Li<sub>4</sub>Ti<sub>5</sub>O<sub>12</sub> Degradation Kinetics during Galvanostatic Cycling
Bibliographic record
Abstract
Spinel lithium titanate Li4Ti5O12 (LTO) has been one of the most prominent electrode materials that are able to exhibit extremely long-term stability1 because of its negligible volume change (0.2%)2 during Li insertion / extraction. During Li insertion, external Li ions enter into the empty octahedral 16c sites in spinel structure, while in the meantime self-diffusion of originally existing Li+ at 8a sites occurs towards the neighboring 16c sites3 to form the rock-salt structure. As a result of this behavior the two-phase partially lithiated LTO, Li4+x Ti5O12 exhibits a very flat voltage plateau around 1.55 V vs. Li/Li+ over a wide range of Li content (e.g., 0.09 < x < 2.91). Of the two phases3,4, one is insulator Li4Ti5O12 (10-9 S/cm) and the other is conductor Li7Ti5O12 (8 S/cm) 5. Besides these intrinsic physicochemical properties of LTO, its nanosizing to enhance its power capability can influence its Li storage properties; e.g., the orientation-dependent 8a – 16c co-intercalation in the near surface region of LTO nanoparticles results in considerable irreversible capacity loss due the formation of overlithiated phase. Furthermore the high surface area of LTO nanoparticles leads to higher reactivity with the electrolyte aggravating the notorious gas generation issue, which may be accompanied structural relaxation in the near surface region6-11. Thanks to many efforts devoting to the size-effect on LTO for the last decade, the mystery of the size-effect mechanism has been largely revealed even though there are still several unanswered questions. One of these is the formation kinetics of the overlithiated phase near surface region, which is particularly critical from an industrial perspective as it can lead to optimized formation protocols and electrolyte recipes for LTO batteries. In this context, the universal Johnson-Mehl-Avrami-Kolmogorov (JMAK) kinetic model was employed to elucidate the correlation between overlithiated phase growth and the resistivity variation of LTO electrodes during standard galvanostatic charge / discharge cycle tests at room temperature. Based on this theoretical model, 8a-16c co-intercalated overlithated phase isotropically grows along the 3D Li diffusion pathway in the spinel framework, until the transition from 8a / 16c occupancy to 8a – 16c co-occupancy gets saturated in the near surface region. (1) Zaghib, K.; Simoneau, M.; Armand, M.; Gauthier, M. J Power Sources 1999, 81, 300. (2) Sun, Y.; Zhao, L.; Pan, H. L.; Lu, X.; Gu, L.; Hu, Y. S.; Li, H.; Armand, M.; Ikuhara, Y.; Chen, L. Q.; Huang, X. J. Nat Commun 2013, 4, 1870. (3) Wagemaker, M.; Simon, D. R.; Kelder, E. M.; Schoonman, J.; Ringpfeil, C.; Haake, U.; Lützenkirchen-Hecht, D.; Frahm, R.; Mulder, F. M. Advanced Materials 2006, 18, 3169. (4) Colbow, K. M.; Dahn, J. R.; Haering, R. R. J Power Sources 1989, 26, 397. (5) Young, D.; Ransil, A.; Amin, R.; Li, Z.; Chiang, Y.-M. Advanced Energy Materials 2013, 3, 1125. (6) Belharouak, I.; Koenig, G. M.; Tan, T.; Yumoto, H.; Ota, N.; Amine, K. J Electrochem Soc 2012, 159, A1165. (7) He, Y.-B.; Li, B.; Liu, M.; Zhang, C.; Lv, W.; Yang, C.; Li, J.; Du, H.; Zhang, B.; Yang, Q.-H.; Kim, J.-K.; Kang, F. Sci. Rep. 2012, 2. (8) He, Y.-B.; Liu, M.; Huang, Z.-D.; Zhang, B.; Yu, Y.; Li, B.; Kang, F.; Kim, J.-K. J Power Sources 2013, 239, 269. (9) Wu, K.; Yang, J.; Liu, Y.; Zhang, Y.; Wang, C.; Xu, J.; Ning, F.; Wang, D. J Power Sources 2013, 237, 285. (10) Bernhard, R.; Meini, S.; Gasteiger, H. A. J Electrochem Soc 2014, 161, A497. (11) He, M.; Castel, E.; Laumann, A.; Nuspl, G.; Novák, P.; Berg, E. J. J Electrochem Soc 2015, 162, A870.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".