Enhanced Capacity and Retention in Lithium Iron Orthosilicate Cathode Via Tuning Its Composition By Hydrothermal Synthesis
Bibliographic record
Abstract
During the past three decades, scientists have designed and tested a range of new materials for application in Li-ion batteries to meet the increasing energy storage demand. Still, the quest for delivering higher energy density while being safer and sustainable remains an ongoing challenge for Li-ion batteries. This work presents our recent efforts on improving an important cathode material, Li 2 FeSiO 4 (LFS), in order to make use of its attractive properties in terms of sustainability and safety. We apply compositional engineering to tune the electronic and crystal structures of LFS and eventually its electrochemical performance. We take advantage of the versatility of hydrothermal synthesis and synthesize various cation-substituted and non-stoichiometric LFS in orthorhombic Pmn 2 1 structure. Partially substituting Co for Fe is found to allow faster phase transformation from pristine Pmn 2 1 to inverse Pmn 2 1 with important positive ramifications in its cycling performance. More interesting, the insertion of Co alters the surface activities of LFS and induces the formation of cathode-electrolyte interphase (CEI) layers with lower resistance and better uniformity that protect the bulk particles from detrimental reactions with the electrolyte. Consequently, the participation of Co helps LFS to have improved capacity retention. To boost the capacity of LFS, we design Fe-rich LFS materials that deliver higher capacity within a reasonable voltage window. By combining DFT calculation with experimental testing, we find that Fe-rich composition LFS shows also promise in facilitating electronic and ionic transport.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".