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Record W4285038110 · doi:10.1039/d2an00645f

Pico-molar electrochemical detection of ciprofloxacin at composite electrodes

2022· article· en· W4285038110 on OpenAlexafffund
Vikram Singh, Sabine Kuss

Bibliographic record

VenueThe Analyst · 2022
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsUniversity of Manitoba
FundersCanada Research Coordinating CommitteeResearch Manitoba
KeywordsX-ray photoelectron spectroscopyCyclic voltammetryMaterials scienceScanning electron microscopeElectrochemical gas sensorElectrodeCarbon nanotubeElectrochemistryTransmission electron microscopyChemical engineeringAnalytical Chemistry (journal)NanotechnologyChemistryChromatographyComposite material

Abstract

fetched live from OpenAlex

and in tap water. The designed sensor was constructed using oxygen functionalized carbon nanotubes (OCNTs), a layer of conductive polymer (polydopamine; PDA), and electro-deposited silver nanoparticles (Ag-NPs) at a glassy carbon electrode. Double-pulse electro-deposition was employed, as it offers numerous advantages including quick analysis time (s, ms), high reproducibility, and does not require expensive complexing agents, even at room temperature. The fabricated chemical sensor was fully optimized and characterized using physiochemical and electrochemical techniques, such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectroscopy (XPS), and voltammetry. Electrochemical measurements demonstrate high applicability of the chemical sensor towards the detection of Cip in the pico-molar concentration range. Detection and quantification levels were determined and the stability of the sensor was assessed. The obtained results demonstrate the potential of the OCNTs-PDA-Ag sensor to detect Cip in environmental samples.

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 score0.010
Threshold uncertainty score0.816

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.213
Teacher spread0.207 · 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

Citations7
Published2022
Admission routes2
Has abstractyes

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