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Record W2940942398 · doi:10.1038/s41467-019-09881-9

Porous metal-metalloporphyrin gel as catalytic binding pocket for highly efficient synergistic catalysis

2019· article· en· W2940942398 on OpenAlexaff
Weijie Zhang, James J. Dynes, Yongfeng Hu, Pingping Jiang, Shengqian Ma

Bibliographic record

VenueNature Communications · 2019
Typearticle
Languageen
FieldMaterials Science
TopicSupramolecular Self-Assembly in Materials
Canadian institutionsCanadian Light Source (Canada)
FundersDivision of Materials ResearchHigher Education Discipline Innovation ProjectMinistry of Education, IndiaJiangnan UniversityNational Science Foundation
KeywordsCatalysisSupramolecular chemistryPorphyrinCooperativityChemistrySupramolecular catalysisMetal-organic frameworkMaterials scienceCombinatorial chemistryNanotechnologyPhotochemistryOrganic chemistryMolecule

Abstract

fetched live from OpenAlex

Synergistic catalysis occurring in an enzyme pocket shows enhanced performance through supramolecular recognition and flexibility. This study presents an aerogel capable of similar function by fabricating a gel catalyst with hierarchical porosity. Here, the as-prepared Co-MMPG, a Co(II) metal-metalloporphyrin gel, maintains enough conformational flexibility and features a binding pocket formed from the co-facial arrangement of the porphyrin rings, as elucidated through the combined studies of solid-state NMR and X-ray absorption near-edge structure (XANES). The cooperativity between two Co(II) sites within the defined nanospace pocket facilitates the binding of different substrates with a favourable geometry thereby rendering Co-MMPG with excellent performance in the context of synergistic catalysis, especially for the kinetic control stereoselective reactions. Our work thus contributes a different enzyme-mimic design strategy to develop a highly efficient heterogeneous catalyst with high chemo/stereo selectivity.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient 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 score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.003

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 head0.014
GPT teacher head0.292
Teacher spread0.278 · 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.

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

Citations57
Published2019
Admission routes1
Has abstractyes

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