Wetting and Reaction of Sn-2.8Ag-0.5Cu-1.0Bi Solder with Cu and Ni Substrates

The wettability of newly developed Sn-2.8Ag-0.5Cu-1.0Bi lead-free solder on Cu and Ni substrates was assessed through the wetting balance tests. The wettability assessment parameters such as contact angle (ϑc) and maximum wetting force (Fw) were documented for three solder bath temperatures with three commercial fluxes, namely, no-clean (NC), nonactivated (R), and water-soluble organic acid flux (WS). It was found that the lead-free Sn-2.8Ag-0.5Cu-1.0Bi solder exhibited less wetting force, i.e., poorer wettability, than the conventional Sn-37Pb solder for all flux types and solder bath temperatures. The wettability of Sn-2.8Ag-0.5Cu-1.0Bi lead-free solder on Cu substrate was much higher than that on Ni substrate. Nonwetting for Sn-2.8Ag-0.5Cu-1.0Bi and Sn-Pb solders on Ni substrate occurred when R-type flux was used. A model was built and simulations were performed for the wetting balance test. The simulation results were found very close to the experimental results. It was also observed that larger values of immersion depth resulted in a decrease of the wetting force and corresponding meniscus height, whereas the increase in substrate perimeter enhanced the wettability. The wetting reactions between the solder and Cu/Ni substrates were also investigated, and it was found that Cu atoms diffused into the solder through the intermetallic compounds (IMCs) much faster than did the Ni atoms. Rapid formation of IMCs inhibited the wettability of Sn-2.8Ag-0.5Cu-1.0Bi solder compared to the Sn-Pb solder.

Multicomponent gas-mixture measurements using an array of gas sensors and an artificial neural-network

An Electronic Nose is being jointly developed between the University of Greenwich and the Institute of Intelligent Machines to detect the gases given off from an oil filled transformer when it begins to break down. The gas sensors being used are very simple, consisting of a layer of Tin Oxide (SnO2) which is heated to approximately 640 K and the conductivity varies with the gas concentrations. Some of the shortcomings introduced by the commercial gas sensors available are being overcome by the use of an integrated array of gas sensors and the use of artificial neural networks which can be 'taught' to recognize when the gas contains several components. At present simulated results have achieved up to a 94% success rate of recognizing two component gases and future work will investigate alternative neural network configurations to maintain this success rate with practical measurements.

Synthesis and enzymatic evaluation of the guanosine analogue 2-amino-6-mercapto-7-methylpurine ribonucleoside (MESG): insights into the phosphorolysis reaction mechanism based on the blueprint transition state: SN1 or SN2?

A modified experimental procedure for the synthesis of MESG (2-amino-6-mercapto-7-methylpurine ribonucleoside) 1 has been successfully performed and its full characterization is presented. High resolution ESI(+)-MSMS indicates both the nucleoside bond cleavage as the main fragmentation in the gas phase and a possible SN1 mechanism. Ab initio transition state calculations based on the blue print transition state support this mechanistic rationale and discard an alternative SN2 mechanism. Assays using purine nucleoside phosphorylase (PNP) enzyme (human and M. tuberculosis sources) indicate its efficiency in the phosphorolysis of MESG and allow the quantitative determination of inorganic phosphate in real time assay.