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Record W4376863316 · doi:10.18280/rcma.330204

Effects of Varying Microstructural Constituents on Corrosion Resistance: A Review

2023· review· fr· W4376863316 on OpenAlexvenueno aff
Sunday L. Lawal, Sunday A. Afolalu, Tien‐Chien Jen, Esther T. Akinlabi

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2023
Typereview
Languagefr
FieldMaterials Science
TopicCorrosion Behavior and Inhibition
Canadian institutionsnot available
Fundersnot available
KeywordsCorrosionMaterials scienceResistance (ecology)MetallurgyComposite materialBiologyAgronomy

Abstract

fetched live from OpenAlex

Several detrimental phases usually result in materials due to improper variation and modification of the microstructural constituents.This usually cause serious defect and result in materials with poor mechanical properties as well as corrosion resistance.Majority of this problem could be traced to even the techniques of manufacturing.Thus, this study focused on a forensic review of the microstructural constituents and its how its variation affects the electrochemical performances of metals and alloys.The various types of microstructures were highlighted and their importance in several applications were explored.Furthermore, the study discussed the various methods of characterizing the microstructures of constituents and several of their mechanical properties that could have effects on the corrosion resistance were presented.Additionally, the effect of pores and composition of microstructural constituents were presented in detail.Also, it was established that it is necessary to modify microstructural constituents in a way that will improve the microstructural and corrosion behavior.The study provided potential information on the techniques of modification of the microstructural constituents that can influence the surface morphology and the mechanical properties of engineering material.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.306
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.002

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 head0.090
GPT teacher head0.347
Teacher spread0.257 · 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 teacher head, not a consensus.

Study designSystematic review
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

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueRevue des composites et des matériaux avancésSame topicCorrosion Behavior and InhibitionFrench-language works237,207