Direct Quantification of Cu Vacancies and Spatial Localization of Surface Plasmon Resonances in Copper Phosphide Nanocrystals

Copper chalcogenides and pnictogenides often behave as heavily doped p-type semiconductors, because of the presence of a high density of Cu vacancies, with corresponding hole carriers in the valence band. If the free-carrier concentration is high enough, localized surface plasmon resonances can be sustained in nanocrystals of these materials, with frequencies that are typically observed in the infrared region of the spectrum (<1 eV), differently from the typical resonances featured in the visible range by metallic nanoparticles. Here, we demonstrate that Cu vacancies in hexagonal Cu3–xP nanocrystals can be directly quantified by scanning transmission electron microscopy (STEM) analysis. We also report, for the first time, the spatial localization of the plasmon resonances in individual Cu3–xP nanocrystals by means of STEM energy loss spectroscopy (EELS), which is an achievement that, to date, had been possible only on nanoparticles of noble metals. Two plasmon modes can be seen from STEM-EELS, which are in agreement with the resonances calculated from the vacancy concentration obtained from the STEM analysis

Formation of colloidal alloy semiconductor CdTeSe magic-size clusters at room temperature

Alloy magic-size clusters (MSCs) are difficult to synthesize, in part because so little is known about how they form. Here, the authors produce single-ensemble alloy CdTeSe MSCs at room temperature by mixing prenucleation-stage solutions of CdTe and CdSe, uncovering a formation pathway that may extend to the synthesis of other alloy MSCs

‘A simple feedforward neural network for the PM10 forecasting: comparison with a radial basis function network and a multivariate linear regression model’

The problem of air pollution is a frequently recurring situation and its management has social and economic considerable effects. Given the interaction of the numerous factors involved in the raising of the atmospheric pollution rates, it should be considered that the relation between the intensity of emission produced by the polluting source and the resulting pollution is not immediate. The aim of this study was to realise and to compare two support decision system (neural networks and multivariate regression model) that, correlating the air quality data with the meteorological information, are able to predict the critical pollution events. The development of a back-propagation neural network is presented to predict the daily PM10 concentration 1, 2 and 3 days early. The measurements obtained by the territorial monitoring stations are one of the primary data sources; the forecasting of the major weather parameters available on the website and the forecasting of the Saharan dust obtained by the "Centro Nacional de Supercomputaciòn" website, satellite images and back trajectories analysis are used for the weather input data. The results obtained with the neural network were compared with those obtained by a multivariate linear regression model for 1 and 2 days forecasting. The relative root mean square error for both methods shows that the artificial neural networks (ANN) gives more accurate results than the multivariate linear regression model mostly for 1 day forecasting; moreover, the regression model used, in spite of ANN, failed when it had to fit spiked high values of PM10 concentration