The Kinetics of Nucleation of Metastable Pits on Metal Surfaces: The Point Defect Model and Its Optimization on Data Obtained on Stainless Steel, Carbon Steel, Iron, Aluminum and Alloy-22
Abstract:The theory of the kinetics of metastable pit nucleation in terms of the Point Defect Model (PDM) has been applied the first time to describing the evolution of the nucleation rate of metastable pits on a variety of metallic substrates. The PDM successfully accounts for the experimental data that have been reported in the literature on stainless steel, carbon steel, iron, aluminum, and Alloy-22, and which are judged to be reliable and reproducible. Important fundamental parameters related to metastable pitting … Show more
“…The role of the gas is to pressurize the blister, which induces a fracture to produce the breakdown event. This is consistent with Luo’s observation that E c becomes more negative as hydrogen is injected into the backside of a thin metal membrane in a Devanathan cell [75], although they did not interpret their data in terms of that concept.…”
Prediction of the accumulated pitting corrosion damage in aluminum-lithium (Al-Li) is of great importance due to the wide application of these alloys in the aerospace industry. The Point Defect Model (PDM) is arguably one of the most well-developed techniques for evaluating the electrochemical behavior of passive metals. In this paper, the passivity breakdown and pitting corrosion performance of AA 2098-T851 was investigated using the PDM with the potentiodynamic polarization (PDP) technique in NaCl solutions at different scan rates, Cl− concentrations and pH. Both the PDM predictions and experiments reveal linear relationships between the critical breakdown potential (Ec) of the alloy and various independent variables, such as a C l − and pH. Optimization of the PDM of the near-normally distributed Ec as measured in at least 20 replicate experiments under each set of conditions, allowing for the estimation of some of the critical parameters on barrier layer generation and dissolution, such as the critical areal concentration of condensed cation vacancies (ξ) at the metal/barrier layer interface and the mean diffusivity of the cation vacancy in the barrier layer (D). With these values obtained—using PDM optimization—in one set of conditions, the Ec distribution can be predicted for any other set of conditions (combinations of a Cl − , pH and T). The PDM predictions and experimental observations in this work are in close agreement.
“…The role of the gas is to pressurize the blister, which induces a fracture to produce the breakdown event. This is consistent with Luo’s observation that E c becomes more negative as hydrogen is injected into the backside of a thin metal membrane in a Devanathan cell [75], although they did not interpret their data in terms of that concept.…”
Prediction of the accumulated pitting corrosion damage in aluminum-lithium (Al-Li) is of great importance due to the wide application of these alloys in the aerospace industry. The Point Defect Model (PDM) is arguably one of the most well-developed techniques for evaluating the electrochemical behavior of passive metals. In this paper, the passivity breakdown and pitting corrosion performance of AA 2098-T851 was investigated using the PDM with the potentiodynamic polarization (PDP) technique in NaCl solutions at different scan rates, Cl− concentrations and pH. Both the PDM predictions and experiments reveal linear relationships between the critical breakdown potential (Ec) of the alloy and various independent variables, such as a C l − and pH. Optimization of the PDM of the near-normally distributed Ec as measured in at least 20 replicate experiments under each set of conditions, allowing for the estimation of some of the critical parameters on barrier layer generation and dissolution, such as the critical areal concentration of condensed cation vacancies (ξ) at the metal/barrier layer interface and the mean diffusivity of the cation vacancy in the barrier layer (D). With these values obtained—using PDM optimization—in one set of conditions, the Ec distribution can be predicted for any other set of conditions (combinations of a Cl − , pH and T). The PDM predictions and experimental observations in this work are in close agreement.
“…A metastable condition can be defined as a stage in which pitting initiation is followed by rapid repassivation. The existence of the metastable condition was widely studied and confirmed by current transients measurements in the repassivation region below the breakdown potential.…”
A probabilistic approach based on Markov chains for the assessment of pitting and crevice corrosion initiation is proposed. A Markov chain is a stochastic process that undergoes transitions from one state to another through a finite number of possible states, until a so‐called “absorbing state” from which the system has no tendency to evolve is attained. The proposed model calculates the probability to have pitting or to maintain a stable passive condition involving a large number of operating parameters related to both metal and environment: steel chemical composition (namely, the PREN index), chlorides content ([Cl−]), chloride critical threshold ([Cl−]cr), temperature, pH, fluid velocity, electrochemical steel potential, E, presence of crevices. Model and algorithms are mainly based on corrosion data collected from literature. Although the proposed model needs experimental confirmation, the simulations here presented seem acceptable, considering the engineering application of stainless steels.
“…In the presence of oxide on a corroding surface a net flux of positive charge flows from the metal to the solution phase through the oxide layer. To account for the net cation transfer through a solid oxide lattice, many mechanisms, such as transport of cation and anion vacancies, transport through interstitials and electron hopping (or ion exchanges), have been proposed and incorporated in corrosion kinetic models [12,[25][26][27][28][29][30][31].…”
Section: Oxide Growth and Dissolution Mechanism During Corrosion Of Imentioning
confidence: 99%
“…The rate of corrosion is controlled by a number of factors beyond the electrochemical potential of the system. Particularly important factors are the type(s) and the thickness(es) of any oxide layer(s) present on an alloy surface [25][26][27][28][29][30][31]. Since the oxide layer plays an important role in determining the corrosion resistance of an alloy, the type of oxide that can be formed and the rate of its formation and growth are of extreme importance in determining the longer term corrosion rate.…”
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