Now a widespread research tool, computational chemistry techniques - or in silico techniques - have many advantages over experimentation.
"Experimentation takes manpower, chemicals, equipment, energy, and time," says Michael York of Continental Tire North America. "Computational chemistry allows one operator to run multiple chemical reactions 24 hours a day. By performing the experiments on the computer, the chemist can eliminate non-productive reaction possibilities and narrow the scope of probable laboratory successes. The end result is a major reduction in laboratory costs (such as materials, energy, and equipment) and time."
On the other side of the coin, the initial investment to buy the software and associated costs, such as new hardware, training and personnel changes, can be relatively high. But, used wisely, the tools can soon repay any initial outlay, not only by bringing laboratory cost savings but also by providing scientific insights that lead to the refinement of processes and procedures.
That's all very well for chemists, but how do these in silico techniques benefit the nanotechnologist? At the nanoscale the limitations of the experimentalist are exposed as the strange and wonderful world of the theoretical scientist comes in to play. Here quantum rules take over as researchers aim to manoeuvre individual atoms to desired positions.
But why do scientists want to go to all the trouble of atom placement? Variations at the nanometre length scale affect the wavelike properties of electrons inside matter. By manoeuvring atoms at this scale, you can vary the fundamental properties of materials (for instance, melting temperature, magnetization and charge capacity) without changing the overall chemical composition.
The prediction of behaviour and properties across a range of length scales is vital to the nanotechnologist. Fortunately, over the past two decades, computational techniques have evolved to the point that they now cover all length and time scales from the electronic to the macroscale.
Computational modelling using first-principles quantum mechanics and/or mesoscale simulations enables scientists to visualize and predict behaviour at or near the nanoscale. Mesoscale models represent solid materials, liquids and gases using larger fundamental units than molecular models, which require atomistic detail. Mesoscale methods function over far longer length and time scales than molecular simulation. You can use them to study complex liquids, polymer blends and structured materials on the nanometre to micron scale.
Modelling clay
Researchers at Queen Mary, University of London, UK, and the Universite Paris Sud in France, for example, have used quantum mechanics-based simulations to study clay-polymer nanocomposites. Such composites are among the most successful nanotechnology materials today, since they simultaneously feature both improved strength and toughness - properties that are usually trade-offs.
Clay-polymer nanocomposites can be prepared by in situ intercalative polymerization, a process that involves mechanical mixing of the clay mineral with the required monomer. The monomer then intercalates within the interlayer - that is, it inserts itself between the layers in the clay sheets - and promotes delamination of the whole structure. Polymerization follows to yield linear or cross-linked polymer matrices.
The researchers studied a recently discovered variation of this method, called self-catalysed in situ intercalative polymerization, using CASTEP - an ab initio quantum mechanical program that employs density functional theory. The project provided theoretical insight into the mechanism of this novel process by determining the role played by the clay matrix. Knowledge gained from the simulations should help scientists to engineer the polymer-silicate interaction.
Scientists at BASF, meanwhile, have used mesoscale simulations to study the science and behaviour of micelles. Micelles are spherical nanoparticles which, formed spontaneously in block-copolymer solutions, have applications in areas such as sensors, cosmetics and drug-delivery devices.
Using MesoDyn, an in silico tool for the prediction of mesoscale structures of soft-condensed matter, the BASF researchers studied concentrated solutions of amphiphilic block copolymers.
The simulations determined which molecular and formulation conditions led to the formation of "reverse micelles" - nanoscale water droplets in a surfactant medium. The findings are vital to understand the behaviour of surfactants. Methods such as solvent casting will produce experimental results but typically take months to run. The simulated experiments, in contrast, took only a few days to complete.
But what about the limitations of these techniques? While modelling tools are now well developed at the quantum and, to some extent, the mesoscale level, there are a number of limitations. For example, applications in the device/electronics field require quantum mechanical calculations for more atoms than current techniques and computing resources can handle. It is also not possible to model whole devices, in particular their functions and properties.
These limitations aside, the ever-increasing power, complexity and pace of software development will continue to produce in silico tools that will enable the nanotechnologist to drive and refine this complex science. Nanotechnology is far from having its chips.