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Enregistrement W2337796128 · doi:10.1149/ma2016-01/39/1964

Development of All Solid State Sensors for Environmental Water Monitoring: Dissolved Oxygen and Free Chlorine

2016· article· en· W2337796128 sur OpenAlexaffabout
HuanHsuan Hsu, Ravi Selvaganapathy

Notice bibliographique

RevueECS Meeting Abstracts · 2016
Typearticle
Langueen
DomaineChemical Engineering
ThématiqueAnalytical Chemistry and Sensors
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésWastewaterEnvironmental scienceContaminationWater qualityAerationChlorinePollutionWater pollutionWater treatmentSewage treatmentEnvironmental engineeringWaste managementEnvironmental chemistryChemistryEngineering

Résumé

récupéré en direct d'OpenAlex

Water contaminations in both natural water resource before collection and in treated drinking water during transportation jeopardise the supply of clean drinking water [1], [2]. Effective wastewater treatments, pollution controls in surface water and contamination free drinking water are three major goals to combat the water pollution sources on public and environmental health. Long term and continuous on-line sensing systems that can provide highly accurate data in real time can be the key component to benefit all these three aspects. However, the development of a comprehensive sensing system that can detect all parameters is labor and cost intensive. Thus, appropriate selection of targeted indicator is important in order to design a sufficient and practical sensing system. Since oxygen is an essential compound during the degradation of water containments, the dissolved oxygen (DO) level is always an important factor for water monitoring. Therefore, DO concentration is one of the primary factors for monitoring pollution in nature water. Additionally, DO is one of the major parameters that has to be precisely controlled during the wastewater treatment [3], [4]. Exceeding required DO concentration can cause waste of energy during aeration, whereas low DO concentration can cause failure treatment processes. Furthermore, free chlorine is one of the most common used disinfectants to ensure the drinking water quality during transportation and in wastewater treatment process for degradation of organic contaminants. The free chlorine concentration has to be strictly controlled to meet the requirement of different applications. For example, the residual chlorine in wastewater discharge is regulated as 0 ppm to prevent the damage in natural eco-system. Nevertheless, the residual chlorine in drinking water has to be maintained between 2 - 0.5 ppm to ensure drinking water safety. The DO and free chlorine is hence selected as the targeted indicators for the on-line sensing systems of water contamination prevention. Generally, the colorimetric methods are always the standard most widely used water monitoring techniques. For example, Winkler Method for DO sensing [5], and N,N-diethyl-p-phenylenediamine (DPD) method for free chlorine sensing [6]. These techniques are accurate and low interference; however, these techniques are expensive, tedious and labor intense which restricted their real-world application in on-line sensor developments. Among all the other sensing strategies, electrochemical methods are usually simple and low cost which are adequate for environmental sensing applications. However, there are still some challenges of the developments of on line DO and free chlorine sensors which needed to be overcome. Conventional DO sensors are expensive as they use platinum in their construction. Additionally, these sensors bio-foul when used in natural or waste water, which leads to reduced sensitivity and unstable performance. These problems are solved by i) replacing Pt with hemin as the low cost alternative for electrocatalysing the oxygen reduction reaction, and ii) using silicone rubber (PDMS) functionalized with polyethyleneglycol (PEG) as the anti-biofouling gas permeable membrane to provide selectivity with an increased device lifetime (a). This DO sensor has a sensitivity of 4.8 (µA/cm2)/(ppm) of dissolved oxygen in a concentration range of 0-20 ppm (b). [7] Additionally, a solid state palladium reference electrode composed by high surface area Pd nanostructure is also integrated with this DO sensor. The Pd/H electrode composed is able to maintain stable electrical potential for more than 5 hours after charging hydrogen. The all solid-state feature allows this sensing device to be applied in long-term on-line DO sensing. On the other hand, although several free chlorine sensors do exist, none of them is capable of long term and continuous on line sensing. We developed a resettable switching of doping states in carbon nanotube (CNT) films due to the chemical oxidation and reduction of phenyl-capped aniline tetramer (PCAT) adsorbed on it (c). The PCAT-CNT sensor obtains sufficient sensitivity (50 nA/ppm) and detection range (0.06-60 ppm) to ensure the safety of free chlorine level contained in drinking water (d). In addition, a cathodic polarization process after the sensing is found to electrochemically reset this sensor back to the original state so that the sensor can be used for subsequent sensing. In short, this sensor is reagent-free and resettable, suitable for long term and continuous monitoring the free chlorine level in drinking water [8]. Reference 1. Environ. Sci. Technol., v. 43, p. 240 2009. 2. Proc. Natl. Acad. Sci. , vol. 103 , pp. 7210 2006. 3. Environ. Int., vol. 30, p. 249 4. Chem. Eng. J., vol. 162, p. 1 5. Mar. Chem., vol. 122, p. 83 6. Guidelines for Canadian Drinking Water Quality - Chlorine, Ottawa 7. Sensors Journal, IEEE, vol. 14 p.3400 8. Appl. Phys. Lett., vol. 106, p. 063102 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)0,001
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: aucune
Score de désaccord entre enseignants0,002
Score d'incertitude au seuil0,006

Scores du classifieur distillé par catégorie (deux têtes)

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

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 Opus0,019
Tête enseignante GPT0,234
Écart entre enseignants0,215 · 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

Citations0
Publié2016
Routes d'admission2
Résumé présentoui

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