Mapping numbers onto space in preschool children and adults

Tese de Doutoramento em Psicologia - Especialidade em Psicologia Experimental e Ciências Cognitivas

Surface plasmon resonance-coupled photoluminescence and resistive switching behavior of pulsed laser-deposited Ag:SiC nanocermet thin films

In this study, Ag:SiC nanocermets were prepared via rapid thermal annealing (RTA) of pulsed laser-deposited SiC/Ag/SiC trilayers grown on Si substrate. Atomic force microscope images show that silver nanoparticles (Ag NPs) are formed after RTA, and the size of NPs increases with increasing Ag deposition time (t Ag). Sharp dip observed in the reflectance spectra confirmed the existence of Ag surface plasmons (SPs). The infrared transmission spectra showed an intense and broad absorption band around 780–800 cm−1 that can be assigned to Si-C stretching vibration mode. Influence of t Ag on the spectral characteristics of SP-enhanced photoluminescence (PL) and electrical properties of silicon carbide (SiC) films has been investigated. The maximum PL enhancement by 5.5 times for Ag:SiC nanocermets is achieved when t Ag ≈ 50 s. This enhancement is due to the strong resonant coupling between SiC and the SP oscillations of the Ag NPs. Presence of Ag NPs in SiC also induces a forming-free resistive switching with switching ratio of 2 × 10−2. The analysis of I–V curves demonstrates that the trap-controlled space-charge-limited conduction with filamentary model is the governing mechanism for the resistive switching in nanocerment thin films.

An R package for the integrated analysis of metabolomics and spectral data

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.