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In this paper, two tin-based alloys (Sn-2.5Ag-0.5Cu and Sn-48Bi-2Zn) are proposed as new lead-free solders. Alloys have been developed by melting pure elements. Samples have been evaluated in terms of microstructure, corrosion resistance and mechanical features. Corrosion tests have been performed in 3% NaCl solution by polarization curves and electrochemical impedance spectroscopy (EIS). SEM observations and EDS analysis were carried out on samples before and after corrosion tests. Static monotonic tensile tests have been performed on three specimens for each alloy. SEM and EDS analysis revealed the presence of Sn-Ag and Sn-Cu intermetallic compounds within the Sn-Ag-Cu alloy. As a result of corrosion test, the Sn-Ag-Cu alloy showed a better corrosion resistance with respect to Sn-Bi-Zn. Both alloys evidenced good mechanical properties higher than the traditional Sn-Pb system. Sn-Ag-Cu seems to be a suitable soldering material

This paper presents emission factors of a class of passenger cars obtained by applying a statistical model developed to evaluate average emission factors based on driving cycle emission measurements. A multivariate regression method based on principal components, namely, the partial least squares (PLS) method, is applied to calculate the model. The method was applied to emission data from a sample of petrol Euro III 1,200-to 1,400-cc passenger cars taken from the ARTEMIS database. A vehicle effect analysis showed that vehicle effect is considerable, in some cases comparable to or greater than the driving cycle effect. Determination of emission factors is obviously affected by these aspects. Thus, the CO2 PLS model fit results are good, CO, HC and NOX more or less sufficient. PLS-predicted quantities were compared with corresponding quantities estimated by a multiple regression model (GLM) based on a quadratic polynomial equation of sub-cycle overall mean speed. GLM goodness of fit was poorer than PLS ones. A validation effort of models is in progress, which is considering the ARTEMIS database extended with tests performed within other national or international projects. In this way, an extended population of combinations of vehicles and driving cycles will provide a better calculation of models and emission factors.

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A novel model has been developed for the analysis and the evaluation of average vehicle emissions in a real driving cycle (emission factors) from data in an emission data base. The model assumes that emission variation can be explained by parameters determined from dynamic vehicle equation and by the frequency of acceleration events at different speed. Because the number of resulting X-variables is large, and variables are correlated, a regression method based on principal components, the Partial Least Squares (PLS) method actually, has been adopted. In this paper, model potentiality is illustrated by an application to a case study taken from the data base built within the UE V Framework Project ARTEMIS. Data are relative to tests performed under hot conditions with a sample of EURO III 1.4-2.0 l gasoline passenger cars. A set of real driving cycles was utilized as representative of urban, rural and motorway operating conditions detected in different European countries. Results for PLS model fit are good for CO2, less than sufficient for CO, HC and NOX; this last result, mostly due to data spread out, is analyzed in the paper by estimating the percentage vehicle's effect.