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Record W1945220039 · doi:10.1002/cphc.201500146

Controlling the Formation of Ionic‐Liquid‐based Aqueous Biphasic Systems by Changing the Hydrogen‐Bonding Ability of Polyethylene Glycol End Groups

2015· article· en· W1945220039 on OpenAlexaff
Jorge F. B. Pereira, Kiki Adi Kurnia, Mara G. Freire, João A. P. Coutinho, Robin D. Rogers

Bibliographic record

VenueChemPhysChem · 2015
Typearticle
Languageen
FieldChemical Engineering
TopicChemical and Physical Properties in Aqueous Solutions
Canadian institutionsMcGill University
FundersFundação de Amparo à Pesquisa do Estado de São PauloMassachusetts Institute of Technology
KeywordsHydrogen bondEthylene glycolAqueous solutionEthylene oxideChemistrySolvationPEG ratioPolyethylene glycolMiscibilityPolymer chemistryEtherIonic bondingIonic liquidPolymerInorganic chemistryChemical engineeringOrganic chemistryMoleculeIonCopolymerCatalysis

Abstract

fetched live from OpenAlex

The formation of aqueous biphasic systems (ABS) when mixing aqueous solutions of polyethylene glycol (PEG) and an ionic liquid (IL) can be controlled by modifying the hydrogen-bond-donating/-accepting ability of the polymer end groups. It is shown that the miscibility/immiscibility in these systems stems from both the solvation of the ether groups in the oxygen chain and the ability of the PEG terminal groups to preferably hydrogen bond with water or the anion of the salt. The removal of even one hydrogen bond in PEG can noticeably affect the phase behavior, especially in the region of the phase diagram in which all the ethylene oxide (EO) units of the polymeric chain are completely solvated. In this region, removing or weakening the hydrogen-bond-donating ability of PEG results in greater immiscibility, and thus, in a higher ability to form ABS, as a result of the much weaker interactions between the IL anion and the PEG end groups.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.022
GPT teacher head0.229
Teacher spread0.207 · 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 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

Citations50
Published2015
Admission routes1
Has abstractyes

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