Probing Electrochemically-Induced Structural Changes and Defects Affecting Li-Ion Intercalation and De-Intercalation in High Capacity Orthosilicate Cathodes
Bibliographic record
Abstract
Li-ion batteries (LIBs) have already made tremendous progress supplying power for multiple devices used in our daily life. Now LIBs are gradually entering the plug-hybrid and fully electric vehicles. Safety and high specific capacity enabling long range driving between charges are among industry’s priorities in this domain. One cathode material group that potentially can provide solutions to these priorities is lithium metal silicates. Orthosilicates, Li2 M SiO4, where M = Fe, Mn, Co, Ni, are characterized by a theoretical specific capacity that is twice that of LiFePO4, namely 340 vs. 170 mAh/g1 ,2. In this presentation, we report on the synthesis and electrochemical/structural evaluation of different phase (monoclinic and orthorhombic) Li2(Fe,Mn)SiO4 materials using a combination of organic-assisted hydrothermal and reducing annealing treatment steps. During the electrochemical process, we find that the Li2FeSiO4 cathode demonstrates rate- and phase- dependent capacity changes upon lithiation/delithiation3. We have probed these changes by conducting both in-situ/postmortem synchrotron XRD/XANES and first-principle calculations of the structure/phase stabilities and Li ion diffusion properties of monoclinic Li2FeSiO4 orthosilicate. It is revealed that formation of Li-Fe antisite defects play a key role in destabilizing the orthosilicate structure and “catalyzing” the monoclinic-to-orthorhombic transition. Furthermore the charge compensation mechanism that is essential in achieving more than one Li extraction is discussed accordingly as well as the Li-ion diffusion properties are analyzed. The rich insight obtained from these Li2FeSiO4 studies is invaluable as we move towards the design and testing of stable orthosilicate structures with fully reversible 2-Li intercalation capacity for high-energy next generation LIB cathodes. References 1. A., Nyten; A., Abouimrane; M., Armand; T., Gustafsson & J. O., Thomas, Electrochemical performance of Li2FeSiO4 as a new Li-battery cathode material. Electrochem. Commun. 7, 156-160, (2005). 2. M. Saiful Islam et al., Silicate cathodes for lithium batteries: alternatives to phosphates? J. Mater. Chem., 2011, 21, 9811. 3. Xia Lu; Huijing Wei; Hsien-Chieh Chiu; Raynald Gauvin; Pierre Hovington; Abdelbast Guerfi; Karim Zaghib and George P. Demopoulos, Rate-dependent phase transitions in Li2FeSiO4 cathode nanocrystals, Sci. Rep. 5 (2015) 8599.
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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".