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Record W4412552534 · doi:10.3390/catal15080697

Carbonation Deactivation of Limestone in a Micro-Fluidized Bed Reactor

2025· article· en· W4412552534 on OpenAlexaff
P. Asiedu-Boateng, Nana Yaw Asiedu, Gregory S. Patience, Jonathan McDonough, Vladimir Živković

Bibliographic record

VenueCatalysts · 2025
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsCarbonationFluidized bedEnvironmental scienceMaterials scienceMetallurgyWaste managementComposite materialEngineering

Abstract

fetched live from OpenAlex

Carbonation–calcination looping using CaO-based natural sorbents such as limestone is a promising technology for the capture of CO2 from fossil fuel-based power plants. In this study, the CO2 capture capacities of Buipe, Oterpkolu, and Nauli limestones from quarries in Ghana were measured in a laboratory-scale micro-fluidized bed reactor through multiple carbonation–calcination cycles. The changes in CO2 capture capacity and conversion with the number of cycles mostly correlated with the changes in the physico-chemical properties: Capture capacity dropped from >60% to <15% after 15 cycles and the surface area dropped to below 5 m2 g−1 from as much as 20 m2 g−1 (for the Oterkpolu). The pore volume of the Nauli limestone was essentially invariant with the number of cycles while it increased for the Buipe limestone, and initially increased and then dropped for the Oterpkolu limestone. This decrease was likely due to sintering and a reduction in the number of micropores. The unusual increase in pore volume after multiple cycles was due to the formation of mesopores with smaller pore diameters.

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 score0.003
Threshold uncertainty score0.273

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 studies0.0000.000
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 head0.005
GPT teacher head0.216
Teacher spread0.211 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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