Bibliographic record
Abstract
Commercially available Li-ion batteries using graphite or graphite-silicon blended anodes are currently approaching a cell level specific energy of 300 Wh kg−1. The use of lithium metal instead as an anode an intriguing possibility to further increase cell level specific energies to 400 Wh kg−1 and beyond. Lithium is an ideal anode because it is the lightest metal and highly electronegative. However, attempts to commercialize cells using lithium metal anodes have been slowed by poor cycle life and safety issues. This is because nonuniform lithium plating leads to the growth of dendrites that cause loss of active lithium and can eventually lead to internal cell shorts. Safe cell cycle life must be improved to 50-100 cycles for special purpose applications like unmanned aerial vehicles, >300 cycles for portable power applications and >1000 cycles for electric vehicle applications. The performance of prototype cells developed at EaglePicher Technologies using lithium metal anode will be highlighted. The figure below on the left shows the specific discharge energy of a 2.5 Ah prototype pouch cell using a lithium anode, high nickel cathode and nonaqueous electrolyte. The cell demonstrates an extremely high specific energy of >400 Wh kg−1 at low rates. The effect of electrolyte on capacity retention is shown in the figure below on the right. The optimized electrolyte demonstrates good retention to >50 cycles. This presentation will focus on design considerations for pouch cells with lithium anodes, as well as improving the cycle life and safety characteristics of these cells. Prototype performance data including cycle life, rate capability, temperature effects and safety testing will be presented. EaglePicher Technologies would like to acknowledge the US Army DEVCOM C5ISR Center in Aberdeen Proving Ground, Maryland for funding this research. Figure 1
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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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".