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To elucidate the mechanism of NaNO2-promoted Pt dissolution, the weight loss of Pt black-deposited Au quartz resonators was measured in 0.1 mol dm–3 HClO4 containing NaNO2 under potential cycling and constant potential conditions. A continuous weight loss of ca. 50–60 ng cm–2 cycle–1 was detected in the presence of 10 mmol dm–3 NaNO2 during potential cycling above a high potential limit of 1.2 V vs RHE (VRHE) and below a low potential limit (EL) of 0.9 VRHE. A weight loss was observed at constant potentials (0.6–0.8 VRHE) when oxides were initially present; the net weight loss (Δw) magnitude increased as the amount of the initially formed Pt oxide increased. These results indicate that Pt dissolution occurred when Pt oxides were reduced in the presence of NaNO2. The good agreement between the Δw per potential cycle and the Δw at a constant potential (EL of the potential cycling = constant potential) revealed that the formation of Pt oxides in the positive sweep was required for the continuous weight loss ...

Platinum (Pt) dissolution has been elucidated by measuring a mass loss of Pt black-deposited Au quartz resonators in 0.1 mol dm-3 HClO4 containing NaNO2. A continuous weight loss of ca. 50 ng cm-2 cycle-1 was detected at [NaNO2] = 0.01 mol dm-3 under the potential cycling between 0.6 and 1.4 V vs. RHE. The magnitude of Pt dissolution depends on the concentration of NaNO2 and the range of potential cycling. The average mass loss decreases to 5~10 ng cm-2 cycle-1 at [NaNO2] = 1 mmol dm-3. The Pt dissolution occurs when the higher potential limit is more than 1.0 V vs. RHE and the lower potential limit is less than 0.9 V vs. RHE, suggesting that the reduction of Pt oxide in the presence of NaNO2 in the bulk solution contributes to the dissolution.

This paper presents a st udy on mixed similarity mea su res (MSM) th at allows doing classification and cluste ring in many sit uations with out discreti eat.ion. For supervised classification we do experimental com parative studies of cl as sifiers bu ilt by dec isio n t ree induction system C4.5 and k ne a rest n eighbor ru le usin g MS:-'L Fo r unsu per vised clustering we first intro duce an extension of k-means algorithm for m ixed numeric and symbolic da ta, t hen evaluate clusters obtained by th is algorithm with natural classes. Exp erimental studies allow us to draw conclusions (meta-knowledge) that are significant in pract ice about t he mutu al use of discreti zation techniques and MSM.

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