Lithium Niobate Coatings on Lithium Iron Phosphate Cathode Materials for Application in Lithium Ion Batteries
Bibliographic record
Abstract
There remains a need to enhance existing cathode materials in lithium ion batteries (LIBs). For example, lithium iron phosphate (LFP) is hindered by low electrical conductivity and slow lithium ion diffusion. While carbon coatings improve LFP performance, alternative materials with higher ionic conductivities have not been thoroughly explored. In this study, we present a method to synthesize a patchwork-type lithium niobate (LiNbO 3 ) coating on LFP particles. LiNbO 3 coatings were fabricated using solvothermal methods to enhance the LFP performance. Electron microscopy and X-ray spectroscopy confirmed the successful deposition of a ∼10 nm thick layer of LiNbO 3 onto the LFP surfaces. The effects of this coating on LFP performance were evaluated through galvanostatic charge-discharge tests. Notably, carbon-coated LFP particles with a 1% w/w LiNbO 3 coating exhibited a specific discharge capacity of 146 mAh g −1 at a 0.1 C rate and retained ∼98% of their capacity after 300 cycles at a 1 C rate. Additionally, cyclic voltammetry and current interruption techniques were employed to assess lithium diffusion coefficients before and after 300+ charge-discharge cycles. The results demonstrate that the LiNbO 3 coating significantly enhanced lithium ion transport by stabilizing areas of the LFP particles with thin or discontinuous carbon coatings, improving performance even after extensive cycling.
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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 | 0.001 | 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".