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Record W4414480455 · doi:10.1016/j.jclepro.2025.146635

Carbon dioxide supply and scaling constraints on direct air capture using calcium oxide powder

2025· article· en· W4414480455 on OpenAlexafffund
Lance Dostie, Ian Power, Kwon Rausis

Bibliographic record

VenueJournal of Cleaner Production · 2025
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsTrent University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsCarbon dioxideScalingCalcium oxideCalciumCalcium loopingOxideCalcium carbonate

Abstract

fetched live from OpenAlex

Calcium oxide (CaO; lime) looping is a carbon dioxide (CO 2 ) removal technology that can mitigate carbon emissions. However, scaling this technology requires a thorough understanding of the rate-limiting effects of CO 2 supply on CO 2 removal rates at and below atmospheric CO 2 concentration levels, as well as with various CaO thicknesses and plot areas. Here, we show that carbonation is readily limited by CO 2 supply even under high flow rates (e.g., 25 mmol CO 2 /h over 100 g CaO). Subsequently, the CO 2 capture efficiencies using CaO and Ca(OH) 2 powders were investigated under ambient CO 2 concentrations (397–490 ppm) to 100 ppm. Ca(OH) 2 carbonation rates increased exponentially with increasing CO 2 concentrations, e.g., 2.1 wt% CaCO 3 /h at 100 ppm compared to 5.2 wt% CaCO 3 /h at ambient CO 2 concentrations, thereby demonstrating the impact of CO 2 concentrations on carbonation efficiency. Column experiments determined CO 2 diffusion limitations for CaO deposits ≥0.5 cm, yielding 64 ± 2 wt% CaCO 3 and an average CO 2 removal rate of 32 kg CO 2 /m 2 /yr (24 days; 85–95% RH). Finally, the CO 2 removal efficiency of CaO plot areas was estimated, revealing the importance of scaling CO 2 supply in concert with increasing CO 2 removal that results from greater thicknesses and masses.

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 score0.242
Threshold uncertainty score0.748

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 integrity0.0000.001
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 head0.011
GPT teacher head0.235
Teacher spread0.224 · 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

Citations4
Published2025
Admission routes2
Has abstractyes

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