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Record W2519751280 · doi:10.1021/acs.oprd.6b00245

Effect of Metals on the Hydrogenolysis of Glycerol to Higher Value Sustainable and Green Chemicals Using a Supported HSiW Catalyst

2016· article· en· W2519751280 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueOrganic Process Research & Development · 2016
Typearticle
Languageen
FieldEngineering
TopicCatalysis for Biomass Conversion
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaBộ Giáo dục và Ðào tạo
KeywordsHydrogenolysisCatalysisChemistrySilicotungstic acidGlycerolAlcoholMetalInorganic chemistryReactivity (psychology)Acid strengthOrganic chemistryZeolite

Abstract

fetched live from OpenAlex

Pt, Pd, Ni, and Cu supported on HSiW/Al 2 O 3 catalysts were studied for the hydrogenolysis of glycerol. It was found that Pt is the best promoter for the production of 1,3-propanediol (1,3-PD) and 1-propanol (1-PO). Ni, a much cheaper metal, has fairly comparable reactivity to Pt, while Cu does not show any activity for the production of 1,3-PD. The catalysts were characterized by XRD and NH 3 -TPD. The strength of the acid sites affects the distribution of products. A reaction mechanism for a NiHSiW/Al 2 O 3 catalyst involving rate-determining parallel dehydration of primary alcohol to produce acetal and of secondary alcohol to produce 3-hydroxypropylaldehdye (3-HPA) was proposed. Hydrogenolysis of 1,3-PD is 15 times slower than that of 1,2-PD. Most of the 1-PO is derived from 1,2-PD. An optimal balance of acid sites of appropriate acid strength and hydrogenation sites will lead to a highly selective catalyst for the production of higher value sustainable chemicals from glycerol.

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)0.002
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 score0.004
Threshold uncertainty score0.479

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.017
GPT teacher head0.288
Teacher spread0.271 · 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