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Record W4409721917 · doi:10.1021/acs.inorgchem.5c00618

A Zirconium-Based Metal–Organic Framework as an Effective Green Catalyst for the Synthesis of Biodiesel

2025· article· en· W4409721917 on OpenAlexafffund
Michelle Pains Duarte, Clara V. Diniz, Hudson A. Bicalho, Rafik Naccache, Ashlee J. Howarth

Bibliographic record

VenueInorganic Chemistry · 2025
Typearticle
Languageen
FieldEngineering
TopicBiodiesel Production and Applications
Canadian institutionsConcordia University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsChemistryZirconiumCatalysisBiodieselBiodiesel productionMetalMetal-organic frameworkOrganic chemistryAdsorption

Abstract

fetched live from OpenAlex

CAU-28 (CAU = Christian-Albrechts-University) is a zirconium-based metal–organic framework (MOF) that features a bio-renewable linker (furan-2,5-dicarboxylic acid) and can be obtained through a green synthesis. In this work, we report an optimized synthesis of CAU-28, substantially enhancing the yield from 2 to 53 %. Moreover, taking advantage of the high thermal and chemical stability of CAU-28 as well as the MOF’s surface area (> 1000 m 2 g –1 ), porosity, and four open metal sites per Zr 6 -cluster, we have demonstrated the high potential of CAU-28 as a green catalyst to produce biodiesel through esterification reactions. Under optimized catalytic conditions, CAU-28 is able to convert oleic acid to its fatty acid methyl ester counterpart with high selectivity, using a catalyst loading of only 5 wt % and 1:24 molar ratio of oleic acid:methanol, at 90 °C for 90 min. Furthermore, the catalyst also shows high stability, maintaining its activity for three reaction cycles.

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 score0.071
Threshold uncertainty score0.554

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 head0.007
GPT teacher head0.231
Teacher spread0.224 · 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

Citations14
Published2025
Admission routes2
Has abstractyes

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