Improving Battery Performance via Mechanical Activation EnhancedSynthesis
Bibliographic record
Abstract
Rechargeable batteries will play a critical role in vehicle electrification, utilization of renewable energy, and the construction of smart city and IoT (internet of things). For these emerging applications, rechargeable batteries with high energy density, fast charging capability, as well as low fabrication cost and long cycle life are urgently needed. This presentation focuses on synthesis of advanced materials to enhance performance of sodium ion batteries (SIBs) with liquid electrolytes or solid electrolytes for large scale, stationary energy storage where ultralong cycle life, high round trip efficiency, low cost, and high safety are important, while the high gravimetric energy densities offered by Li-ion batteries (LIBs) are not critical. To achieve long-cycle life and high safety, we have developed a mechanical-activation-enhanced reactions (MAER) method to synthesize Na-cathode material and Na-ion conductor with controlled structural defects and larger Na diffusion pathways. Using this MAER method, we have achieved one of the best cycle stabilities of O3-NaCrO2 cathodes over 300 charge/discharge cycles without doping and one of the highest Na ion conductivities of Na3Zr2Si2PO12 solid electrolyte at room temperature (> 10 -3 S/cm). Detailed structural analyses reveal that MAER can minimize Cr 3+ ion misplacement at Na sites to improve the cycle stability of O3-NaCrO2 and increase the bottleneck size of Na3Zr2Si2PO12 crystals to enhance its Na ion conductivity at room temperature. These studies have provided a new direction and offered guidelines to synthesize high performance Na-ion battery materials in the near future.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".