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Record W4396969359 · doi:10.1177/03019233241251605

Study on structure and viscosity evolution mechanism of titanium-containing iron liquid in full viscosity range

2024· article· en· W4396969359 on OpenAlexaff
Xiaoyue Fan, Yining Huang, Shanchao Gao, Jianliang Zhang

Bibliographic record

VenueIronmaking & Steelmaking Processes Products and Applications · 2024
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsWestern University
Fundersnot available
KeywordsViscosityTitaniumMechanism (biology)Temperature dependence of liquid viscosityRange (aeronautics)ThermodynamicsMaterials scienceRelative viscosityChemistryMetallurgyComposite materialPhysics

Abstract

fetched live from OpenAlex

This study aims to investigate the relationship between the viscosity of Fe-4.5wt%C-0.2wt%Ti melt and its liquid structure. The actual experimental results of viscosity measurement reveal that the viscosity range of Fe-4.5wt%C-0.2wt%Ti melt can be segmented into three intervals. Through thermodynamically calculated equilibrium phases and high-temperature confocal experiments, it is observed that the abrupt change in viscosity in the lower temperature range is attributed to the precipitation of the graphite phase. Moreover, differential scanning calorimetry experiments exhibit a distinct phase transition in the liquid phase region. To delve into the atomic structure of the liquid phase region, molecular dynamics simulations are employed. The high-temperature liquid structure of Fe-4.5wt%C-0.2wt%Ti melt predominantly consists of vacancies, atomic clusters with Ti as the core, and atomic clusters with C as the core. Within the high-temperature liquid phase region, the conversion of Free-Fe and C 1 -Fe to C x -Fe occurs, resulting in a reduction of free volume and enhanced stability of the cluster. Consequently, the inhomogeneity of the Fe-4.5wt%C-0.2wt%Ti melt increases, and the viscosity exhibits a significant increase with temperature, leading to a variation in the viscosity within the liquid phase interval.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.239
Teacher spread0.228 · 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 designTheoretical or conceptual
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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