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Enregistrement W3183871026 · doi:10.1149/ma2021-0114675mtgabs

Direct Exfoliation of Conductive MoS<sub>2</sub> Using Peroxide for Solid State Sensor and Catalytic Applications

2021· article· en· W3183871026 sur OpenAlexaff
Dipankar Saha, Vinay Patel, P. Ravi Selvaganapathy, Peter Kruse

Notice bibliographique

RevueECS Meeting Abstracts · 2021
Typearticle
Langueen
DomaineMaterials Science
Thématique2D Materials and Applications
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésMaterials scienceNanotechnologyMolybdenum disulfideGrapheneElectrical conductorSemiconductorExfoliation jointOptoelectronicsInertChemistryComposite material

Résumé

récupéré en direct d'OpenAlex

Two-dimensional (2D) materials have attracted much attention over the last decade due to their high performance in nanoelectronic devices. The discovery of graphene opened up many opportunities to investigate and explore other 2D materials. There has been a drive to expand the toolbox of 2D materials to also include insulators and semiconductors with a variety of bandgaps. As a result, a wide range of materials have been discovered or predicted, with molybdenum disulfide (MoS 2 ) being particularly popular. Using the semiconducting phase of MoS 2 (2H-MoS 2 ) requires a relatively high voltage to get sufficient conductivity due to the presence of a band gap. However, for applications in batteries, supercapacitors, electrocatalysts and solar cells, a substantially increased conductivity is required in order to achieve reasonable currents. 1 The most common source of conductive MoS 2 is metallic MoS 2 (1T-MoS 2 ) that has been prepared via the lithium intercalation process, which requires inert atmosphere processing and safety procedures. 2 Hence, there is a desire to develop a safer and more efficient process to yield conductive MoS 2 . Defects play a very important role in modulating the electrical properties of MoS 2 . Sonication of MoS 2 in an appropriate solvent results in many disordered structural defects. The most common defects on MoS 2 are sulfur defects. These defects increase the energy level of the gap state and eventually deteriorate the device performance. Thiol based molecules are commonly used to reduce the number of sulfur defects on MoS 2 . Other molecules such as oxygen or organic super acids like bis(trifluoromethane) sulfonamide (TFSI) have also been reported to passivate the surface defect. 3 Past research has mainly focused on the theoretical study of defective MoS 2 and how to utilize those defects for improving photoluminescent efficiency. However, those defects can also be utilized to improve the conductivity of MoS 2 as a safer alternative for applications in batteries, supercapacitors, solar cells, electrocatalyst and sensors. Conductive MoS 2 (c-MoS 2 ) can be used as active material for low-cost solid-state chemiresistive pH sensors. 4 In chemiresistive sensors, conductivity changes are observed based on direct interactions between the active material and the analyte. 5 Even though chemiresistive pH sensors based on exfoliated graphene, carbon nanotubes, or graphitic materials are available, their sensing response is limited to less than 20%. 6 On the other hand, MoS 2 has attracted great attention as a promising electrocatalyst for the hydrogen evaluation reaction (HER) because of abundant active sites at edge sites and on the basal plane for facilitating hydrogen production. Water splitting is the simplest and most convenient method to generate hydrogen. In industrial applications, an electrocatalyst is commonly used to accelerate the HER and reduce the overpotential. Even though 2H-MoS 2 has good catalytic activity at the edge sites, its low electrical conductivity limits the achievable current density, resulting in a high Tafel value and making it unsuitable for practical application in HER. In this work, we show a simple and effective way to prepare few layer c-MoS 2 under ambient conditions using 0.06 vol% aqueous hydrogen peroxide. We have demonstrated that the bulk conductivity of the conductive MoS 2 that we prepared is up to seven orders of magnitude higher than that of the semiconducting phase of MoS 2 . The samples were also characterized with Hall measurements, X-ray photoelectron spectroscopy (XPS) and Raman spectroscopy which showed that hydrogen molybdenum bronze (H x MoO 3 ) and substoichiometric MoO 3−y help tune the conductivity of the nanometer-scale thin films without impacting the sulfur-to-molybdenum ratio. C-MoS 2 was further functionalized with thiols to determine the number of residual reactive sites. An important goal of our work is to control the conductivity of the MoS 2 thin films in safe and facile ways that enable their application in low-cost chemiresistive sensors in liquid environments. We fabricated chemiresistive pH sensors with centimeter channel lengths while maintaining low measurement voltages. We further measured the catalytic activity of c-MoS 2 films in 0.5 M H 2 SO 4 electrolyte solution with three electrode systems using linear sweep voltammetry (LSV) which showed a lower Tafel value at 10 mA/cm 2 current density. The lower Tafel value demonstrated that c-MoS 2 has potential to use as catalyst for HER. Our study furthers the understanding of conductive forms of MoS 2 , and also opens up a new pathway for next generation electronic and energy conversion devices. References: Saha. D; Selvaganapathy. P R; Kruse. P, J. Electrochem. Soc., 167 , 126517 (2020). Eda. G et al ., Nano Lett ., 11 , 5111−5116 (2011). Lu. H et al. APL Mater ., 6 , 066104 (2018). Saha. D; Kruse. P, ACS Appl. Nano Mater ., 3 , 10864-10877 (2020). Kruse. P, J. Phys., D , 51 , 203002 (2018). Gou, P et al ., Sci. Rep., 4 , 4468, (2015). 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 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)0,000
Version: codex-gemma-dda1882f352aStatut 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 enseignants0,011
Score d'incertitude au seuil0,545

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
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 recherche0,0000,000
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 Opus0,027
Tête enseignante GPT0,283
Écart entre enseignants0,256 · 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.

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
Publié2021
Routes d'admission1
Résumé présentoui

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