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Record W2131269885 · doi:10.1680/emr.13.00002

Microstructural evolution and characterization of a ferroniobium alloy

2013· article· en· W2131269885 on OpenAlexaff
Syed Jawad Ali Shah, H. Henein, Douglas G. Ivey

Bibliographic record

VenueEmerging Materials Research · 2013
Typearticle
Languageen
FieldEngineering
TopicIntermetallics and Advanced Alloy Properties
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIntermetallicEutectic systemMaterials scienceAlloyDifferential scanning calorimetryMicrostructureMetallurgyPhase (matter)NiobiumThermodynamics

Abstract

fetched live from OpenAlex

Niobium in the form of a ferroniobium alloy is added during the steel-making process to improve the mechanical properties of steel, but it may contain phase(s) with high melting temperatures that may be slow to melt or dissolve. It has been suggested that these phases lead to the presence of coarse Nb-rich particles in the resultant steel, which may adversely affect the mechanical properties. In the present study, electron microscopy and differential scanning calorimetry (DSC) were used to identify phases and microstructural evolution of a commercial grade ferroniobium alloy. The ferroniobium alloy was composed of two main phases, that is, Nb-rich solid solution and μ phase (Fe 7 Nb 6 ). The μ phase (Fe 7 Nb 6 ) was formed as a result of three reactions and exhibited three different morphologies based on their formation temperatures (proeutectic intermetallic, eutectic intermetallic and eutectoid intermetallic). The intermetallic that formed via the eutectic reaction was slightly Nb-rich and was heavily faulted. The Nb-rich eutectic portion of the Fe-Nb binary-phase diagram was also modified based on the DSC results to incorporate the effect of impurities in commercial alloys. On the basis of solidification study, the estimated cooling rate for the as received ferroniobium alloy was 10 K/min.

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)0.000
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 score0.016
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

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 head0.022
GPT teacher head0.273
Teacher spread0.251 · 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

Citations2
Published2013
Admission routes1
Has abstractyes

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