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Record W4404924110 · doi:10.37934/aram.128.1.129137

A Case Study: Investigation of Untreated and Treated 304 Stainless Steel on Corrosion Behaviour

2024· article· en· W4404924110 on OpenAlexaff
Nur Izzah Atirah Jaffar Sidek, Siti Nurthoiyibatul Solehah Hussein, Shahrul Azmir Osman, Ali Ourdjini, Saliza Azlina Osman

Bibliographic record

VenueJournal of Advanced Research in Applied Mechanics · 2024
Typearticle
Languageen
FieldEngineering
TopicNon-Destructive Testing Techniques
Canadian institutionsUniversity of Ottawa
FundersUniversiti Tun Hussein Onn Malaysia
KeywordsMetallurgyCorrosionMaterials science

Abstract

fetched live from OpenAlex

A titanium coating on an iron-based metal surface significantly enhanced its resistance to localised corrosion. The research thoroughly investigated the microstructure and corrosion behaviour of both the untreated and treated 304 stainless steel substrates. The coating’s morphology was meticulously examined using scanning electron microscopy (SEM), while its chemical composition was determined via energy-dispersive X-ray spectroscopy (EDX). Electrochemical impedance spectroscopy (EIS) was employed in an open circuit potential experiment to evaluate the coating’s resistance to localised corrosion in an alkaline solution. SEM was again utilised to assess the coating’s morphologies and cross-sectional view. The result revealed that untreated samples showed small and large pits on the microstructure, while no pit was detected in treated samples. Only fine dimples and voids were observed for the treated sample. The treated sample exhibited superior corrosion resistance to the untreated sample with a corrosion rate of 0.002348 mm/year and 0.007109 mm/year, respectively. This is attributed to the presence of the coating for a treated sample with curing for 10 minutes. The corrosion rate value is still considered excellent and accepted for stainless steel because the corrosion rate penetration is below 1 mils per year (mpy).

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.421
Threshold uncertainty score0.590

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.060
GPT teacher head0.350
Teacher spread0.290 · 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.

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
Published2024
Admission routes1
Has abstractyes

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