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Record W4412165803 · doi:10.1021/acs.jpcc.5c02283

Electrochemical Quantitation of Supramolecular Complexation upon “Clicking” Cucurbit[7]uril Hosts on the Surface: From Drug Excipients to Steroids

2025· article· en· W4412165803 on OpenAlexafffund
Yuguo Zhang, Qi Lin, Jia Chen, Yujing Guo, Ruibing Wang, Hua‐Zhong Yu

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2025
Typearticle
Languageen
FieldChemistry
TopicSupramolecular Chemistry and Complexes
Canadian institutionsSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaUniversidade de Macau
KeywordsSupramolecular chemistryElectrochemistryChemistryDrugCombinatorial chemistryCucurbiturilNanotechnologyMaterials sciencePharmacologyOrganic chemistryMedicineElectrodePhysical chemistryMolecule

Abstract

fetched live from OpenAlex

Cucurbit[7]uril (CB[7]), which is highly favorable for binding ferrocene (Fc) derivatives in solution and on the surface, has stood out as a promising electrochemical sensing motif due to its readily quantifiable redox responses. Given that understanding the complexation between CB[7] and drug candidates or steroids is crucial for pharmaceutical and steroidal applications, we report herein our electrochemical investigation to quantitate the affinity between surface-bound CB[7] and nonredox-active guests through competitive binding against ferrocene methanol (FcMeOH). The immobilization of the supramolecular host relies on the formation of azide-terminated alkanethiolate self-assembled monolayers (SAMs) on gold and subsequent copper(I)-catalyzed azide–alkyne cycloaddition (CuAAC) with alkyne-modified CB[7]. By incubating FcMeOH and subsequently a drug/steroid molecule, the competitive binding between CB[7]@drug/steroid and CB[7]@ferrocene complexes on the surface can be quantified with cyclic voltammetry, despite the nonredox-active nature of drug or steroid compounds. The formation constants of CB[7]@drug/steroid complexes are obtained with high accuracy, and a quantitative assay method is developed by establishing a linear relationship between the electrochemical signal and the guest concentration. Not only are the determined binding constants consistent with the literature values from conventional instrumental analyses (e.g., HPLC), but the obtained limits of detection (LODs) are also remarkable (i.e., from sub to low μM range).

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 score0.004
Threshold uncertainty score0.788

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 science0.0010.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.261
Teacher spread0.250 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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