Laser treatment of Ag@ZnO nanorods as long-life-span SERS surfaces.

This is the accepted manuscript. The final version is available from ACS at http://pubs.acs.org/doi/abs/10.1021/am506622x.UV nanosecond laser pulses have been used to produce a unique surface nanostructuration of Ag@ZnO supported nanorods (NRs). The NRs were fabricated by plasma enhanced chemical vapor deposition (PECVD) at low temperature applying a silver layer as promoter. The irradiation of these structures with single nanosecond pulses of an ArF laser produces the melting and reshaping of the end of the NRs that aggregate in the form of bundles terminated by melted ZnO spherical particles. Well-defined silver nanoparticles (NPs), formed by phase separation at the surface of these melted ZnO particles, give rise to a broad plasmonic response consistent with their anisotropic shape. Surface enhanced Raman scattering (SERS) in the as-prepared Ag@ZnO NRs arrays was proved by using a Rhodamine 6G (Rh6G) chromophore as standard analyte. The surface modifications induced by laser treatment improve the stability of this system as SERS substrate while preserving its activity.We thank the Junta de Andalucía (TEP8067, FQM-6900 and P12-FQM-2265) and the Spanish Ministry of Economy and Competitiveness (Projects CONSOLIDER-CSD 2008-00023, MAT2011-28345-C02-02, MAT2013-40852-R, MAT2013-42900-P and RECUPERA 2020) for financial support. The authors also thank the European Union Seventh Framework Programme under Grant Agreements 312483-ESTEEM2 (Integrated Infrastructure Initiative-I3) and REGPOT-CT-2011-285895-Al-NANOFUNC, and the European Research Council under the European Union’s Seventh Framework Programme (FP/2007-2013)/ERC grant agreement 291522 - 3DIMAGE. R. J. Peláez acknowledges the grant JCI-2012_13034 from the Juan de la Cierva program

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Copper complexes as a source of redox active MRI contrast agents

The study reports an advance in designing copper-based redox sensing MRI contrast agents. Although the data demonstrate that copper(II) complexes are not able to compete with lanthanoids species in terms of contrast, the redox-dependent switch between diamagnetic copper(I) and paramagnetic copper(II) yields a novel redox-sensitive contrast moiety with potential for reversibility

Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance