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Record W4417118289 · doi:10.1002/cssc.202502031

Formic Acid Pursues Efficient Hydrodeoxygenation of Naphthols and Phenolic Derivatives to Arenes

2025· article· en· W4417118289 on OpenAlexafffund
Benedetta Di Erasmo, Edoardo Bazzica, Giulia Brufani, Luigi Vaccaro, Chao‐Jun Li

Bibliographic record

VenueChemSusChem · 2025
Typearticle
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsCentre in Green Chemistry and Catalysis
FundersFonds de recherche du Québec – Nature et technologiesMcGill UniversityCentre in Green Chemistry and CatalysisFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaEuropean CommissionUniversità degli Studi di PerugiaCanada Research Chairs
KeywordsHydrodeoxygenationFormic acidHydrogenPhenolsLigninCatalysisSubstrate (aquarium)Biofuel

Abstract

fetched live from OpenAlex

The use of high-pressure hydrogen poses significant challenges, including safety risks, storage problems, and elevated costs. Consequently, developing reductive chemical processes utilizing low-pressure hydrogen is highly appealing for industrial-scale applications. This emphasizes the critical need for sustainable alternatives that offer safer and more accessible reaction conditions. Liquid organic hydrogen carriers (LOHCs) are appealing materials due to their capability to generate hydrogen in situ, which can be directly utilized to produce target biofuel precursors, fuels, or fuel additives. Hydrodeoxygenation (HDO) is an efficient approach for transforming lignin and its phenolic derivatives into valuable aromatic chemicals and fuels. Herein, we present an alternative HDO of naphthols and phenols using a commercial heterogeneous catalyst, Pd/C, employing formic acid as LOHC. This paper presents a consistent substrate scope for the HDO of different naphthols and phenols, including pharmaceutically relevant molecules such as amylmetacresol and menthol.

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 score0.015
Threshold uncertainty score0.430

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 head0.006
GPT teacher head0.226
Teacher spread0.220 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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