Strained bubbles in van der Waals heterostructures as local emitters of photoluminescence with adjustable wavelength

The possibility to tailor photoluminescence (PL) of monolayer transition metal dichalcogenides (TMDCs) using external factors such as strain, doping, and external environment is of significant interest for optoelectronic applications. Strain in particular can be exploited as a means to continuously vary the band gap. Micrometer-scale strain gradients were proposed for creating “artificial atoms” that can utilize the so-called exciton funneling effect and work, for example, as exciton condensers. Here we describe room-temperature PL emitters that naturally occur whenever monolayer TMDC is deposited on an atomically flat substrate. These are hydrocarbon-filled bubbles, which provide predictable, localized PL from well-separated sub-micrometer areas. Their emission energy is determined by the built-in strain controlled only by the substrate material, such that both the maximum strain and the strain profile are universal for all bubbles on a given substrate, i.e., independent of the bubble size. We show that for bubbles formed by monolayer MoS₂, PL can be tuned between 1.72 and 1.81 eV by choosing bulk PtSe₂, WS₂, MoS₂, or graphite as a substrate, and its intensity is strongly enhanced by the funneling effect. Strong substrate-dependent quenching of the PL in areas of good contact between MoS₂ and the substrate ensures localization of the luminescence to bubbles only; by employing optical reflectivity measurements we identify the mechanisms responsible for the quenching. Given the variety of available monolayer TMDCs and atomically flat substrates and the ease of creating such bubbles, our findings open a venue for making and studying the discussed light-emitting “artificial atoms” that could be used in applications

Loss of the Orphan Nuclear Receptor SHP Is More Pronounced in Fibrolamellar Carcinoma than in Typical Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) remains a major problem in oncology. The molecular mechanisms which underlie its pathogenesis are poorly understood. Recently the Small Heterodimer Partner (SHP), an orphan nuclear receptor, was suggested to be involved as a tumor suppressor in hepatocellular carcinoma development. To date, there are no such studies regarding fibrolamellar carcinoma, a less common variant of HCC, which usually affects young people and displays distinct morphological features. The aim of our project was to evaluate the SHP levels in typical and fibrolamellar hepatocellular carcinoma with respect to the levels of one of the cell cycle regulators, cyclin D1. We assessed the immunoreactivity levels of SHP and cyclin D1 in 48 typical hepatocellular carcinomas, 9 tumors representing the fibrolamellar variant, 29 non malignant liver tissues and 7 macroregenerative nodules. We detected significantly lower SHP immunoreactivity in hepatocellular carcinoma when compared to non malignant liver tissue. Moreover, we found that SHP immunoreactivity is reduced in fibrolamellar carcinoma when compared to typical hepatocellular carcinoma. We also found that SHP is more commonly lost in HCC which arises in the liver with steatosis. The comparison between the cyclin D1 and SHP expression revealed the negative correlation between these proteins in the high grade HCC. Our results indicate that the impact of loss of SHP protein may be even more pronounced in fibrolamellar carcinoma than in a typical form of HCC. Further investigation of mechanisms through which the loss of SHP function may influence HCC formation may provide important information in order to design more effective HCC therapy

Indirect to direct gap crossover in two-dimensional InSe revealed by ARPES

EPSRC, CDT Graphene-NOWNANO, the EPSRC Doctoral Prize Fellowship, e European Graphene Flagship Project, ERC Synergy Grarant Hetero2D,the ARCHER National UK Supercomputer RAP Project e547, Royal Society URF, and Llyod Register Foundation Nanotechnolog

Transductive Learning for Spatial Data Classification

Learning classifiers of spatial data presents several issues, such as the heterogeneity of spatial objects, the implicit definition of spatial relationships among objects, the spatial autocorrelation and the abundance of unlabelled data which potentially convey a large amount of information. The first three issues are due to the inherent structure of spatial units of analysis, which can be easily accommodated if a (multi-)relational data mining approach is considered. The fourth issue demands for the adoption of a transductive setting, which aims to make predictions for a given set of unlabelled data. Transduction is also motivated by the contiguity of the concept of positive autocorrelation, which typically affect spatial phenomena, with the smoothness assumption which characterize the transductive setting. In this work, we investigate a relational approach to spatial classification in a transductive setting. Computational solutions to the main difficulties met in this approach are presented. In particular, a relational upgrade of the nave Bayes classifier is proposed as discriminative model, an iterative algorithm is designed for the transductive classification of unlabelled data, and a distance measure between relational descriptions of spatial objects is defined in order to determine the k-nearest neighbors of each example in the dataset. Computational solutions have been tested on two real-world spatial datasets. The transformation of spatial data into a multi-relational representation and experimental results are reported and commented

Optogalvanic signals from iron positive ions in hollow cathode discharge

Dynamic optogalvanic signals from Fe positive ions in Kr/Fe hollow cathode discharge are experimentally registered. Peculiarities in amplitude and time shapes dependences on discharge current and their correlation with the voltage-current characteristic of the lamp are shown. The resonant signals from Kr atoms and nonresonant signals are also presented

Mining Spatio-temporal Data at Different Levels of Detail

Presented at the 11th AGILE International Conference on Geographic Information Science (AGILE 2008), Girona, Spain, 5-8 May 2008In this paper we propose a methodology for mining very large spatio-temporal datasets. We propose a two-pass strategy for mining and manipulating spatio-temporal datasets at different levels of detail (i.e., granularities). The approach takes advantage of the multi-granular capability of the underlying spatio-temporal model to reduce the amount of data that can be accessed initially. The approach is implemented and applied to real-world spatio-temporal datasets. We show that the technique can deal easily with very large datasets without losing the accuracy of the extracted patterns, as demonstrated in the experimental results.Science Foundation Ireland; Irish Research Council for Science, Engineering & TechnologyConference detailshttp://plone.itc.nl/agile/agile-conference