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Record W4407302797 · doi:10.1021/acscatal.4c06061

Accessing Arenes via the Hydrodeoxygenation of Phenolic Derivatives Enabled by Hydrazine

2025· article· en· W4407302797 on OpenAlexafffund
Benedetta Di Erasmo, Inna Perepichka, Hui Su, Luigi Vaccaro, Chao‐Jun Li

Bibliographic record

VenueACS Catalysis · 2025
Typearticle
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsMcGill UniversityCentre in Green Chemistry and Catalysis
FundersNextGenerationEUNatural Sciences and Engineering Research Council of CanadaMinistero dell’Istruzione, dell’Università e della RicercaMcGill UniversityFonds Québécois de la Recherche sur la Nature et les TechnologiesCentre in Green Chemistry and CatalysisUniversità degli Studi di PerugiaCanada Research Chairs
KeywordsHydrodeoxygenationChemistryCatalysisPhenolsHydrazine (antidepressant)ReagentOrganic chemistryLigninHydrazoneCombinatorial chemistrySelectivity

Abstract

fetched live from OpenAlex

Hydrodeoxygenation (HDO) is an effective method for converting lignin and its derived phenolic compounds to value-added aromatic chemicals and fuels. Efforts to exploit molecular hydrogen have been made to remove the hydroxyl group in lignin-derived phenolic compounds to make them appealing for the chemical industry. However, these processes rely on high pressure and expensive catalysts, presenting challenges in terms of safety, hydrogen storage, and cost-effectiveness. This highlights the demand for alternatives under more accessible reaction conditions. Herein, we present a methodology for the HDO of phenols and naphthols using Pd/C as a commercial heterogeneous catalyst employing hydrazine as a dual reagent for reducing and hydrazone formation. This paper presents an applicable substrate scope for the HDO of different naphthols and phenols including pharmaceutically relevant molecules such as paracetamol. Additionally, highly challenging steroid derivatives, such as β-estradiol, have been hydrodeoxygenated.

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 score0.075
Threshold uncertainty score0.491

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 head0.006
GPT teacher head0.225
Teacher spread0.219 · 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

Citations8
Published2025
Admission routes2
Has abstractyes

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