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Record W4408118517 · doi:10.1080/21650373.2025.2471820

An overview on polymorphs of calcium carbonate formed during carbon mineralization of cementitious materials

2025· article· en· W4408118517 on OpenAlexaff
Zhiqiang Xiao, Jian Zhang, Xiang Hu, Amani Khaskhoussi, Pingping He, Xujia You, Xing Su, Wei Chen, Caijun Shi

Bibliographic record

VenueJournal of Sustainable Cement-Based Materials · 2025
Typearticle
Languageen
FieldMaterials Science
TopicCalcium Carbonate Crystallization and Inhibition
Canadian institutionsUniversity of British Columbia
FundersNational Natural Science Foundation of China
KeywordsMineralization (soil science)Calcium carbonateCarbonateCementitiousGeologyCalciumGeochemistryMineralogyMaterials scienceMetallurgyCementSoil scienceComposite material

Abstract

fetched live from OpenAlex

Carbon mineralization presents a promising pathway for reducing the carbon footprint within the cement industry. However, the performance of carbonated cementitious materials is closely tied to the polymorphs of calcium carbonate. This paper examines the factors and strategies governing the formation of calcium carbonate polymorphs in carbonated cementitious materials, including carbonation conditions, additives and physical treatments. Temperature emerges as a critical factor among carbonation conditions, favoring the formation of vaterite at 30 ∼ 40 °C and aragonite above 60 °C. Chemical additives can effectively regulate polymorph formation through multiple mechanisms, such as embedding in the crystals and adsorbing on the surface of calcium carbonate. Additionally, ultrasound enhances vaterite formation, while under high power, it promotes aragonite formation. Magnetic field treatment can enhance aragonite precipitation, with its content increasing with field strength. This review aims to advance research on polymorph regulation, improving performance and broadening applications of carbonated cementitious materials.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.016
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.018
GPT teacher head0.288
Teacher spread0.270 · 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

Citations9
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Sustainable Cement-Based MaterialsSame topicCalcium Carbonate Crystallization and InhibitionFrench-language works237,207