Impact of Nickel on Next-Generation Li-Rich Oxide Cathodes for Li-Ion Batteries
Bibliographic record
Abstract
As developments in lithium-ion batteries have focused on improving energy density, cycle life, and reducing cost, an important class of layered oxides with excess lithium, Li[Li,Ni,Mn,Co]O2, has been heavily studied as a next-generation electrode material. However, our limited understanding of the underlying electrochemical mechanisms hinders our ability to mitigate the negative impacts of these mechanisms, and has to date prevented commercialization. Previous studies have shown that these materials show a myriad of simultaneous mechanisms including changes in the nickel oxidation state, reversible oxygen redox accounting for capacities exceeding the theoretical limit1, oxygen gas release decreasing cycle efficiency2, as well as structural transformations leading to voltage fade3. Due to this complex mixture of electrochemical processes, model systems have been used extensively to observe these processes one at a time. This work on model systems is continued here by studying materials that show both nickel and oxygen redox but no structural transformations during cycling. This builds on work performed on Li-Fe-Sb-O and Li-Fe-Te-O where oxygen gas release was found to reduce Fe during charging of the electrode, an unexpected observation that has not been seen in other materials until the present study4. Given that nickel, and not iron, is of interest for next-generation materials, herein we use Li-Ni-Sb-O and Li-Ni-Te-O as model systems to better understand the electrochemical consequences of combining Ni and O redox processes during cycling. Herein, we show extensive electrochemical tests, ex-situ powder X-ray diffraction, online electrochemical mass spectrometry, X-ray absorption near-edge spectroscopy (XANES) and X-ray photoemission spectroscopy (XPS). Results show limited oxygen release takes place from the particle surfaces only, coupled with a marked contrast between the nickel oxidation states at the surface (as seen by XPS) and those of the bulk of the particles (seen with XANES). XPS data also demonstrates a reduction of surface nickel during oxygen gas release, confirming that the mechanism identified with Li-Fe-Sb-O does in fact occur in Ni-containing materials, though this is limited to the particle surface. Despite this surface effect, the reversibility of the nickel in the bulk is lost resulting in poor long-term cycling. Given that a great deal of work is currently being done to maximize the amount of nickel in next-generation electrode material, the consequences of nickel use in Li-rich oxides observed here are of high importance to further our understanding of these important materials. References 1. McCalla, E. et al, Science 2015, 350 (6267), 1516. 2. Jung, R. et al, Journal of The Electrochemical Society 2017, 164 (7), A1361-A1377. 3. Gallagher, K. G et al, Electrochemistry Communications 2013, 33, 96-98. 4. McCalla, E. et al, Journal of the American Chemical Society 2015, 137 (14), 4804-4814.
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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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".