Chemical Speed Dating: The Impact of 52 Dopants in Na–Mn–O Cathodes
Bibliographic record
Abstract
Na–Mn–O cathodes are very promising for sodium-ion batteries but suffer major setbacks related to long-term cycling and stability in air. With our high-throughput approach, a systematic investigation of 52 different dopants of Na 0.66 MnO 2 from across the periodic table was performed. The chemical composition of Na 0.66 Mn 0.9 M 0.1 O 2+δ (M = dopant) is utilized to unravel the impact of dopants on the layered structure and investigate how different dopants influence the battery performance and air and moisture stability. A broad range of doping was possible, with 20 different dopants fully integrating into the Na–Mn–O structures, including several previously unstudied dopants (Si, Sc, Ga, Rb, Rh, Cs, Re, and Tl). This yields high-interest novel cathodes, including a Rb-doped sample with a high specific capacity of 200 mA h g –1, as well as Mo- and Nb-doped samples with excellent capacity retentions of 98% and 100%, respectively, after 10 cycles compared to 92% in undoped Na 0.66 MnO 2 . The air and moisture stability of the cathode material is studied systematically, and a number of compositions show ultrahigh stability in air. This systematic approach provides a rapid overview of the benefits of individual dopants and also provides an excellent opportunity to elucidate trends across the periodic table. Significantly, we find that the presence of reversible anionic redox (absent in the undoped samples) correlates remarkably well to the bond valence sum of the dopants, implying that dopants can be used to tune the polarity of M–O bonds and encourage anionic redox behavior. Such “speed dating” reveals fundamental chemical insights and guides further design.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".