Pioneering Combinatorial Investigation to Unlock the Potential of Lithium Borosilicate Glasses as Solid Electrolytes
Bibliographic record
Abstract
The development of solid-state electrolytes for lithium-ion batteries (LIBs) focuses on enhancing the safety, lifetime, and energy density. Lithium borosilicate glass ceramics (LBS) have garnered interest due to their electrochemical stability and deformability. However, achieving highly ionic conductive glasses requires fully glassy LBS compositions at high lithium contents and this remains a challenge. To date, only a handful of Li–B–Si–O compositions have been studied as prospective solid electrolytes. Herein, we developed the combinatorial synthesis of glasses by the melt-quench method. We adapted our high-throughput techniques to be able to obtain XRD, ionic conductivity, electronic conductivity, and the electrochemical stability window on these glasses. Furthermore, we designed a high-throughput softness measuring system with exceptional precision for effective determination of deformability and this test demonstrates excellent correlation with the glass transition temperature (a measurement that cannot be performed in high-throughput). Our investigations explored the influence of composition in over 360 different combinations of Li–B–Si–X–O where X are substituents from a list of 55 elements. Low level substitution (1%) was found to increase the solubility of Li in the glasses which in turn dramatically increased the deformability and gave a moderate improvement in ionic conductivity. In addition, our study unveiled that substitutions have an impact on the electrochemical stability window with the Zn-substituted glass demonstrating a greater anodic stability limit compared to the unsubstituted LBS electrolyte. Overall, this study provides valuable insights into lithium borosilicate composition–property relations. Extending combinatorial techniques to the study of glassy solid electrolytes opens up many avenues for accelerated design of this class of materials.
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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".