MétaCan
Menu
Back to cohort
Record W2490965648 · doi:10.1021/bk-2013-1132.ch013

The Effect of Calcination Temperature on the Properties and Hydrodeoxygenation Activity of Ni<sub>2</sub>P Catalysts Prepared Using Citric Acid

2013· book-chapter· en· W2490965648 on OpenAlexafffund
Victoria M. L. Whiffen, Kevin J. Smith

Bibliographic record

VenueACS symposium series · 2013
Typebook-chapter
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsCalcinationCatalysisHydrodeoxygenationCitric acidInorganic chemistryCrystalliteChemistryAqueous solutionNuclear chemistryNickelSpecific surface areaMaterials scienceOrganic chemistrySelectivityCrystallography

Abstract

fetched live from OpenAlex

The effect of calcination temperature on the preparation of unsupported high surface area Ni2P catalysts, synthesized by adding citric acid (CA) to an aqueous solution of nickel nitrate and diammonium hydrogen phosphate, is reported. The addition of CA led to increased surface area, decreased particle size, and increased CO uptake of the reduced Ni2P. However, increases in the Ni2P-CA calcination temperature from 773 to 823 and to 973 K led to a deterioration in the catalyst properties. All Ni2P catalysts deactivated following the hydrodeoxygenation (HDO) of 4-methylphenol (4-MP) at 623 K and 4.4 MPa. The deactivation was due to coking and was modeled by an exponential decay law. All Ni2P catalysts had similar deactivation parameters, indicating the loss in activity was due to C deposition on similar sites. The Ni2P-CA catalysts, with crystallite size in the range of 34–50 nm, had comparable initial TOFs, indicating that the HDO of 4-MP was structure insensitive over Ni2P catalysts of this size.

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 score0.005
Threshold uncertainty score0.829

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 head0.010
GPT teacher head0.185
Teacher spread0.175 · 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

Citations3
Published2013
Admission routes2
Has abstractyes

Explore more

Same venueACS symposium seriesSame topicCatalysis and Hydrodesulfurization StudiesFrench-language works237,207