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Record W2736022621 · doi:10.1021/acsomega.7b00554

Acyclovir as an Ionic Liquid Cation or Anion Can Improve Aqueous Solubility

2017· article· en· W2736022621 on OpenAlexafffund
Julia L. Shamshina, O. Andreea Cojocaru, Steven P. Kelley, Katharina Bica, Sergey P. Wallace, Gabriela Gurău, Robin D. Rogers

Bibliographic record

VenueACS Omega · 2017
Typearticle
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsMcGill University
FundersCanada Excellence Research Chairs, Government of CanadaCanada Research Chairs
KeywordsCounterionChemistrySolubilityAqueous solutionIonic liquidCationic polymerizationInorganic chemistryIonIonic bondingChloridePolymer chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Six ionic liquid (IL)-forming ions (choline, tetrabutylphosphonium, tetrabutylammonium, and trimethylhexadecylammonium cations, and chloride and docusate anions) were paired with acyclovir as the counterion to form four low melting solid salts and two waxes; five of these compounds could be classified as ILs. All of the newly synthesized acyclovir ILs exhibited increased aqueous solubilities by at least 2 orders of magnitude when compared to that of neutral acyclovir. For three of the prepared compounds, the solubilities in simulated body fluids (phosphate-buffered saline, simulated gastric, and simulated intestinal fluids) were also greatly enhanced when compared to that of neutral acyclovir. Acyclovir in its anionic form was more water- or buffer-soluble than acyclovir in its cationic form, though this might be the effect of the particular ions, indicating that the solubilities can be finely tuned by proper choice of the cationic or anionic form of acyclovir and the counterion paired with it.

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 score0.004

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 communication0.0000.001
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 head0.282
Teacher spread0.260 · 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

Citations54
Published2017
Admission routes2
Has abstractyes

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