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Record W3199890670 · doi:10.1002/adfm.202105453

Hemin‐Doped, Ionically Crosslinked Silicone Elastomers with Peroxidase‐Like Reactivity

2021· article· en· W3199890670 on OpenAlexafffund
Cody B. Gale, Michael A. Brook

Bibliographic record

VenueAdvanced Functional Materials · 2021
Typearticle
Languageen
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsHeminElastomerMaterials scienceSelf-healing hydrogelsPolymer chemistryMicelleAqueous solutionPolymerChemical engineeringOrganic chemistryChemistryComposite material

Abstract

fetched live from OpenAlex

Abstract The encapsulation of hemin in polymer systems represents a powerful tool for the preparation of synthetic enzymes. Reported systems largely focus on the creation of hydrogels or microreactors that require a degree of modification to the hemin molecule itself. The straightforward synthesis of hydrophobic hemin‐aminosilicone crosslinked silicone elastomers is described with peroxidase like reactivity. Elastomers are formed from purely ionic interactions between commercial aminoalkylsilicones and native hemin, which acts as a crosslinker. Elastomers prepared from ethylene diamine‐based silicones are robust elastomers in which hemin is retained, while hemin can leach monoamine analogues into the adjacent aqueous environment. The system is easily tunable, allowing for precise control of the hemin concentration and the physical properties of the elastomer. Upon exposure to peroxide‐containing solutions, elastomers readily oxidize the substrate 3′,3′,5′,5′‐tetramethylbenzidine at the interface. Elastomers containing excess amine show greater catalytic efficiency due to the coordination of the amines to the iron center of hemin, which is known to be activating. The reaction can be followed using UV‐visible spectroscopy; rates of reaction and Michaelis–Menten parameters are derived.

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 score0.906

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)0.0010.000

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 head0.006
GPT teacher head0.187
Teacher spread0.181 · 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

Citations13
Published2021
Admission routes2
Has abstractyes

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