Week 5: Representing and Interpreting Data in Single-Molecule Electronics
Rohan V -
Hi all, it’s Rohan! Last week, I explained how data is commonly modeled in single-molecule experiments. This week, I’ll explain how researchers try and interpret the vast amount of data that comes out of these experiments. As a refresher, because of the small scale of individual molecules, thousands of individual ‘experiments’ need to be run on any given molecule to ensure consistency of results. Any individual experiment might be affected by a slight variation in ambient temperature, a change in machine voltage, etc; this inconsistency is counteracted by performing the experiment many times.
Due to the size of the experimental output, it’s inefficient for researchers to look at thousands of graphs of how the junction conductance evolves as it is stretched apart. To more quickly and accurately analyze their data, researchers often represent trace data in a two-dimensional (2-D) histogram. These histograms aggregate the distance-conductance data from all of the thousands of traces in the experimental dataset, grouping them together into a single graph.
Above is a 2-D histogram generated from the experiment we ran this past week! Here, a deeper shade of blue indicates that more traces pass through that specific point in distance-conductance space. Recalling how a general trace from this molecule looks, we can see that the 2-D histogram broadly represents an initial exponential conductance decay, followed by a ‘molecular plateau’ where the molecule is bound to gold electrodes.
However, the 2-D histogram is much broader than the individual trace. This phenomenon arises because traces collected from the same molecule can vary greatly in behavior, as explained above. The idea of representing single-molecule data with 2-D histograms has become a staple feature of molecular electronics research. However, the way that researchers extract information about molecular conductance from these outputs is still in development. Oftentimes, the ‘conductance’ of a molecule is defined to be the conductance value that the most traces pass through (represented as the darkest shade on the 2-D histogram above). This method of extracting conductance, while effective for a general understanding of molecular behavior, fails to account for the variable conductance behavior of many traces. In fact, in the single trace depicted below, conductance varies by a significant amount even within the molecular plateau.
My project involves developing a more accurate and efficient way to model single-molecule conductance. Over the last few weeks, I’ve spent a significant amount of my time working with MATLAB code, using existing documentation from my lab as well as programs developed by other researchers, to achieve this goal. In the coming weeks, I’ll dive into more specifics: explaining existing algorithms to model single-molecule conductance, their limitations, and the approach I’ve been using to create an improved program that can be widely applied to many molecules.
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