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Record W2988641454 · doi:10.1039/9781788015967-00031

Host–Guest Chemistry of the Cucurbituril Family

2019· book-chapter· en· W2988641454 on OpenAlexaff
Shengke Li, Donal H. Macartney, Ruibing Wang

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldChemistry
TopicSupramolecular Chemistry and Complexes
Canadian institutionsQueen's University
Fundersnot available
KeywordsCucurbiturilHost–guest chemistrySupramolecular chemistryChemistryCationic polymerizationMoleculeStereochemistryCrystallographyHydrophobic effectBiomoleculePolymer chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Chapter 3 describes the supramolecular host–guest chemistry of unmodified cucurbit[n]urils (single-cavity CB[n], n=5–8, 10, and twisted tCB[n], n=13–15). The ranges of host–guest complexes formed with the single-cavity and twisted CB[n] hosts are surveyed, and their applications, notably in the field of drug and biomolecule recognition, are described. With inner cavity volumes ranging from 68 to 691 Å3, the single-cavity CB[n] hosts exhibit unique selectivity for differently sized guest molecules and ions: the CB[5] can bind small gas molecules, CB[6] binds aliphatic chains, CB[7] can include aromatic and polycyclic guests, CB[8] allows for the binding of two complementary guests, while CB[10] can bind other small host molecules, as well as transition metal complexes. The host–guest complexation is driven primarily by the hydrophobic effect in terms of the release of high-energy waters from the cavity upon guest inclusion, along with favorable ion–dipole interactions between the polar portals and charged centers on cationic guests. The ultra-high stability constants (up to 1017 M−1) observed with certain dicationic guests and CB[7] result from optimal packing of the hydrophobic cavity with the guest core and the placement of an ammonium group adjacent to each portal.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.386
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.000

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 head0.013
GPT teacher head0.196
Teacher spread0.183 · 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.

Study designBench or experimental
Domainnot available
GenreOther

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

Citations1
Published2019
Admission routes1
Has abstractyes

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