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Record W2883797988 · doi:10.1515/corrrev-2018-0023

Cathodic modification of stainless steels with ruthenium: a review of recent advances in making the cheaper option cheaper

2018· review· en· W2883797988 on OpenAlexfundno aff
Fortunate Moyo, Josias W. van der Merwe, Daniel Wamwangi, Yonela A. Mgwebi

Bibliographic record

VenueCorrosion Reviews · 2018
Typereview
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsnot available
FundersMcGill University
KeywordsRutheniumCathodic protectionMaterials scienceCorrosionMetallurgyAlloyPlatinumCatalysisAnodeElectrode

Abstract

fetched live from OpenAlex

Abstract Corrosion resistance of stainless steels is significantly compromised in oxygen-deficient environments, leading to limited service life as well as unsatisfactory performance of the structures made of these alloys. Cathodic modification with ruthenium has been demonstrated to remarkably improve the corrosion resistance of stainless steels in reducing acidic media, even in the presence of an abrasive suspension. Although ruthenium is several times cheaper than other platinum group metals (PGMs), alloying with ruthenium remains prohibitively more expensive than alloying with conventional, although less effective, metals. A number of strategies have been explored to reduce the amount of ruthenium required to cathodically modify stainless steels in a bid to make this cheaper option much cheaper and thus increase the cost-benefits of using these alloys. Some of the strategies include partially substituting ruthenium with cheaper metals, as well as introducing the PGM as a surface alloy.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.111
GPT teacher head0.406
Teacher spread0.294 · 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 designNot applicable
Domainnot available
GenreReview

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
Published2018
Admission routes1
Has abstractyes

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