MétaCan
Menu
Back to cohort
Record W3049060004 · doi:10.3390/catal10080934

Hydrogenation of Furfural to Furfuryl Alcohol over Ru Particles Supported on Mildly Oxidized Biochar

2020· article· en· W3049060004 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCatalysts · 2020
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsFurfuryl alcoholFurfuralBiocharCatalysisSelectivityChemistryRutheniumAdsorptionAutoclaveDispersion (optics)Nuclear chemistryInorganic chemistryChemical engineeringOrganic chemistryPyrolysis

Abstract

fetched live from OpenAlex

Catalytic hydrogenation of aldehydes is required as the stabilizing step in bio-oils conversion. Ruthenium supported on carbon was used in the present work for hydrogenation of furfural (FF) to furfuryl alcohol (FA). Converting a biochar with no surface area and low carboxyl groups surface density to an outstanding catalyst support using a very simple mild air/steam oxidation is the original contribution of this work. The mildly oxidized biochar is impregnated with a targeted loading of 2.5 wt.% Ru via ion-exchange, using Ru(NH3)6Cl2 precursor. ICP analysis shows that the mild oxidation increases Ru adsorption capacity of untreated biochar from 1.2 to 2.2 wt.%. H2 chemisorption and TEM analysis indicate that the preliminary mild oxidation leads to higher Ru dispersion. XPS analysis also shows that the treatment prevents Ru from surface segregation. The highest value of 93% FA selectivity at 53% FF conversion was obtained in a batch autoclave reactor under optimized conditions.

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.

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 score0.007
Threshold uncertainty score0.922

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)0.0000.001

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 head0.018
GPT teacher head0.228
Teacher spread0.210 · 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