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Record W2402154162 · doi:10.1504/ijmsi.2015.071108

Pseudo-dynamic carbonation for concrete curing and carbon storage

2015· article· en· W2402154162 on OpenAlexaff
Yixin Shao, Arthur Zhou, Mehrdad Mahoutian

Bibliographic record

VenueInternational Journal of Materials and Structural Integrity · 2015
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsCarbonationFlue gasCuring (chemistry)Carbon dioxideCementMaterials scienceCarbonatationCarbonateWaste managementComposite materialPulp and paper industryChemistryMetallurgyOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Concrete demonstrates the capacity to absorb carbon dioxide. The reaction between concrete and carbon dioxide accelerates concrete curing at early age and converts CO2 into calcium carbonate for carbon storage. As-captured flue gas without recovery can be directly used in this application. Because of low CO2 concentration in flue gas, a pseudo-dynamic carbonation process was developed with multiple injection and releasing cycles. The effect of process parameters on carbon uptake and strength gain was examined. It was found that, with a flue gas of 25% CO2 concentration and 2-hour carbonation, cement paste could uptake CO2 of 8-11% based on cement content in immediate carbonation and concrete could absorb CO2 at 7-9% after 18-hour initial curing. The maximum possible carbon uptake from flue gas carbonation was also determined. It was about 15-17% based on cement mass. While immediate carbonation could significantly enhance the early strength, carbonation after initial curing did not improve significantly the late strength.

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 score0.195
Threshold uncertainty score0.351

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 head0.025
GPT teacher head0.297
Teacher spread0.272 · 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

Citations5
Published2015
Admission routes1
Has abstractyes

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