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Record W4400917733 · doi:10.1021/acs.biomac.4c00547

Compact Polyelectrolyte Complexes of Poly(<scp>l</scp>-Lysine) and Anionic Polysaccharides

2024· article· en· W4400917733 on OpenAlexafffund
Jaehak Yu, Burak Tavsanli, Micah J. Tamminga, Elizabeth R. Gillies

Bibliographic record

VenueBiomacromolecules · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicHydrogels: synthesis, properties, applications
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPolyelectrolytePolysaccharideLysineChemistryPolymer chemistryChemical engineeringPolymerBiochemistryOrganic chemistryAmino acid

Abstract

fetched live from OpenAlex

Compact polyelectrolyte complexes (CoPECs) can exhibit mechanical properties similar to those of biological tissues and other interesting properties, such as self-healing. To date, a variety of CoPECs prepared from synthetic polyelectrolytes have been investigated, but there are very few examples based entirely on biopolymers. We describe here an investigation of CoPECs based on poly(l-lysine) (PLL) with sodium hyaluronate (HA) and alginate (Alg). A 2:1 ratio of cation:anion and 0.25 M NaBr was beneficial for the formation of viscoelastic PLL-HA CoPECs, with the favorable ratio attributed to the spacing of carboxylates on HA being one every two saccharide units. In contrast, 1.0 M NaBr and a 1:1 ratio were better for PLL-Alg CoPECs. Both CoPECs swelled or retained a constant volume when immersed in hypertonic media, but contracted in hypotonic media. The loading of molecules into the PLL-HA (2:1) CoPECs was investigated. Higher loadings were achieved for anionic molecules compared to cations, presumably due to the excess cationic binding sites on the networks. The times required for full release of the molecules ranged from less than 2 h for neutral paracetamol to about 48 h for crystal violet and diclofenac.

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 score0.051
Threshold uncertainty score0.989

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 head0.253
Teacher spread0.238 · 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

Citations2
Published2024
Admission routes2
Has abstractyes

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