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Record W4402614944 · doi:10.3390/surfaces7030049

Supramolecular Chemistry of Polymer-Based Molecular Tweezers: A Minireview

2024· article· en· W4402614944 on OpenAlexafffund
Bahareh Vafakish, Lee D. Wilson

Bibliographic record

VenueSurfaces · 2024
Typearticle
Languageen
FieldChemistry
TopicSupramolecular Chemistry and Complexes
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaUniversity of Saskatchewan
KeywordsSupramolecular chemistryMolecular tweezersNanotechnologyOptical tweezersChemistryTweezersSupramolecular polymersMaterials sciencePhysicsMoleculePhysical chemistryOrganic chemistryOptics

Abstract

fetched live from OpenAlex

Polymer-based molecular tweezers have emerged as a prominent research area due to their enhanced ability to form host–guest complexes, driven by advancements in their design and synthesis. The impact of the spacer structure on the tweezers is predominant. They can be rigid, flexible, and stimuli-responsive. Herein, a new generation of molecular tweezers is introduced as polymer-based molecular tweezers. The integration of molecular tweezers onto biopolymers has significantly expanded their potential applications, making them promising candidates, especially in drug delivery, owing to their biocompatibility, adaptive structural features, and versatile interaction capabilities. The unique structure of polymer-based molecular tweezers, particularly when integrated with biopolymers, creates a unique nano-environment that enhances their interaction with guest molecules. This minireview focuses on the synthesis and applications of polymer-based molecular tweezers and examines how the incorporation of various spacers affects their binding affinity and specificity. These features highlight the advancement of these polymer-based systems, emphasizing their potential applications, particularly in drug delivery, water treatment technology, and future research opportunities.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.252
Teacher spread0.239 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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