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Record W3165379868 · doi:10.1021/acs.iecr.1c01010

Preparation of Aerogel-Supported Copper Oxide for the Methane Chemical Looping Combustion (CLC) Process

2021· article· en· W3165379868 on OpenAlexaff
Sanaz Daneshmand-Jahromi, Davood Karami, Nader Mahinpey

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2021
Typearticle
Languageen
FieldEngineering
TopicChemical Looping and Thermochemical Processes
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAerogelThermogravimetric analysisCubic zirconiaChemical looping combustionChemical engineeringCombustionScanning electron microscopeOxideMaterials scienceCopperMethaneSpecific surface areaDispersion (optics)Copper oxideChemistryNanotechnologyOrganic chemistryComposite materialMetallurgyCatalysis

Abstract

fetched live from OpenAlex

The objective of this research is to investigate the effect of different aerogel supports on the stability of copper oxide during limited cycles of CH4 chemical looping combustion (CLC) in a thermogravimetric analyzer (TGA). Supports proved to be significantly effective not only for the fuel utilization efficiency of CLC but also for the stability of the oxygen carrier (OC). The evaluation tests were performed by employing CuO/aerogel (zirconia, alumina, and silica) OCs synthesized utilizing a novel patented method. The results showed that the OC prepared using zirconia aerogel support had the highest activity over a medium-temperature range (550–800 °C). The OCs were characterized using different methods, including surface area and pore analysis, TGA, X-ray diffraction (XRD), scanning electron microscope (SEM), and dynamic light scattering analysis. The results demonstrated that the proper support can stabilize Cu dispersion, intensify the redox degree of OCs at high temperatures, and strengthen the distribution of Cu on the surface of the support.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.805

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.081
GPT teacher head0.363
Teacher spread0.281 · 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

Citations20
Published2021
Admission routes1
Has abstractyes

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