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Record W2373190818 · doi:10.1149/ma2016-01/1/81

(Energy Technology Division Graduate Student Award) Designing Polyoxometalate-Carbon Hybrid Materials for Supercapacitor Electrodes

2016· article· en· W2373190818 on OpenAlexaff
Matthew Genovese, Yee Wei Foong, Keryn Lian

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldMaterials Science
TopicPolyoxometalates: Synthesis and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPolyoxometalateSupercapacitorElectrochemistryMaterials scienceElectrodeNanotechnologyCarbon fibersChemical engineeringMoleculeLayer (electronics)Substrate (aquarium)Energy storageCoatingChemistryOrganic chemistryComposite materialPhysical chemistryCatalysisComposite number

Abstract

fetched live from OpenAlex

This talk will provide an overview of our recent work in the development of supercapacitor electrode materials based on the modification of carbon substrates (MWCNTs and Activated Carbons) with custom designed polyoxometalate (POM) molecular coatings. POMs undergo fast reversible multi-electron transfer reactions, an ideal characteristic for electrochemical energy storage. When immobilized on a carbon substrate these POM molecules provide enhanced charge storage capacity due to their faradaic reactions, while the carbon material contributes mechanical support, improved conductivity, and physical capacitive effects. Different POM molecules such as PMo 12 O 40 3- (PMo 12 ) and PW 12 O 40 3- (PW 12 ) demonstrate characteristic electrochemical activity related to their chemical composition. However, we have discovered that when these molecules are combined in aqueous solutions they do not just physically mix, but instead react spontaneously to form PMo 12-x W x mixed addenda chemistries with unique electrochemical properties different from that of individual PMo 12 or PW 12 . We have also demonstrated that this mixture behaviour can be extended to additional POM combinations such as SiMo 12 O 40 4- (SiMo 12 ) and SiW 12 O 40 4- (SiW 12 ). These novel POM combinations have electrochemical properties that can be easily tuned based upon the compositions of the mixtures. This control over POM redox behavior can be used along with layer-by-layer (LbL) deposition to design molecular coatings that demonstrate desired pseudocapacitive characteristics. The best performance was achieved with a coating that superimposed a 3:1 PMo 12 O 40 3− -PW 12 O 40 3− mixed layer on a 1:1 GeMo 12 O 40 4− -SiMo 12 O 40 4− mixed layer, which resulted in a 5X capacitance enhancement over unmodified MWCNT. This dual layer electrode also demonstrated a close to rectangular CV profile due to the overlapping redox features of the POM combination (Figure 1). In addition to our work on the POM active layers, our current research on the development of alterative carbon substrates will also be discussed. While MWCNT is an effective substrate for POM modification, we have recently focused on the fabrication of less expensive activated carbon materials based on waste biomass precursors. We have developed a nanostructured carbon material based on corn-cob biomass via a simple high temperature exfoliation procedure. This exfoliated corn biochar had excellent energy storage performance, demonstrating a capacitance over 100 times higher than natural corn biochar produced without exfoliation treatment. Additionally, high surface area activated carbons from pine cone biomass were synthesized via a two-step carbonization and KOH activation method. These high surface area substrates result in carbon-POM hybrids with further enhanced specific capacitance compared to nanocarbon based materials. The capacitive performance of these different carbon-POM composites will be compared along with a discussion of how our approach can be leveraged to design the optimal hybrid material for different energy storage applications. Figure 1

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.026
GPT teacher head0.266
Teacher spread0.240 · 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 teacher head, not a consensus.

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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Citations1
Published2016
Admission routes1
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