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Record W2801524799 · doi:10.1139/cjc-2017-0492

Synthesis of a simply modified electrochemical nicotine sensor based on silver nanoparticles

2018· article· en· W2801524799 on OpenAlexvenueno aff
M.A. Ameer, Amany M. Fekry, Shereen M. Azab, Mohamed R. Shehata

Bibliographic record

VenueCanadian Journal of Chemistry · 2018
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryDetection limitCyclic voltammetryDielectric spectroscopyElectrochemical gas sensorElectrodeVoltammetryElectrochemistrySodium dodecyl sulfateCarbon paste electrodeAnalytical Chemistry (journal)NanoparticleScanning electron microscopeSilver nanoparticleNuclear chemistryChromatographyNanotechnologyMaterials science

Abstract

fetched live from OpenAlex

An innovative electrochemical nicotine modified sensor was created by chemically mixing a carbon paste with silver nano powder to prepare an Ag nanoparticle modified carbon paste electrode. Different electrochemical techniques including cyclic voltammetry, linear sweep voltammetry, and electrochemical impedance spectroscopy in both aqueous and micellar media were used. The surface morphology was also detected using scanning electron microscope and energy dispersive X-ray analysis techniques. Nicotine measurements were investigated in Britton–Robinson buffer solutions with a pH range of 2.0–8.0 containing 1.0 mmol/L sodium dodecyl sulfate. The electrode-based NIC sensor exhibited a high sensitivity in quantitative analysis, and its detection limit could be as low as 0.0036 μmol/L with linearity ranging from 0.8 μmol/L to 800 μmol/L. In addition, due to its good reproducibility, anti-interference performance, and long-term stability, the proposed sensor is capable of detecting trace levels of nicotine in urine samples and different brands of commercial cigarettes.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score1.000

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)0.0010.000

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 head0.007
GPT teacher head0.209
Teacher spread0.202 · 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 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

Citations19
Published2018
Admission routes1
Has abstractyes

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