Development of High-Throughput Methods for Sodium-Ion Battery Cathodes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Combinatorial synthesis of Li-ion batteries has proven extremely powerful in screening complex compositional spaces for next-generation materials. To date, no Na-ion counterpart exists wherein Na-ion cathodes can be synthesized in such a way to be comparable to that obtained in bulk synthesis. Herein, we develop a synthesis route wherein hundreds of milligram-scale powder samples can be made in a total time of 3 days. We focus on materials in the Na–Fe–Mn–O pseudoternary system of high immediate interest. Using a sol–gel method, developed herein, yields both phase-pure combinatorial samples of Na2/3Fe1/2Mn1/2O2 and NaFe1/2Mn1/2O2, consistent with previous reports on bulk samples of interest commercially. By contrast, the synthesis route used for Li-ion cathodes (namely coprecipitations) does not yield phase pure materials, suggesting that the sol–gel method is more effective in mixing the Na, Fe, and Mn than coprecipitation. This has important consequences for all attempts to make these materials, even in bulk. Finally, we demonstrate that these milligram-scale powder samples can be tested electrochemically in a combinatorial cell. The resulting cyclic voltammograms are in excellent agreement with those found on bulk samples in the literature. This demonstrates that the methodology developed here will be effective in characterizing the hundreds of samples needed to understand the complex ternary systems of interest and that such results will scale-up well to the gram and kilogram scale.
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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 | 0.001 | 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 | 0.001 | 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 it