Single-Molecule Conductance Studies of Organometallic Complexes Bearing 3-Thienyl Contacting Groups.

The compounds and complexes 1,4-C6 H4 (C≡C-cyclo-3-C4 H3 S)2 (2), trans-[Pt(C≡C-cyclo-3-C4 H3 S)2 (PEt3 )2 ] (3), trans-[Ru(C≡C-cyclo-3-C4 H3 S)2 (dppe)2 ] (4; dppe=1,2-bis(diphenylphosphino)ethane) and trans-[Ru(C≡C-cyclo-3-C4 H3 S)2 {P(OEt)3 }4 ] (5) featuring the 3-thienyl moiety as a surface contacting group for gold electrodes have been prepared, crystallographically characterised in the case of 3-5 and studied in metal|molecule|metal junctions by using both scanning tunnelling microscope break-junction (STM-BJ) and STM-I(s) methods (measuring the tunnelling current (I) as a function of distance (s)). The compounds exhibit similar conductance profiles, with a low conductance feature being more readily identified by STM-I(s) methods, and a higher feature by the STM-BJ method. The lower conductance feature was further characterised by analysis using an unsupervised, automated multi-parameter vector classification (MPVC) of the conductance traces. The combination of similarly structured HOMOs and non-resonant tunnelling mechanism accounts for the remarkably similar conductance values across the chemically distinct members of the family 2-5.

New Insights into Single-Molecule Junctions Using a Robust, Unsupervised Approach to Data Collection and Analysis.

We have applied a new, robust and unsupervised approach to data collection, sorting and analysis that provides fresh insights into the nature of single-molecule junctions. Automation of tunneling current-distance (I(s)) spectroscopy facilitates the collection of very large data sets (up to 100,000 traces for a single experiment), enabling comprehensive statistical interrogations with respect to underlying tunneling characteristics, noise and junction formation probability (JFP). We frequently observe unusual low-to-high through-molecule conductance features with increasing electrode separation, in addition to numerous other "plateau" shapes, which may be related to changes in interfacial or molecular bridge structure. Furthermore, for the first time we use the JFP to characterize the homogeneity of functionalized surfaces at the nanoscale.

Effective ligand passivation of Cu2O nanoparticles through solid-state treatment with mercaptopropionic acid.

In colloidal nanoparticle (NPs) devices, trap state densities at their surface exert a profound impact on the rate of charge carrier recombination and, consequently, on the deterioration of the device performance. Here, we report on the successful application of a ligand exchange strategy to effectively passivate the surface of cuprite (Cu2O) NPs. Cu2O NPs were prepared by means of a novel synthetic route based on flame spray pyrolysis. FTIR, XRD, XPS, and HRTEM measurements corroborate the formation of cubic cuprite Cu2O nanocrystals, excluding the possible presence of undesired CuO or Cu phases. Most importantly, steady-state emission and transient absorption assays document that surface passivation results in substantial changes in the intensity of emissive excitonic states--centered at copper and oxygen vacancies--and in the lifetime of excitons near the band edge. To shed light onto ultrafast processes in Cu2O nanocrystals additional pump probe experiments on the femtosecond and nanosecond time scales were carried out. Two discernible species were observed: on one hand, an ultrafast component (~ps) that relates to the excitons; on the other hand, a long-lived component (~µs) that originates from the defects/trap states.

Unsupervised vector-based classification of single-molecule charge transport data.

The stochastic nature of single-molecule charge transport measurements requires collection of large data sets to capture the full complexity of a molecular system. Data analysis is then guided by certain expectations, for example, a plateau feature in the tunnelling current distance trace, and the molecular conductance extracted from suitable histogram analysis. However, differences in molecular conformation or electrode contact geometry, the number of molecules in the junction or dynamic effects may lead to very different molecular signatures. Since their manifestation is a priori unknown, an unsupervised classification algorithm, making no prior assumptions regarding the data is clearly desirable. Here we present such an approach based on multivariate pattern analysis and apply it to simulated and experimental single-molecule charge transport data. We demonstrate how different event shapes are clearly separated using this algorithm and how statistics about different event classes can be extracted, when conventional methods of analysis fail.