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Record W2514428825 · doi:10.1002/9781119053859.ch3

Anion, Cation and Ion‐Pair Recognition by Macrocyclic and Interlocked Host Systems

2016· other· en· W2514428825 on OpenAlexfundno aff
Paul D. Beer, Matthew J. Langton

Bibliographic record

Venuenot available
Typeother
Languageen
FieldChemistry
TopicMolecular Sensors and Ion Detection
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaRoyal Commission for the Exhibition of 1851
KeywordsSupramolecular chemistryMolecular recognitionIonElectrochemistryCatenaneChemistryMetal ions in aqueous solutionNanotechnologyCombinatorial chemistryMaterials scienceMoleculeElectrodeOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

This chapter reviews selected highlights from the research contributions of the Beer group to the field of macrocyclic and supramolecular chemistry. We begin by discussing the group's early interests in the area of electrochemical molecular recognition, in which metallocene redox-active hosts were designed for cation, anion and neutral guest species electrochemical sensing applications. The emerging field of anion coordination chemistry stimulated our interest in the development of transition metal based photo-active macrocyclic receptors for anion sensing and led on to the anion templated construction of mechanically bonded molecular frameworks. This strategic anion templation methodology proved notably significant in advancing interlocked host anion recognition and sensing capabilities and has recently been exploited further within the rapidly developing field of halogen bonding anion recognition. We have also made significant contributions to the fields of ion-pair recognition and metal directed self-assembly which are also highlighted in this Chapter.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.233
Threshold uncertainty score0.998

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)0.0030.000

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 head0.005
GPT teacher head0.202
Teacher spread0.197 · 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 designNot applicable
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

Citations6
Published2016
Admission routes1
Has abstractyes

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