Modifying Nanocarbon Films with Switchable Dopant Molecules for the Detection of Aqueous Permanganate
Notice bibliographique
Résumé
Monitoring disinfectants is essential to maintain water free from pathogens. Permanganate (MnO 4 - ) is one of the commonly used disinfectants and is applied as potassium permanganate (KMnO 4 ) solution in drinking water and wastewater treatment plants. It is used as a pre-oxidant at the beginning of the treatment process for the control of reduced iron (II) and manganese (II) concentration. 1,2 Though dilute KMnO 4 solutions are sometimes used as topical antiseptics and astringents, higher concentration (over 200 mg/L) can cause gastrointestinal distress. 3 A Do Not Consume concentration of 7 mg/L KMnO 4 is recommended based on clinical experience. 3 Therefore, to ensure the proper level of permanganate during and after the water treatment process, it is necessary to monitor the concentration of permanganate. Current methods for permanganate detection focus on spectrophotometric measurements (direct and indirect). The direct method involves no additional chemical, however, lacks the suitability for the samples with less than 0.75 mg/L of MnO 4 - . 4 Indirect methods provide better sensitivity but require the need for reagents like NaI and ABTS (unstable). 4 A sensitive single-particle-detection (SPD) method has been reported recently which uses dark-field optical microscopy on graphene nanoplatelets silver (GNP@Ag) core-shell nanoparticles, but, this method is quite complex and requires sample preparation before analysis. 1 Moreover, these methods are unsuitable for the implementation in a treatment plant for the online monitoring of disinfectant. To tackle this problem, we introduced a chemiresistive sensing platform capable of quantifying permanganate in aqueous media. Chemiresistive sensors are becoming popular in sensing due to their low cost, easy fabrication technique, and the ability to detect different analyte using appropriate ligand. 5 Chemiresisive sensors have been reported before for the continuous measurement of free chlorine using carbon nanotube (CNT) substrate doped with redox-active aniline oligomer named phenyl capped aniline tetramer (PCAT). 6 The sensor consists of two parallel electrodes connected by a layer of carbon nanotubes. Oligoanilines are immobilized on the substrate through noncovalent interactions. Oligoanilines are known to dope CNTs differently depending on which one of the three oxidation states they are in (fully reduced, half oxidized, and fully oxidized). 7 , 8 Oxidation of this oligoaniline (PCAT) attached to the surface of CNT by permanganate molecule leads to a change in the oxidation state of the oligoaniline; this change in oxidation state in the molecule changes the doping characteristics of CNT leading to a change in resistance of the CNT film. This resistance change can be used to quantify the concentration of permanganate. 9 This sensor can be reset with fresh water and reused for further analysis. Here we present a chemiresistive sensing array that can detect permanganate in the aqueous environment. We have functionalized the nanocarbon substrate with five different redox-active molecules. Four of them are pH-responsive and one is not responsive to the pH range used for the analysis. The sensors were tested for the range of 0.17 mg/L to 1.33 mg/L of MnO 4 - at a pH range of 6.5 to 9.5. These redox-active molecules create active sites on the substrate, and when in contact with the analyte, dopant molecules attached on the surface react with analytes and give responses. These responses vary in magnitude depending on pH and the redox-active molecule. The sensor responses at different pHs were then analyzed using principal component analysis (PCA) to identify the pH of the permanganate solution. PCA analysis shows a clear separation of different pH solutions. We have therefore demonstrated a nanocarbon based sensing array that is not only capable of continuously measuring the low concentration of permanganate, but also the pH of the aqueous media. References: Z. Ye et al., Anal. Chem. , 90 , 13044–13050 (2018). J. L. Cleasby, J. Am. Water Works Assoc. , 67 , 147–149 (1975). C. C. Willhite, V. S. Bhat, G. L. Ball, and C. J. McLellan, Hum. Exp. Toxicol. , 32 , 275–298 (2013). S. T. McBeath, D. P. Wilkinson, and N. J. D. Graham, Chemosphere , 251 , 126626 (2020). J. Dalmieda, A. Zubiarrain-Laserna, D. Ganepola, P. R. Selvaganapathy, and P. Kruse, Sensors Actuators B Chem. , 328 , 129023 (2020). L. H. H. Hsu, E. Hoque, P. Kruse, and P. Ravi Selvaganapathy, Appl. Phys. Lett. , 106 , 063102 (2015). A. Mohtasebi, A. D. Broomfield, T. Chowdhury, P. R. Selvaganapathy, and P. Kruse, ACS Appl. Mater. Interfaces , 9 , 20748–20761 (2017). E. Hoque, T. Chowdhury, and P. Kruse, Surf. Sci. , 676 , 61–70 (2018). E. Hoque, L. H. H. Hsu, A. Aryasomayajula, P. R. Selvaganapathy, and P. Kruse, IEEE Sensors Lett. , 1 , 4500504 (2017). Figure 1
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| 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 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 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 tête enseignante, 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 ».