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Record W2334826291 · doi:10.1149/ma2014-02/27/1540

Keynote: In-Situ Visualization of Non-Linear Phenomena during Metastable Pitting Corrosion on Stainless Steel

2014· article· en· W2334826291 on OpenAlexaff
Harm Hinrich Rotermund

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsDalhousie University
Fundersnot available
KeywordsMaterials sciencePitting corrosionCorrosionMetallurgyNucleationOxideMetastabilityChemistry

Abstract

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Stainless steels and many other alloys are designed to be corrosion resistant. Nevertheless they can undergo localized pitting corrosion, which may rapidly lead to their failure, with the possibility of catastrophic events as a result. To study the initial steps of metastable pit formation in situ and in real time we adapted the ellipso-microscope for surface imaging (EMSI) [1] and a high resolution, contrast enhanced optical microscope to the electrolyte - stainless steel interface. Utilizing these methods we have been able to explain the sudden corrosion onset by an explosive autocatalytic growth in the number of metastable pits [2]. By applying those different but complementary imaging methods simultaneously the correlation between oxide film weakening and the nucleation of individual pits could be confirmed. The existence of front propagation as a component of the transition to pitting corrosion shows that characteristics of this process are consistent with the behavior of stochastic reaction-diffusion systems [3]. Recently we implemented digital in-line holography as a third imaging tool simultaneously to gather three-dimensional information within the electrolyte, in close proximity to the exposed surface. This enhances the sensitivity for small pitting events and opens new avenues for investigating and understanding the underlying processes. For instance we are now able to follow the trajectories of particles ejected from a pit [4]. Using this arsenal of in situ and real time imaging tools allows us to efficiently study methods to enhance the pitting corrosion resistance of stainless steels; initial results of simple treatments such as emerging the sample just in high purity deionized water at 90 °C for an hour, but nevertheless improving the pitting corrosion resistance in NaCl solutions dramatically, will be discussed [5]. [1] H.H. Rotermund et al. Science 270, 608-610 (1995) [2] C. Punckt et al., Science 305, 1133-1136 (2004) [3] M. Dornhege et al., J. Electrochem. Soc. 154, C24-C27 (2007) [4] P.E. Klages et al., Corrosion Science 65, 128–135 (2012) [5] P.E. Klages et al., Electrochemistry Communications 15, 54–58 (2012)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0120.002

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.018
GPT teacher head0.273
Teacher spread0.255 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

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