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Record W4309972039 · doi:10.1002/ange.202216029

Cycling a Tether into Multiple Rings: Pt‐Bridged Macrocycles for Differentiated Guest Recognition, Pseudorotaxane Transformations, and Guest Capture and Release

2022· article· en· W4309972039 on OpenAlexafffund
Miguel A. Soto, Veronica Carta, Ingrid Suzana, Brian O. Patrick, Francesco Lelj, Mark J. MacLachlan

Bibliographic record

VenueAngewandte Chemie · 2022
Typearticle
Languageen
FieldChemistry
TopicSupramolecular Chemistry and Complexes
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSupramolecular chemistryRing (chemistry)ChemistryMoleculeRotaxaneRing sizeHost–guest chemistryMolecular switchReactivity (psychology)CrystallographyCombinatorial chemistryNanotechnologyStereochemistryMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Macrocycle engineering is a key topic in supramolecular chemistry. When synthesizing a ring, one can obtain either complex mixtures of macrocycles of different sizes or a single ring if a template is utilized. Here, we unite these approaches along with post‐synthetic modifications to transform a single tether into multiple rings—up to five per tether. The macrocycles contain two bridged phenylpyridine ligands that are connected through a Pt atom, which defines the rings’ shape, size, and host activity. All rings undergo redox reactions (between Pt II and Pt IV ) that allow for large conformational changes. Their reactivity, together with their host performance, is a convenient way to control the capture and release of guests, to mediate ring transformations, and to control pseudorotaxane‐to‐pseudorotaxane conversions. This novel approach could serve to assemble other libraries of small ring molecules, create cyclic polymers bridged by responsive‐at‐metal nodes, and produce processable mechanically interlocked molecules.

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)
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 score0.012
Threshold uncertainty score1.000

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 studies0.0010.000
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 head0.015
GPT teacher head0.223
Teacher spread0.208 · 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
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

Citations0
Published2022
Admission routes2
Has abstractyes

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