The synthesis, characterization, and water oxidation activity of mononuclear ruthenium complexes with tris(2-pyridylmethyl)amine (TPA), tris(6-methyl-2-pyridylmethyl)amine (Me(3)TPA), and a new pentadentate ligand N,N-bis(2-pyridinylmethyl)-2,2'-bipyridine-6-methanamine (DPA-Bpy) have been described. The electrochemical properties of these mononuclear Ru complexes have been investigated by both experimental and computational methods. Using Ce(IV) as oxidant, stoichiometric oxidation of water by [Ru(TPA)(H(2)O)(2)](2+) was observed, while Ru(Me(3)TPA)(H(2)O)(2)](2+) has much less activity for water oxidation. Compared to [Ru(TPA)(H(2)O)(2)](2+) and [Ru(Me(3)TPA)(H(2)O)(2)](2+), [Ru(DPA-Bpy)(H(2)O)](2+) exhibited 20 times higher activity for water oxidation. This study demonstrates a new type of ligand scaffold to support water oxidation by mononuclear Ru complexes.
Improved understanding of charge-transport in single molecules is essential for harnessing the potential of molecules, e.g., as circuit components at the ultimate size limit. However, interpretation and analysis of the large, stochastic data sets produced by most quantum transport experiments remain an ongoing challenge to discovering much-needed structure–property relationships. Here, we introduce segment clustering, a novel unsupervised hypothesis generation tool for investigating single molecule break junction distance–conductance traces. In contrast to previous machine learning approaches for single molecule data, segment clustering identifies groupings of similar pieces of traces instead of entire traces. This offers a new and advantageous perspective into data set structure because it facilitates the identification of meaningful local trace behaviors that may otherwise be obscured by random fluctuations over longer distance scales. We illustrate the power and broad applicability of this approach with two case studies that address common challenges encountered in single molecule studies: First, segment clustering is used to extract primary molecular features from a varying background to increase the precision and robustness of conductance measurements, enabling small changes in conductance in response to molecular design to be identified with confidence. Second, segment clustering is applied to a known data mixture to qualitatively separate distinct molecular features in a rigorous and unbiased manner. These examples demonstrate two powerful ways in which segment clustering can aid in the development of structure–property relationships in molecular quantum transport, an outstanding challenge in the field of molecular electronics.
Interpretation of single molecule transport data is complicated by the fact that all such data are inherently highly stochastic in nature. Features are often broad, seemingly unstructured and distributed over more than an order of magnitude. However, the distribution contains information necessary for capturing the full variety of processes relevant in nanoscale transport, and a better understanding of its hierarchical structure is needed to gain deeper insight into the physics and chemistry of single molecule electronics. Here, we describe a novel data analysis approach based on hierarchical clustering to aid in the interpretation of single molecule conductance-displacement histograms. The primary purpose of statistically partitioning transport data is to provide avenues for unbiased hypothesis generation in single molecule break junction experiments by revealing otherwise potentially hidden aspects in the conductance data. Our approach is generalizable to the analysis of a wide variety of other single molecule experiments in molecular electronics, as well as in single molecule fluorescence spectroscopy, force microscopy, and ion-channel conductance measurements.
Diborane (B 2 H 6 ) is a promising molecular precursor for atomic precision p-type doping of silicon that has recently been experimentally demonstrated [S ̌kereňet al. Nat. Electron. 2020]. We use density functional theory (DFT) calculations to determine the reaction pathway for diborane dissociating into a species that will incorporate as electrically active substitutional boron after adsorbing onto the Si(100)-2×1 surface. Our calculations indicate that diborane must overcome an energy barrier to adsorb, explaining the experimentally observed low sticking coefficient (<1 × 10 −4 at room temperature) and suggesting that heating can be used to increase the adsorption rate. Upon sticking, diborane has an ≈50% chance of splitting into two BH 3 fragments versus merely losing hydrogen to form a dimer such as B 2 H 4 . As boron dimers are likely electrically inactive, whether this latter reaction occurs is shown to be predictive of the incorporation rate. The dissociation process proceeds with significant energy barriers, necessitating the use of high temperatures for incorporation. Using the barriers calculated from DFT, we parameterize a Kinetic Monte Carlo model that predicts the incorporation statistics of boron as a function of the initial depassivation geometry, dose, and anneal temperature. Our results suggest that the dimer nature of diborane inherently limits its doping density as an acceptor precursor and furthermore that heating the boron dimers to split before exposure to silicon can lead to poor selectivity on hydrogen and halogen resists. This suggests that, while diborane works as an atomic precision acceptor precursor, other non-dimerized acceptor precursors may lead to higher incorporation rates at lower temperatures.
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