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Record W4221014618 · doi:10.3390/molecules27061869

Chelating Silicone Dendrons: Trying to Impact Organisms by Disrupting Ions at Interfaces

2022· article· en· W4221014618 on OpenAlexafffund
Miguel Meléndez‐Zamudio, Kevina Chavda, Michael A. Brook

Bibliographic record

VenueMolecules · 2022
Typearticle
Languageen
FieldMaterials Science
TopicLuminescence and Fluorescent Materials
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChelationChemistrySiliconeElastomerMetal ions in aqueous solutionDendrimerPolymer chemistryCationic polymerizationIonHydrosilylationMetalCombinatorial chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The viability of pathogens at interfaces can be disrupted by the presence of (cationic) charge and chelating groups. We report on the synthesis of silicone dendrimers and linear polymers based on a motif of hexadentate ligands with the ability to capture and deliver metal ions. Mono-, di- or trialkoxysilanes are converted in G1 to analogous vinylsilicones and then, iteratively using the Piers-Rubinsztajn reaction and hydrosilylation, each vinyl group is transformed into a trivinyl cluster at G2. The thiol-ene reaction with cysteamine or 3-mercaptopropionic acid and the trivinyl cluster leads to hexadentate ligands 3 × N-S or 3 × HOOC-S. The compounds were shown to effectively capture a variety of metals ions. Copper ion chelation was pursued in more detail, because of its toxicity. On average, metal ions form chelates with 2.4 of the three ligands in a cluster. Upon chelation, viscous oils are converted to (very) soft elastomers. Most of the ions could be stripped from the elastomers using aqueous EDTA solutions, demonstrating the ability of the silicones to both sequester and deliver ions. However, complete ion removal is not observed; at equilibrium, the silicones remain ionically crosslinked.

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 categoriesInsufficient 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 score0.996

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.009
GPT teacher head0.264
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.

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

Citations10
Published2022
Admission routes2
Has abstractyes

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