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Record W2999066200 · doi:10.1021/acsomega.9b03690

Charge Matters: Electrostatic Complexation As a Green Approach to Assemble Advanced Functional Materials

2020· review· en· W2999066200 on OpenAlexafffund
Caio G. Otoni, Marcos Vinícius Aquino Queirós, Júlia Bonesso Sabadini, Orlando J. Rojas, Watson Loh

Bibliographic record

VenueACS Omega · 2020
Typereview
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaH2020 European Research CouncilFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsPolyelectrolyteBiomoleculeElectrostaticsColloidRheologySpinningNanotechnologyAdhesionIonic strengthChemical physicsChemistryMaterials scienceChemical engineeringPolymer chemistryPolymerOrganic chemistryAqueous solutionPhysical chemistryComposite material

Abstract

fetched live from OpenAlex

We report on electrostatically complexed materials bearing advanced functions that are not possible for other assemblies. The fundamentals of electrostatic association between oppositely charged polyelectrolytes and colloidal particles are introduced together with the conditions needed for complexation, including those related to ionic strength, pH, and hydration. Related considerations allow us to control the properties of the formed complexes and to develop features such as self-healing and underwater adhesion. In contrast to assemblies produced by typical hydrophobic and chemical interactions, electrostatic complexation leads to reversible systems. A state-of-the-art account of the field of electrostatically complexed materials is provided, including those formed from biomolecules and for salt-controlled rheology, underwater adhesiveness, and interfacial spinning. Finally, we present an outlook of electrostatic complexation from the colloidal chemistry perspective.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.070
GPT teacher head0.339
Teacher spread0.269 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations42
Published2020
Admission routes2
Has abstractyes

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