(Invited) Composition and Interfacial Engineering of Lithium Iron Orthosilicate Cathodes with Superior Intercalation Properties
Bibliographic record
Abstract
The quest for Li-ion batteries (LIBs) delivering higher energy density while being safer and sustainable has intensified recently. This work presents our advances towards improving the intercalation properties of an important cathode material, Li2FeSiO4(LFS), in order to take advantage of its attractive properties in terms of sustainability and safety. LFS theoretically has 330 mAh g-1capacity (2 Li+ per formula unit), however, inherently it exhibits relatively poor Li-ion intercalation kinetics, interfacial reactivity and complex phase transitions resulting in lower than one Li+ storage and poor capacity retention.In this work, we apply a multi-prong strategy to overcome these obstacles making use of compositional engineering to tune the electronic and crystal structures of LFS in concert withmechanochemical processing and polymer coating to provide a stabilizing interphase. We take advantage of the versatility of hydrothermal synthesis [1a] and mechanochemical activation and synthesize various cation-substituted and non-stoichiometric LFS nanomaterials with annealed orthorhombic Pmn21structure. Firstly, mechanochemical annealing leads to activation of Li-ion diffusion (DLi) by one order of magnitude enhancement [2a,b]. Secondly, partial substituting Co for Fe is found to allow faster phase transformation from pristine Pmn21to inverse Pmn21with important positive ramifications in its cycling performance [1b]. Thirdly, we boost the capacity of LFS, bydesigning Fe-rich (and not Li-rich!) LFS materials that deliver higher capacity within a reasonable voltage window [1c]. Lastly, PEDOT coating and near the surface composition alteration allows for stable cycling performance with >1.2 Li capacity [2c]. Acknowledgments:This research was supported by a NSERC grant and the McGill Sustainability Systems Initiative (MSSI). References: [1] Yan Zeng et al., (a) Hydrothermal Crystallization of Pmn21Li2FeSiO4Hollow Mesocrystals for Li-Ion Cathode Application,Chem. Eng. J., 359 (2019), 1592-1602; (b) Unveiling the mechanism of improved capacity retention in Pmn21Li2FeSiO4 cathode by cobalt substitution, J. Materials Chem. A2019, 7, 25399 – 25414; (c) Defect Engineering of Fe-Rich Orthosilicate Cathode Materials with Enhanced Li-Ion Intercalation Capacity and Kinetics, ACS Applied Energy Materials, 2020, 3, 675−686 [2] Majid Rasool et al., (a) Mechanochemically-tuned structural annealing: a new pathway to enhancing Li-ion intercalation activity in nanosized βIILi2FeSiO4 , J Mater. Chem. A 2019, 7, 13705 – 13713; (b) Unusual Li-ion intercalation activation with stepwise capacity increase in orthosilicates, J. Phys. Chem. C;DOI: 10.1021/acs.jpcc.9b11896; (c) PEDOT encapsulated and mechanochemically engineered silicate nanocrystals for high energy density cathodes, Advanced Materials Interfaces, 2020, 2000226; DOI: 10.1002/admi.202000226
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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".