High-Throughput Combinatorial Analysis of Mechanical and Electrochemical Properties of Li[Ni<sub>x</sub>Co<sub>y</sub>Mn<sub>z</sub>]O<sub>2</sub> Thin Film Battery Material
Bibliographic record
Abstract
Lithium-ion battery cathode material has been the subject of much research in both academia and industry in order to improve its electrochemical properties and cyclability. A prime candidate for replacing the relatively expensive and unstable LiCoO2, is the Lil[NiCoMn]O2 cathode material. Being a ternary material, the Li[NiCoMn]O2 has ample room for composition optimization. As such, extended investigations have been conducted to optimize the electrochemical properties of Li[NiCoMn]O2. However, there arose a need for the evaluation of the mechanical properties of Li[NiCoMn]O2 in order to remedy the deterioration of cathode cycle life due to stress development during repeated charge/discharge cycles. In this study, an efficient and high throughput combinatorial methodology is developed using sputter deposition technique and applied to the characterization of mechanical properties degradation of Li[NiCoMn]O2 as a result of repeated charge/discharge cycling. Co-sputter deposition of LiCoO2, LiNiO2, and LiMn2O4 compound targets was carried out in order to create the necessary concentration gradient to efficiently fabricate a Li[NiCoMn]O2 composition library. EDS analysis confirmed that the fabricated composition library encompassed a broad composition range of 20~80 at. % Ni content, 3~44 at. % Co content, and 5~50 at. % Mn content. XRD analysis confirmed a layered α -NaFeO2 (R3-m) structure for all compositions. Mechanical properties characterization by nanoindentation was carried out pre and post five charge/discharge cycles conducted via voltammetry sweep cycles between 2.5V and 4.5V at a scan rate of 1mV/s. Elastic modulus and hardness values pre and post charge/discharge cycles were found to both exhibit a strong composition dependency; Ni-rich compositions exhibited highest hardness values of 12GPa and Mn-rich compositions exhibited highest modulus values of 170GPa pre charge/discharge cycling. However, post charge/discharge cycling nanoindentation results indicated that Mn-rich compositions were characterized to have highest retention of its mechanical properties whereas the properties degraded more significantly for Ni-rich and Co-rich compositions. Electrochemical performance was analyzed at Ni-rich Li[Ni0.80Co0.10Mn0.10]O2, Co-rich Li[Ni0.40Co0.50Mn0.10]O2, and Mn-rich Li[Ni0.60Co0.03Mn0.37]O2, Li[Ni0.45Co0.10Mn0.45]O2, and Li[Ni0.33Co0.30Mn0.37]O2 compositions. Overall, a strong correlation for enhanced mechanical properties retention leading to superior discharge capacity retention was found with high Mn compositions demonstrating both superior mechanical properties retention and discharge capacity retention. Li[Ni0.33Co0.30Mn0.37]O2 composition, which retained 50% and 38% of its hardness and elastic modulus after cycling, demonstrated 90.61% discharge capacity retention after 20 cycles at 1C rate. Our results, obtained via the developed high throughput and efficient combinatorial analysis, indicate that the manganese rich compositions, which were characterized to have superior mechanical properties, demonstrate the best discharge capacity retention capability even after multiple charge/discharge cycles.
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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) | 0.001 | 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".