Exploring the Impact of Mechanical Pressure on the Performance of Anode-Free Lithium Metal Cells
Bibliographic record
Abstract
In the pursuit of surpassing the energy density of conventional lithium ion cells, significant efforts have been made to develop lithium metal cells. However, many reports in the literature utilize Li-metal cells with significant excess lithium, resulting in a dramatically reduced practical energy density. In contrast, anode-free cells do not utilize excess lithium; instead, a lithium metal anode is formed in-situ from the stored lithium within the positive electrode during the first charge. Here, we evaluate anode-free lithium metal pouch cells (NMC532||Cu) with operando pressure measurements constrained to different stack pressures between 75–2205 kPa with two different electrolytes, 1M LiPF 6 FEC:DEC (1:2) and 1M LiPF 6 FEC:TFEC (1:2). Increasing the initial average pressure from 75–2200 kPa was found to generally improve cycle life, with the most significant benefits achieved up to 1200 kPa. Cells containing FEC:TFEC electrolyte exhibited a superior initial performance compared to FEC:DEC cells, as evidenced by cycling data and SEM analysis of the lithium morphology. Although generally beneficial, we found that the effect of increased pressure on the performance of cells with different solvent systems was not equal, indicating that the physical properties of electrolyte play an important roll in cells constrained to higher pressures between 1200–2200 kPa.
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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 | 0.001 |
| 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 | 0.000 | 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".