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When Molecules Leave Tire Tracks
A New Approach to Optimizing Molecular Self-Organization
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. 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 (Germany), 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.
To develop the new molecular-interaction site model, Dr. Herrmann's group collaborated with Priv. Doz. Dr. Thomas Franosch und Professor Erwin Frey within the Cluster of Excellence "Nanosystems Initiative Munich" (NIM). The problem was tackled using an approach from statistical physics known as Monte Carlo method, which allows one to conduct a detailed computer simulation on the statistics of molecular interactions. The structural motifs so generated were compared with experimental high-resolution images of molecular patterns obtained by STM. Marta Balbás Gambra, a doctoral student, began each simulation with a mathematical representation of a collection of hundreds of randomly oriented particles of defined conformation. These schematic molecules were then perturbed by - computationally - adding energy, causing the population to adopt a new configuration.
Original publication:
Rohr C., et. al.: Molecular Jigsaw: Pattern Diversity Encoded by Elementary Geometrical Features, NanoLetters online, 16 February 2009 DOI: 10.1021/nl903225j
http://www.en.uni-muenchen.de
Keywords: CeNS Center for Nanoscience LMU Munich Nanosystems Initiative Munich NIM scanning tunnelling microscopy STM
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