MétaCan
Menu
Back to cohort
Record W1804485921 · doi:10.14288/1.0078677

The hydraulic potential of high iron bearing steel slags

2009· article· en· W1804485921 on OpenAlexaff
D. Ionescu

Bibliographic record

VenuecIRcle (University of British Columbia) · 2009
Typearticle
Languageen
FieldEngineering
TopicMetallurgical Processes and Thermodynamics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMetallurgyBearing (navigation)Materials scienceComputer science

Abstract

fetched live from OpenAlex

The incorporation of additives to the clinker or to the raw materials stream is a common practice in cement manufacture. However, steel slag, unlike its ironmaking parent the blast furnace slag, it is not a conventional admixture for cement. Currently most steel slags are slow cooled rendering stable crystalline compounds with minor hydraulic value. Nevertheless, if steel slags would be quenched and granulated, the resulting glassy product might display increased hydration and strength development potential. The use of steel slag in cement could contribute to important savings for both cement and steelmaking industries and provide a solution for the environmental problems linked to CO₂ emissions and costs of transport and disposal. The purpose of this research is to explore the thermodynamics and kinetics of steel slag hydration in an effort to produce a cement additive, or a more promising material of near Portland cement composition. An important criteria used in the assessment of slags as potential cements is the presence of a glassy phase. At present, it is not very clear why glass enhances the hydration process. However, it is known that the free energy of formation for glasses is less than for crystals so that glasses are easier to hydrate compared to crystalline materials. In the particular case of steel slag, the glassy phase would have to contain high amounts of iron. Steel slags are known to display iron levels approximately 10 times higher than Portland cement and commonly used blast furnace slags. However, the effect of high Fe₂0₃ levels on the setting and strengthening of cement paste is not clearly understood due to the fact that most cement additives do not present this characteristic. The present work looks at the progress made in recycling steel slag as cement additive, the complexity of the hydration process in slags, the possibilities of improving the hydration potential of slags at laboratory and industrial level, and the problems that still need to be addressed. However, the focus is on the glassy phase present in quenched steel slag and its influence on the hydration rate. B-SEM, Image Analysis, XRD techniques and a series of isothermal calorimetric experiments on synthetic as well as oxidized industrial steel slags vis-avis Portland cement assist in this endeavor. Temperature is a thermodynamic and kinetic factor modifying the enthalpy of hydrate formation (heat of hydration) and accelerating the hydration reaction. Hydration tests were carried out at temperatures ranging from 25 to 70 °C to determine the heat release, the rate of reaction and the apparent activation energy for steel slag and Portland cement hydration. The kinetics of hydration were explored in synthetic steel slags in both amorphous and fully crystalline form. The mechanism of hydration for both amorphous and crystalline steel slag was found to be a combination of nucleation and growth and diffusion, with higher reaction rates for the glassy slags. The higher reactivity in the glassy slags was explained by a lower activation energy when compared to the crystalline parent. Also, it was confirmed that slags have higher activation energies than Portland cement. As expected, and comparably to granulated blast furnace slags, quenched steels slags exhibited a significant hydraulic potential and hydrated at longer times, thus being expected to contribute to the late (> 5-180 days) strength development in slag - cements with compression strength superior to pure Portland cement.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.981
Threshold uncertainty score0.999

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 head0.003
GPT teacher head0.144
Teacher spread0.141 · 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 designOther design
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
Published2009
Admission routes1
Has abstractyes

Explore more

Same venuecIRcle (University of British Columbia)Same topicMetallurgical Processes and ThermodynamicsFrench-language works237,207