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Enregistrement W2529690474 · doi:10.1149/ma2016-02/7/934

New Colloidal Techniques for the Fabrication of Manganese Dioxide-Carbon Nanotube Electrodes of Supercapacitors

2016· article· en· W2529690474 sur OpenAlexaff
Igor Zhitomirsky

Notice bibliographique

RevueECS Meeting Abstracts · 2016
Typearticle
Langueen
DomaineMaterials Science
ThématiqueSupercapacitor Materials and Fabrication
Établissements canadiensMcMaster University
Organismes subventionnairesnon disponible
Mots-clésMaterials scienceSupercapacitorFabricationDispersantNanotechnologyCarbon nanotubeNanocompositeNanoparticlePseudocapacitanceElectrodeChemical engineeringElectrochemistryDispersion (optics)Chemistry

Résumé

récupéré en direct d'OpenAlex

Electrochemical supercapacitors based on MnO2 electrode materials are currently attracting significant interest due to the high specific capacitance obtained using environmentally friendly aqueous electrolytes and low cost of MnO2. A complicating factor in the application and commercialization of MnO2 electrodes for electrochemical supercapacitors is low electronic and ionic conductivity of MnO2. This problem is usually addressed by the fabrication of porous nanocomposite electrodes, containing MnO2 nanoparticles and carbon nanotubes (CNT) or other conductive additives. Despite the impressive progress achieved in the fabrication of MnO2-CNT electrodes, there is a need for simple and versatile methods for the fabrication of MnO2 nanoparticles, efficient dispersion of MnO2 and CNT, and fabrication of porous electrodes. We report new strategies for the fabrication of MnO2-CNT composites, which are based on the use of new dispersing agents and new techniques for mixing of MnO2 and CNT. The electrochemical performance of the composites, prepared by different methods was compared. The results were used for the fabrication of advanced electrodes and devices. Efficient dispersing agents are of critical importance for the fabrication of MnO2 nanoparticles and formation of stable suspensions for colloidal processing. An important property of a dispersant is its adsorption on the particle surface. Therefore, there is a need in the development of charged dispersing agents with strong interfacial adhesion. New methods have been developed for the synthesis, surface modification and dispersion of MnO2 nanoparticles using advanced dispersing agents, which provide strong bidentate, polydentate or chelating bonding to the particle surface. Various dispersants were developed and compared, such as molecules from catechol, chromotropic acid, gallic acid, salicylic acid and bicinchoninic acid families. The influence of the nature of functional groups, number of aromatic rings, length of the hydrocarbon chain on the adsorption, dispersion and electrostastic charging in the suspensions was investigated. New dispersants were used for the synthesis of non-agglomerated MnO2 particles. Various aromatic and steroid dispersants were investigated for the dispersion of CNT. It was found that steroid dispersants outperform other dispersants in the dispersion of CNT. The polyaromatic molecules, containing chelating and charging functional groups can be used as co-dispersants for MnO2 and CNT. Further progress in the application of new dispersants was achieved by the development of liquid-liquid interface synthesis and extraction method, which allowed agglomerate free synthesis of MnO2 and their mixing with CNT. In this strategy, the problems related to particle agglomeration during the drying stage were avoided. As an extension of these investigations, chelating polymers were used for dispersion. The unique feature of this strategy is that chelating aromatic ligands of the monomers provide multiple adsorption sites for attachment on MnO2 and CNT and impart electrical charges for electrosteric dispersion. In another strategy, complexes of polymers and chelating agents were used for co-dispersion of MnO2 and CNT. As a result, we achieved an outstanding efficiency in dispersion. Heterocoagulation methods have been developed for the nanotechnology of MnO2 - CNT composites. The methods are based on selective strong adsorption of dispersants with specific adsorption ligands on MnO2 or CNT and heterocoagulation of the materials using electrostatic forces, related to opposite charges of individual dispersants or click chemistry methods. Proof of concept studies in aqueous and non-aqueous suspensions showed significant improvement in component dispersion and mixing. The capacitive performance of MnO2-CNT composites, prepared by different methods was compared. Ni foams were used as current collectors for the fabrication of electrodes with active mass loading of 30-50 mg cm-2 and mass ratio of active material to current collector of 0.3-0.42. The capacitive behavior of the composite electrodes was studied in Na2SO4 electrolyte using cyclic voltammetry, chronopotentiometry and impedance spectroscopy. The use of new dispersants and mixing techniques allowed for significant improvement in electrode performance. The composites, prepared in the presence of dispersants using liquid-liquid extraction and mixing methods showed the highest specific capacitance of 8 F cm-2. The liquid-liquid extraction method for the synthesis of MnO2 particles and their mixing with CNT allowed good capacitance retention at high charge-discharge rates. Significant improvement in capacitive behavior was achieved using heterocoagulation methods for the fabrication of MnO2-CNT composites. The new strategies allowed significant reduction in electrode resistance due to the use of efficient colloidal dispersion and mixing techniques. The capacitance retention of above 70% was achieved in the scan rate range of 2-100 mV s-1. The electrodes showed cyclic stability above 90% after 5000 cycles. The composite electrodes were used for the fabrication of asymmetric capacitors with voltage window of 1.6V. We report capacitances, power-energy characteristics and cyclic behavior of electrodes, prepared using different methods.

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)0,000
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: aucune
Score de désaccord entre enseignants0,001
Score d'incertitude au seuil0,003

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0010,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0010,000

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 Opus0,016
Tête enseignante GPT0,241
Écart entre enseignants0,225 · 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
Publié2016
Routes d'admission1
Résumé présentoui

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