotechnology, as well as existing and future computer science research for nanotechnology. The second half of this chapter introduces the area of swarm intelligence and then summarises investigations into how nanotechnology and self-assembling devices may be controlled by such techniques.

Section snippets

Development in Nanotechnology

To describe Feynman's grand visions that have inspired many researchers in several fields of study, Drexler5 introduced the term “Nanotechnology” and “Molecular Engineering” in his book, “Engines of Creation”[1]. He explored and characterised an extensive view of Feynman's visions in many aspects including potential benefits and possible dangers to humanity. According to the vision, building products with atomic precision by bottom–up technology

Benefits of Computer Science for Nanotechnology

Recently, M.C. Roco of the National Nanotechnology Initiative (NNI), an organisation officially founded in 2001 to initiate the coordination among agencies of nanometre-scale science and technology in the USA, gave a timeline for nanotechnology to reach commercialisation.27 For the next twenty years,

Swarm Intelligence

Swarm intelligence is inspired by collaborative behaviours in social animals such as birds, ants, fish and termites. Collaborative behaviour among social animals exhibits a remarkable degree of intelligence. These social animals require no leader. Their collaborative behaviours emerge from interactions among individuals. Often the behaviour of flocks, swarms and insect colonies, arises through interactions among the individuals in the group and through interactions with their environment. For

Perceptive Particle Swarm Optimisation

In particle swarm optimisation, all individuals in the swarm have the same behaviours and characteristics. It is assumed that the information on the position and the performance of particles can be exchanged during social interaction among particles in the neighbourhood. Importantly, conventional particle swarm optimisation relies on social interaction among particles through exchanging detailed information on position and performance. However, in the physical world, this type of complex

Perceptive Particle Swarm Optimisation for Nanotechnology

Using this model of particle movement and perception, computers can be used to simulate the aggregation of various desired forms. While the PPSO algorithm can be used for function optimisation as described above, a more direct simulation enables the same algorithm to model bottom–up form generation. Instead of simulated particles randomly flocking in a virtual “function optimisation space,” we can make them randomly flock in a virtual “form aggregation space.” From the computer science

Self-Assembling Nanotechnology

Swarm intelligence is not the only field that may inform future nanotechnology. The development of self-assembling robots has also taught us much.

Self-assembly (the autonomous construction of a device by itself) is a dream of robotics engineers and may be an essential requirement for future nanorobots. A payload of self-assembling components would be easier to transport to hazardous and distant locations compared to complete robots. A device that can self-assemble also has the ability to

Conclusions

As the development of nanotechnology progresses in several disciplines including physics, chemistry, biology and material science, computer scientists must be aware of their roles and brace themselves for the greater advancement of nanotechnology in the future. This chapter has outlined the development of nanotechnology. It is hoped that this gentle review will benefit computer scientists who are keen to contribute their works to the field of nanotechnology. We also suggested the possible

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