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Record W2893641003 · doi:10.1002/srin.201800367

Application of Optical Floating Zone Method to Dissolution Kinetics of Inclusions in a Steelmaking Slag

2018· article· en· W2893641003 on OpenAlexafffund
Mukesh Sharma, H. A. Dabkowska, Neslihan Dogan

Bibliographic record

Venuesteel research international · 2018
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsMcMaster UniversityBrockhouse Institute for Materials ResearchMcMaster University Medical Centre
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsSteelmakingDissolutionMaterials sciencePorositySlag (welding)MetallurgyOptical microscopeSinteringKineticsMineralogyScanning electron microscopeComposite materialChemical engineeringChemistry

Abstract

fetched live from OpenAlex

The dissolution kinetics of micro‐particles (inclusions) in steelmaking slags is investigated using the high temperature confocal scanning laser microscope (HT ‐ CSLM). However, these studies focus on the limited type of inclusions such as Al 2 O 3 , SiO 2, MgO, CaO, and MgAl 2 O 4 . To experimentally study the removability of various problematic inclusions that are not available in the market, optical floating zone and sintering techniques are presented here for the production of high purity micro‐particles. The syntheses of TiO 2 and Al 2 TiO 5 inclusions are employed to demonstrate the advantages and potential of both techniques. These inclusions are then dissolved in the steelmaking slags using CSLM at 1430 °C. In situ observation shows that there is gas evolution during the reaction between slag and Al 2 TiO 5 particles prepared by both techniques. However, the gas evolution is more rapid during the dissolution of particles prepared by sintering and hinders in situ observations and measurements. The optical floating zone technique is capable of preparation of micro‐particles with high purity and less porosity. At 1430 °C, the Al 2 TiO 5 particles do not dissolve at all, whereas TiO 2 particles completely dissolve in 200 s.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.930
Threshold uncertainty score0.269

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.042
GPT teacher head0.400
Teacher spread0.358 · 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 designSimulation or modeling
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

Citations7
Published2018
Admission routes2
Has abstractyes

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