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Record W4408189953 · doi:10.1021/acs.iecr.4c04749

CO<sub>2</sub> Capture with Mg-, Al-, and Zr- Assisted CaO-Based Sorbents in the Calcium Looping Process Under Mild and Realistic Conditions

2025· article· en· W4408189953 on OpenAlexafffund
Seyed Mojtaba Hashemi, Nader Mahinpey

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2025
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCalcium loopingProcess (computing)CalciumChemistryMaterials scienceProcess engineeringSorbentMetallurgyComputer sciencePhysical chemistryAdsorptionEngineering

Abstract

fetched live from OpenAlex

The calcium looping (CaL) process is a promising carbon capture technology for CO 2 capture from point source emitters. A key challenge in the CaL process is the loss of sorbent capacity over successive capture-regeneration cycles due to sintering, which affects long-term stability. This study addresses this issue by a novel approach of incorporating MgO, Al 2 O 3, and ZrO 2 as promoters into calcium-based sorbents synthesized using the solution combustion synthesis (SCS) method. Sorbents were developed in mono-, bi-, and trimetallic configurations using soluble metal nitrates as precursors. Among the tested sorbents, Ca/(Zr–Al) demonstrated the highest CO 2 uptake of 0.46 g of CO 2 /g of sorbent, while Ca/(Mg–Zr–Al) achieved 0.43 g of CO 2 /g of sorbent. Both configurations exhibited exceptional stability, maintaining over 90% of their initial capacity after 50 cycles at elevated temperatures. These results highlight the effectiveness of bi- and trimetallic sorbents in enhancing the performance and durability of the CaL process.

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 score0.004
Threshold uncertainty score0.860

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.060
GPT teacher head0.337
Teacher spread0.277 · 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

Citations5
Published2025
Admission routes2
Has abstractyes

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