In the course of my restless perambulations across nanotechnology under dreaming spires and between punting on the rivers Cherwell and Isis, I discovered in the depth of a Microscopy Suite an artist with an eye for the truly small: Dr. Louise Hughes and her website, Miniature Horizons. An expert in scanning and transmission electron microscopy specializing in electron tomography, she is an award-winning science communicator. Louise has created, visualized and preserved some of the most stunning images of nature in the realm beyond small and promptly turned them into wearable scientific artwork. As science photography becomes the object of increasing competition, one can easily see why.
A Swiss microsystems laboratory headed by Jürgen Brugger at the École Polytechnique Fédérale de Lausanne developed a new method to place nanoparticles on a surface. No one will be surprised that the Swiss accomplished great precision of 1 nm at this task. Thus far, the greatest measure of positioning accuracy was, with luck, 10-20 nm. This opens new perspectives for nano devices such as miniaturized optical and electro-optical nanodevices including measuring sensors where predetermined and selective placement onto large-area substrates such as 1 cm² is needed to utilize the benefits of nanoparticle assembly.
Like most great solutions, this one is simple and elegant: gold nanoparticles suspended in a liquid are first heated so they gather in one spot, and then drawn across the surface. Not unlike a miniature golf course, the surface is lithographed with funneled traps and auxiliary sidewalls, thus patterned with barriers and holes. When the nanoparticles hit a barrier (an auxiliary sidewall), they disengage from the liquid and can be deterministically directed to sink into the hole, attaining simultaneous control of position, orientation and interparticle distance at the nanometer level. In this way, position and orientation of the slightly oblong gold nanorods can be steered very precisely. The Swiss research group demonstrated this by writing the world’s smallest version of the alphabet and also shaped complex patterns. This will open new doors for vastly improved assembly of nanodevices.
In light of groundbreaking advances in the field, it comes as no surprise that the 2016 Nobel Memorial Prize in Chemistry was just awarded to Jean-Pierre Sauvage (U. Strasbourg), J. Fraser Stoddart (Northwestern U.) and Bernard L. Feringa (U. Groningen) for work on nanometer-size “molecular machines” that feature characteristics of “smart materials” – an emerging area not only of materials science that opens extremely bright perspectives to nanotechnology overall, bringing nanomachines and microrobots within reach.
One of the challenges – and advantages – of nanotechnology is that many aspects of its use can, but also ought to be, tested by simulation. If minuscule applications are to carry active components with extreme precision to their target within the human body, be it to mark cells for diagnostic procedures or to support the buildup of tissues and structures or to deliver therapeutic agents, their functionality needs to be simulated by mathematical models and methods.
One such application is the development of biosensors capable of identifying tumor markers in blood that holds significant potential for cancer therapy. Nanowires composed of semiconductor material enable recognition of proteins that indicate the presence of existing tumors. Physical simulation of such an electronic system with biological application presents novel mathematical problems. Nanowires used as sensors are approximately 1 μm in length (one-hundredth of the diameter of a human hair) and 50 μm in diameter, i.e., one-twentieth of their length. DNA molecules examined by these nanowires are only 2 μm in diameter and tied to receptors on the wire permeated by a certain electric current. This changes conductivity and current flow of the sensor. A mathematical model suitable for simulation needs to reflect the relevant subsystems of this process by equations that describe the distribution of the charges, coupled with equations reflecting the movement of charges. This creates a system of partial differential equations reflective of the transport of charges that can be connected with equations descriptive of the motion of molecules on the outside of the sensor. Currently, Clemens Heitzinger at Vienna University of Technology’s Institute of Analysis and Scientific Computing is developing such models.
Certain systems of equations can also use multiscalar analysis problems that are common in nanotechnology. Both the behavior of fine structures (for example during bonding of a molecule) and the properties of the sensor itself are of interest. If one were to describe it all through a numerical simulation, it would require computing time beyond realizable limits. But partial differential equations combined with a solution of multiscalar problems permit simulation of both relatively large and very small structures in a single system. This can save extremely expensive custom-built items for lab experiments. It also permits conclusions about data and connections that could not be deducted by physical measurement techniques. The technology can target any molecule identifiable through antibodies, not only biological molecules but also poisonous gases.
However, the smaller the examined systems, the greater the importance of random movements and fluctuations. To account for such events, probability theories need to be integrated into systems of differential equations, transforming partial differential equations into stochastic partial differential equations that have random forcing terms and coefficients, can be exceedingly difficult to solve, and have strong connections with quantum field theory and statistical mechanics. This also has numerous applications outside of medicine, for example in information technology. One example is microchips that contain billions of transistors approximately 20 μm in size that cannot all be perfectly identical but need to function despite their fluctuation range. The same mathematical modeling approach permits optimal numerical simulation of such systems.