Qualification and Quantification Analyses of Iron (III) Ions in a Variety of Samples, Using Nano-Structured Metal-Oxide Based Electrochemical Sensors
Notice bibliographique
Résumé
The purpose of this work is to design and develop a sensitive and selective nanostructured electrochemical sensor for the analyses of iron (III) ions in different samples, such as drinking water, seawater, pharmaceutical products, soil and food. 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 [1]. 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. Fabrication of new ion selective electrochemical and optical electrodes with high selectivity and sensitivity, wide linear concentration range, long lifetime, good reproducibility and low cost has been always of a great interest to the scientists. In this work, we have successfully designed a nanostructured electrochemical ion-selective sensor by anchoring the Desferal ion-receptors onto the surface of TiO2/FTO glass substrates, and applied these frameworks as iron-selective electrodes. 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 iron-determination methods. The first component of the proposed potentiometric sensor is the working electrode. TiO2/FTO glass substrates were chosen as the working electrode materials. The TiO2 film was prepared following a standard method described elsewhere [2]. 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 ionophore of interest (desferal) and the potentials of the varying concentration solutions of 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 Fe (III) ions over a wide concentration range (1.0×10-1 M to 1.0×10-7 M). They also showed a fast response time (=< 1 min), and their potential response remained unaffected of PH in a wide range (2.5-7.2). Moreover, the performance of the sensors was studied 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 second component of the designed ion-selective potentiometric sensors is the ionophore or the ion receptor. In this work Desferal was used as the ionophore, which was responsible for complexation with Fe (III) ions, and therefore producing a signal (voltage). Self-assembled monolayers (SAMs) being chemisorbed in an ordered manner on surfaces such as metal-oxides (TiO2), provide a unique way to alter the properties of a surface at will. We have introduced a new method to increase the immobilization of the Desferal ionophores onto the TiO2 electrodes using self-assembled monolayers (SAMs) of phosphonic acids with different alkyl chain-lengths. These alkylphosphonate SAMs covalently bind to the surface of TiO2 from their head groups; the chemical anchoring of the alkylphosphonates to TiO2is then characterized by several changes in the FTIR spectrum. The surface modifications were performed with the aim of increasing the stability as well as the life time of our fabricated nano-sensors. The fabricated iron-selective nanosensors are to be tested for the analysis of some water samples and food materials for the determination Fe (III) ions. The designed TiO2-based nanosensors offered simplicity, rapidity, and reliability as an analytical tool. References [1]. Cabantchik, Z. I.; Breuer, W.; Zanninelli, G.; Cianciulli, P. 18 (2005) 277. [2]. Sepehrifard, A.; Stublla, A.; Haftchenary, A.; Chen, A.; Potvin, P.G.; Morin, S. J. New Mat, Electrochem. Systems. 11 (2008) 281. Figure 1
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Prédiction machine sur la base complète
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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».