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Record W4411927618 · doi:10.3390/cmd6030028

A Comparative Study of the Tensile Behavior of Wrought 44W Steel, Monel 400, 304L Stainless Steel, and Arc-Directed Energy Deposited 308L Stainless Steel in Simulated Hydrogen Environments

2025· article· en· W4411927618 on OpenAlexafffund
Emmanuel Sey, Zoheir Farhat, Ali Nasiri

Bibliographic record

VenueCorrosion and Materials Degradation · 2025
Typearticle
Languageen
FieldMaterials Science
TopicHydrogen embrittlement and corrosion behaviors in metals
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMonelMaterials scienceMetallurgyUltimate tensile strengthHydrogenArc (geometry)AlloyEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

This study investigates the tensile behaviors of wrought 44W steel, Monel 400, 304L austenitic stainless steel, and arc-directed energy deposited (arc-DED) 308L austenitic stainless steel under simulated hydrogen environments to evaluate their endurance to hydrogen embrittlement (HE). The specimens were subjected to cathodic hydrogen charging in an alkaline solution, followed by uniaxial tensile testing at a strain rate of 0.2 min−1. Based on measurements of elongation and toughness, the resistance to HE was ranked as follows: 304L stainless steel > Monel 400 > arc-DED 308L stainless steel > 44W steel. Notably, no significant changes were observed in the yield strengths, ultimate tensile strengths, or elastic modulus of 304L austenitic stainless steel, Monel 400, and 44W steel across all the levels of hydrogenation. However, the arc-DED 308L stainless steel exhibited a slight increase in these properties, attributed to its unique microstructural characteristics and strengthening mechanisms inherent to additive manufacturing processes. These outcomes contribute to a better understanding of the mechanical performance and suitability of these structural alloys in hydrogen-rich environments, highlighting the superior HE resistance of 304L stainless steel and Monel 400 for such applications.

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 categoriesMeta-epidemiology (narrow)
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 score0.040
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.019
GPT teacher head0.269
Teacher spread0.250 · 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 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
Published2025
Admission routes2
Has abstractyes

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