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Record W2327375591 · doi:10.1021/ie5008008

Effect of Synthesis Route on Properties of CuO as a High Temperature Oxygen Carrier

2014· article· en· W2327375591 on OpenAlexaff
Mehdi Alipour, John A. Nychka, Rajender Gupta

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2014
Typearticle
Languageen
FieldMaterials Science
TopicCopper-based nanomaterials and applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsX-ray photoelectron spectroscopyThermogravimetric analysisOxygenChemical looping combustionScanning electron microscopeOxideCopperDesorptionMaterials scienceAnalytical Chemistry (journal)CombustionCopper oxideChemistryChemical engineeringPhysical chemistryChromatographyAdsorptionOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

Copper oxide powders intended for use as oxygen carriers in high temperature air separation and chemical looping combustion have been synthesized by a range of ceramic synthesis techniques including citrate gel, Pechini, precipitation, alanine assisted combustion, and high temperature oxidation. The evolution of morphology and crystal structure in the synthesis of powders was characterized using scanning electron microscopy (SEM) and X-ray diffraction (XRD). The surface chemical properties of the powders were studied using X-ray photoelectron spectroscopy (XPS). The oxygen sorptive/desorptive kinetics was studied using thermogravimetric analysis (TGA). Kinetics of the oxygen exchange reactions were analyzed and explained using empirical kinetics models with minimum error. A strong correlation was observed between the oxygen desorption parameters and oxygen to copper ratio calculated from measured XPS spectra. Copper oxide powders synthesized using the alanine assisted combustion and citrate gel methods resulted in optimum kinetic properties for use as an oxygen carrier at high temperature.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.032
GPT teacher head0.288
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), 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
Published2014
Admission routes1
Has abstractyes

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