Suite of High-Throughput Experiments for Screening Solid Electrolytes for Li Batteries
Bibliographic record
Abstract
All-solid lithium batteries are an important technology to achieve safer batteries with potentially longer life. Efforts over the past decade have generated a vast list of candidate solid electrolytes. High-throughput methods have already been useful in this context, but studies have been limited to room temperature ionic conductivities. Although a high ionic conductivity is necessary, this single property is insufficient to ensure function in a solid battery. Herein, a suite of high-throughput methods is introduced where 64 samples are synthesized simultaneously. Herein, we demonstrate for the first time the high-throughput capability of obtaining: (1) ionic conductivities at and above room temperature to extract activation energies, (2) electronic conductivities to evaluate the risk of dendrite growth within the electrolytes, (3) electrochemical stability window, and (4) chemical stability against lithium. Importantly, the stability window is obtained by testing the electrolyte in a composite electrode with conductive carbon, thereby avoiding the overestimations of stability that are rampant in the literature. Each method was validated using two reference materials chosen as they show high contrast for all properties. The results systematically show excellent reproducibility and good agreement with the literature. This suite of techniques provides meaningful properties necessary to evaluate candidate solid electrolytes.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".