Effects of Fluorine Doping on Nickel-Rich Positive Electrode Materials for Lithium-Ion Batteries
Bibliographic record
Abstract
Three fluorine-doped lithium nickel oxide samples series (LiNiO2−xFx, LiNi1−xMgxO2−xFx; Li1+x/2Ni1−x/2O2−xFx) were prepared and investigated. It is suggested that fluorine was introduced into the lattice structure during the calcination. As fluorine is introduced into LiNiO2−xFx and LiNi1−xMgxO2−xFx the percentage of Ni (or Ni and Mg) in the Li layer increases for x > 0.05. However, adding excess Li in Li1+x/2Ni1−x/2O2−xFx sucessfully balances the charge differential introduced by fluorine doping therefore very little Ni2+ was created and the lithium layers remain “uncontaminated” by other metals. Data from Li/LiNiO2−xFx, Li/LiNi1−xMgxO2−xFx and Li/Li1+x/2Ni1−x/2O2−xFx cells mirror the percent of cation mixing as determined by X-ray diffraction (XRD) and Rietveld refinement in each case. In situ XRD of Li1.1−xNi0.9O1.8F0.2 shows no multipule phase transitions which further suggests fluorine was successfully doped into the lattice. Acclelerating rate calorimetry (ARC) experiments show a potential safety advantage brought by fluorine doping. pH titration was used to explore if residual LiF (if any) at the surface converted to other lithium compounds (LiOH, Li2O or Li2CO3). No evidence of residual LiF was found.
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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 | 0.001 | 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".