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Record W1994747781 · doi:10.1155/2015/750689

Synthesis of CuNi/C and CuNi/<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mo>γ</mml:mo></mml:mrow></mml:math>-Al<sub>2</sub>O<sub>3</sub>Catalysts for the Reverse Water Gas Shift Reaction

2015· article· en· W1994747781 on OpenAlexafffund
Maxime Lortie, Rima J. Isaifan, Yun Liu, Sander Mommers

Bibliographic record

VenueInternational Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsX-ray photoelectron spectroscopyCatalysisAlloyPolyolMaterials scienceYield (engineering)MetalNuclear chemistryAnalytical Chemistry (journal)Chemical engineeringMetallurgyChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

A new polyol synthesis method is described in which CuNi nanoparticles of different Cu/Ni atomic ratios were supported on both carbon and gamma-alumina and compared with Pt catalysts using the reverse water gas shift, RWGS, reaction. All catalysts were highly selective for CO formation. The concentration of CH 4 was less than the detection limit. Cu was the most abundant metal on the CuNi alloy surfaces, as determined by X-ray photoelectron spectroscopy, XPS, measurements. Only one CuNi alloy catalyst, Cu 50 Ni 50 /C, appeared to be as thermally stable as the Pt/C catalysts. After three temperature cycles, from 400 to 700°C, the CO yield at 700°C obtained using the Cu 50 Ni 50 /C catalyst was comparable to that obtained using a Pt/C catalyst.

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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.013
GPT teacher head0.230
Teacher spread0.217 · 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

Citations24
Published2015
Admission routes2
Has abstractyes

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