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Record W4213181414 · doi:10.21577/0103-5053.20220035

Metal-Supported Niobium Catalysts Investigation for Conversion of Bio-Renewable Glycerol to Organic Compounds

2022· article· en· W4213181414 on OpenAlexaff
Carolina F. M. Pestana, Bianca P. Pinto, Daniella Fernandes, Cláudio J. A. Mota

Bibliographic record

VenueJournal of the Brazilian Chemical Society · 2022
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsDiscovery Air (Canada)
FundersFinanciadora de Estudos e ProjetosFundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de JaneiroConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsAcroleinCatalysisGlycerolChemistryNiobium oxideAcetic acidAcrylic acidAcetaldehydeInorganic chemistrySelectivityOrganic chemistryOxideNuclear chemistryEthanol

Abstract

fetched live from OpenAlex

The glycerol oxidative dehydration to organic compounds, especially acrolein and acrylic acid, was studied over vanadium, molybdenum, and cobalt oxides supported on niobium oxide and niobium phosphate catalysts. At 300 °C, the glycerol conversion was 100% over all catalysts tested, and acrolein was the main product observed, without significant deactivation within the time on stream studied. Except for Co/V/Nb2O5, acrylic acid was formed over all catalysts, but the selectivity was not higher than 10%. Acetaldehyde and acetic acid were the other organic oxygenated products formed. Although the catalysts supported over Nb2O5 presented significantly lower acidity than the catalysts supported over NbOPO4, they showed 100% glycerol conversion, indicating that the dehydration of glycerol does not require strong acidity. These data indicate the potential of niobium-based supports in the development of an active and selective catalyst for the glycerol oxidehydration to acrolein and acrylic acid, chemicals of great importance in the industry.

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 score0.095
Threshold uncertainty score0.491

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.008
GPT teacher head0.201
Teacher spread0.193 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Brazilian Chemical SocietySame topicCatalysis for Biomass ConversionFrench-language works237,207