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Record W2816687309 · doi:10.1139/cjc-2018-0178

A novel copper selective sensor based on ion imprinted 2-vinylpyridine polymer

2018· article· en· W2816687309 on OpenAlexvenueno aff
Murat Yolcu, Nurşen Dere

Bibliographic record

VenueCanadian Journal of Chemistry · 2018
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsnot available
Fundersnot available
KeywordsPotentiometric titrationChemistryEthylene glycol dimethacrylatePotentiometric sensorIonophoreCopperDetection limitPolymerMethacrylic acidMonomerInorganic chemistryMetal ions in aqueous solutionTitrationEthylene glycolMembraneIonChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

A novel potentiometric Cu 2+ -selective microsensor has been developed that is based upon ion imprinted 2-vinylpyridine polymer. The polymer was synthesized using Cu(II) ions, 2-vinylpyridine, methacrylic acid, and ethylene glycol dimethacrylate as a template, functional monomer, and cross-linker, respectively. The resultant polymer was used as ionophore to obtain a selective potentiometric response towards Cu(II) ions in the structure of the PVC membrane. The detection limit of the microsensor was determined to be 8.4 × 10 −7 mol/L, and its response time was considerably short (less than 15 s). The prepared microsensor exhibited a near-Nernstian response for Cu(II) ions over the concentration range of 10 −1 to 10 −6 mol/L, with a slope of 28.5 mV per decade over 2 months, and without any considerable divergence in potentials. The microsensor was effectively performed in a pH range between 4.0 and 7.0 and used as an indicator electrode in the potentiometric titration of Cu(II) ions with EDTA. The proposed microsensor has been successfully demonstrated for the determination of copper in a number of environmental water samples. The obtained potentiometric results were in good harmony with the results obtained by the AAS method.

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)0.001
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 score0.058
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.009
GPT teacher head0.211
Teacher spread0.201 · 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

Citations8
Published2018
Admission routes1
Has abstractyes

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