Towards All Solid State Batteries Using Perovskite Solid Electrolytes
Bibliographic record
Abstract
Safety and environmental concerns are some of the main issues when considering lithium batteries technology. All Solid State Batteries (ASSB) may give a fundamental solution to solve those concerns. These solvent-free ASSB show major advantages: thermal stability, possibility of multicell assembly, possibility to use high potential electrode materials. We recently reported the approach to assemble ASSB in one step by Spark Plasma Sintering (SPS) using phosphate materials [1, 2]. Investigations on oxide electrolytes were considered. These electrolytes require not only being chemically stable towards electrode materials, but should also provide a good electrochemical stability over a wide potential range. Candidate materials (e.g. perovskite materials) should be relatively low cost, environmentally benign and easy to produce at an industrial scale. Several perovskite electrolytes (covering most of these requirements) were synthesized and characterized by XRD and impedance spectroscopy after sintering by SPS. Their thermal stability towards well-known electrode materials was also investigated, as well as electrodes and electrolyte interfaces using SEM and EDS. This presentation will focus on the required steps to develop ASSB using perovskite solid electrolytes. [1] A. Aboulaich et al,Advanced Energy Materials, 1, 179-183 (2011). [2] G. Delaizir et al, Adv. Funct. Mat., 22, 2140-2147 (2012).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".