Reporting on the Unusual Electrochemical Performance of the Low Temperature Orthorhombic Phase of Lithium Iron Silicate
Bibliographic record
Abstract
Lithium transition metal silicates Li2 M SiO4 ( M = Fe, Mn, Co, etc.) have been under intense research due to their double theoretical capacity of 330 mAh/g compared to LiFePO4 (170 mAh/g)1,2,3 . Their development as cathode materials however has been hampered due in part of their rich polymorphism and phase transitions occurring during cycling not allowing full capacity storage and reversible retention. In this work we focus on the electrochemistry of lithium iron silicate (LFS) 5-8. Almost all previous studies have focused solely on the electrochemistry of the high temperature monoclinic phase (m-LFS). According to these studies4,5,8, the monoclinic phase undergoes electrochemically-induced phase transition during cycling to thermodynamically stable low-temperature orthorhombic phase (o-LFS). This prompted us to take a deep look into the structural and electrochemical behavior of the latter phase as it might hold the key in designing high performance lithium iron orthosilicate cathodes. During this investigation we discovered that upon galvanostatic cycling, the specific capacity of an o-LFS/C nanocomposite cathode exhibited gradual increase from 40 to 165 mAh/g and a shift in Li-storage mechanism from solid solution to biphasic type. This intriguing behavior is currently the subject of further characterizations to elucidate the underlying phenomena that can have significant implications to the development of high energy density LIB cathode materials. References Islam, M.S., et al., Silicate cathodes for lithium batteries: alternatives to phosphates? Journal of Materials Chemistry, 2011. 21(27): p. 9811-9818. Zaghib, K., et al., Review and analysis of nanostructured olivine-based lithium recheargeable batteries: Status and trends. Journal of Power Sources, 2013. 232: p. 357-369. Ferrari, S., et al., Electrochemistry of orthosilicate-based lithium battery cathodes: a perspective. Physical Chemistry Chemical Physics, 2014. 16(22): p. 10353-10366. Masese, T., et al., Relationship between Phase Transition Involving Cationic Exchange and Charge–Discharge Rate in Li2FeSiO4. Chemistry of Materials, 2014. 26(3): p. 1380-1384. Lu, X.; Wei, H. J.; Chiu, H. C.; Gauvin, R.; Hovington, P.; Guerfi, A.; Zaghib, K.; Demopoulos, G. P. Rate-dependent phase transitions in Li2FeSiO4 cathode nanocrystals. Sci Rep-UK 2015, 5, 8599. Arthur, Z.; Chiu, H.-C.; Lu, X.; Chen, N.; Emond, V.; Zaghib, K.; Jiang, D.-T.; Demopoulos, G. P. Spontaneous reaction between uncharged lithium iron silicate cathode and LiPF6-based electrolyte Chem Commun 2016, 52, 190-193. Lu, X.; Chiu, H.-C.; Bevan, H. K.; Jiang, D.-T.; Zaghib, K.; Demopoulos, P. G. Density functional theory insight into the structure stability and Li diffusion properties of monoclinic and orthorhombic Li2FeSiO4 cathodes. J Power Sources 2016, In revision. Lu, X.; Chiu, H.-C.; Arthur, Z.; Zhou, J. G.; Wang, J.; Chen, N.; Jiang, D.-T.; Zaghib, K.; Demopoulos, G. P. Quasi-equilibrium Li storage in metastable Li2FeSiO4 cathode. 2016, Under review.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".