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Enregistrement W2277254775 · doi:10.1149/ma2015-02/47/1906

Novel Potentiometric Sensors Based on Nanostructured TiO2 Electrodes for Selective Determination of Biologically Relevant Transition Metals

2015· article· en· W2277254775 sur OpenAlexaff
Maryam Hariri, Sylvie Morin

Notice bibliographique

RevueECS Meeting Abstracts · 2015
Typearticle
Langueen
DomaineChemistry
ThématiqueElectrochemical Analysis and Applications
Établissements canadiensYork University
Organismes subventionnairesnon disponible
Mots-clésChromiumPotentiometric titrationSeawaterChemistryMetalElectrochemistryEnvironmental chemistryElectrodeMetal ions in aqueous solutionDetection limitRedoxInorganic chemistryChromatographyEcology

Résumé

récupéré en direct d'OpenAlex

The purpose of this work is to design and develop sensitive and selective nanostructured electrochemical sensors for the analyses of chromium (III) and iron (III) ions in different samples, such as drinking water, seawater, pharmaceutical products, soil and food. Chromium is an essential metal to life in small amounts while toxic in larger quantities. Recommended chromium intakes are provided in the dietary reference intakes (DRIs) developed by the Institute of Medicine of the National Academy of science and serum chromium levels normally range from less than 0.05 up to 0.5 micrograms/milliliter (mcg/mL) in humans. For medical and environmental reasons, it is of great importance to determine how much of this metal ion is present in such media [1]. To date, many Cr (III) selective electrodes with PVC membrane, based on various ionophores, have been introduced; however, these sensors suffer from the disadvantages of a) significant interferences from other cations, b) deviation from Nernstian behavior, c) small linear range and d) narrow pH range of operation. In addition, iron is an essential metal especially for biological systems since it plays an important role in many metabolic pathways, has an effective role in oxygen transport, storage and also in electron transport. The concentration of Fe(III) in biological systems has to be efficiently balanced as both its deficiency and excess can cause various biological disorders. Also iron is a vital element in environment systems as well as industries. The same iron limit-importance holds for environmental systems, such as fresh and seawaters, in which the iron concentration is claimed to be of crucial relevance [2]. It is therefore very important for biological, clinical, environmental and industrial purposes to efficiently detect Fe3+ion. Several methods such as AAS, ICP-MS have been reported for the determination of iron. However, these methods suffer from being time consuming as they involve multiple sample manipulations, instability if a large number of samples analysis is needed, and high cost. The proposed potentiometric sensors were prepared by anchoring glyoxalbis (2-hydroxyanil) (GBHA) as well as Desferal ionophores onto TiO2/FTO glass as Cr (III) and Fe (III)-selective electrodes, respectively. These analytical devices based on nanostructured titanium dioxide were highly sensitive due to the large surface-to-volume ratio of the nanostructure, and additionally showed excellent selectivity as they did not have the disadvantages of the above-mentioned transition metal-determination methods. One of the most important components of the proposed potentiometric sensor is the working electrode. TiO2/FTO glass substrate was chosen as the working electrode material. The TiO2 film was prepared following a standard method described elsewhere [3]. The morphology of the TiO2 films was investigated by scanning electron microscopy (SEM) to assure that the TiO2 particles formed a homogenous porous film and to monitor film thickness. The TiO2/FTO substrates were subsequently modified with the ionophores of interest (GBHA and Desferal) and the potentials of the varying concentration solutions of Cr (III) and Fe (III) were read form the voltmeter upon increasing the concentration of the test solutions. Our results demonstrated that the proposed potentiometric sensors exhibit a nernstian response for Cr (III) as well as Fe (III) ions over a wide concentration range (1.0×10-7 M-1.0×10-2 M for Cr (III)), and (1.0×10-7 - 2.2×10-1 for Fe (III)). They also showed a fast response time (< 1 min), and their potential response remained unaffected of PH in a wide range (2.5-6.3 for the Cr (III)-selective nanosensor, and 2.5-7.2 for the Fe (III)-selective nanosensor). Moreover, the performance of the sensors is discussed in terms of stability, response time, and possible interferences from other ions. For the latter studies, potentiometric selectivity coefficients were determined by the Matched Potential Method (MPM) for the alkali, transition metal and other heavy metal ions as interfering ions. According to our results, the interfering ions could not disturb the functioning of the proposed sensor electrodes significantly, indicating negligible interference in the performance of the nanostructured sensor assemblies. The fabricated sensors are to be tested for the analysis of some water samples and food materials for the determination of Cr (III) and Fe (III) ions. The designed TiO2-based nanosensors offered simplicity, rapidity, and reliability as an analytical tool. References: [1].Ganjali, M.R.; Zamani, H.A.; Norouzi, P.; Adib, M.; Rezapour, M.; Aceedy, M. Korean Chem. Soc. 26 (2005) 579. [2]. Cabantchik, Z. I.; Breuer, W.; Zanninelli, G.; Cianciulli, P. 18 (2005) 277. [3]. Sepehrifard, A.; Stublla, A.; Haftchenary, A.; Chen, A.; Potvin, P.G.; Morin, S. J. New Mat, Electrochem. Systems. 11 (2008) 281. Figure 1

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Expérimental (laboratoire) · Signal consensuel: Expérimental (laboratoire)
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,003

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,001
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0010,001
Science ouverte0,0020,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,022
Tête enseignante GPT0,262
Écart entre enseignants0,240 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeExpérimental (laboratoire)
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2015
Routes d'admission1
Résumé présentoui

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