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Record W2314316515 · doi:10.1021/es405449v

Reaction Mechanism for the Aqueous-Phase Mineral Carbonation of Heat-Activated Serpentine at Low Temperatures and Pressures in Flue Gas Conditions

2014· article· en· W2314316515 on OpenAlexaff
Louis-César Pasquier, Guy Mercier, Jean‐François Blais, Emmanuelle Cecchi, Sandra E. Kentish

Bibliographic record

VenueEnvironmental Science & Technology · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsCarbonationFlue gasAqueous solutionMineralChemistryMechanism (biology)Gas phaseChemical engineeringPhase (matter)MineralogyWaste managementMaterials scienceOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Mineral carbonation is known as one of the safest ways to sequester CO2. Nevertheless, the slow kinetics and low carbonation rates constitute a major barrier for any possible industrial application. To date, no studies have focused on reacting serpentinite with a relatively low partial pressure of CO2 (pCO2) close to flue gas conditions. In this work, finely ground and heat-treated serpentinite [Mg3Si2O5(OH)4] extracted from mining residues was reacted with a 18.2 vol % CO2 gas stream at moderate global pressures to investigate the effect on CO2 solubility and Mg leaching. Serpentinite dissolution rates were also measured to define the rate-limiting step. Successive batches of gas were contacted with the same serpentinite to identify surface-limiting factors using scanning electron microscopy (SEM) analysis. Investigation of the serpentinite carbonation reaction mechanisms under conditions close to a direct flue gas treatment showed that increased dissolution rates could be achieved relative to prior work, with an average Mg dissolution rate of 3.55 × 10(-11) mol cm(-2) s(-1). This study provides another perspective of the feasibility of applying a mineral carbonation process to reduce industrial greenhouse gas (GHG) emissions from large emission sources.

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 score0.100
Threshold uncertainty score0.467

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 studies0.0000.001
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 head0.005
GPT teacher head0.242
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 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

Citations81
Published2014
Admission routes1
Has abstractyes

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