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Record W2048299653 · doi:10.1021/ac7022876

Noncovalent Modification of Carbon Nanotubes with Ferrocene−Amino Acid Conjugates for Electrochemical Sensing of Chemical Warfare Agent Mimics

2008· article· en· W2048299653 on OpenAlexafffund
Mohammad A. K. Khan, Kağan Kerman, Michael W. P. Petryk, Heinz‐Bernhard Kraatz

Bibliographic record

VenueAnalytical Chemistry · 2008
Typearticle
Languageen
FieldMaterials Science
TopicCarbon Nanotubes in Composites
Canadian institutionsUniversity of SaskatchewanWestern University
FundersOntario Ministry of Research and Innovation
KeywordsChemistryFerroceneCarbon nanotubeBoronic acidElectrochemistryCombinatorial chemistryIndium tin oxideCyclic voltammetryChemical modificationSurface modificationConjugateAqueous solutionInorganic chemistryOrganic chemistryNanotechnologyPolymer chemistryElectrodeLayer (electronics)Materials science

Abstract

fetched live from OpenAlex

The electrochemical detection of chemical warfare agent (CWA) mimics was explored using multiwalled carbon nanotubes (MWCNTs) on indium tin oxide (ITO) surfaces in connection with ferrocene-amino acid conjugates. Various ferrocene-amino acid conjugates were synthesized and utilized as the recognition layer for the detection of CWA mimics. The ferrocene-amino acid conjugates were noncovalently attached to the pretreated MWCNTs on the ITO surface and reacted with CWA mimics, upon which the electrical properties of the MWCNTs and the Fc group were affected significantly. Alternating current voltammetry and capacitance-based detection offered large dynamic ranges for the detection of methylphosphonic acid, diethyl cyanophosphonate, ethylmethylphosphonate, and pinacolyl methylphosphonate in water. Electrochemical measurements showed dramatic changes upon the electrostatic interaction between the CWA mimics and the ferrocene-amino acid conjugates immobilized on MWCNTs on ITO surfaces. Electrochemical sensing in connection with MWCNTs is shown to be a promising analytical tool for the trace-level detection of CWA mimics in aqueous solutions.

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 score0.015
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.023
GPT teacher head0.256
Teacher spread0.233 · 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

Citations65
Published2008
Admission routes2
Has abstractyes

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