The objective of this study was to determine if a distribution of pit induction times ͑from potentiostatic experiments͒ could be used to predict a distribution of pitting potentials ͑from potentiodynamic experiments͒ for high-purity aluminum. Pit induction times were measured for 99.99 Al in 50 mM NaCl at potentials of Ϫ0.35, Ϫ0.3, Ϫ0.25, and Ϫ0.2 V vs. saturated calomel electrode. Analysis of the data showed that the pit germination rate generally was an exponential function of the applied potential; however, a subset of the germination rate data appeared to be mostly potential insensitive. The germination rate behavior was used as an input into a mathematical relationship that provided a prediction of pitting potential distribution. Good general agreement was found between the predicted distribution and an experimentally determined pitting potential distribution, suggesting that the relationships presented here provide a suitable means for quantitatively describing pit germination rate.The objective of this work is to determine if pit induction time data can be used to predict the potentiodynamic pitting data for pure aluminum: i.e., if the experimental conditions are carefully chosen, are the induction time ͑͒ and pitting potential (E pit ) data mathematically interdependent? Equilibrium models, such as Macdonald's point defect model, 1 suggest that the two parameters are linked and the scanning of potential in the potentiodynamic case does not alter the mechanism by which pitting occurs. In contrast, experimental evidence exists that suggests a complex influence of scan rate on pitting behavior. For example, Baroux reported that a decrease in the scan rate could result in an increase in pitting potential for sufficiently slow scan rates. 2 Here we have attempted to determine if and E pit are relatable for pure aluminum under carefully controlled experimental conditions.To test the link between and E pit , we must have a mathematical model that relates these parameters. Recently, we reviewed the derivation of such a model; the details can be found elsewhere. 3 Here we briefly summarize the equations necessary to calculate the pitting potential distribution from parameters that can be extracted from induction time data.In the mid-1970s, Shibata developed a statistical treatment of pit nucleation in stainless steel which expressed both E pit and in terms of a germination rate, ͑s Ϫ1 ͒. 4 More recently, we applied this treatment to the pitting behavior of aluminum and found that could best be described by an exponential dependence on potential 3
͑E ͒ ϭ A exp͑␥E ͒ ͓1͔where A is the area of the electrode ͑m 2 ͒, E is the potential ͑V͒, and  ͑m Ϫ2 s Ϫ1 ͒ and ␥ ͑V Ϫ1 ͒ are constants. At a particular potential, can be experimentally determined by taking the negative of the slope of log͑survival probability͒ vs.where P s is the probability that a sample survives for a given time and is calculated fromwhere n is the nth sample to pit out of a total population of N values. Thus a value for can be experimentally determi...