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Record W2061595583 · doi:10.1021/ie900703d

CO<sub>2</sub>Sequestration in Concrete through Accelerated Carbonation Curing in a Flow-through Reactor

2009· article· en· W2061595583 on OpenAlexafffund
Sormeh Kashef-Haghighi, Subhasis Ghoshal

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2009
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesConcordia University
KeywordsCarbonationCarbon dioxideDissolutionCuring (chemistry)Materials scienceVolumetric flow rateCarbonateCementCarbonatationEnvironmental scienceCarbon sequestrationChemical engineeringWaste managementPulp and paper industryChemistryComposite materialMetallurgyThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

CO 2 accelerated concrete curing has been recently suggested as a carbon dioxide mitigation technology in which CO 2 is reacted with cement and stored as a thermodynamically stable carbonate in concrete construction products. In this research, the rate and extent of CO 2 uptake by concrete is assessed in a flow-through reactor. Carbonation efficiencies of 16−20% attained in a flow-through reactor was comparable to those obtained for static CO 2 pressure chambers employed in previous studies for accelerated concrete curing. However, significantly less energy is required in a flow-through reactor compared to a CO 2 pressure chamber. Intermittent carbonation experiments showed that the carbonation efficiency was limited in part by slow dissolution and/or diffusion of dissolved reactive components in the concrete matrix.

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 score0.003
Threshold uncertainty score0.005

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 head0.099
GPT teacher head0.336
Teacher spread0.237 · 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

Citations163
Published2009
Admission routes2
Has abstractyes

Explore more

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