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Enregistrement W3025500926 · doi:10.1149/ma2020-01381666mtgabs

Controlling Electrochemical Reactivity of Mesoporous Carbons By Surface Modification

2020· article· en· W3025500926 sur OpenAlexaff
Viola Birss, Samantha Luong, Marwa Atwa, Xiaoan Li, Xia Tong, Erwan Bertin, Ruohong Sui, Robert A. Marriott

Notice bibliographique

RevueECS Meeting Abstracts · 2020
Typearticle
Langueen
DomaineEnergy
ThématiqueElectrocatalysts for Energy Conversion
Établissements canadiensSt. Francis Xavier UniversityUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésMicroporous materialMaterials scienceMesoporous materialSurface modificationChemical engineeringCarbon fibersGrapheneNanomaterialsElectrochemistryNanotechnologyWettingNanoparticleElectrolyteReactivity (psychology)Specific surface areaCatalysisOrganic chemistryChemistryElectrodeComposite materialComposite number

Résumé

récupéré en direct d'OpenAlex

Carbon nanomaterials are used in a wide array of electrochemical applications, such as in fuel cells, batteries, capacitors, and sensors. These materials typically have the high surface area and conductivity required for good electrochemical performance and are low in cost. Many different types of carbon nanomaterials are studied, including microporous Vulcan carbon and Ketjenblack, carbon nanotubes, templated carbons, graphene and its derivatives. While microporous carbons are often used as cathode supports in polymer electrolyte membrane fuel cells (PEMFCs), most carbons are susceptible to corrosion, with pores too small to fit catalytic Pt nanoparticles or contain Nafion ionomer, and have varying and uncontrolled pathways between carbon particles, yielding transport limitations that influence electrochemical performance. Thus, interest is increasingly being focused on mesoporous carbons as catalyst support materials. Colloid imprinted carbons (CICs) are one example of promising mesoporous carbon materials and are the focus of this presentation. CICs are prepared from abundant and inexpensive precursors that are templated with silica colloids of a precise size to achieve ordered mesoporosity. Previously, CIC powders with pore sizes of 12, 22, and 50 nm (with still smaller internal necks) were studied, focusing on their corrosion resistance and wettability before and after functionalization with a hydrophobic surface group (-PhF 5 ), achieved through diazonium chemistry. 1–3 In the present work, we investigated CICs with larger pore sizes in the range of 85 nm to better simulate the typical size of the pathways and pores present in common, ink-derived microporous carbons, attaching both –PhF 5 and a hydrophilic surface functional group, -PhSO 3 H, to the CIC-85 surface. The larger CIC pore size should be beneficial for use in PEMFC cathode supports, as they can accommodate both Pt nanoparticles and ionomer without hindering mass transport. The CIC-85 material was prepared by mixing a suspension of 85 nm silica particles and ground mesophase pitch precursor in ethanol and then imprinting at 400 °C for 2 hrs under nitrogen before being carbonized at 900 °C for 2 hrs in nitrogen. The silica was removed by 24 hr NaOH reflux, then washed with water until neutralized. Surface functionalization was achieved by refluxing the CIC-85 powder for 24 hr under nitrogen with amyl nitrate and either 2,3,4,5,6-pentafluoroaniline in acetonitrile, or sulfanilic acid in water, to form hydrophobic or hydrophilic CIC-85 surface properties, respectively. The product was then treated to a 24 hr acetonitrile Soxhlet extraction and air-dried. Characterization techniques, including Raman spectroscopy, XRD, SEM, and gas sorption (BET analysis), showed that the ordered porosity of the CIC-85 was fully preserved after functionalization and that average pore sizes decreased by a few nm, consistent with the presence of surface functional groups. Neutron activation analysis and CHNS elemental analysis confirmed the successful functionalization of the CIC-85 powder with hydrophobic -PhF 5 and hydrophilic -PhSO 3 H groups, respectively. The electrochemistry of the CICs, before and after surface functionalization, were studied by using a CIC-85, 12% sulfuric acid, and 1 wt% Nafion in ethanol ink mixture, with 0.2 mg of the CIC-85 loaded onto a glassy carbon rod electrode. The CIC material was tested in 0.5 M sulfuric acid for its CV response in the normal potential range for carbon and also exposed to an electrochemical accelerated stress test (AST) protocol, designed to reduce testing time and to account for real time surface area changes as a result of surface roughening and mass loss. The AST involved holding at 1.0 V vs RHE for 10 s and then stepping to and holding at 1.4 V for 50 s, repeatedly, and then reporting the difference in the cumulative oxidation vs reduction charges, equivalent to the amount of total oxidized carbon. CVs collected before and after the AST to determine surface area changes from the double layer capacitance and changes in the density of surface oxide groups from the pseudocapacitance. The results showed that the presence of the hydrophobic –PhF 5 group protects the CIC-85 material from corrosion, while the hydrophilic –PhSO 3 H group also decreases the extent of CIC-85 oxidation, but by a lesser extent. A new model is suggested, wherein steric hindrance introduced by the functional groups, in addition to surface wettability alteration, prevents access of solution to the surface and protects carbon from oxidation. XPS analysis, before and after the corrosion experiments, showed that the surface functionalities are not lost after repeated exposure of the CIC-85 to the more aggressive anodic potentials. 1. D. Bélanger and J. Pinson, Chem. Soc. Rev. , 40 , 3995–4048 (2011). 2. X. Li et al., ACS Appl. Mater. Interfaces , 10 , 2130–2142 (2018). 3. F. Forouzandeh et al., J. Electrochem. Soc. , 165 , F3230–F3240 (2018). 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)0,001
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 enseignants0,051
Score d'incertitude au seuil0,946

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,001
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 Opus0,013
Tête enseignante GPT0,226
Écart entre enseignants0,213 · 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

Citations1
Publié2020
Routes d'admission1
Résumé présentoui

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