We report a series of single-molecule transport measurements carried out in an ionic environment with oligophenylenediamine wires. These molecules exhibit three discrete conducting states accessed by electrochemically modifying the contacts. Transport in these junctions is defined by the oligophenylene backbone, but the conductance is increased by factors of ∼20 and ∼400 when compared to traditional dative junctions. We propose that the higher-conducting states arise from in situ electrochemical conversion of the dative Au←N bond into a new type of Au-N contact. Density functional theory-based transport calculations establish that the new contacts dramatically increase the electronic coupling of the oligophenylene backbone to the Au electrodes, consistent with experimental transport data. The resulting contact resistance is the lowest reported to date; more generally, our work demonstrates a facile method for creating electronically transparent metal-organic interfaces.

We study the single-molecule transport properties of small bandgap diketopyrrolopyrrole oligomers (DPPn, n = 1–4) with lengths varying from 1 to 5 nm. At a low bias voltage, the conductance decays exponentially as a function of length indicative of nonresonant transport. However, at a high bias voltage, we observe a remarkably high conductance close to 10–2 G0 with currents reaching over 0.1 μA across all four oligomers. These unique transport properties, together with density functional theory-based transport calculations, suggest a mechanism of resonant transport across the highly delocalized DPP backbones in the high bias regime. This study thus demonstrates the unique properties of diketopyrrolopyrrole derivatives in achieving highly efficient long-range charge transport in single-molecule devices.

The scanning tunneling microscope-based break junction (STM-BJ) is used widely to create and characterize single metal-molecule-metal junctions. In this technique, conductance is continuously recorded as a metal point contact is broken in a solution of molecules. Conductance plateaus are seen when stable molecular junctions are formed. Typically, thousands of junctions are created and measured, yielding thousands of distinct conductance versus extension traces. However, such traces are rarely analyzed individually to recognize the types of junctions formed. Here, we present a deep learning-based method to identify molecular junctions and show that it performs better than several commonly used and recently reported techniques. We demonstrate molecular junction identification from mixed solution measurements with accuracies as high as 97%. We also apply this model to an in situ electric field-driven isomerization reaction of a [3]­cumulene to follow the reaction over time. Furthermore, we demonstrate that our model can remain accurate even when a key parameter, the average junction conductance, is eliminated from the analysis, showing that our model goes beyond conventional analysis in existing methods.

Conjugated marcocycles cycloparaphenylenes can be used to make single-molecule devices with highly tunable conductivity.