Some classes of molecules are capable of arranging themselves in specific patterns on surfaces. This ability to self-organize is crucial for many technological applications, which are dependend on the assembly of ordered structures on surfaces. However, it has so far been virtually impossible to predict or control the result of such processes. Now a group of researchers led by Dr. Bianca Hermann, a physicist from the Center for Nanoscience (CeNS) at LMU Munich, reports a significant breakthrough: By combining statistical physics and detailed simulations with images obtained by scanning tunnelling microscopy (STM), the team has been able to formulate a simple model that can predict the patterns observed. "With the help of the model, we can generate a wide variety of patterns that reproduce surprisingly well the arrangements observed experimentally", says Hermann. "We want to extend this approach to other surface symmetries. Already now the areas of molecular electronics, sensor applications, surface catalysis and organic photovoltaics can profit from our model. Its ability to predict structures formed by self-organization allows optimization of molecular building blocks prior to synthesis."
When "mother nature" does the engineering, molecules can self-organize into complex structures – a first step in the formation of membranes, cells and other molecular systems. The principle of self-organization, which allows very economical use of resources, is also exploited in the production of functionalized surfaces required in molecular electronics, sensor applications, catalysis and photovoltaic components. The idea of the manufacturing process is that molecular components are brought into contact with a substrate material, and then "magically" find their preferred positions in the desired molecular network. The starting components are selected to display specific structural and chemical features intended for the envisaged application. However, the optimization of the molecular adlayers depends largely on a trial-and-error approach, and is therefore complicated and time-consuming.
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