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Record W4407279082 · doi:10.1126/sciadv.ads4626

Direct carbonyl reductive functionalizations by diphenylphosphine oxide

2025· article· en· W4407279082 on OpenAlexaff
Jianyu Dong, Ruofei Cheng, Shuang‐Feng Yin, Lang Chen, Lebin Su, Renhua Qiu, Yongbo Zhou, Li‐Biao Han, Chao‐Jun Li

Bibliographic record

VenueScience Advances · 2025
Typearticle
Languageen
FieldChemistry
TopicAsymmetric Hydrogenation and Catalysis
Canadian institutionsCentre in Green Chemistry and Catalysis
FundersNational Natural Science Foundation of China
KeywordsDiphenylphosphine oxideSurface modificationReductive aminationChemistryCombinatorial chemistryPhosphineDiphenylphosphineArylPhosphine oxideOrganic chemistryCatalysisAlkyl

Abstract

fetched live from OpenAlex

Reductive functionalization of aldehydes and ketones is one of the most challenging but ultimately rewarding areas in synthetic chemistry and related sciences. We report a simple and extremely versatile carbonyl reductive functionalization strategy achieving direct, highly selective, and efficient reductive amination, etherification, esterification, and phosphinylation reactions of (hetero)aryl aldehydes and ketones, which are extremely challenging or unattainable to achieve by traditional strategies, using only diphenylphosphine oxide and an inorganic base. It enables modular synthesis of functionally and structurally diverse tertiary amines, ethers, esters, phosphine oxides, etc., as well as related pesticides, drug intermediates, and pharmaceuticals. Compared to phosphorus-mediated name reactions, this strategy firstly transformed C═O bonds into C-element single bonds. Mechanistically, phosphinates are formed as intermediates, which undergo unconventional nucleophilic substitution at the C atom within their C─O─P unit. Thus, this work provides important strides in the field of reductive functionalization of aldehydes/ketones, phosphorus-mediated transformation, and various fundamental reactions.

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: none
Teacher disagreement score0.735
Threshold uncertainty score0.397

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.005
GPT teacher head0.258
Teacher spread0.253 · 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 routes1
Has abstractyes

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