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Effect of temperature on CO2 curing, compressive strength and microstructure of cement paste

2022· article· en· W4229066383 on OpenAlexaff
Bao Lu, Sarra Drissi, Jianhui Liu, Xiang Hu, Baixin Song, Caijun Shi

Bibliographic record

VenueCement and Concrete Research · 2022
Typearticle
Languageen
FieldEngineering
TopicConcrete and Cement Materials Research
Canadian institutionsUniversity of British Columbia
FundersScience and Technology Program of Gansu ProvinceScience and Technology Support Program of Jiangsu ProvinceNational Natural Science Foundation of ChinaJiangsu Provincial Key Laboratory of New Environmental Protection
KeywordsVateriteCarbonationMaterials scienceMicrostructureCompressive strengthCuring (chemistry)AragoniteCrystallinityComposite materialCalciteCalcium carbonateCementMineralogyChemistry

Abstract

fetched live from OpenAlex

This study investigated the effect of temperature (5 °C–50 °C) on the carbonation process, compressive strength and microstructure of CO2-cured cement paste. Results showed that the carbonation process and rate were significantly affected by temperature and time. When the curing temperature increased, the rate of improvement of cement paste's properties was accelerated. The carbonation reaction was mainly kinetically controlled by product layer diffusion with an activation energy of about 10.8 kJ/mol. Temperature had greatly affected the structural transformation, morphological changes, size and amounts of calcium carbonate polymorphs (calcite, aragonite and vaterite) as well as their degree of crystallinity and decomposition temperature. Alongside calcite, vaterite and aragonite were formed at low and high curing temperatures, respectively. Apart from microstructure, the compressive strength was also found to be very sensitive to temperature and carbonation products. The relationship between the amount of different carbonate polymorphs and the compressive strength was also provided.

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)0.001
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.014
GPT teacher head0.288
Teacher spread0.274 · 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

Citations268
Published2022
Admission routes1
Has abstractyes

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