Characterization of Engineered Complex Cathode Materials for Li-ion Batteries
Bibliographic record
Abstract
Lithium-ion batteries have become a vital part of our modern life and play an essential role in electric vehicle development. One of the most feasible strategies to enhance the energy density of Li-ion batteries is to use layered, Ni-rich cathode materials. However, higher nickel content causes several problems and therefore, several methods, including doping and coating, have been utilized to stabilize their structure and boost their performance. This thesis aims to understand the microstructure of such engineered complex cathodes and provide valuable contributions by comprehensively understanding and establishing a link between the composition, structure, performance, and properties of these complex materials. In this regard, the most advanced electron- and photon-based techniques have been used to uncover the fundamental underlying reasons for the enhanced performance or degradation in these complex cathode structures. This study shows that introducing W cation inside the LiNiO2 results in new W-variants with a heterogeneous concentration on the top surface and through grain boundaries of the host secondary particles. These W-rich regions play a reinforcing role in grain boundaries and protect the outer surface of LiNiO2 particles. However, synthesis defects, such as porosities, could reduce these benefits by increasing the electrolyte infiltration inside the cathode particles. It is also demonstrated that the degradation process can be studied through the changes in electron energy loss near-edge structure spectra. The investigation of a coating approach on LiNi0.8Co0.15Al0.05O2 materials through the mechanofusion process illustrates more microscopic-scale details regarding the thickness unevenness of the coating and some degree of physical intermixing between the core (LiNi0.8Co0.15Al0.05O2) and coating (LiFePO4 and alumina) precursors. In addition to good physical contact between the core and coating materials, further analysis at higher resolution reveals some nanoscale grains and defective areas near the top surface of the secondary particles following the mechanofusion coating process.
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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 | 0.001 | 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) | 0.002 | 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".