Bibliographic record
Abstract
Research on lithium metal combined with polymer electrolyte in lithium rechargeable batteries was started in 1979. Since that time, battery research has expanded worldwide. Several new polymers, solid electrolytes and ionic liquids with improved conductivity were identified. These advances resulted from a better understanding of the major parameters controlling ion migration, such as favorable polymer structure, phase diagram between solvating polymer and lithium salt, and the development of new lithium counter-anions. In spite of the progress so far, the quest for a highly conductive dry polymer at room temperature is still not available. However, effort is continuing, and all-lithium polymer battery (LPB) developers presently face the challenge of whether to heat the polymer electrolyte to enable high-power performance, as required for electric vehicle and energy storage. LPB developers have explored both the high-temperature and low-temperature options. This presentation discusses the challenges and opportunities in developing thin lithium metal with stable SEI as negative electrode for three battery technologies using: 1. Batteries made from dry polymer and ionic liquid-polymer electrolytes for rechargeable lithium batteries 2. All solid-state Li-sulfur batteries 3. Li-air batteries. In addition, we will discuss the safety of lithium, dendrite mechanism, interface phenomena, side reactions, protection of lithium metal, and lithium alloys.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
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".