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Enregistrement W3025330831 · doi:10.1149/ma2020-01292201mtgabs

Design of an Arsine Gas Free Rapid Arsenic Detector Using a Novel Luminescent Metal-Organic Framework

2020· article· en· W3025330831 sur OpenAlexaff
Alan Huang, Osama Abuzalat, Setareh Homayoonnia, D. Wong, Naga Siva Kumar Gunda, Seonghwan Kim, Sushanta K. Mitra

Notice bibliographique

RevueECS Meeting Abstracts · 2020
Typearticle
Langueen
DomaineChemical Engineering
ThématiqueAnalytical Chemistry and Sensors
Établissements canadiensUniversity of CalgaryUniversity of Waterloo
Organismes subventionnairesnon disponible
Mots-clésArsineArsenicBromideChemistryEnvironmental chemistryParts-per notationInorganic chemistryOrganic chemistryCatalysisPhosphine

Résumé

récupéré en direct d'OpenAlex

An estimated 140 million people have access to drinking water with an excessively high concentration of arsenic. Many of them are located in countries that lack the infrastructure and funding to support laboratories, thus requiring the use of cheaper rapid field tests to determine arsenic levels in water. These tests typically first reduce all the dissolved arsenic to arsine gas, then a paper strip laden with mercuric bromide is inserted into the reaction vessel. The arsine gas reacts with the mercuric bromide on the strip for a colorimetric change. Semi-quantitative results can be determined by comparing the color change to a provided color chart. These rapid tests are non-ideal since the user can be easily exposed to toxic arsine gas if the test enclosure is poorly sealed. Additionally, the colorimetric reaction between mercuric bromide and arsine gas does not work if the paper strip gets wet and likely has reduced performance in high humidity conditions. To improve the safety and ease of testing arsenic in water, we propose the use of a Zinc-Trimesic acid metal-organic framework (MOF) functionalized with 8-Hydroxyquiniline (ZnQ@Zn-BTC). When exposed to arsenate ions, the ZnQ@Zn-BTC MOF exhibits fluorescence quenching upon excitation with UV light. This luminescent reaction required no intermediate reactions, thereby simplifying the assay and eliminating the need for arsine gas. The MOF is available in a dehydrated solid form which can be re-suspended in solution to prepare for testing. The addition of microlitre-scale volumes of sample is enough for detection, indicating that the MOF may be amenable for low-volume detection applications. Preliminary testing in solution has been performed where an arsenic-laden water sample was first added to a ZnQ@Zn-BTC suspension in ethanol and allowed to rest for 10 minutes for binding events. The fluorescence of the sample was then measured using fluorescence spectroscopy, with excitation using 387 nm light, and peak emission observed at 510nm. The quenching was subsequently calculated as the simple difference between the fluorescence of the sample and the fluorescence of a control ZnQ@Zn-BTC solution. Current tests in solution reveal sensitivity for arsenic compounds down to a concentration of 100 ppb for sample volumes as small as 20 µL. There is maximum fluorescence quenching at 1.5 ppm of arsenic and samples with concentrations higher than this level would likely not demonstrate further quenching. Future development includes immobilization of the ZnQ@Zn-BTC on a paper substrate for an easy, rapid and quantitative arsenic testing method. By moving to a paper-based platform the assay can be simplified to a one-step process: simply introduce the sample and the capillary action of the paper will handle the rest. In addition, a reader for the strip can be developed for quantitative results with greater reliability than the color chart comparison that current rapid tests utilize. Paper-based microfluidics also has the advantages of being easily scalable and extremely stable, with little influence from environmental factors. These qualities make it suitable for deployment in less developed countries that may lack resources for more complex assays. Early identification of arsenic in water sources is a key step towards the reduction and prevention of arsenic poisoning. By introducing the proposed assay with a suitable education and awareness program, identification of arsenic in water can be performed quicker and its negative effects on the health of the community can be restrained faster.

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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,004
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
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 enseignants0,079
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,004
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,039
Tête enseignante GPT0,243
Écart entre enseignants0,203 · 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 tête enseignante, pas un consensus.

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

Citations1
Publié2020
Routes d'admission1
Résumé présentoui

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