MétaCan
Menu
Back to cohort
Record W2888137063 · doi:10.1021/acscatal.8b02214

Formal Cross-Coupling of Diaryl Ethers with Ammonia by Dual C(Ar)–O Bond Cleavages

2018· article· en· W2888137063 on OpenAlexaff
Dawei Cao, Huiying Zeng, Chao‐Jun Li

Bibliographic record

VenueACS Catalysis · 2018
Typearticle
Languageen
FieldEngineering
TopicLignin and Wood Chemistry
Canadian institutionsMcGill UniversityCentre in Green Chemistry and Catalysis
FundersLanzhou University of TechnologyRecruitment Program of Global ExpertsMinistry of Education of the People's Republic of China
KeywordsChemistryBond cleavageEtherAnilineAmmoniaOrganic chemistryArylLigninCatalysisCommodity chemicalsAlkyl

Abstract

fetched live from OpenAlex

The conversion of renewable resources and inexpensive inorganic chemicals directly into higher value-added organic chemicals is becoming more and more important for our society’s future sustainable development. Lignin, being the second most abundant organic carbon renewable resource on Earth, has been treated as waste in the pulp and paper industry. The 4-O-5 linkage diaryl ether bond is the strongest among the three types of ether linkages in lignins. Selective cleavage of this linkage can potentially generate smaller processable bio-based aromatic polymeric materials and compounds. Furthermore, there has been a long synthetic interest in coupling reactions with aryl ethers via C(Ar)–O bond cleavage, for example, for polyphenylene oxide (PPO) waste recycling. On the other hand, ammonia is a very inexpensive industrial inorganic chemical. Herein, we report a direct conversion of diaryl ethers and ammonia into aniline derivatives and arenes, providing a model for selective lignin 4-O-5 linkage modification and PPO recycling with inexpensive ammonia. Both symmetrical and unsymmetrical diaryl ethers were successfully cross-coupled with ammonia via dual C(Ar)–O bond cleavages, generating the corresponding cyclohexylanilines and arenes.

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 score0.045
Threshold uncertainty score0.536

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 head0.004
GPT teacher head0.209
Teacher spread0.205 · 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

Citations61
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueACS CatalysisSame topicLignin and Wood ChemistryFrench-language works237,207