Synthesis of Honeycomb-Shaped LiNi0.5Mn0.5O2 Using a Sol-Gel Method with Polymethylmethacrylate (PMMA) and Organic Surfactant
Bibliographic record
Abstract
Layered-structural LiNi0.5Mn0.5O2 has high discharge capacity, abundant availability, enhanced chemical stability, convenient environmental benignancy, and low cost. However, LiNi0.5Mn0.5O2 suffers from poor intrinsic rate capability due to its poor ionic conductivity (2.54 × 10-7 S/cm) and poor cycle stability owing to the volume change of the cathode materials during cycling. To address this issue, honeycomb-shaped LiNi0.5Mn0.5O2 was developed for lithium-ion batteries using a sol-gel method with spherical polymethylmethacrylate (PMMA) particles. PMMA particles provide spherical voids in LiNi0.5Mn0.5O2 cathode materials due to their relatively low decomposition temperature (< 350 °C). Honeycomb-shaped LiNi0.5Mn0.5O2 has a higher surface area (2.63 m2/g) than the LiNi0.5Mn0.5O2 (2.00 m2/g) produced by conventional sol-gel method. The initial discharge capacities of conventional nano LiNi0.5Mn0.5O2 and honeycomb-shaped LiNi0.5Mn0.5O2 are 151.9 mAh g-1 and 200.4 mAh g-1, respectively at 0.1 C. After 50 cycles at 1 C, honeycomb-shaped LiNi0.5Mn0.5O2 has a larger capacity retention than conventional nano LiNi0.5Mn0.5O2, measuring 67.9% and 58.8%, respectively. The superior electrochemical performance of honeycomb-shaped LiNi0.5Mn0.5O2 increases the effective surface area for Li-ion diffusion, leading to better rate capability, and buffers the volume change during Li+ion insertion/extraction, improving the cycling stability.
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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) | 0.001 | 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 | 0.000 | 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".