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Record W2970061819 · doi:10.1021/acsomega.9b01953

Impact of Counter Ions of Cationic Monomers on the Production and Characteristics of Chitosan-Based Hydrogel

2019· article· en· W2970061819 on OpenAlexafffund
Solmaz Heydarifard, Weijue Gao, Pedram Fatehi

Bibliographic record

VenueACS Omega · 2019
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsLakehead University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for InnovationNorthern Ontario Heritage Fund Corporation
KeywordsCationic polymerizationChitosanMonomerSelf-healing hydrogelsPolymer chemistryPolymerizationChemistryChemical engineeringNuclear chemistryOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

Chitosan-based hydrogel has received considerable interests because of its appealing properties and applications in many areas. The primary objective of this work was to produce novel cationic chitosan-based hydrogels via polymerizing chitosan with two cationic monomers of the same structure but with different counter ions [2-(methacryloyloxy)ethyl]trimethylammonium methyl sulfate (METMS) and [2-(methacryloyloxy)ethyl]trimethylammonium chloride (METAC). Polymerization of chitosan with the cationic monomers performed under the conditions of 50 °C, 5 h, 7 pH, and 2/1 mol/mol monomer/chitosan led to chitosan-METMS and -METAC with the cationic charge densities of 3.22 and 2.88 mequiv/g, respectively. Elemental analysis, gel permeation chromatography, Fourier transform infrared, X-ray diffraction, and differential scanning calorimetry analyses were used to confirm the impact of counter ions of cationic monomers (i.e., polarizability of monomers) on their polymerization performance and the characteristics of induced chitosan-based hydrogels. Also, the results of this work postulated that the counter ions associated with the monomers could dramatically impact the water uptake and swelling properties of the generated chitosan-based hydrogels as well as their performance in adsorbing an anionic dye from a simulated solution.

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 score0.076
Threshold uncertainty score0.148

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 head0.016
GPT teacher head0.261
Teacher spread0.246 · 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

Citations17
Published2019
Admission routes2
Has abstractyes

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