Impact of Nickel Substitution into Model Li-Rich Oxide Cathode Materials for Li-Ion Batteries
Bibliographic record
Abstract
Developments in lithium-ion batteries for energy storage are currently focused on improving energy density, increase cycle life, and reducing cost to match targets set by the automotive industry. An important class of cathodes, known as Li-rich layered oxides, Li–Ni–Mn–Co–O, is considered promising for next-generation electrode materials, yet a poor understanding of a number of detrimental processes, for which the underlying mechanisms are not clear, has hindered their commercialization. Numerous model systems have been studied in an effort to fully understand the discrete mechanisms taking place during battery operation. Given that Ni is relied upon more and more in commercial materials, we build here upon the previous work on model systems by studying Li–Ni–Sb–O and Li–Ni–Te–O materials to better understand the impact of Ni substitution into this complex class of materials. Using a combination of detailed electrochemical tests, X-ray diffraction, online electrochemical mass spectrometry, X-ray absorption near-edge spectroscopy, and X-ray photoemission spectroscopy, we find a stark contrast between the electrochemistry taking place in the bulk of particles as compared to that taking place at the surface. We find that oxidation of oxygen results in reduction of nickel, as was seen previously in Li–Fe–Sb–O, and this has a detrimental impact on the discharge capacity. However, the reductive couple occurs solely at the surface of particles in Ni-containing materials because of mitigated oxygen gas production in these materials. The consequences of this contrast between the surface and the bulk are discussed to guide further development of next-generation electrodes.
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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 | 0.001 | 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".