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Record W4388419228 · doi:10.26434/chemrxiv-2023-0bbgm

Selective Recognition of Quaternary Ammonium Cations

2023· preprint· en· W4388419228 on OpenAlexfundno aff
Callum S. Begg, Mark P. Walsh, Joseph M. Phelps, Emma H. Wolpert, Emanuella F. Fiandra, Emma Winful, Abby R. Haworth, Dmitry S. Yufit, Karen E. Johnston, Clare S. Mahon, Kim E. Jelfs, Matthew O. Kitching

Bibliographic record

VenueChemRxiv · 2023
Typepreprint
Languageen
FieldChemistry
TopicMolecular Sensors and Ion Detection
Canadian institutionsnot available
FundersEngineering and Physical Sciences Research CouncilLaidlaw FoundationDurham UniversityRoyal Society
KeywordsAmmoniumCounterionSelectivityIsostructuralChemistrySupramolecular chemistryMolecular recognitionAqueous solutionCombinatorial chemistryInorganic chemistryMoleculeIonCrystallographyOrganic chemistryCrystal structure

Abstract

fetched live from OpenAlex

Selective recognition of ammonium cations fundamentally relies on their degree of substitution. In biological systems, proteins can preferentially bind more substituted ammonium cations over less substituted homologues. To date, a general methodology to mimic this behavior remains elusive as synthetic hosts principally observe the inverse order (i.e., 1°>2°>3°>4°). Here we show that, through combining supramolecular recognition with solid-phase abstraction, we can overturn the canonical order of synthetic receptor selectivity across a diverse range of ammonium cation scaffolds. Quaternary ammonium cations access a lower energy solid-state than tertiary counterparts through multipoint binding to an adaptive array of isostructural BINOL·counterion networks. The preferential abstraction of quaternary ammonium cations from mixtures of homologous cations proceeds under thermodynamic control with excellent selectivity and remains operative even under aqueous conditions.

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 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 score0.011
Threshold uncertainty score0.877

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)0.0000.000

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 head0.041
GPT teacher head0.265
Teacher spread0.224 · 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.

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

Citations2
Published2023
Admission routes1
Has abstractyes

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