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Record W3185640514 · doi:10.1021/acs.macromol.1c00586

Enthalpy of the Complexation in Electrolyte Solutions of Polycations and Polyzwitterions of Different Structures and Topologies

2021· article· en· W3185640514 on OpenAlexaff
Jukka Niskanen, Alexander J. Peltekoff, Jean-Richard Bullet, Benoît H. Lessard, Françoise M. Winnik

Bibliographic record

VenueMacromolecules · 2021
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsUniversité de MontréalUniversity of Ottawa
Fundersnot available
KeywordsEnthalpyElectrolyteNetwork topologyPolyelectrolyteThermodynamicsChemistryChemical engineeringPolymer scienceMaterials sciencePhysical chemistryOrganic chemistryPolymerPhysicsComputer scienceEngineering

Abstract

fetched live from OpenAlex

Our understanding of the intricate complexation behavior of polyzwitterions with polycations is less than that of polyanion–polycation complexes. In addition, the effect of the topology of polyzwitterions on the complexation of polyzwitterions with polycations is also unclear. We investigated the complexation of methacrylate-based linear, star, and branched poly(sulfobetaine methacrylate)s (PB) with the cationic poly(methacryl oxyethyl trimethylammonium chloride) (PMOTAC), as well as the complexation of linear imidazolium-based polyzwitterions and polycations, by isothermal titration calorimetry (ITC) in saline media. The complexation enthalpies increased with increasing salt concentration and molecular weight up to 0.25 M NaCl. A low degree of branching had little effect on the complexation, whereas highly branched PB did not form complexes. The obtained complexes had nonstoichiometric composition, with high enthalpies per mole of injected polycation due to additional water being released by cascading complexation. Understanding the complexation behavior of polyzwitterions with polycations provides alternatives to traditional polyanion–polycation complexes.

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 score0.177
Threshold uncertainty score0.201

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 head0.011
GPT teacher head0.228
Teacher spread0.217 · 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

Citations20
Published2021
Admission routes1
Has abstractyes

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