Resolving the Effects of Surface Area and Porosity on the Capacitance of Activated Carbon
Notice bibliographique
Résumé
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.
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Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
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