The addition of a lateral alkyl chain is a well-known strategy to reduce π-stacked ensembles of molecules in solution, with the intention to minimize the interactions between the molecules’ backbones. In this paper, we study whether this concept generalizes to single-molecule junctions by using a combination of mechanically controllable break junction (MCBJ) measurements and clustering-based data analysis with two small series of model compounds decorated with various bulky groups. The systematic study suggests that introducing alkyl side chains also favors the formation of electrode-molecule configurations that are not observed in their absence, thereby inducing broadening of the conductance peak in the one-dimensional histograms. Thus, the introduction of alkyl chains in aromatic compounds for molecular electronics must be carefully designed and optimized for the specific purpose, balancing between increased solubility and the possibility of additional junction configurations.

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Break-junction experiments are used to statistically study the electronic properties of individual molecules. The measurements consist of repeatedly breaking and merging a gold wire while measuring the conductance as a function of displacement. When a molecule is captured, a plateau is observed in the conductance traces otherwise exponentially decaying tunnel traces are measured. Clustering methods are widely used to separate these traces and identify potential sub-populations in the data corresponding to different molecular junction configurations. As these configurations are typically a priori unknown, unsupervised methods are most suitable for the classification. However, most of the unsupervised methods used for the classification perform poorly in the identification of these small sub-populations of molecular traces. Robust removal of tunnelling-only traces before clustering is thus of great interest. Neural networks have been proven to be powerful in the classification of data samples with predictable behaviour, but often show large sensitivity to the underlying training data. In this study we report on a neural network method for the separation of tunnelling-only traces in conductance vs. displacement measurements that achieves excellent classification performance for complete and unseen data sets. This method is particularly useful for data sets in which the yield of molecular traces is low or which comprise of a significant number of traces displaying a jump from tunneling features to a molecular plateau.@en

Creating a good contact between electrodes and graphene nanoribbons (GNRs) has been a long-standing challenge in searching for the next GNR-based nanoelectronics. This quest requires the controlled fabrication of sub-20 nm metallic gaps, a clean GNR transfer minimizing damage and organic contamination during the device fabrication, as well as work function matching to minimize the contact resistance. Here, we transfer 9-atom-wide armchair-edged GNRs (9-AGNRs) grown on Au(111)/mica substrates to pre-patterned platinum electrodes, yielding polymer-free 9-AGNR field-effect transistor devices. Our devices have a resistance in the range of 10⁶-10⁸ Ω in the low-bias regime, which is 2-4 orders of magnitude lower than previous reports. Density functional theory calculations combined with the non-equilibrium Green's function method explain the observed p-type electrical characteristics and further demonstrate that platinum gives strong coupling and higher transmission in comparison to other materials, such as graphene.

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Alkanes serve an important role as benchmark system in molecular electronics. However, a large variation in the conductance values is reported in the literature. To better understand these fluctuations, in this study we measure large molecular data sets (up to 100 000 breaking traces) of a series of alkanes with different lengths and anchoring groups using the mechanically controlled break junction (MCBJ) technique. Employing an unsupervised learning algorithm, we investigate both the time evolution and the distance dependence of the measured traces. For all the molecules considered, we have been able to identify the single-molecule conductance value for the fully stretched molecular configuration. For alkanedithiols, the corresponding extracted β decay constant of 1.05 ± 0.08 per CH₂group agrees well with literature values. In the case of the stronger thiol bonding, additional peaks in the conductance histograms are found, suggesting the formation of molecular junctions containing a single molecule plus additional gold/molecule unit(s). The results shine light on the dispersion in reported conductance values and show that the evolution of the molecule as a function of stretching and time contains crucial information in determining the molecular junction configuration in MCBJs.

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Switching effects are key elements in the design and characterization of nanoscale molecular electronics systems. They are used to achieve functionality through the transition between different conducting states. In this study, we analyze the presence of switching events in reference molecular systems, which are not designed to have switching behavior, such as oligo(phenylene ethynylene)s and alkanes, using the mechanically controllable break junction technique. These events can be classified in two groups, depending on whether the breaking trace shows exponential decay or plateau-like features before the switch happens. We argue that the former correspond to junctions forming after rupture of the gold atomic point contact, while the latter can be related to a change in the contact geometry of the junction. These results highlight how a proper choice of anchoring group and careful comparison with reference compounds are essential to understanding the origin of switching in molecular break junctions.

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Unsupervised machine learning, and in particular data clustering, is a powerful approach for the analysis of datasets and identification of characteristic features occurring throughout a dataset. It is gaining popularity across scientific disciplines and is particularly useful for applications without a priori knowledge of the data structure. Here, we introduce an approach for unsupervised data classification of any dataset consisting of a series of univariate measurements. It is therefore ideally suited for a wide range of measurement types. We apply it to the field of nanoelectronics and spectroscopy to identify meaningful structures in data sets. We also provide guidelines for the estimation of the optimum number of clusters. In addition, we have performed an extensive benchmark of novel and existing machine learning approaches and observe significant performance differences. Careful selection of the feature space construction method and clustering algorithms for a specific measurement type can therefore greatly improve classification accuracies.

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Atomically precise graphene nanoribbons (GNRs) are a promising emerging class of designer quantum materials with electronic properties that are tunable by chemical design. However, many challenges remain in the device integration of these materials, especially regarding contacting strategies. We report on the device integration of uniaxially aligned and non-aligned 9-atom wide armchair graphene nanoribbons (9-AGNRs) in a field-effect transistor geometry using electron beam lithography-defined graphene electrodes. This approach yields controlled electrode geometries and enables higher fabrication throughput compared to previous approaches using an electrical breakdown technique. Thermal annealing is found to be a crucial step for successful device operation resulting in electronic transport characteristics showing a strong gate dependence. Raman spectroscopy confirms the integrity of the graphene electrodes after patterning and of the GNRs after device integration. Our results demonstrate the importance of the GNR-graphene electrode interface and pave the way for GNR device integration with structurally well-defined electrodes.

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We report on charge transport across single short peptides using the Mechanically Controlled Break Junction (MCBJ) method. We record thousands of electron transport events across single-molecule junctions and with an unsupervised machine learning algorithm, we identify several classes of traces with multifarious conductance values that may correspond to different peptide conformations. Data analysis shows that very short peptides, which are more rigid, show conductance plateaus at low conductance values of about 10-3G0 and below, with G0 being the conductance quantum, whereas slightly longer, more flexible peptides also show plateaus at higher values. Fully stretched peptide chains exhibit conductance values that are of the same order as that of alkane chains of similar length. The measurements show that in the case of short peptides, different compositions and molecular lengths offer a wide range of junction conformations. Such information is crucial to understand mechanism(s) of charge transport in and across peptide-based biomolecules. This journal is

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This paper describes the conductance of single-molecules and self-assembled monolayers comprising an oligophenyleneethynylene core, functionalized with acenes of increasing length that extend conjugation perpendicular to the path of tunneling electrons. In the Mechanically Controlled Break Junction (MCBJ) experiment, multiple conductance plateaus were identified. The high conductance plateau, which we attribute to the single molecule conformation, shows an increase of conductance as a function of acene length, in good agreement with theoretical predictions. The lower plateau is attributed to multiple molecules bridging the junctions with intermolecular interactions playing a role. In junctions comprising a self-assembled monolayer with eutectic Ga-In top-contacts (EGaIn), the pentacene derivative exhibits unusually low conductance, which we ascribe to the inability of these molecules to pack in a monolayer without introducing significant intermolecular contacts. This hypothesis is supported by the MCBJ data and theoretical calculations showing suppressed conductance through the PC films. These results highlight the role of intermolecular effects and junction geometries in the observed fluctuations of conductance values between single-molecule and ensemble junctions, and the importance of studying molecules in both platforms.

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Porphyrin cyclophane 1, consisting of two rigidly fixed but still movable cofacial porphyrins and exposing acetate-masked thiols in opposed directions of the macrocycle, is designed, synthesized, and characterized. The functional cyclophane 1, as pioneer of mechanosensitive 3D materials, forms stable single-molecule junctions in a mechanically controlled break-junction setup. Its reliable integration in a single-molecule junction is a fundamental prerequisite to explore the potential of these structures as mechanically triggered functional units and devices.

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Graphene nanoribbons (GNRs) have attracted strong interest from researchers worldwide, as they constitute an emerging class of quantum-designed materials. The major challenges toward their exploitation in electronic applications include reliable contacting, complicated by their small size (<50 nm), and the preservation of their physical properties upon device integration. In this combined experimental and theoretical study, we report on the quantum dot behavior of atomically precise GNRs integrated in a device geometry. The devices consist of a film of aligned five-atom-wide GNRs (5-AGNRs) transferred onto graphene electrodes with a sub 5 nm nanogap. We demonstrate that these narrow-bandgap 5-AGNRs exhibit metal-like behavior at room temperature and single-electron transistor behavior for temperatures below 150 K. By performing spectroscopy of the molecular levels at 13 K, we obtain addition energies in the range of 200-300 meV. DFT calculations predict comparable addition energies and reveal the presence of two electronic states within the bandgap of infinite ribbons when the finite length of the 5-AGNR is accounted for. By demonstrating the preservation of the 5-AGNRs' molecular levels upon device integration, as demonstrated by transport spectroscopy, our study provides a critical step forward in the realization of more exotic GNR-based nanoelectronic devices.

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A new concept to improve the reliability of functional single molecule junctions is presented using the E-field triggered switching of Fe^IIbis-terpyridine complexes in a mechanically controlled break junction experiment as model system. The complexes comprise a push-pull ligand sensing the applied E-field and the resulting distortion of the Fe^II ligand field is expected to trigger a spin-crossover event reflected in a sudden jump of the transport current. By molecular engineering, the active centre of the complex is separated from the gold electrodes in order to eliminate undesired side-effects. Two aspects are considered to isolate the central metal ion, namely the spacing by introducing additional alkynes, and the steric shielding achieved by bulky isopropyl groups. With this small series of model complexes, a pronounced correlation is observed between the occurrence of bistable junctions and the extent of separation of the central metal ion, affirming the hypothesized Enhanced Separation Concept (ESC).

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One of the main challenges to upscale the fabrication of molecular devices is to achieve a mechanically stable device with reproducible and controllable electronic features that operates at room temperature1,2. This is crucial because structural and electronic fluctuations can lead to significant changes in the transport characteristics at the electrode-molecule interface3,4. In this study, we report on the realization of a mechanically and electronically robust graphene-based molecular junction. Robustness was achieved by separating the requirements for mechanical and electronic stability at the molecular level. Mechanical stability was obtained by anchoring molecules directly to the substrate, rather than to graphene electrodes, using a silanization reaction. Electronic stability was achieved by adjusting the π-π orbitals overlap of the conjugated head groups between neighbouring molecules. The molecular devices exhibited stable current-voltage (I-V) characteristics up to bias voltages of 2.0 V with reproducible transport features in the temperature range from 20 to 300 K.

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Single-molecule break-junction measurements are intrinsically stochastic in nature, requiring the acquisition of large datasets of “breaking traces” to gain insight into the generic electronic properties of the molecule under study. For example, the most probable conductance value of the molecule is often extracted from the conductance histogram built from these traces. In this letter, we present an unsupervised and reference-free machine learning tool to improve the determination of the conductance of oligo(phenylene ethynylene)dithiol from mechanically controlled break-junction (MCBJ) measurements. Our method allows for the classification of individual breaking traces based on an image recognition technique. Moreover, applying this technique to multiple merged datasets makes it possible to identify common breaking behaviors present across different samples, and therefore to recognize global trends. In particular, we find that the variation in the extracted molecular conductance can be significantly reduced resulting in a more reliable estimation of molecular conductance values from MCBJ datasets. Finally, our approach can be more widely applied to different measurement types which can be converted to two-dimensional images.

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Porphyrin derivatives are key components in natural machinery enabling us to store sunlight as chemical energy. In spite of their prominent role in cascades separating electrical charges and their potential as sensitizers in molecular devices, reports concerning their electronic transport characteristics are inconsistent. Here we report a systematic investigation of electronic transport paths through single porphyrin junctions. The transport through seven structurally related porphyrin derivatives was repeatedly measured in an automatized mechanically controlled break-junction set-up and the recorded data were analyzed by an unsupervised clustering algorithm. The correlation between the appearances of similar clusters in particular sub-sets of the porphyrins with a common structural motif allowed us to assign the corresponding current path. The small series of model porphyrins allowed us to identify and distinguish three different electronic paths covering more than four orders of magnitude in conductance.@en