Enhancement of Electrochemical Properties of Lithium Rich Li <sub>2</sub> RuO <sub>3</sub> Cathode Material
Bibliographic record
Abstract
Lithium-rich layered Ru-based oxides are interesting cathode materials due to their high energy density and reversible capacity. However, their poor structural stability and voltage decay hinder their broad commercial applicability. To address this, we investigate the co-doping strategy on Li 2 RuO 3 (LRO) for improved battery performance using a combination of quantum mechanics, molecular dynamics, and pseudo-1-dimensional (P1D) formulations. Specifically, in addition to the effect of Ti as a dopant in Li 2 Ru 0.5 Ti 0.5 O 3 (LTO), the effect of three co-dopants, Tc, Rh, or Pd in Li 2 Ru 0.5 Ti 0.25 M 0.25 O 3 has also been studied. It has been found that the co-doping strategy significantly improves the thermal stability of LRO. Tc and Ti improve structural stability by reducing the oxygen removal reaction. Pd and Tc reduce the bandgap considerably, leading to higher electrical conductivity. The results show that co-doping minimizes the energy required for Li-ions diffusion. In particular, Tc significantly enhances the Li-ions diffusion in LRO and LTO. Further co-dopants Rh, Pd, and Tc improve the maximum voltage of LRO, as well as the voltage stability by reducing the voltage reduction. Finally, P1D simulations show that while LTO provides the highest voltage and power operation, doping it with Tc and Pd increases its efficiency by reducing the ohmic potential drop and diffusion polarization.
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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".