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Record W4399600474 · doi:10.1002/cctc.202400225

Catalytic Relevance of Mg‐Al‐O Basic Centers in the Upgrade of Ethanol to n‐Butanol

2024· article· en· W4399600474 on OpenAlexafffund
Wei Tian, José E. Herrera

Bibliographic record

VenueChemCatChem · 2024
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaSuncor Energy Incorporated
KeywordsCatalysisChemistryButanolHydrotalciteInorganic chemistryTitrationRedoxMetalOxideMixed oxideHeterogeneous catalysisEthanolOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The catalytic relevance of different surface functionalities present on hydrotalcite‐derived Mg−Al mixed metal oxides (MgAlO) are investigated in the context of ethanol upgrade to n‐butanol. The catalyst structure and active sites were altered through changes in the Mg/Al ratio and introduction of additional redox metal oxide functionalities. After performing a series of catalytic activity tests, operando FTIR characterization, TGA measurements and in situ active center titration; qualitative and quantitative relationships between catalyst structure and catalytic performance are obtained. We found the Mg−Al mixed metal oxides system can catalyze the ethanol to n‐butanol process through a Guerbet reaction pathway, though the process is kinetically limited. By introducing redox active metal oxides to the catalyst formulation, such as MoO 3 or V 2 O 5 , this problem can be partially solved, but at a cost of losing a significant fraction of high‐strength basic centers which are the most catalytically relevant function in the system, as they control enolate formation rates leading to the C−C coupling step required for n‐butanol formation.

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 score0.014
Threshold uncertainty score0.473

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.222
Teacher spread0.214 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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