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Record 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 on OpenAlexaff
Igor Zhitomirsky

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMaterials scienceSupercapacitorFabricationDispersantNanotechnologyCarbon nanotubeNanocompositeNanoparticlePseudocapacitanceElectrodeChemical engineeringElectrochemistryDispersion (optics)Chemistry

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.241
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2016
Admission routes1
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