New Insight in the Charge Storage Mechanism of Vanadium Nitride as a Pseudocapacitve Electrode
Bibliographic record
Abstract
More than 10 years after the initial studies on the pseudocapacitive performance of molybdenum nitride electrode in acid media 1,2,a specific capacity of 1340F/g was reported for a vanadium nitride electrode in alkaline electrolyte3,4. This impressive specific capacity and the high electronic conductivity of VN make it very attractive for energy storage application in electrochemical capacitor. Accordingly, new synthesis methods such as high energy milling of Li3N and V2O3 5, xerogel6–8, nanotubes coating9,10 or TiN/VN core/shell nanostructure11 have been developed. On one hand, Choi et al. suggested that the pseudocapacitive behavior of VN can be explained by an equilibrium reaction involving the adsorption of OH- ions and a redox reaction of an oxidized surface, formed by oxidation of VN. While Pande et al. confirmed the role of OH- anions in the redox reaction12, only few studies were aimed at getting an understanding of the charge storage mechanism. On the other hand, significant differences in specific capacity and cyclability have been reported in the literature 6,8,10,13. These differences can be explained by the diversity in the synthesis methods, which often use oxide precursors leading to oxy-nitrides more than nitrides. However, most studies show a lack of understanding in the different charge storage mechanisms which could help determining the conditions that will insure a long cycle life when use in electrochemical capacitor. This presentation will describe the synthesis of a model material VN with various thicknesses that will be followed by an electrochemical investigation assisted with in-situ and ex-situ characterizations. The major aim of this work is to get some insights in the different redox reactions involved in the charge storage mechanism and to determine the suitable conditions for utilization of VN as active electrode material. Our results show a capacity retention of up to 96% after 10 000 cycles. (1) Liu, T.-C.; Pell, W. G.; Conway, B. E. J. Electrochem. Soc. 1998, 145, 1882–1888. (2) Roberson, S. L.; Finello, D.; Davis, R. F. J. Appl. Electrochem. 1999, 29, 75–80. (3) Choi, D.; Blomgren, G. E.; Kumta, P. N. Adv. Mater. 2006, 18, 1178–1182. (4) Choi, D.; Kumta, P. N. Electrochem. Solid-State Lett. 2005, 8, A418–A422. (5) Hanumantha, P. J.; Datta, M. K.; Kadakia, K. S.; Hong, D. H.; Chung, S. J.; Tam, M. C.; Poston, J. a.; Manivannan, a.; Kumta, P. N. J. Electrochem. Soc. 2013, 160, A2195–A2206. (6) Zhou, X.; Chen, H.; Shu, D.; He, C.; Nan, J. J. Phys. Chem. Solids 2009, 70, 495–500. (7) Cheng, F.; He, C.; Shu, D.; Chen, H.; Zhang, J.; Tang, S.; Finlow, D. E. Mater. Chem. Phys. 2011, 131, 268–273. (8) Shu, D.; Lv, C.; Cheng, F.; He, C.; Yang, K.; Nan, J.; Long, L. Int. J. Electrochem. Sci. 2013, 8, 1209–1225. (9) Ghimbeu, C. M.; Raymundo-Piñero, E.; Fioux, P.; Béguin, F.; Vix-Guterl, C. J. Mater. Chem. 2011, 21, 13268–13275. (10) Zhang, L.; Holt, C. M. B.; Luber, E. J.; Olsen, B. C.; Wang, H.; Danaie, M.; Cui, X.; Tan, X.; W. Lui, V.; Kalisvaart, W. P.; Mitlin, D. J. Phys. Chem. C 2011, 115, 24381–24393. (11) Dong, S.; Chen, X.; Gu, L.; Zhou, X.; Wang, H.; Liu, Z.; Han, P.; Yao, J.; Wang, L.; Cui, G.; Chen, L. Mater. Res. Bull. 2011, 46, 835–839. (12) Pande, P.; Rasmussen, P. G.; Thompson, L. T. J. Power Sources 2012, 207, 212–215. (13) Glushenkov, A. M.; Hulicova-jurcakova, D.; Llewellyn, D.; Lu, G. Q.; Chen, Y. Chem. Mater. 2010, 22, 914–921.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".