MétaCan
Menu
Back to cohort
Record W4394602239 · doi:10.1016/j.jmrt.2024.04.070

Effects of steel slag on the properties and microstructure of magnesium oxysulfate cement prepared by magnesium desulfurization byproducts

2024· article· en· W4394602239 on OpenAlexaff
Huihui Du, Jiajie Li, Dong Xu, Gang Li, Wen Ni, Xinli Mu, Bateer Sayn, Junxiang Guo, Zhaokun Shi, Mingyuan Gu, Youhao Yin, Zhiqiang Yang, Michael Hitch

Bibliographic record

VenueJournal of Materials Research and Technology · 2024
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Oxide Properties and Applications
Canadian institutionsUniversity of the Fraser Valley
FundersNational Natural Science Foundation of China
KeywordsMaterials scienceCementMicrostructureFlue-gas desulfurizationMagnesiumMetallurgySlag (welding)ShrinkageAmorphous solidCorrosionVolume (thermodynamics)Chemical engineeringComposite materialWaste managementOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

The magnesium oxysulfate (MOS) cement has problems of short setting time, poor water resistance, and volume instability. This paper investigates the potential of steel slag (SS) to modify the properties and microstructure of the MOS cement prepared by magnesium desulfurization byproducts (MDBs). The results showed that SS delayed the setting time, restrained the volume shrinkage, and improved the water resistance of the MOS cement. The hydration products of the MOS cement were the fibrous 318 phase (3Mg(OH)2•MgSO4•8H2O), lamellar Mg(OH)2, and MgCO3. The addition of SS increased the pH and decreased the overall hydration reaction rate and the amount of Mg(OH)2 in the MOS cement. SS reacted with Mg(OH)2 at the late stage to form an amorphous M–S–H gel, which increased the gel pore volume and the density of the cement. In conclusion, SS enhanced the application potential of MOS cement, through improving its macroscopic properties.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.024
GPT teacher head0.264
Teacher spread0.241 · 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 source (direct Gemma or distilled Codex), 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

Citations27
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Materials Research and TechnologySame topicMagnesium Oxide Properties and ApplicationsFrench-language works237,207