Characterization of Large Microplastic Debris in Beach Sediments in the Po Delta Area

The use of single-use or disposable plastic objects has massively increased during the last few decades, and plastic has become the main type of litter found in marine environments. The Adriatic Sea is seriously prone to marine litter pollution, and it collects about one-third of all the freshwater flowing into the Mediterranean, mainly via the river Po. This study investigated the type and composition of large microplastic debris collected in different sites in the Po Delta area. Visual classification was performed by relevant criteria, while chemical composition was assessed by infrared spectroscopy. The main plastic fraction is composed of polyolefin (76%), followed by polystyrene (19%). This proportion roughly matches global plastic production, rescaled after excluding plastics with negative buoyancy: all the identified compounds have a specific gravity lower than that of the seawater. Fragments (irregularly shaped debris) represent the most abundant category fraction (85%), followed by pellets, which represent roughly 10% of the total. Overall, the results provided an insight into large microplastic pollution in beach sediments in the Po delta area

Development of n-DoF preloaded structures for impact mitigation in cobots

A core issue in collaborative robotics is that of impact mitigation, especially when collisions happen with operators. Passively compliant structures can be used as the frame of the cobot, although usually they are implemented by means of a single DoF. However, n-DoF preloaded structures offer a number of advantages, in terms of flexibility in designing their behavior. In this work we propose a comprehensive framework for classifying n-DoF preloaded structures, including 1-, 2-, and 3-dimensional arrays. Also, we study the implications of the peculiar behavior of these structures - which present sharp stiff-to-compliant transitions at design-determined load thresholds - on impact mitigation. To this regard, an analytical n-DoF dynamic model was developed and numerically implemented. A prototype of a 10-DoF structure was tested under static and impact loads, showing a very good agreement with the model. Future developments will see the application of n-DoF preloaded structures to impact-mitigation on cobots and in the field of mobile robots, as well as to the field of novel architected materials

Advanced Methods for Photovoltaic Output Power Forecasting: A Review

Forecasting is a crucial task for successfully integrating photovoltaic (PV) output power into the grid. The design of accurate photovoltaic output forecasters remains a challenging issue, particularly for multistep-ahead prediction. Accurate PV output power forecasting is critical in a number of applications, such as micro-grids (MGs), energy optimization and management, PV integrated in smart buildings, and electrical vehicle chartering. Over the last decade, a vast literature has been produced on this topic, investigating numerical and probabilistic methods, physical models, and artificial intelligence (AI) techniques. This paper aims at providing a complete and critical review on the recent applications of AI techniques; we will focus particularly on machine learning (ML), deep learning (DL), and hybrid methods, as these branches of AI are becoming increasingly attractive. Special attention will be paid to the recent development of the application of DL, as well as to the future trends in this topic

A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks

This work proposes a novel fault diagnostic technique for photovoltaic systems based on Artificial Neural Networks (ANN). For a given set of working conditions - solar irradiance and photovoltaic (PV) module's temperature - a number of attributes such as current, voltage, and number of peaks in the current-voltage (I-V) characteristics of the PV strings are calculated using a simulation model. The simulated attributes are then compared with the ones obtained from the field measurements, leading to the identification of possible faulty operating conditions. Two different algorithms are then developed in order to isolate and identify eight different types of faults. The method has been validated using an experimental database of climatic and electrical parameters from a PV string installed at the Renewable Energy Laboratory (REL) of the University of Jijel (Algeria). The obtained results show that the proposed technique can accurately detect and classify the different faults occurring in a PV array. This work also shows the implementation of the developed method into a Field Programmable Gate Array (FPGA) using a Xilinx System Generator (XSG) and an Integrated Software Environment (ISE)

Correlation of Shear Bond Strength and Degree of Conversion in Conventional and Self-Adhesive Systems Used in Orthodontic Bonding Procedures

Abstract (1) Background: Self-adhesive systems have been proposed for the orthodontic bonding with the intention to reduce the traditional three-component system. (2) Methods: The sample consisted of 32 extracted intact permanent premolars randomly divided into two groups (n = 16). In Group I the metal brackets were bonded with Transbond XT Primer and Transbond XT Paste. In Group II the metal brackets were bonded with GC Ortho connect. The resin was polymerized for 20 s from two directions (mesial and occlusal) using a Bluephase light-curing unit. The shear bond strength (SBS) was measured using a universal testing machine. Immediately after SBS testing, Raman microspectrometry was performed for each sample to calculate the degree of conversion (DC). (3) Results: There was no statistically significant difference in the SBS between the two groups. A significantly higher DC (p < 0.001) value was recorded in Group II, in which the brackets were bonded with GC. Very weak or no correlation (0.01) was recorded between SBS and DC in Group I and moderate positive correlation was recorded in Group II (0.33). (4) Conclusions: No difference was found in SBS between the conventional and two-step systems used in orthodontics. The two-step system demonstrated higher DC compared to the conventional system. There is a very weak or moderate correlation between DC and SBS

Experimental evidence of PID effect on CIGS photovoltaic modules

As well known, potential induced degradation (PID) strongly decreases the performance of photovoltaic (PV) strings made of several crystalline silicon modules in hot and wet climates. In this paper, PID tests have been performed on commercial copper indium gallium selenide (CIGS) modules to investigate if this degradation may be remarkable also for CIGS technology. The tests have been conducted inside an environmental chamber where the temperature has been set to 85 \ub0C and the relative humidity to 85%. A negative potential of 1000 V has been applied to the PV modules in different configurations. The results demonstrate that there is a degradation affecting the maximum power point and the fill factor of the current\u2010voltage (I\u2010V) curves. In fact, the measurement of the I\u2010V curves at standard test condition show that all the parameters of the PV modules are influenced. This reveals that CIGS modules suffer PID under high negative voltage: this degradation occurs by different mechanisms, such as shunting, observed only in electroluminescence images of modules tested with negative bias. After the stress test, PID is partially recovered by applying a positive voltage of 1000 V and measuring the performance recovery of the degraded modules. The leakage currents flowing during the PID test in the chamber are measured with both positive and negative voltages; this analysis indicates a correlation between leakage current and power losses in case of negative potential

Precise Engineering of Nanocrystal Shells via Colloidal Atomic Layer Deposition

We present a general strategy for a facile synthesis of complex multifunctional nanoscale materials via colloidal atomic layer deposition (c-ALD). The c-ALD technique is based on self-limiting half-reactions of ionic precursors on the surface of a nanocrystal (NC) occurring at room temperature. Using this technique, uniform layers of CdS and ZnS semiconductor shells were epitaxially grown on CdSe semiconductor cores with different shell combinations, leading to the precise control of the optical properties of these heterostructures. All core-shell multicomponent nanoparticles preserve narrow size distributions, phase crystallinity, and shape homogeneity of the initial NCs. Furthermore, we attempted to extend the toolbox of the c-ALD to combine materials with intrinsically different properties, such as Au/CdS core/shell structures with substantial lattice mismatch. The results presented in this work demonstrate great opportunities for creating functional materials with programmable properties for electronics and optoelectronics

Trace Elements in Chromian Spinels from Four Siberian Kimberlites

We analysed the major, minor and trace elements chemistry of forty-two Cr-spinels from four Siberian kimberlites. They showed a wide range in Mg# (Mg/(Mg + Fe2+); 0.42–0.78) and Cr# (Cr/(Cr + Al); 0.32–0.92) and a common trend of increasing Cr# with decreasing Mg#. The major element classification schemes suggested that there were spinels deriving from a peridotitic source (Xen) and spinels crystallised from kimberlitic melts (Chr). Laser-Ablation Inductively Coupled Plasma Mass Spectrometry on both groups showed that the trace elements with the highest abundance were Mn (985–3390 ppm), Ni (531–3162 ppm), V (694–2510 ppm) and Zn (475–2230 ppm). Testing the effectiveness of trace elements in determining the source for Cr-spinels, we found out that Cr-spinels crystallised directly from a kimberlitic melt usually showed higher Mn, Ni, Sc and V concentrations with respect to those of peridotitic origin. In addition, using the available partitioning models, we found that the correlations between major elements and Ni, Co, Sc and Ga in the Xen group could be explained by subsolidus equilibration between spinel, olivine and clinopyroxene at 800–1000 °C, thus supporting a peridotitic source for this group. Finally, we calculated the composition of the possible melts in equilibrium with the Cr-spinels of the Chr group, using a selected set of partition coefficients. Calculated abundances of Cu, Ga and Zr were comparable to those of the kimberlite, while V was never close to the kimberlite composition. This simulation highlighted the need for new data on the trace elements partition coefficients between kimberlitic melts and Cr-spinel

Towards long lasting zirconia-based composites for dental implants: Transformation induced plasticity and its consequence on ceramic reliability

Zirconia-based composites were developed through an innovative processing route able to tune compositional and microstructural features very precisely. Fully-dense ceria-stabilized zirconia ceramics (84 vol% Ce-TZP) containing equiaxed alumina (8 vol%Al2O3) and elongated strontium hexa-aluminate (8 vol% SrAl12O19) second phases were obtained by conventional sintering. This work deals with the effect of the zirconia stabilization degree (CeO2 in the range 10.0\u201311.5 mol%) on the transformability and mechanical properties of Ce-TZP-Al2O3-SrAl12O19 materials. Vickers hardness, biaxial flexural strength and Single-edge V-notched beam tests revealed a strong influence of ceria content on the mechanical properties. Composites with 11.0 mol% CeO2 or above exhibited the classical behaviour of brittle ceramics, with no apparent plasticity and very low strain to failure. On the contrary, composites with 10.5 mol% CeO2 or less showed large transformation-induced plasticity and almost no dispersion in strength data. Materials with 10.5 mol% of ceria showed the highest values in terms of biaxial bending strength (up to 1.1 GPa) and fracture toughness (>10 MPa 1am). In these ceramics, as zirconia transformation precedes failure, the Weibull modulus was exceptionally high and reached a value of 60, which is in the range typically reported for metals. The results achieved demonstrate the high potential of using these new strong, tough and stable zirconia-based composites in structural biomedical applications

il progetto MUSE

MUSE \ue8 una \u201cCollaborazione Transfrontaliera per la Mobilit\ue0 Universitaria Sostenibile Energeticamente efficiente\u201d che unisce 6 partner (3 Italiani, 3 Sloveni), e 5 partner associati (3 italiani e 2 sloveni) in una stretta collaborazione per rendere energeticamente efficiente e ridurre le emissioni di anidride carbonica nella mobilit\ue0 urbana ed extra urbana degli Enti Locali transfrontalieri. La partnership \ue8 guidata dall\u2019Universit\ue0 di Trieste e comprende, oltre a questa, l\u2019Universit\ue0 di Lubiana, la Regione Friuli Venezia Giulia, l\u2019UTI di Noncello (Comuni di Fontanafredda, Porcia, Pordenone, Roveredo in Piano e Zoppola), l\u2019Agenzia regionale di Nova Gorica e l\u2019Agenzia regionale di Lubiana nelle attivit\ue0 progettuali volte al raggiungimento degli obiettivi di progetto. Il progetto e mira a rendere pi\uf9 efficiente la mobilit\ue0 della popolazione universitaria, utilizzandola come banco di prova per la sperimentazione di servizi di mobilit\ue0 sostenibili, applicabili e replicabili a tutto il contesto della mobilit\ue0 urbana, extraurbana e transfrontaliera