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Record W2080950777 · doi:10.1021/ef200015a

Model for Self-Reactivation of Highly Sintered CaO Particles during CO<sub>2</sub> Capture Looping Cycles

2011· article· en· W2080950777 on OpenAlexaff
B. Arias, J.C. Abánades, Edward J. Anthony

Bibliographic record

VenueEnergy & Fuels · 2011
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsNatural Resources Canada
Fundersnot available
KeywordsCarbonationCalcium loopingSorbentCalcinationChemical engineeringDiffusionMaterials scienceThermodynamicsChemistryCatalysisAdsorptionPhysical chemistryPhysics

Abstract

fetched live from OpenAlex

Calcium looping is an emerging high-temperature, energy-efficient, CO 2 capture technology using CaO as a regenerable sorbent of CO 2 through the reversible carbonation/calcination reaction. The stability of the sorbent plays a key role in the design of these systems. This paper revisits the self-reactivation phenomenon that has been reported for some highly deactivated CaO materials when submitted to repeated carbonation/calcination cycles under certain conditions. Self-reactivation is modeled in this paper as the result of a dynamic balance between the loss of activity in one cycle and the accumulated gain of activity by extended carbonation times, because of a product layer of CaCO 3 that keeps building up on all surfaces, controlled by the slow diffusion of CO 2 . The model describes reasonably well the trends observed for some limestones and conditions. For other limestones and conditions, the carbonation mechanism is more complex and the model does not fit the evolution of the maximum Ca conversion with the number of cycles as well, although the general patterns of self-reactivation are still well-reproduced.

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 score0.025
Threshold uncertainty score0.667

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 head0.019
GPT teacher head0.205
Teacher spread0.185 · 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

Citations32
Published2011
Admission routes1
Has abstractyes

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