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Record W4234532192 · doi:10.1149/ma2016-02/7/1016

Resolving the Effects of Surface Area and Porosity on the Capacitance of Activated Carbon

2016· article· en· W4234532192 on OpenAlexaff
Jocelyn E. Zuliani, Donald W. Kirk, Charles Q. Jia

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceCapacitanceElectrolytePorositySpecific surface areaSupercapacitorMesoporous materialSurface-area-to-volume ratioElectrodeVolume (thermodynamics)WettingCarbon fibersChemical engineeringFaraday efficiencyElectrochemistrySpecific energyComposite materialChemistryThermodynamics

Abstract

fetched live from OpenAlex

Electrochemical double-layer capacitors (EDLCs) store energy at the interface of the electrode and electrolyte, known as the electrical double layer. One of the challenges facing EDLC technology is their moderate energy density compared to batteries and fuel cells. Therefore, improving EDLC performance requires identification of the key electrode and electrolyte parameters that affect energy density. Some of the key parameters that are known to impact the energy density of an electrode material are the material’s specific surface area (SSA), its pore size distribution, the macroscopic morphology, and the material’s chemical composition, relating to the wettability of the material and the faradaic reactions that may occur during charging. However, published research to date has not isolated the intercoupled relationship between specific surface area and pore size distribution. While high surface area materials often have high microporosity (pores less than 2 nm in diameter) due to the high surface to volume ratio of these small pores, as the pore size is increased into the mesopores range (2-50 nm diameter pores), the material’s SSA tends to decrease, since mesopores have a lower surface to volume ratio [1, 2]. As such, there remains an ongoing unresolved debate as to the relationship between pore size and SSA-normalized capacitance [2], with some researchers demonstrating an increased SSA-normalized capacitance in pores with diameters less than 1 nm [3], some researchers suggesting that there is no correlation between pore size and SSA-normalized capacitance [4], and some researchers demonstrating that there is a relationship between SSA-normalized capacitance and pores greater than 2 nm in diameter [5]. One of the main challenges to unravel the correlation between pore size and SSA-normalized capacitance is the variation in a material’s structural features, including total SSA, and its chemical composition. This makes it challenging to determine the effect of a single parameter [2]. In order to investigate the effect of pore size on SSA-normalized capacitance, the ideal materials for comparison would be those of similar specific surface area, and chemical composition, but differing pore size distribution. This approach ensures that the effects of the material’s total SSA are constant. When materials have different total SSA values, it is difficult to determine if the variations in performance are associated to differing pore size or differing surface area. Additionally, the relationship is more complicated in a sample with multiple pore sizes, which is common in activated carbon materials. It is critical to determine the relationship between pore size and capacitance in materials with narrow pore size distributions, and materials that have broad pore size distributions. By identifying the key structural features that improve the energy density of a material, the overall performance of EDLCs may also be improved. This study investigates the relationship between SSA-normalized capacitance and pore size distribution. Activated carbon materials were prepared with various pore size, but with similar specific surface area and chemical composition. The relationship between pore size and both SSA-normalized capacitance and gravimetric capacitance as well as the rate performance of the activated carbon materials is reported. Finally, the dependence of capacitance on maximum operating voltage is discussed in order to better elucidate the relationship between porosity and capacitance. Initial results have demonstrated that as the pore size increases, the SSA-normalized capacitance decreases. However, a similar decrease was not observed in the gravimetric capacitance. This indicates that the variation in bulk density of broad pore size distribution carbon materials may mask the effects of enhanced SSA-normalized capacitance in sub-nanometer pores. The results suggest that there may be a critical pore size distribution that will maximize both SSA-normalized capacitance and gravimetric capacitance. References [1] P. Simon, Y. Gogotsi, Nature Materials, 7 (2008) 845-854. [2] W.T. Gu, G. Yushin, Wiley Interdisciplinary Reviews-Energy and Environment, 3 (2014) 424-473. [3] J. Chmiola, G. Yushin, Y. Gogotsi, C. Portet, P. Simon, P.L. Taberna, Science, 313 (2006) 1760-1763. [4] T.A. Centeno, O. Sereda, F. Stoeckli, Physical Chemistry Chemical Physics, 13 (2011) 12403-12406. [5] M. Lazzari, F. Soavi, M. Mastragostino, Fuel Cells, 10 (2010) 840-847.

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)0.001
metaresearch head score (Gemma)0.005
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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.014
GPT teacher head0.211
Teacher spread0.197 · 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
Has abstractyes

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