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Record W2329239928 · doi:10.1021/ef401541b

Evaluation of Copper–Aluminum Oxides as Sorbents for High-Temperature Air Separation

2013· article· en· W2329239928 on OpenAlexaff
Mehdi Alipour, John A. Nychka, Rajender Gupta

Bibliographic record

VenueEnergy & Fuels · 2013
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOxygenFlue gasCombustionSpinelDesorptionSorbentThermogravimetric analysisChemical engineeringSorptionAir separationChemistryCopperMaterials scienceInert gasAdsorptionMetallurgyOrganic chemistry

Abstract

fetched live from OpenAlex

Oxygen production in an economic way is critical to oxy-firing combustion, a carbon capture technology. Oxygen-deficient oxides have been used for absorption of oxygen from air and desorption of oxygen in recycled flue gas for oxy-firing combustion. Cuprous/cupric oxide equilibrium with alumina can be used as an alternative for oxygen production and absorption. In this work, the effect of the spinel phase (CuAl 2 O 4 ) content on oxygen sorptive/desorptive properties of CuO–CuAl 2 O 4 sorbents has been investigated using thermogravimetric analysis. The desorption rate and oxygen sorption capacity were shown to be dependent upon the amount of alumina addition. The effect of SO 2 and H 2 O in flue gas was investigated using FACTSage over a range of conditions. It was found that, at temperatures above 750 °C, CuO is inert to these species, making it a proper choice for oxygen carrier. Cyclic stability was also investigated using the same instrument. The molar CuAl 2 O 4 content of 20% was observed to have the most positive cyclic stability effect. A stable morphology was observed in the scanning electron microscopy microstructure of the sorbent with this CuAl 2 O 4 content. Sintering at a lower CuAl 2 O 4 content and attrition because of second-phase agglomeration can destabilize the sorbents.

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 score0.017
Threshold uncertainty score0.449

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 head0.012
GPT teacher head0.251
Teacher spread0.240 · 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

Citations8
Published2013
Admission routes1
Has abstractyes

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