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Enregistrement W3024896620 · doi:10.1149/ma2020-01292232mtgabs

Chemiresistive Detection of Silver Ions in Aqueous Media

2020· article· en· W3024896620 sur OpenAlexaffabout
Johnson Dalmieda, Ana Zubiarrain-Laserna, Ravi Selvaganapathy, Peter Kruse

Notice bibliographique

RevueECS Meeting Abstracts · 2020
Typearticle
Langueen
DomaineChemistry
ThématiqueElectrochemical Analysis and Applications
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésSilver nitrateSilver nanoparticleGraphiteMetal ions in aqueous solutionIonic bondingIonChemistryAqueous solutionNanotechnologyMaterials scienceNuclear chemistryOrganic chemistry

Résumé

récupéré en direct d'OpenAlex

Silver is a precious metal that is commonly used in water filters to reduce growth of biofilm within the filter itself. Ionic silver is used as an effective disinfectant for potable water, giving a log10 reduction for L. pneumophilia, P. aeruginosa, and E. coli of 2.4, 4, and 7, respectively.1 In terms of human exposure, silver is not an essential metal therefore any exposure to silver is unwanted. When silver is ingested orally, the most common adverse effect is argyria, which is an extreme blue pigmentation of the skin and abdominal viscera.2 Occupational exposure to silver nitrate has been correlated to respiratory tract irritation.3 Currently there are no guidelines for silver ions in drinking water, and the World Health Organization (WHO) has set a health advisory (not a guideline value) of 100 ppb. The only country with a Maximum Allowable Content (MAC) is Germany, whose drinking water regulations (Trinkwasserverordnung) have a MAC of 80 ppb.4 Here we demonstrate a chemiresistive sensor for the in situ detection of silver (I) in aqueous media. Chemiresistive sensors function through the changes that occur in the electronic structure of the sensor itself. A graphite film attached to two copper contacts at either end is exposed to the silver ions such that only the graphite and not the contacts interact with the ions. To functionalize the graphite, silver (I)-specific ligands, such as bathocuproine, can be deposited onto the film to adsorb onto it, protecting it from interfering ions.5 Chemiresistive sensors have been demonstrated before for the detection of free chlorine in aqueous media. Rather than using graphite, carbon nanotubes (CNT) were utilized as the conductive film, with phenyl-capped aniline tetramer (PCAT) as the chlorine-specific ligand. As the PCAT doped CNT was exposed to chlorine, the PCAT oxidized, and the electronic changes were probed using a bias voltage. The linear range for this sensor was from 60 ppb to 60 ppm, providing sufficient sensitivity for household use.6 When testing other common ions, there were no significant interference's that compete with the response of silver (I) in solution, making this sensor quite selective to silver (I). pH tests show that there is no change in current induced by pH between the range of 6-10 pH. Below 6, the sensor functions as a "proton sensor" due to the protonation of the adsorbed bathocuproine. Above pH 10, AgOH may be formed, which will precipitate out of solution. All tests were performed at an analyte conductance of 31 μS/cm, which is typical for freshwater samples.7 When the fabricated sensor was exposed to silver (I) in aqueous solution, detection of the ions was observed through a step up in the current going through the sensor. Each step up in current was proportional to the concentration of silver (I) in solution. Based on this, a calibration curve was made using the Langmuir Isotherm model and a first-order exponential decay model. For the range of 3 ppb to 1 ppm, both the Langmuir Isotherm and the first-order exponential decay model gave R2 values of 0.9982 and 0.9939, respectively. The sensor could also be reliably reset to the same 0 ppm baseline after use by exposing it to a pH 3 solution. When exposed to silver (I) post-reset, current changes were also quite reproducible, with the values having a relative standard deviation no greater than 1.24%, highlighting the re-usability of this sensor. The limit of detection, calculated using a signal:noise ratio of 3, for this sensor would be 3 ppb. We have therefore demonstrated that chemiresistors based on functionalized nanocarbon films can be used as selective ion sensors in addition to their previously demonstrated application as redox sensors. The toolbox of organometallic chemistry can now be applied to extend this sensing platform to other cations for water quality sensing. References 1. Kim, J. S.; Kuk, E.; Yu, K. N.; Kim, J.-H.; Park, S. J.; Lee, H. J.; Kim, S. H.; Park, Y. K.; Park, Y. H.; Hwang, C.-Y.; et al. Antimicrobial Effects of Silver Nanoparticles. Nanomedicine: Nanotechnology, Biology and Medicine 2007, 3 (1), 95–101. 2. Marshall, J. P. Systemic Argyria Secondary to Topical Silver Nitrate. Archives of Dermatology 1977, 113 (8), 1077. 3. Toxicological Profile for Silver. Agency for Toxic Substances and Disease Registry 1990. 4. Silver as a Drinking-Water Disinfectant. World Health Organization 2018. 5. Saito, T. Transport of Silver(I) Ion through a Supported Liquid Membrane Using Bathocuproine as a Carrier. Separation Science and Technology 1998, 33 (6), 855–866. 6. Hsu, L. H. H.; Hoque, E.; Kruse, P.; Selvaganapathy, P. R. A Carbon Nanotube Based Resettable Sensor for Measuring Free Chlorine in Drinking Water. Applied Physics Letters 2015, 106 (6), 063102. 7. https://www.ontario.ca/data/provincial-stream-water-quality-monitoring-network (accessed on October 22, 2019) 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 enseignants0,003
Score d'incertitude au seuil0,012

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 savante0,0000,001
Science ouverte0,0010,001
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0030,002

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 Opus0,012
Tête enseignante GPT0,223
Écart entre enseignants0,211 · 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
Publié2020
Routes d'admission2
Résumé présentoui

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