Toward a Better Understanding on Na-Air Batteries
Bibliographic record
Abstract
Na-air battery (or Na-oxygen battery) is a newly developed member of metal-air batteries and is attracting increasing research interest due to its environmentally benign characteristics and high theoretical energy density, which can be comparable to gasoline, making them attractive candidates for use in electrical vehicles.1–3 The concept of sodium-air batteries (SABs) are similar to lithium-air batteries (LABs) that were widely studied over the last decades.4 However, the poor cycling life and low energy efficiency (high charging overpotential) of LABs and SABs hinder their commercialization.4-5 Compared to the numerous reports of LABs, the research on SABs is still in its infancy. Although SABs show a number of attractive properties such as low charging overpotential and high round-trip energy efficiency, their cycling life is currently limited to a few tens of cycles. Lithium and sodium elements share similar chemical properties, however, the chemistry and electrochemistry of LABs and SABs are not the same. While the discharge product of LABs is well-recognized to be lithium peroxide (Li2O2), both sodium peroxide (Na2O2) and superoxide (NaO2) have been detected as the discharge product of SABs in a number of different studies. Therefore, understanding the chemistry behind SABs is critical towards enhancing their performance and advancing their development. In order to deepen the understandings on Na-air batteries, our group have applied nanostructured carbon materials as cathodes to investigate various effects including functional groups on graphene,6 surface area of porous carbon black,7 current density on CNTs/NCNTs,8 building 3D electrodes,9 and influence of humidity on rechargeability.10 Furthermore, the determining kinetics factors for controlling the chemical composition of the discharge products in SABs will be discussed and the potential research directions toward improving SABs are proposed. The perspectives in this field will be also anticipated. This poster will present the summary of the recent studies 6-10 and newfound results 11 on Na-air batteries from our group. 1. E. Peled, D. Golodnitsky, H. Mazor, M. Goor, S. Avshalomov, J. Power Sources 2011, 196, 6835. 2. Q. Sun, Y. Yang, Z. W. Fu, Electrochem. Commun. 2012, 16, 22. 3. P. Hartmann, C. L. Bender, M. Vracar, A. K. Durr, A. Garsuch, J. Janek, P. Adelhelm, Nat. Mater. 2013, 12, 228. 4. J. Wang, Y. Li, X. Sun, Nano Energy 2013, 2, 443. 5. H. Yadegari, Q. Sun, X. Sun, Na-O2 Batteries-A Review, submitted. 2015 6. Y. Li, H. Yadegari, X. Li, M. N. Banis, R. Li, X. Sun, Chem. Commun. 2013, 49, 11731. 7. H. Yadegari, Y. Li, M. N. Banis, X. Li, B. Wang, Q. Sun, R. Li, T. K. Sham, X. Cui, X. Sun, Energy Environ. Sci. 2014, 7, 3747. 8. Q. Sun, H. Yadegari, M. N. Banis, J. Liu, B. Xiao, B. Wang, S. Lawes, X. Li, R. Li, X. Sun, Nano Energy, 2015, 12, 698. 9. H. Yadegari, M. N. Banis, B. Xiao, Q. Sun, X. Li, A. Lushington, B. Wang, R. Li, T. K. Sham, X. Cui, X. Sun, Chem. Mater. 2015, 27, 3040. 10. Q. Sun, H. Yadegari, M. N. Banis, J. Liu, B. Xiao, X. Li, C. Langford, R. Li, X. Sun, J. Phys. Chem. C 2015, 119, 13433. 11. X. Sun et al., submitted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.013 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".