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Record W4398765308 · doi:10.1039/d4sc01409j

Aqueous photo-RAFT polymerization under ambient conditions: synthesis of protein–polymer hybrids in open air

2024· article· en· W4398765308 on OpenAlexfundno aff
Arman Moini Jazani, Hironobu Murata, Martin Cvek, Anna Lewandowska-Andrałojć, Roksana Bernat, Kriti Kapil, Xiaolei Hu, Ferdinando De Luca Bossa, Grzegorz Szczepaniak, Krzysztof Matyjaszewski

Bibliographic record

VenueChemical Science · 2024
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsnot available
FundersFonds de recherche du Québec – Nature et technologiesNarodowa Agencja Wymiany AkademickiejNarodowe Centrum NaukiFundacja na rzecz Nauki PolskiejDivision of Materials ResearchMinisterstvo Školství, Mládeže a TělovýchovyUS-UK Fulbright CommissionNational Science Foundation
KeywordsRaftChain transferPolymerizationReversible addition−fragmentation chain-transfer polymerizationAqueous solutionChemistryPolymerPhotochemistryFragmentation (computing)Radical polymerizationPyruvic acidPolymer chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

interaction with the excited state RAFT agent. This method enabled the polymerization of a range of hydrophilic monomers in reaction volumes up to 250 mL, eliminating the need to remove radical inhibitors from the monomers. In addition, photo-RAFT polymerization using SP allowed for the facile synthesis of protein-polymer hybrids in short reaction times (<1 h), low organic content (≤16%), and without rigorous deoxygenation and the use of transition metal photocatalysts. Enzymatic studies of a model protein (chymotrypsin) showed that despite a significant loss of protein activity after conjugation with RAFT chain transfer agents, the grafting polymers from proteins resulted in a 3-4-fold recovery of protein activity.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.275
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), 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

Citations21
Published2024
Admission routes1
Has abstractyes

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