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Record W4414715917 · doi:10.1016/j.jmrt.2025.09.271

Influence of molybdenum additions on the microstructural properties of medium-Mn steels

2025· article· en· W4414715917 on OpenAlexafffund
Felisters Zvavamwe, Jubert Pasco, Min‐Kyu Paek, Clodualdo Aranas

Bibliographic record

VenueJournal of Materials Research and Technology · 2025
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsUniversity of New Brunswick
FundersPhilippine Council for Industry, Energy, and Emerging Technology Research and DevelopmentNatural Sciences and Engineering Research Council of CanadaNew Brunswick Innovation FoundationCanada Foundation for Innovation
KeywordsAusteniteMolybdenumManganeseMartensiteMicrostructureEquiaxed crystals

Abstract

fetched live from OpenAlex

In this work, the impact of microalloying with molybdenum, ranging from 0.05 to 0.25 wt%, on the microstructural properties of as-cast medium manganese steel was explored. Four medium manganese steels with different molybdenum contents were designed using a CALPHAD approach. A duplex phase microstructure consisting of martensite and retained austenite was observed in all the steels investigated. Increasing Mo content delays the martensitic transformation and promotes the formation of equiaxed prior austenite grains, effectively suppressing γ-lean band formation observed in low-Mo alloys. The prior austenite grain refinement also offers additional nucleation pathways for solid-state martensitic transformation and reduces the preferential <100> casting texture. Mo addition therefore mitigates the effects of directional solidification and leads to a more uniform distribution of retained austenite throughout the microstructure. The element distribution in the constituent phases was investigated in detail. Mn, Mo and Si segregation to austenite was observed. The origins of elemental banding in medium manganese steels were also analyzed. The findings also revealed that increasing the molybdenum content led to an increase in the hardness of the steel.

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 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 score0.007
Threshold uncertainty score0.190

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.022
GPT teacher head0.264
Teacher spread0.242 · 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

Citations4
Published2025
Admission routes2
Has abstractyes

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