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Record W2604406681 · doi:10.1002/ajoc.201700130

Unsupported Nanoporous Gold‐Catalyzed Chemoselective Reduction of α,β‐Unsaturated Aldehydes Using Formic Acid as Hydrogen Source

2017· article· en· W2604406681 on OpenAlex
Madiha Butt, Xiujuan Feng, Yoshinori Yamamoto, Abdulrahman I. Almansour, Natarajan Arumugam, Raju Suresh Kumar, Ming Bao

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAsian Journal of Organic Chemistry · 2017
Typearticle
Languageen
FieldMaterials Science
TopicNanoporous metals and alloys
Canadian institutionsnot available
FundersInstitute of Population and Public HealthKing Saud UniversityNational Natural Science Foundation of China
KeywordsChemistryFormic acidCatalysisNanoporousSelectivityAllylic rearrangementHydrogenLeaching (pedology)ChemoselectivityOrganic chemistryCombinatorial chemistry

Abstract

fetched live from OpenAlex

Abstract A straightforward, highly chemoselective hydrogenation of α,β‐unsaturated aldehydes was developed using unsupported nanoporous gold (AuNPore) as a heterogeneous catalyst with bio‐renewable formic acid (HCO 2 H) as hydrogen source. Various α,β‐unsaturated aldehydes were reduced to their corresponding allylic alcohols in good to high chemical yields with excellent selectivity. AuNPore offers several attractive features as a catalyst, such as high activity and selectivity, easy recyclability, and no leaching.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.011
GPT teacher head0.247
Teacher spread0.236 · 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