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Register a new userTemporal correlations and structural memory effects in break junction measurements
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We review data analysis techniques that can be used to study temporal correlations among conductance traces in break junction measurements. We show that temporal histograms are a simple but efficient tool to check the temporal homogeneity of the conductance traces, or to follow spontaneous or triggered temporal variations, like structural modifications in trained contacts, or the emergence of single-molecule signatures after molecule dosing. To statistically analyze the presence and the decay time of temporal correlations, we introduce shifted correlation plots. Finally, we demonstrate that correlations between opening and subsequent closing traces may indicate structural memory effects in atomic-sized metallic and molecular junctions. Applying these methods on measured and simulated gold metallic contacts as a test system, we show that the surface diffusion induced flattening of the broken junctions helps to produce statistically independent conductance traces at room temperature, whereas at low temperature repeating tendencies are observed as long as the contacts are not closed to sufficiently high conductance setpoints. Applying opening-closing correlation analysis on Pt-CO-Pt single-molecule junctions, we demonstrate pronounced contact memory effects and recovery of the molecule for junctions breaking before atomic chains are formed. However, if chains are pulled the random relaxation of the chain and molecule after rupture prevents opening-closing correlations.
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Single-molecule break junction measurements deliver a huge number of conductance vs. electrode separation traces. Along such measurements the target molecules may bind to the electrodes in different geometries, and the evolution and rupture of the single-molecule junction may also follow distinct trajectories. The unraveling of the various typical trace classes is a prerequisite of the proper physical interpretation of the data. Here we exploit the efficient feature recognition properties of neural networks to automatically find the relevant trace classes. To eliminate the need for manually labeled training data we apply a combined method, which automatically selects training traces according to the extreme values of principal component projections or some auxiliary measured quantities, and then the network captures the features of these characteristic traces, and generalizes its inference to the entire dataset. The use of a simple neural network structure also enables a direct insight to the decision making mechanism. We demonstrate that this combined machine learning method is efficient in the unsupervised recognition of unobvious, but highly relevant trace classes within low and room temperature gold-4,4 bipyridine-gold single molecule break junction data.
Graphene quantum dots (QDs) are intensively studied as platforms for the next generation of quantum electronic devices. Fine tuning of the transport properties in monolayer graphene QDs, in particular with respect to the independent modulation of the tunnel barrier transparencies, remains challenging and is typically addressed using electrostatic gating. We investigate charge transport in back-gated graphene mechanical break junctions and reveal Coulomb blockade physics characteristic of a single, high-quality QD when a nanogap is opened in a graphene constriction. By mechanically controlling the distance across the newly-formed graphene nanogap, we achieve reversible tunability of the tunnel coupling to the drain electrode by five orders of magnitude, while keeping the source-QD tunnel coupling constant. These findings indicate that the tunnel coupling asymmetry can be significantly modulated with a mechanical tuning knob and has important implications for the development of future graphene-based devices, including energy converters and quantum calorimeters.
A possible notion of nonclassicality for single systems can be defined on the basis of the notion of memory cost of classically simulating probabilities observed in a temporal sequence of measurements. We further explore this idea in a theory-independent framework, namely, from the perspective of general probability theories (GPTs), which includes classical and quantum theory as special examples. Under the assumption that each system has a finite memory capacity, identified with the maximal number of states perfectly distinguishable with a single measurement, we investigate what are the temporal correlations achievable with different theories, namely, classical, quantum, and GPTs beyond quantum mechanics. Already for the simplest nontrivial scenario, we derive inequalities able to distinguish temporal correlations where the underlying system is classical, quantum, or more general.
Using wave function matching approach and employing the Landauer-Buttiker formula a ferromagnetic graphene junction with a temperature gradient across the system, is studied. We calculate the thermally induced charge and spin current as well as the thermoelectric voltage (Seebeck effect) in the linear and nonlinear regimes. Our calculation revealed that due to the electron-hole symmetry, the charge Seebeck coefficient is, for an undoped magnetic graphene, an odd function of chemical potential while the spin Seebeck coefficient is an even function regardless of the temperature gradient and junction length. We have also found with an accurate tuning external parameter, namely the exchange filed and gate voltage, the temperature gradient across the junction drives a pure spin current without accompanying the charge current. Another important characteristic of thermoelectric transport, thermally induced current in the nonlinear regime, is examined. It would be our main finding that with increasing thermal gradient applied to the junction the spin and charge thermovoltages decrease and even become zero for non-zero temperature bias.
The choice of impedance used to shunt a Josephson junction determines if the charge transferred through the circuit is quantized: a capacitive shunt renders the charge discrete, whereas an inductive shunt leads to continuous charge. This discrepancy leads to a paradox in the limit of large inductances L. We show that while the energy spectra of the capacitively and inductively shunted junction are vastly different, their high-frequency responses become identical for large L. Inductive shunting thus opens the possibility to observe charging effects unimpeded by charge noise.
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