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Light emission of nanocrystals (NCs) can depend not only on NC size but also – and even more importantly in wide-band-gap NCs – on the occurrence of optically active sites, such as donor–acceptor pairs (DAPs). Here, we demonstrate that controlling the thermo-chemical conditions of NC nucleation when NCs are forming in a solid host – an approach often used for driving NC size dispersion – can be an innovative strategy for tailoring DAP population. Our data show that light emission from DAP recombination and decay in defect sites can be controlled in γ-Ga2O3 NCs in alkali-germanosilicate glass – a prototypal oxide-in-oxide nanostructured system – by oxygen and gallium vacancy formation during nucleation. Time-resolved UV-excited photoluminescence, combined with differential scanning calorimetry, X-ray diffraction, and transmission electron microscopy, reveal how nucleation pretreatment determines, besides NC size and concentration, also the DAP number via promotion of acceptor formation or their passivation during interaction with the host. The results envisage the possibility of nucleation-based design of light-emitting NCs in a wide range of oxide systems. [ABSTRACT FROM AUTHOR]

Phase transformations at the nanoscale represent a challenging field of research, mainly in the case of nanocrystals (NCs) in a solid host, with size-effects and interactions with the matrix. Here we report the study of the structural evolution of γ-Ga2O3 NCs in alkali-germanosilicate glass – a technologically relevant system for its light emission and UV-to-visible conversion – showing an evolution drastically different from the expected transformation of γ-Ga2O3 into β-Ga2O3. Differential scanning calorimetry registers an irreversible endothermic process at ∼1300 K, well above the exothermic peak of γ-Ga2O3 nano-crystallization (∼960 K) and below the melting temperature (∼1620 K). Transmission electron microscopy and X-ray diffraction data clarify that glass-embedded γ-Ga2O3 NCs transform into LiGa5O8via diffusion-driven kinetics of Li incorporation into NCs. At the endothermic peak, β-Ga2O3 forms from LiGa5O8 dissociation, following a nucleation-limited kinetics promoted by size-dependent order–disorder change between LiGa5O8 polymorphs. As a result of the changes, modifications of UV-excited NC light emission are registered, with potential interest for applications. [ABSTRACT FROM AUTHOR]

In order to assess indoor air quality (IAQ), two 1-week monitoring campaigns of volatile organic compounds (VOC) were performed in different areas of a multistorey shopping mall. High-spatial-resolution monitoring was conducted at 32 indoor sites located in two storehouses and in different departments of a supermarket. At the same time, VOC concentrations were monitored in the mall and parking lot area as well as outdoors. VOC were sampled at 48-h periods using diffusive samplers suitable for thermal desorption. The samples were then analyzed with gas chromatography-mass spectrometry (GC-MS). The data analysis and chromatic maps indicated that the two storehouses had the highest VOC concentrations consisting principally of terpenes. These higher TVOC concentrations could be a result of the low efficiency of the air exchange and intake systems, as well as the large quantity of articles stored in these small spaces. Instead, inside the supermarket, the food department was the most critical area for VOC concentrations. To identify potential emission sources in this department, a continuous VOC analyzer was used. The findings indicated that the highest total VOC concentrations were present during cleaning activities and that these activities were carried out frequently in the food department. The study highlights the importance of conducting both high-spatial-resolution monitoring and high-temporal-resolution monitoring. The former was able to identify critical issues in environments with a complex emission scenario while the latter was useful in interpreting the dynamics of each emission source. [ABSTRACT FROM AUTHOR]

Multivariate statistical techniques, including discriminant function analysis (DFA), cluster analysis (CA), principal component analysis (PCA), absolute principal component score (APCS), and radial basis function neural network (RBF-NN), were applied to a data set formed by 905 samples and 15 parameters, including pH, electrical conductivity (EC), total dissolved solids (TDS), dissolved oxygen (O2), chemical oxygen demand (COD), Na+, Ca2+, Mg2+, K+, Cl, NO3,- SO42-, HCO3, and vital organism at 22°C and 36°C, of groundwater samples collected in the Apulia region (southern Italy). Among all collected samples, only samples showing values for all parameters were used to compose the data set on which the multivariate statistical techniques were applied PCA and APCS allowed us to identify, for each province as well as the sites diverging from the main cluster, the pollution sources pressuring the sampling sites investigated: they were identified as fertilizer applications, the use of unpurified irrigation water, marine water intrusion, and calcareous characteristics of the soil. We found that the groundwater pollution sources pressuring the sites were similar among the five Apulian provinces (Foggia, Bari, Brindisi, Lecce, and Taranto). Moreover, for each province, marine water intrusion showed the highest contribution. The application of DFA to the data set allowed us to obtain good results in discriminating among four provinces, with the exception of Taranto. The model also gave good performance results in forecasting. However, RBF-NN provided more accurate results than DFA and confirmed that EC had the greatest relative importance. This is probably due to the different salinity among the sites (Na+ also showed good discriminant importance). In fact, with PCA and APCS, it was possible to observe that EC, together with Na+, Cl, and TDS, was the parameter that most often showed high loading values, and the scattered samples with these loading values were collected at sites in which marine water intrusion had been hypothesized The results obtained by multivariate statistical methods can be useful both in guiding stakeholders and in providing a valid tool to authorities for assessing and managing groundwater resources. [ABSTRACT FROM AUTHOR]

Background: An effective screening tool for colorectal cancer is still lacking. Analysis of the volatile organic compounds (VOCs) linked to cancer is a new frontier in cancer screening, as tumour growth involves several metabolic changes leading to the production of specific compounds that can be detected in exhaled breath. This study investigated whether patients with colorectal cancer have a specific VOC pattern compared with the healthy population. Methods: Exhaled breath was collected in an inert bag (Tedlar®) from patients with colorectal cancer and healthy controls (negative at colonoscopy), and processed offline by thermal-desorber gas chromatography-mass spectrometry to evaluate the VOC profile. During the trial phase VOCs of interest were identified and selected, and VOC patterns able to discriminate patients from controls were set up; in the validation phase their discriminant performance was tested on blinded samples. A probabilistic neural network (PNN) validated by the leave-one-out method was used to identify the pattern of VOCs that better discriminated between the two groups. Results: Some 37 patients and 41 controls were included in the trial phase. Application of a PNN to a pattern of 15 compounds showed a discriminant performance with a sensitivity of 86 per cent, a specificity of 83 per cent and an accuracy of 85 per cent (area under the receiver operating characteristic (ROC) curve 0·852). The accuracy of PNN analysis was confirmed in the validation phase on a further 25 subjects; the model correctly assigned 19 patients, giving an overall accuracy of 76 per cent. Conclusion: The pattern of VOCs in patients with colorectal cancer was different from that in healthy controls. The PNN in this study was able to discriminate patients with colorectal cancer with an accuracy of over 75 per cent. Breath VOC analysis appears to have potential clinical application in colorectal cancer screening, although further studies are required to confirm its reliability in heterogeneous clinical settings. Copyright © 2012 British Journal of Surgery Society Ltd. Published by John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Malignant pleural mesothelioma (MPM) is an aggressive tumour whose main aetiology is the long-term exposure to asbestos fibres. The diagnostic procedure of MPM is difficult and often requires invasive approaches; therefore, it is clinically important to find accurate markers for MPM by new noninvasive methods that may facilitate the diagnostic process and identify patients at an earlier stage. In the present study, the exhaled breath of 13 patients with histology-established diagnosis of MPM, 13 subjects with long-term certified professional exposure to asbestos (EXP) and 13 healthy subjects without exposure to asbestos (healthy controls, HC) were analysed. An analytical procedure to determine volatile organic compounds by sampling of air on a bed of solid sorbent and thermal desorption GC-MS analysis was developed in order to identify the compounds capable of discriminating among the three groups. The application of univariate (ANOVA) and multivariate statistical treatments (PCA, DFA and CP-ANN) showed that cyclopentane and cyclohexane were the dominant variables able to discriminate among the three groups. In particular, it was found that cyclohexane is the only compound able to differentiate the MPM group from the other two; therefore, it can be a possible marker of MPM. Cyclopentane is the dominant compound in the discrimination between EXP and the other groups (MPM and HC); then, it can be considered a good indicator for long-term asbestos exposure. This result suggests the need to perform frequent and thorough investigations on people exposed to asbestos in order to constantly monitor their state of health or possibly to study the evolution of disease over time. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]