Enhanced noise at high bias in atomic-scale Au break junctions

Heating in nanoscale systems driven out of equilibrium is of fundamental importance, has ramifications for technological applications, and is a challenge to characterize experimentally. Prior experiments using nanoscale junctions have largely focused on heating of ionic degrees of freedom, while heating of the electrons has been mostly neglected. We report measurements in atomic-scale Au break junctions, in which the bias-driven component of the current noise is used as a probe of the electronic distribution. At low biases ($<$ 150~mV) the noise is consistent with expectations of shot noise at a fixed electronic temperature. At higher biases, a nonlinear dependence of the noise power is observed. We consider candidate mechanisms for this increase, including flicker noise (due to ionic motion), heating of the bulk electrodes, nonequilibrium electron-phonon effects, and local heating of the electronic distribution impinging on the ballistic junction. We find that flicker noise and bulk heating are quantitatively unlikely to explain the observations. We discuss the implications of these observations for other nanoscale systems, and experimental tests to distinguish vibrational and electron interaction mechanisms for the enhanced noise.Comment: 30 pages, 7 figure

Shot noise variation within ensembles of gold atomic break junctions at room temperature

Atomic-scale junctions are a powerful tool to study quantum transport, and are frequently examined through the mechanically controllable break junction technique (MCBJ). The junction-to-junction variation of atomic configurations often leads to a statistical approach, with ensemble-averaged properties providing access to the relevant physics. However, the full ensemble contains considerable additional information. We report a new analysis of shot noise over entire ensembles of junction configurations using scanning tunneling microscope (STM)-style gold break junctions at room temperature in ambient conditions, and compare this data with simulations based on molecular dynamics (MD), a sophisticated tight-binding model, and nonequilibrium Green's functions. The experimental data show a suppression in the variation of the noise near conductances dominated by fully transmitting channels, and a surprising participation of multiple channels in the nominal tunneling regime. Comparison with the simulations, which agree well with published work at low temperatures and ultrahigh vacuum (UHV) conditions, suggests that these effects likely result from surface contamination and disorder in the electrodes. We propose additional experiments that can distinguish the relative contributions of these factors.Comment: 21 pages, 6 figures. To appear in J. Phys: Condens. Matt., special issue on break junction

Self-supervised deep clustering of single-cell RNA-seq data to hierarchically detect rare cell populations.

Single-cell RNA sequencing (scRNA-seq) is a widely used technique for characterizing individual cells and studying gene expression at the single-cell level. Clustering plays a vital role in grouping similar cells together for various downstream analyses. However, the high sparsity and dimensionality of large scRNA-seq data pose challenges to clustering performance. Although several deep learning-based clustering algorithms have been proposed, most existing clustering methods have limitations in capturing the precise distribution types of the data or fully utilizing the relationships between cells, leaving a considerable scope for improving the clustering performance, particularly in detecting rare cell populations from large scRNA-seq data. We introduce DeepScena, a novel single-cell hierarchical clustering tool that fully incorporates nonlinear dimension reduction, negative binomial-based convolutional autoencoder for data fitting, and a self-supervision model for cell similarity enhancement. In comprehensive evaluation using multiple large-scale scRNA-seq datasets, DeepScena consistently outperformed seven popular clustering tools in terms of accuracy. Notably, DeepScena exhibits high proficiency in identifying rare cell populations within large datasets that contain large numbers of clusters. When applied to scRNA-seq data of multiple myeloma cells, DeepScena successfully identified not only previously labeled large cell types but also subpopulations in CD14 monocytes, T cells and natural killer cells, respectively

Survey of the current status of sustainable concrete production in the U.S.

The wide use of concrete in construction has significantly impacted energy use and environmental quality. Fortunately, the emergence of sustainable concrete, often made with alternative or recycled waste materials, offers great opportunities to improve concrete sustainability. This paper studies the current status of sustainable concrete production in the U.S. through a questionnaire survey. It revealed that the surveyed companies varied largely in their recognition and adoption of supplementary cementitious materials (SCMs) and alternative aggregates (AAs). Of the various alternative materials available, the most widely used by survey participants were limited to the three SCMs (fly ash, slag cement, and silica fume) and two types of aggregates (lightweight and recycled concrete aggregates). Multiple benefits and barriers to the adoption of SCMs and AAs, e.g., concrete properties, cost, and local availability, were also disclosed by survey participants. Statistical comparisons identified differences in sustainable concrete production between ready mixed concrete suppliers and concrete prefabricators, as well as in its applications in structural and non-structural concrete components. The findings provide a better understanding of the U.S. sustainable concrete production and offer insights into how researchers can help address industry concerns about the implementation of sustainable concrete

Excess noise in STM-style break junctions at room temperature

Current noise in nanoscale systems provides additional information beyond the electronic conductance. We report measurements at room temperature of the nonequilibrium ﾓexcessﾔ noise in ensembles of atomic-scale gold junctions repeatedly formed and broken between a tip and a film, as a function of bias conditions. We observe suppression of the noise near conductances associated with conductance quantization in such junctions, as expected from the finite temperature theory of shot noise in the limit of few quantum channels. In higher conductance junctions, the Fano factor of the noise approaches 1/3 the value seen in the low conductance tunneling limit, consistent with theoretical expectations for the approach to the diffusive regime. At conductance values where the shot noise is comparatively suppressed, there is a residual contribution to the noise that scales quadratically with the applied bias, likely due to a flicker noise/conductance fluctuation mechanism

Noise in electromigrated nanojunctions

Noise measurements are a probe beyond simple electronic transport that can reveal additional information about electronic correlations and inelastic processes. Here we report noise measurements in individual electromigrated nanojunctions, examining the evolution from the many channel regime to the tunneling regime, using a radio frequency technique. While we generally observe the dependence of noise on bias expected for shot noise, in approximately 12% of junction configurations we find discrete changes in the bias dependence at threshold values of the bias, consistent with electronic excitation of local vibrational modes. Moreover, with some regularity we find significant mesoscopic variation in the magnitude of the noise in particular junctions even with small changes in the accompanying conductance. In another $\sim$17% of junctions we observe pronounced asymmetries in the inferred noise magnitude as a function of bias polarity, suggesting that investigators should be concerned about current-driven ionic motion in the electrodes even at biases well below those used for deliberate electromigration.Comment: 13 pages, 3 figures. To appear in PR