(Invited) Building Better Batteries in the Solid State
Bibliographic record
Abstract
Most of the current commercialized lithium batteries employ liquid electrolytes, despite their vulnerability to battery fire hazards, because they avoid the formation of dendrites on the anode side, which is commonly encountered in solid-state batteries. In a review two years ago, we focused on the challenges and issues facing lithium metal for solid-state rechargeable batteries (see J. Power Sources 353 (2017) 333-342), pointed to the progress made in addressing this drawback, and concluded that a situation could be envisioned where solid-state batteries would again win over liquid batteries for different applications in the near future. However, an additional drawback of solid-state batteries is the lower ionic conductivity of the electrolyte. Therefore, extensive research efforts have been invested in the last few years to overcome this problem, the reward of which has been significant progress. It is the purpose of this review to report these recent works and the state-of-the-art on solid electrolytes. In addition to solid electrolytes stricto sensu , there are other electrolytes that are mainly solids, but with some added liquid. In some cases, the amount of liquid added is only on the microliter scale; the addition of liquid is aimed at only improving the contact between a solid-state electrolyte and an electrode, for instance. In some other cases, the amount of liquid is larger, as in the case of gel polymers. It is also an acceptable solution if the amount of liquid is small enough to maintain the safety of the cell; such cases are also considered in this review. Different chemistries are examined, including not only Li-air, Li-O 2 , and Li-S, but also sodium-ion batteries, which are also subject to intensive research. The challenges toward commercialization are also considered.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".