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Record W2905264317 · doi:10.3390/su10124709

Slag and Activator Chemistry Control the Reaction Kinetics of Sodium Metasilicate-Activated Slag Cements

2018· article· en· W2905264317 on OpenAlexfundno aff
M. Criado, Brant Walkley, Xinyuan Ke, John L. Provis, Susan A. Bernal

Bibliographic record

VenueSustainability · 2018
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsnot available
FundersFP7 Ideas: European Research CouncilEngineering and Physical Sciences Research CouncilEuropean CommissionUniversité Laval
KeywordsGround granulated blast-furnace slagChemistryKineticsActivator (genetics)Isothermal processSlag (welding)Isothermal titration calorimetryAlkali metalChemical kineticsSodiumMetallurgyInorganic chemistryCalorimetryReactivity (psychology)Nuclear chemistryChemical engineeringMineralogyMaterials scienceFly ashPhysical chemistryOrganic chemistryThermodynamicsBiochemistry

Abstract

fetched live from OpenAlex

The reaction kinetics of four commercial ground granulated blast furnace slags with varying percentages of MgO (6 to 14 wt.%), activated with four different doses of sodium metasilicate, were evaluated using isothermal calorimetry. The reaction kinetics were strongly dependent on the dose of the alkaline activator used, and the chemical and physical properties of the slag. When using low concentrations of sodium metasilicate as an activator, the MgO content in the slag influences the kinetics of the reaction, while the CaO content plays a more significant role when the concentration of metasilicate is increased. This study elucidated a close relationship between the dose of the alkali-activator and the chemistry of the slag used, although it was not possible to identify a clear correlation between any of the published chemically-based “slag quality moduli” and the calorimetry results, highlighting the complexity of blast furnace slag glass chemistry, and the importance of the physical properties of the slag in defining its reactivity.

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 score0.013
Threshold uncertainty score0.391

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 head0.009
GPT teacher head0.252
Teacher spread0.243 · 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

Citations61
Published2018
Admission routes1
Has abstractyes

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