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Structure–Property Relationships in Atomic-Scale Junctions: Histograms and Beyond

Author(s):

Mark S. Hybertsen, Latha Venkataraman

Journal:

Accounts of Chemical Research

Year:

2016

Volume:

49

Pages

452-460

DOI:

10.1021/acs.accounts.6b00004

Abstract:

Over the past 10 years, there has been tremendous progress in the measurement, modeling and understanding of structure–function relationships in single molecule junctions. Numerous research groups have addressed significant scientific questions, directed both to conductance phenomena at the single molecule level and to the fundamental chemistry that controls junction functionality. Many different functionalities have been demonstrated, including single-molecule diodes, optically and mechanically activated switches, and, significantly, physical phenomena with no classical analogues, such as those based on quantum interference effects. Experimental techniques for reliable and reproducible single molecule junction formation and characterization have led to this progress. In particular, the scanning tunneling microscope based break-junction (STM-BJ) technique has enabled rapid, sequential measurement of large numbers of nanoscale junctions allowing a statistical analysis to readily distinguish reproducible characteristics. Harnessing fundamental link chemistry has provided the necessary chemical control over junction formation, enabling measurements that revealed clear relationships between molecular structure and conductance characteristics. Such link groups (amines, methylsuflides, pyridines, etc.) maintain a stable lone pair configuration that selectively bonds to specific, undercoordinated transition metal atoms available following rupture of a metal point contact in the STM-BJ experiments. This basic chemical principle rationalizes the observation of highly reproducible conductance signatures. Subsequently, the method has been extended to probe a variety of physical phenomena ranging from basic I–V characteristics to more complex properties such as thermopower and electrochemical response. By adapting the technique to a conducting cantilever atomic force microscope (AFM-BJ), simultaneous measurement of the mechanical characteristics of nanoscale junctions as they are pulled apart has given complementary information such as the stiffness and rupture force of the molecule-metal link bond. Overall, while the BJ technique does not produce a single molecule circuit for practical applications, it has proved remarkably versatile for fundamental studies. Measured data and analysis have been combined with atomic-scale theory and calculations, typically performed for representative junction structures, to provide fundamental physical understanding of structure–function relationships.

This Account integrates across an extensive series of our specific nanoscale junction studies which were carried out with the STM- and AFM-BJ techniques and supported by theoretical analysis and density functional theory based calculations, with emphasis on the physical characteristics of the measurement process and the rich data sets that emerge. Several examples illustrate the impact of measured trends based on the most probable values for key characteristics (obtained from ensembles of order 1000–10 000 individual junctions) to build a solid picture of conductance phenomena as well as attributes of the link bond chemistry. The key forward-looking question posed here is the extent to which the full data sets represented by the individual trajectories can be analyzed to address structure–function questions at the level of individual junctions. Initial progress toward physical modeling of conductance of individual junctions indicates trends consistent with physical junction structures. Analysis of junction mechanics reveals a scaling procedure that collapses existing data onto a universal force–extension curve. This research directed to understanding the distribution of structures and physical characteristics addresses fundamental questions concerning the interplay between chemical control and stochastically driven diversity.

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