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Record W4382064756 · doi:10.1021/acs.macromol.3c00250

Comparison of Mechano- and PhotoATRP with ZnO Nanocrystals

2023· article· en· W4382064756 on OpenAlexfundno aff
Martin Cvek, Arman Moini Jazani, Julian Sobieski, Thaiskang Jamatia, Krzysztof Matyjaszewski

Bibliographic record

VenueMacromolecules · 2023
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsnot available
FundersFonds de recherche du QuébecMinisterstvo Školství, Mládeže a TělovýchovyDivision of Materials ResearchCarnegie Mellon University
KeywordsDeoxygenationAcrylateNanocrystalMonomerZincPhotochemistryPolymerizationMaterials scienceCopolymerPhotocatalysisLigand (biochemistry)PolymerChemical engineeringPolymer chemistryChemistryNanotechnologyCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Zinc oxide (ZnO) was previously reported as an excellent cocatalyst for mechanically controlled atom transfer radical polymerization (mechanoATRP), but its photocatalytic properties in photoinduced ATRP (photoATRP) have been much less explored. Herein, well-defined ZnO nanocrystals were prepared via microwave-assisted synthesis and applied as a heterogeneous cocatalyst in mechano- and photoATRP. Both techniques yielded polymers with outstanding control over the molecular weight, but ZnO-cocatalyzed photoATRP was much faster than analogous mechanoATRP (conversion of 91% in 1 h vs 54% in 5 h). The kinetics of photoATRP was tuned by loadings of ZnO nanocrystals. PhotoATRP with ZnO did not require any excess of ligand versus Cu, in contrast to mechanoATRP, requiring an excess of ligand, acting as a reducing agent. ZnO-cocatalyzed photoATRP proceeded controllably without prior deoxygenation, since ZnO was involved in a cascade of reactions, leading to the rapid elimination of oxygen. The versatility and robustness of the technique were demonstrated for various (meth)acrylate monomers with good temporal control and preservation of end-group functionality, illustrated by the formation of tailored block copolymers.

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 score0.005
Threshold uncertainty score0.340

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 head0.015
GPT teacher head0.270
Teacher spread0.255 · 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

Citations23
Published2023
Admission routes1
Has abstractyes

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