Mesocrystallizing Nanograins for Enhanced Li<sup>+</sup> Storage
Bibliographic record
Abstract
Abstract The morphology and crystallinity of electrode materials have a major effect on their charge carrier storage properties when applied in rechargeable batteries. While nanosizing electrode particles (with larger surface area) and maintaining electrode integrity are both good for performance enhancement, they seem to contradict each other and are challenging to balanced. Herein, electrode particles consisting of numerous nanograins with uniform crystalline orientation are designed to guarantee both high surface area and high structural integrity, allowing the significant improvement of Li + storage kinetics and performance. Applying this “mesocrystallizing” strategy to an NiCo 2 O 4 ‐based anode, results in various degrees of pseudocapacitance response, the long‐term cyclability and rate performance of this material are also significantly enhanced. Impressively, the mesocrystalline NiCo 2 O 4 electrode exhibits a high specific capacity of 1403 mAh g –1 after 200 cycles at 1.6 A g –1 (a rate of 1.8 C). The growth mechanism of mesocrystalline materials with different morphologies is identified to be a topotactic structural transition process featuring a gradual edge‐to‐core corrosion process. This work presents an important synthetic clue to balance the morphology and crystallinity of battery electrode materials for their performance optimization and is expected to inspire future structural design for battery materials beyond the one prototyped here.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".