Tunneling of two-dimensional surface polaritons through nanogaps in atomically thin crystals

We theoretically investigate the tunneling of two-dimensional surface polaritons (2DSPs) through nanometer-wide gaps in atomically thin crystals. For quantitatively accurate results, we developed a rigorous model based on the diffraction of 2DSPs for strongly confined surface polaritons (i.e., the polariton wavelength much shorter than the free-pace photon wavelength). We find distinctive features of the tunneling of 2DSPs. First, radiation loss during the tunneling is shown to be negligible. Second, the reflection coefficient R and tunneling coefficient T are shown to exhibit an anomalous logarithm singularity in their dependency on the gap width. Even for a gap size over two orders of magnitude smaller than the surface polariton wavelength, an appreciable reflection coefficient was observed in our calculation. Finally, we show that when the gap size increases, the phase of R saturates very rapidly to a nontrivial value of π/4. Based on these results, we further examine resonant tunneling of 2DSP through two identical gaps separated by a distance L, and establish a resonance condition defined by L≈λsp(4n-1)/8 with a positive integer n

Benefits of current percolation in superconducting coated conductors

The critical currents of MOD/RABiTS and PLD/IBAD coated conductors have been measured as a function of magnetic field orientation and compared to films grown on single crystal substrates. By varying the orientation of magnetic field applied in the plane of the film, we are able to determine the extent to which current flow in each type of conductor is percolative. Standard MOD/RABiTS conductors have also been compared to samples whose grain boundaries have been doped by diffusing Ca from an overlayer. We find that undoped MOD/RABiTS tapes have a less anisotropic in-plane field dependence than PLD/IBAD tapes and that the uniformity of critical current as a function of in-plane field angle is greater for MOD/RABiTS samples doped with Ca.EPSRC US Department of Energ

Protein expression and purification of integrin I domains and IgSF ligands for crystallography

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Stable and Efficient Nanofilm Pure Evaporation on Nanopillar Surfaces

Molecular dynamics simulations were conducted to systematically investigate how to maintain and enhance nanofilm pure evaporation on nanopillar surfaces. First, the dynamics of the evaporation meniscus and the onset and evolution of nanobubbles on nanopillar surfaces were characterized. The meniscus can be pinned at the top surface of the nanopillars during evaporation for perfectly wetting fluid. The curvature of the meniscus close to nanopillars varies dramatically. Nanobubbles do not originate from the solid surface, where there is an ultrathin nonevaporation film due to strong solid–fluid interaction, but originate and evolve from the corner of nanopillars, where there is a quick increase in potential energy of the fluid. Second, according to a parametric study, the smaller pitch between nanopillars (P) and larger diameter of nanopillars (D) are found to enhance evaporation but also raise the possibility of boiling, whereas the smaller height of nanopillars (H) is found to enhance evaporation and suppress boiling. Finally, it is revealed that the nanofilm thickness should be maintained beyond a threshold, which is 20 Å in this work, to avoid the suppression effect of disjoining pressure on evaporation. Moreover, it is revealed that whether the evaporative heat transfer is enhanced on the nanopillar surface compared with the smooth surface is also affected by the nanofilm thickness. The value of nanofilm thickness should be determined by the competition between the suppression effect on evaporation due to the decrease in the volume of supplied fluid and the existence of capillary pressure and the enhancement effect on evaporation due to the increase in the heating area. Our work serves as the guidelines to achieve stable and efficient nanofilm pure evaporative heat transfer on nanopillar surfaces

Magnetotransport and dielectric properties of perovskite ruthenate and titanate thin films

2007-2008 > Academic research: refereed > Publication in refereed journalVersion of RecordPublishe

The role of adaptation in generating monotonic rate codes in auditory cortex

In primary auditory cortex, slowly repeated acoustic events are represented temporally by the stimulus-locked activity of single neurons. Single-unit studies in awake marmosets (Callithrix jacchus) have shown that a sub-population of these neurons also monotonically increase or decrease their average discharge rate during stimulus presentation for higher repetition rates. Building on a computational single-neuron model that generates stimulus-locked responses with stimulus evoked excitation followed by strong inhibition, we find that stimulus-evoked short-term depression is sufficient to produce synchronized monotonic positive and negative responses to slowly repeated stimuli. By exploring model robustness and comparing it to other models for adaptation to such stimuli, we conclude that short-term depression best explains our observations in single-unit recordings in awake marmosets. Together, our results show how a simple biophysical mechanism in single neurons can generate complementary neural codes for acoustic stimuli

Evolution of superconductivity in K2-xFe4+ySe5: Spectroscopic studies of X-ray absorption and emission.

This study investigates the evolution of superconductivity in K2-xFe4+ySe5 using temperature-dependent X-ray absorption and resonant inelastic X-ray scattering techniques. Magnetization measurements show that polycrystalline superconducting (SC) K1.9Fe4.2Se5 has a critical temperature (T c) of ∼31 K with a varying superconducting volume fraction, which strongly depends on its synthesis temperature. An increase in Fe-structural/vacancy disorder in SC samples with more Fe atoms occupying vacant 4d sites is found to be closely related to the decrease in the spin magnetic moment of Fe. Moreover, the nearest-neighbor Fe-Se bond length in SC samples exceeds that in the non-SC (NS) sample, K2Fe4Se5, which indicates a weaker hybridization between the Fe 3d and Se 4p states in SC samples. These results clearly demonstrate the correlations among the local electronic and atomic structures and the magnetic properties of K2-xFe4+ySe5 superconductors, providing deeper insight into the electron pairing mechanisms of superconductivity

Factorizing LambdaMART for cold start recommendations

Recommendation systems often rely on point-wise loss metrics such as the mean squared error. However, in real recommendation settings only few items are presented to a user. This observation has recently encouraged the use of rank-based metrics. LambdaMART is the state-of-the-art algorithm in learning to rank which relies on such a metric. Despite its success it does not have a principled regularization mechanism relying in empirical approaches to control model complexity leaving it thus prone to overfitting. Motivated by the fact that very often the users' and items' descriptions as well as the preference behavior can be well summarized by a small number of hidden factors, we propose a novel algorithm, LambdaMART Matrix Factorization (LambdaMART-MF), that learns a low rank latent representation of users and items using gradient boosted trees. The algorithm factorizes lambdaMART by defining relevance scores as the inner product of the learned representations of the users and items. The low rank is essentially a model complexity controller; on top of it we propose additional regularizers to constraint the learned latent representations that reflect the user and item manifolds as these are defined by their original feature based descriptors and the preference behavior. Finally we also propose to use a weighted variant of NDCG to reduce the penalty for similar items with large rating discrepancy. We experiment on two very different recommendation datasets, meta-mining and movies-users, and evaluate the performance of LambdaMART-MF, with and without regularization, in the cold start setting as well as in the simpler matrix completion setting. In both cases it outperforms in a significant manner current state of the art algorithms