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Record W2160468086 · doi:10.1021/ie202693q

CO<sub>2</sub> Sequestration in Chrysotile Mining Residues—Implication of Watering and Passivation under Environmental Conditions

2012· article· en· W2160468086 on OpenAlexaff
Gnouyaro P. Assima, Faı̈çal Larachi, Georges Beaudoin, John Molson

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsPassivationCarbonationDissolutionChemistryLeaching (pedology)Environmental chemistryMaterials scienceEnvironmental science

Abstract

fetched live from OpenAlex

Factors affecting carbon dioxide fixation in chrysotile mining residues (CMR) under environmental conditions were studied by reproducing mineral dissolution and carbonation in laboratory columns packed with CMR particles. Carbonation is very sensitive to water saturation and watering frequency of the CMR porous media. CO 2 uptake by dry residues subjected to dry CO 2 flow for several days at ambient temperature was below 0.02%. However, an increase by a factor of 20 in CO 2 uptake was achieved by periodic addition of small amounts of water with respect to a moistened CO 2 stream over dry CMR samples. The highest MgCO 3 conversion resulted in nearly 22 mg of CO 2 captured per gram of residue, revealing that up to 93% of Mg remained noncarbonated because of surface obstructing processes. Magnesium leaching from CMR was hindered by two concomitant passivation phenomena limiting the residue’s CO 2 storage capacity. A unique cyclic voltammetry technique using oxic and anoxic aqueous solutions contacted with CMR fixed beds was implemented to assess the relative importance from CMR-borne iron electrochemical passivation and silica-deposit nonelectrochemical passivation. Passivation around the dissolving CMR particles by iron hydroxide precipitation was found to develop very rapidly in comparison to silica gel polymerization.

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)0.000
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 score0.079
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.062
GPT teacher head0.320
Teacher spread0.258 · 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

Citations80
Published2012
Admission routes1
Has abstractyes

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