MétaCan
Menu
Back to cohort
Record W2802376845 · doi:10.3390/ma11050730

Early Age Carbonation Heat and Products of Tricalcium Silicate Paste Subject to Carbon Dioxide Curing

2018· article· en· W2802376845 on OpenAlexaff
Zhen Li, Zhen He, Yixin Shao

Bibliographic record

VenueMaterials · 2018
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsMcGill University
FundersMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsCarbonationMaterials scienceCuring (chemistry)Calcium silicate hydrateVateriteCalcium silicateCarbon dioxideComposite materialCompressive strengthCarbonatationChemical engineeringCalciteSilicateMineralogyCalcium carbonateCementChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

This paper presents a study on the carbonation reaction heat and products of tricalcium silicate (C3S) paste exposed to carbon dioxide (CO2) for rapid curing. Reaction heat was measured using a retrofitted micro-calorimeter. The highest heat flow of a C3S paste subject to carbonation curing was 200 times higher than that by hydration, and the cumulative heat released by carbonation was three times higher. The compressive strength of a C3S paste carbonated for 2 h and 24 h was 27.5 MPa and 62.9 MPa, respectively. The 24-h carbonation strength had exceeded the hydration strength at 28 days. The CO2 uptake of a C3S paste carbonated for 2 h and 24 h was 17% and 26%, respectively. The X-ray diffraction (XRD), transmission electron microscope coupled with energy dispersive spectrometer (TEM-EDS), and 29Si magic angle spinning–nuclear magnetic resonance (29Si MAS-NMR) results showed that the products of a carbonated C3S paste were amorphous silica (SiO2) and calcite crystal. There was no trace of calcium silicate hydrate (C–S–H) or other polymorphs of calcium carbonate (CaCO3) detected.

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 score0.002
Threshold uncertainty score0.006

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)0.0020.000

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 head0.019
GPT teacher head0.249
Teacher spread0.230 · 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

Citations51
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueMaterialsSame topicConcrete and Cement Materials ResearchFrench-language works237,207