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Electrochemical insight into the passivity and corrosion of 316 L stainless steel fabricated through wire arc additive manufacturing

2024· article· en· W4395673913 on OpenAlexafffund
Khashayar Morshed-Behbahani, Amir Hadadzadeh, Ali Nasiri

Bibliographic record

VenueColloids and Surfaces A Physicochemical and Engineering Aspects · 2024
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing Materials and Processes
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaDalhousie UniversityCanada Research ChairsUniversity of MemphisOcean Frontier InstituteHerff College of Engineering, University of Memphis
KeywordsPassivityMaterials scienceAlloyMicrostructureMetallurgyAusteniteCorrosionElectrochemistryFerrite (magnet)Pitting corrosionElectrodeComposite material

Abstract

fetched live from OpenAlex

This study explores the microstructure of AISI 316 L stainless steel fabricated by wire arc additive manufacturing (WAAM) and establishes correlations with its passivity and corrosion characteristics in a 0.9 wt.% NaCl solution, while making comparisons with the wrought alloy counterpart. The WAAM-fabricated alloy demonstrates a favorable chemical composition in comparison to the wrought alloy, exhibiting a heterogeneous microstructure comprised of residual delta ferrite within the austenitic matrix. The pitting vulnerability of the WAAM-printed alloy is observed to be lower than that of the wrought counterpart, a phenomenon further investigated through passive film behavior analysis. Mott-Schottky analysis and the point defect model (PDM) revealed the development of a less defective passive layer on the WAAM-fabricated alloy, enhancing overall passivity and electrochemical response, attributed to the interplay between microstructural features and chemical composition.

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 score0.001
Threshold uncertainty score0.002

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)0.0000.000

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 head0.005
GPT teacher head0.191
Teacher spread0.186 · 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

Citations40
Published2024
Admission routes2
Has abstractyes

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