Organic distributed feedback laser to monitor solvent extraction upon thermal annealing in solution-processed polymer films

Solution-processed polymer films are used in multiple technological applications. The presence of residual solvent in the film, as a consequence of the preparation method, affects the material properties, so films are typically subjected to post-deposition thermal annealing treatments aiming at its elimination. Monitoring the amount of solvent eliminated as a function of the annealing parameters is important to design a proper treatment to ensure complete solvent elimination, crucial to obtain reproducible and stable material properties and therefore, device performance. Here we demonstrate, for the first time to our knowledge, the use of an organic distributed feedback (DFB) laser to monitor with high precision the amount of solvent extracted from a spin-coated polymer film as a function of the thermal annealing time. The polymer film of interest, polystyrene in the present work, is doped with a small amount of a laser dye as to constitute the active layer of the laser device and deposited over a reusable DFB resonator. It is shown that solvent elimination translates into shifts in the DFB laser wavelength, as a consequence of changes in film thickness and refractive index. The proposed method is expected to be applicable to other types of annealing treatments, polymer-solvent combinations or film deposition methods, thus constituting a valuable tool to accurately control the quality and reproducibility of solution-processed polymer thin films.

Stratigraphic and geochemical study of the organic-rich black shales in the Tarcău Nappe of the Moldavidian Domain (Carpathian Chain, Romania)

An integrated stratigraphic analysis has been made of the Tarcău Nappe (Moldavidian Domain, Eastern Romanian Carpathians), coupled with a geochemical study of organic-rich beds. Two Main Sequence Boundaries (Early Oligocene and near to the Oligocene–Aquitanian boundary, respectively) divide the sedimentary record into three depositional sequences. The sedimentation occurred in the central area of a basin supplied by different and opposite sources. The high amount of siliciclastics at the beginning of the Miocene marks the activation of the “foredeep stage”. The successions studied are younger than previously thought and they more accurately date the deformation of the different Miocene phases affecting the Moldavidian Basin. The intervals with black shales identified are related to two main separate anoxic episodes with an age not older than Late Rupelian and not before Late Chattian. The most important organic-rich beds correspond to the Lower Menilites, Bituminous Marls and Lower Dysodilic Shales Members (Interval 2). These constitute a good potential source rock for petroleum, with homogeneous Type II oil-prone organic matter, highly lipidic and thermally immature. The deposition of black shales has been interpreted as occurring within a deep, periodically isolated and tectonically controlled basin.

Sentiment classification for early detection of health alerts in the chemical textile domain

In the chemical textile domain experts have to analyse chemical components and substances that might be harmful for their usage in clothing and textiles. Part of this analysis is performed searching opinions and reports people have expressed concerning these products in the Social Web. However, this type of information on the Internet is not as frequent for this domain as for others, so its detection and classification is difficult and time-consuming. Consequently, problems associated to the use of chemical substances in textiles may not be detected early enough, and could lead to health problems, such as allergies or burns. In this paper, we propose a framework able to detect, retrieve, and classify subjective sentences related to the chemical textile domain, that could be integrated into a wider health surveillance system. We also describe the creation of several datasets with opinions from this domain, the experiments performed using machine learning techniques and different lexical resources such as WordNet, and the evaluation focusing on the sentiment classification, and complaint detection (i.e., negativity). Despite the challenges involved in this domain, our approach obtains promising results with an F-score of 65% for polarity classification and 82% for complaint detection.

Phrase-based statistical machine translation: explanation of its processes and statistical models and evaluation of the English to Spanish translations produced

Statistical machine translation (SMT) is an approach to Machine Translation (MT) that uses statistical models whose parameter estimation is based on the analysis of existing human translations (contained in bilingual corpora). From a translation student’s standpoint, this dissertation aims to explain how a phrase-based SMT system works, to determine the role of the statistical models it uses in the translation process and to assess the quality of the translations provided that system is trained with in-domain goodquality corpora. To that end, a phrase-based SMT system based on Moses has been trained and subsequently used for the English to Spanish translation of two texts related in topic to the training data. Finally, the quality of this output texts produced by the system has been assessed through a quantitative evaluation carried out with three different automatic evaluation measures and a qualitative evaluation based on the Multidimensional Quality Metrics (MQM).