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Record W3118826756 · doi:10.5006/3782

Magnesium Corrosion Research Special Issue

2021· article· en· W3118826756 on OpenAlexaffabout
G. Williams, Joey Kish

Bibliographic record

VenueCORROSION · 2021
Typearticle
Languageen
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsMcMaster University
Fundersnot available
KeywordsCorrosionCathodic protectionPolitical scienceMetallurgyMaterials scienceChemistry

Abstract

fetched live from OpenAlex

Welcome to the second special issue of CORROSION devoted to current trends in magnesium corrosion research. This issue follows our original guest co-edited special issue published in May 2017, as well as two prior issues dedicated to magnesium and aluminum-magnesium alloys. The high susceptibility of magnesium and its alloys to corrosion is acknowledged to be the principal barrier to its widespread use as a structural metal. Hence, the subject of corrosion and protection of magnesium and its alloys remains one of the most highly cited fields in corrosion science and continues to stimulate significant interest and debate.It is with great pleasure that we serve as guest co-editors for this issue, which presents invited original articles contributed by research groups from the United States, Canada, Germany, Australia, Malaysia, Sweden, and the United Kingdom. We are extremely grateful to the authors for finding the time to compile their manuscripts and to the reviewers for the rigor of their efforts, given the current unprecedented challenges of the COVID-19 pandemic. In addition, we would like to extend our gratitude to the editorial team of Prof. John Scully, Sammy Miles, and Marlene Walters for their invaluable advice and guidance.For this latest special issue, we have sought to compile a selection of articles that cover a broad range of interests, which reflect the current cutting-edge of magnesium corrosion research. The first papers, by Glover and coauthors, seek to further the understanding of the phenomenon of cathodic activation of a corroding magnesium surface and demonstrate how a novel germanium alloying addition mitigates this effect, thus reducing the corrosion rate.These are followed by a description of the development of a novel experimental method that allows dynamic pH control of an unbuffered test electrolyte, by Curioni, et al., along with preliminary findings on the pH-dependent electrochemical response of pure Mg. Several articles focus on the localized corrosion behavior of technologically important Mg alloys such as ZEK100 (Kousis, et al.) and Magnox Al-80 alloy (Clark, et al.), used in the automotive sector and first generation of U.K. nuclear reactions, respectively, and characterized using a scanning vibrating electrode technique. The same methodology, in combination with scanning electrochemical microscopy, is used by others (Thomas and coauthors) to investigate the time-dependent localized corrosion behavior of welded AM series alloy surfaces.Another two papers focus on Mg corrosion inhibition, one using an empirical approach to investigate the effectiveness of lithium carbonate technology on AZ31 alloy (Kish and coauthors) and the other using density functional theory calculations to optimize the inhibitive properties of bipyridyl-based derivatives (Feiler, et al.). The importance of magnesium alloys as biodegradable materials for potential in vivo applications is addressed in contributions by Höhlinger, et al., and Chen and coauthors. The former describes a multifaceted approach to evaluate the corrosion rate of WE43 alloy in the presence of different types of surface coatings, while the latter quantifies the biocompatibility of a Mg surface immersed in simulated body fluid as a function of corrosion product film composition. Finally, there are also valuable contributions which investigate the corrosion of binary Mg alloys prepared by high energy ball milling of nanocrystalline powders, by Gupta and coauthors, and the influence of carbon dioxide on the atmospheric corrosion of Mg-Al alloys in the presence of sodium chloride deposits, by Esmaily and coauthors.We hope that you find the selection of articles both stimulating and informative.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
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 score0.026
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0350.009

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 head0.056
GPT teacher head0.343
Teacher spread0.286 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2021
Admission routes2
Has abstractyes

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