Investigating Parasitic Reactions in Anode-Free Li Metal Cells with Isothermal Microcalorimetry
Bibliographic record
Abstract
Anode-free Li metal cells are one of the most appealing energy storage technologies beyond Li-ion batteries due to their superior theoretical specific and volumetric energy densities. However, long cycle life in an anode-free cell remains elusive due to difficulties reversibly plating and stripping metallic lithium. Isothermal microcalorimetry was used to study parasitic reactions in anode-free Li metal cells for electrolytes containing different Li salts. A new cycling protocol was used to measure the parasitic heat flow both on freshly plated Li surfaces and anode surfaces after stripping lithium. Existing methods were used to measure parasitic reactions occurring at high voltage. In both low- and high-voltage measurements, electrolytes containing LiDFOB had the highest parasitic heat flow compared to an electrolyte with LiPF6 salt. In contrast to previous studies of parasitic reactions in Li-ion batteries using isothermal microcalorimetry, the LiDFOB-containing electrolytes gave the longest lifetime despite having higher parasitic heat flow. This observation was attributed to decomposition of the LiDFOB salt, and subsequent formation of a favorable SEI layer that greatly improves plating and stripping efficiency. In-situ detection of parasitic heat flow with isothermal microcalorimetry techniques will be valuable for future studies of electrolyte design in anode-free Li metal cells.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".