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Record W4200211494 · doi:10.26434/chemrxiv-2021-w5kz4

Plug-and-Play Heterogeneous Catalysis Enabled by Metal–Organic Cage-Crosslinked Polymers

2021· preprint· en· W4200211494 on OpenAlexfundno aff
Christopher L. Brown, David Lundberg, Jessica V. Lamb, Denise Kleinschmidt, Yasmeen AlFaraj, M. Francesca Ottaviani, Nathan J. Oldenhuis, Jeremiah A. Johnson

Bibliographic record

VenueChemRxiv · 2021
Typepreprint
Languageen
FieldMaterials Science
TopicNanocluster Synthesis and Applications
Canadian institutionsnot available
FundersDivision of ChemistryNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsCatalysisPolymerHomogeneousMaterials scienceHeterogeneous catalysisHomogeneous catalysisDecoupling (probability)Organic polymerNanotechnologyMetal-organic frameworkCombinatorial chemistryChemical engineeringChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The immobilization of homogeneous catalysts onto solid supports to improve recyclability while maintaining catalytic efficiency is often a trial-and-error process limited by poor control of the local catalyst environment and a lack of modular strategies to append catalysts to support materials. Here, we introduce a “plug-and-play” heterogenous catalysis platform that overcomes these challenges. Our approach leverages the well-defined interiors of self-assembled Pd12L24 metal–organic cages/polyhedra (MOCs): through a simple combination of catalyst-ligands, polymeric ligands, and spacer ligands, we demonstrate facile self-assembly of a diverse range of polymer gels featuring endohedrally-catalyst-functionalized junctions. Through decoupling catalyst incorporation and environment from the physical properties of the support (polymer matrix), this simple strategy is shown to enhance the recyclability of various catalyst systems (e.g., TEMPO-catalyzed oxidation and Au(I)-catalyzed cyclization) and enable catalysis in environments where homogeneous catalyst analogs are not viable.

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 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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.013
GPT teacher head0.236
Teacher spread0.223 · 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

Citations0
Published2021
Admission routes1
Has abstractyes

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