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Record W2317243088 · doi:10.1021/ie303275e

Physico–Chemical Processes Limiting CO<sub>2</sub> Uptake in Concrete during Accelerated Carbonation Curing

2013· article· en· W2317243088 on OpenAlexaff
Sormeh Kashef-Haghighi, Subhasis Ghoshal

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2013
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsMcGill University
Fundersnot available
KeywordsCarbonationCementCuring (chemistry)Materials scienceMortarPortlanditeChemical engineeringMicrostructureCarbonatationComposite materialPortland cement

Abstract

fetched live from OpenAlex

Accelerated curing of fresh concrete using CO 2 is a possible approach for value-added, high-volume usage products from waste CO 2 emitted from stationary sources. The extent of CO 2 uptake and the spatial distribution of the CaCO 3 (s) precipitates formed during accelerated carbonation curing of compacted, 4-h hydrated cement mortar (fresh concrete mixture with fine aggregates) samples were investigated in this study. The maximum carbonation efficiency achieved was 20% of the theoretical uptake. Microprobe imaging was used to analyze the composition of the compacted cement mortar microstructure and showed extensive filling of pores of diameters 4 μm and smaller, with CaCO 3 (s). The carbonation efficiency, however, reached 67% when an aqueous suspension of cement was carbonated in a completely mixed reactor, where interparticle pores do not exist and a higher surface area of cement particles is exposed to dissolved CO 2 . The theoretical efficiency was not achieved because all reactive cement surfaces were saturated with carbonation products, as indicated by equilibrium concentrations of dissolved calcium, silica, inorganic carbon, and pH. This study shows that both deposition of CaCO 3 (s), on reactive surfaces, and pore filling may regulate the extent of CO 2 uptake during accelerated carbonation curing of concrete.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.015
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.064
GPT teacher head0.291
Teacher spread0.227 · 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.

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

Citations97
Published2013
Admission routes1
Has abstractyes

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