We compare the density functional theory (DFT) results on the adsorption of small aromatic molecules (benzene, pyridine and thiophene) on gold surfaces obtained by using three types of van der Waals exchange-correlation functionals and localized basis set calculations. We show that the value of the molecule-surface binding energy depends on the interplay between the BSSE effect and the tendency of the exchange-correlation functionals to overestimate both the molecule-surface as well as the gold-gold distances within the relaxed systems. Consequently, we find that by using different types of LCAO basis sets or geometric models for the adsorption of the molecules on the surface, the binding energy can vary up to 100 %. A critical analysis of the physical parameters resulting from the calculations is presented for each exchange-correlation functional.

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Acenes and azaacenes lie at the core of molecular materials’ applications due to their important optical and electronic features. A critical aspect is provided by their heteroatom multiplicity, which can strongly affect their properties. Here we report pyrazinacenes containing the dihydro-decaazapentacene and dihydro-octaazatetracene chromophores and compare their properties/functions as a model case at an oxidizing metal substrate. We find a distinguished, oxidation-state-dependent conformational adaptation and self-assembly behaviour and discuss the analogies and differences of planar benzo-substituted decaazapentacene and octaazatetracene forms. Our broad experimental and theoretical study reveals that decaazapentacene is stable against oxidation but unstable against reduction, which is in contrast to pentacene, its C–H only analogue. Decaazapentacenes studied here combine a planar molecular backbone with conformationally flexible substituents. They provide a rich model case to understand the properties of a redox-switchable π-electronic system in solution and at interfaces. Pyrazinacenes represent an unusual class of redox-active chromophores.

The physical properties of cysteine clusters formed on the Au( 110)-(1 × 1) surface are investigated by means of density-functional theory. We take into account the clusters formed from three types of rotational conformers of cysteine; for each structure we investigate the protonated and unprotonated forms. Our investigations are based on three different geometrical models for the surface−adsorbate system, allowing us to describe the formation of new nucleation centers on the surface as well as the properties of long chains of cysteine molecules adsorbed on the Au( 110)-(1 × 1) surface. We point out significant differences between the relaxed structures formed by each conformer as well as the specific physicochemical properties leading to formation of new nucleation centers compared with those of already formed large cysteine structures. In particular, we emphasize the role of the NH 2 group of cysteine in these processes, by correlating our data with its relative position with respect to the gold surface.

Autoregressive processes (AR) have typical short-range memory. Detrended Fluctuation Analysis (DFA) was basically designed to reveal long range correlation in non stationary processes. However DFA can also be regarded as a suitable method to investigate both long-range and short range correlation in non-stationary and stationary systems. Applying DFA to AR processes can help understanding the non uniform correlation structure of such processes. We systematically investigated a first order autoregressive model AR (1) by DFA and established the relationship between the interaction constant of AR(1) and the DFA correlation exponent. The higher the interaction constant the higher is the short range correlation exponent. They are exponentially related. The investigation was extended to AR(2) processes. The presence of a distant positive interaction in addition to a near by interaction will increase the correlation exponent and the range of correlation while the effect of a distant negative interaction will decrease significantly only the range of interaction. This analysis demonstrate the possibility to identify and AR(1) model in an unknown DFA plot or to distinguish among AR(1) and AR(2) models. The analysis was performed on medium long series of 1000 terms.