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Record W3024162958 · doi:10.1149/ma2020-01512835mtgabs

Voltammetric Detection of Dihydroxybenzene Isomers By Modified Graphite Paste Electrode with Prussian Blue-Nanosized Polyaniline Hybrid

2020· article· en· W3024162958 on OpenAlexaffabout
Bhargav R. Patel, Meissam Noroozifar, Kağan Kerman

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldMaterials Science
TopicConducting polymers and applications
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsPrussian bluePolyanilineElectrocatalystConductive polymerDifferential pulse voltammetryAnilineCyclic voltammetryMaterials scienceChemistryElectrochemistryInorganic chemistryChemical engineeringPolymerOrganic chemistryElectrodePolymerization

Abstract

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The dihydroxybenzene isomers (DHB); catechol (1, 2-dihydroxybenzene, CC), resorcinol (1, 3-dihydroxybenzene, RC) and hydroquinone (1,4-dihydroxybenzene, HQ) are well-known organic pollutants and are widely distributed in the environment. DHB isomers are frequently used as starting materials and industrial reagents in various industries for production of plastic, tanning, pharmaceuticals, paint, paper, cosmetics and rubber. 1–4 Furthermore, these isomers are also found as important side products from the industrial effluents of photo-processing companies, oil refineries, coal-tar, steel, paper and pulp mills in concentrations ranging between 1 to 1000 ppm. 4,5 Due to their toxicity and the health risks associated with their use, national environmental agencies from United States and European Union have categorized them as primary wastewater pollutants. 6 In addition, Health Canada has also listed them under the List of Prohibited and Restricted Cosmetic Ingredients (Cosmetic Ingredient Hotlist). 7 As an electroactive polymer, polyaniline (PANI) is known to have good environmental stability, conductivity and redox properties. It has also been recently used in electrochemical biosensors due to the ease of synthesis, cheap cost and simple doping mechanisms. Prussian blue (PB) is another redox substance which also possesses good conductivity and unique electrocatalytic characteristics. In this study, modified graphite paste electrode (GPE) with PB doped nanosized-PANI hybrid (PB@n-PANI/GPE) was used for simultaneous detection of DHB isomers using differential pulse voltammetry (DPV). Prussian blue doped nanosized-polyaniline (PB@n-PANI) was synthesized in two-steps, firstly, aniline and ferric (III) chloride salt reacted through in situ mechano-chemical reactions to produce nanosized-PANI polymer. Secondly, potassium hexacyanoferrate (II) solution was added to the product of step one and the polymer was doped by PB in a solid-state and template-free technique to generate PB@n-PANI. The synthesized hybrid was then characterized by different microscopic and spectroscopic techniques. The PB@n-PANI modified GPE exhibited three well-separated oxidation peaks at potentials 0.24, 0.36 and 0.74 V for HQ, CC and RC, respectively (Fig. 1). The analytical performance of the PB@n-PANI/GPE has been studied for simultaneous determination of HQ, CC and RS in different real sample matrices with satisfactory recoveries. Furthermore, different electrochemical techniques such as cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and chronoamperometry have been used to perform electrochemical surface characterization of the modified GPE. The results from CV and ESI were used to evaluate the charge transfer properties of the PB@n-PANI/GPE and the results from chronoamperometric studies were used to calculate the diffusion coefficients of the DHB isomers. In addition, active surface area of the modified GPE and the standard heterogenous rate constant for the DHB isomers have also been calculated. Overall, we have demonstrated that the presence of PB and n-PANI enhances the redox-active surface area and plays a synergistic role in improving the electrocatalytic activity of the PB@n-PANI/GPE. Therefore, PB@n-PANI/GPE is a promising analytical platform for simultaneous detection of DHB isomers. References 1. C. Terashima, T. N. Rao, B. V. Sarada, D. A. Tryk, and A. Fujishima, Anal. Chem. , 74 , 895–902 (2002). 2. W. Xiao and D. Xiao, Talanta , 72 , 1288–1292 (2007). 3. J. Yu, W. Du, F. Zhao, and B. Zeng, Electrochim. Acta , 54 , 984–988 (2009). 4. B. Nasr, G. Abdellatif, P. Cañizares, C. Sáez, J. Lobato, and M. A. Rodrigo, Environ. Sci. Technol. , 39 , 7234–7239 (2005). 5. W. Phutdhawong, S. Chowwanapoonpohn, and D. Buddhasukh, Anal. Sci. , 16 , 1083–1084 (2000). 6. T. Xie, Q. Liu, Y. Shi, and Q. Liu, J. Chromatogr. A , 1109 , 317–321 (2006). 7. Health Canada, (2018) https://www.canada.ca/en/health-canada/services/consumer-product-safety/cosmetics/cosmetic-ingredient-hotlist-prohibited-restricted-ingredients/hotlist.html. Figure 1

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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 score0.002
Threshold uncertainty score0.821

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 studies0.0000.000
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 head0.012
GPT teacher head0.216
Teacher spread0.204 · 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".

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Citations1
Published2020
Admission routes2
Has abstractyes

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