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Enregistrement W4285397868 · doi:10.1149/ma2022-01522135mtgabs

(Invited) Chemiresistive Water Quality Sensors: Challenges and Progress

2022· article· en· W4285397868 sur OpenAlexaff
Peter Kruse

Notice bibliographique

RevueECS Meeting Abstracts · 2022
Typearticle
Langueen
DomaineChemical Engineering
ThématiqueAnalytical Chemistry and Sensors
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésChemiresistorNanotechnologyConductive polymerMaterials scienceDopingNanomaterialsConductivityAnalyteActive layerLayer (electronics)OptoelectronicsPolymerChemistryComposite material

Résumé

récupéré en direct d'OpenAlex

Chemiresistors are solid state devices that change their electronic properties (more specifically, the resistance of a conductive thin film or percolation network) as a result of chemical interactions with their environment. They are a well-established and widely commercialized technology for gas or vapor sensor applications. The active layer may consist of metal oxides, polymers, nanomaterials or composites. In most cases, chemisorption or catalytic activity involving the analyte results in surface doping of the active layer, although other mechanisms (such as conductivity changes due to swelling) have also been reported. A significant part of the sensing literature is taken up by reports of ChemFETs, in which case the conductivity of the active layer can also be modulated by an applied gate voltage. Gate voltage modulation is helpful for establishing the sensing mechanism and - on occasion - for distinguishing multiple simultaneous target analytes. In most cases, however, the actual sensor operation occurs at zero gate voltage, thus reducing the ChemFET to a chemiresistor. [1] Gas sensors can be operated at high voltages and without shielding the contacts to the film from gas exposure, two simplifications that are not afforded to sensors operating in aqueous environments. Water quality sensors are a surprisingly underserved area of sensor applications.[2] Important chemical water quality parameters include pH, dissolved gases, common ions and a range of toxic trace contaminants which may be ionic or uncharged, inorganic or organic. All these water quality parameters are usually monitored using colorimetric sensors, electrochemical sensors and large lab-based instruments. None of these lend themselves to low maintenance, reagent free, low power continuous operation for online monitoring. In particular, colorimetric sensors need a resupply of reagents and electrochemical sensors require reference electrodes. Chemiresistors have the potential to eliminate all these disadvantages, but there has been slow progress in adapting them to aqueous analytes. They are simple and economical to manufacture, and can operate reagent-free and with low or no maintenance. Unlike electrochemical sensors they do not require reference electrodes. Challenges include the need to prevent electrical shorts through the aqueous medium and the need to keep the sensing voltage low enough to avoid electrochemical reactions at the sensor. We have built a chemiresistive sensing platform for aqueous media. The active sensor element consists of a percolation network of low-dimensional materials particles that form a conducting film, e.g. from carbon nanotubes, pencil trace, exfoliated graphene or MoS2. The first members of that platform were free chlorine sensors,[3-5] but we have also demonstrated pH sensitive films [6,7] and cation sensors.[8] While there are some challenges associated with expanding the range of accessible analytes,[9] we have recently expanded the applicability of our platform, in particular anions and cations that are commonly present as pollutants in surface and drinking water. Our sensors can be incorporated into a variety of systems and will also be suitable for online monitoring in remote and resource-poor locations. References: [1] A. Zubiarrain-Laserna and P. Kruse, Graphene-Based Water Quality Sensors. J. Electrochem. Soc. 167 (2020) 037539. [2] P. Kruse, Review on Water Quality Sensors. J. Phys. D 51 (2018) 203002. [3] L. H. H. Hsu, E. Hoque, P. Kruse, and P. R. Selvaganapathy, A carbon nanotube based resettable sensor for measuring free chlorine in drinking water. Appl. Phys. Lett. 106 (2015) 063102. [4] E. Hoque, L. H. H. Hsu, A. Aryasomayajula, P. R. Selvaganapathy, and P. Kruse, Pencil-Drawn Chemiresistive Sensor for Free Chlorine in Water. IEEE Sens. Lett. 1 (2017) 4500504. [5] A. Mohtasebi, A. D. Broomfield, T. Chowdhury, P. R. Selvaganapathy, and P. Kruse, Reagent-Free Quantification of Aqueous Free Chlorine via Electrical Readout of Colorimetrically Functionalized Pencil Lines. ACS Appl. Mater. Interfaces 9 (2017) 20748-20761. [6] D. Saha, P. R. Selvaganapathy and P. Kruse, Peroxide-Induced Tuning of the Conductivity of Nanometer-Thick MoS2 Films for Solid State Sensors. ACS Appl. Nano Mater. 3 (2020) 10864-10877. [7] S. Angizi, E. Y. C. Yu, J. Dalmieda, D. Saha, P. R. Selvaganapathy and P. Kruse, Defect Engineering of Graphene to Modulate pH Response of Graphene Devices. Langmuir 37 (2021) 12163-12178. [8] J. Dalmieda, A. Zubiarrain-Laserna, D. Ganepola, P. R. Selvaganapathy and P. Kruse, Chemiresistive Detection of Silver Ions in Aqueous Media. Sens. Actuators B:Chem 328 (2021) 129023. [9] J. Dalmieda, A. Zubiarrain-Laserna, D. Saha, P. R. Selvaganapathy and P. Kruse, Impact of Surface Adsorption on Metal-Ligand Binding of Phenanthrolines. J. Phys. Chem. C 125 (2021) 21112-21123. 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)0,002
score de la tête « metaresearch » (Gemma)0,003
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,043
Score d'incertitude au seuil0,143

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,003
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0010,001
Communication savante0,0030,004
Science ouverte0,0010,002
Intégrité de la recherche0,0040,005
Charge utile insuffisante (le modèle a refusé de juger)0,0430,028

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 Opus0,030
Tête enseignante GPT0,262
Écart entre enseignants0,232 · 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'étudeSans objet
Domainenon disponible
GenreSynthèse

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
Publié2022
Routes d'admission1
Résumé présentoui

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