A chemical map of NaSICON electrode materials for sodium-ion batteries
Bibliographic record
Abstract
Na-ion batteries are promising devices for smart grids and electric vehicles due to cost effectiveness arising from the overall abundance of sodium (Na) and its even geographical distribution. Among other factors, the energy density of Na-ion batteries is limited by the positive electrode chemistry. NaSICON-based positive electrode materials are known for their wide range of electrochemical potentials,[1],[2],[3] high ionic conductivity, and most importantly their structural and thermal stabilities. Using first- principles calculations, we chart the chemical space of 3 d transition metal-based NaSICON phosphates of formula Na x MM’(PO 4 ) 3 (with M and M’= Ti, V, Cr, Mn, Fe, Co and Ni), to analyze their thermodynamic stabilities and the intercalation voltages for Na+ ions. Specifically, we computed the Na insertion voltages and related properties of 28 distinct NaSICON compositions. We investigated the thermodynamic stability of Na-intercalation in previously unreported NaxMn 2 (PO 4 ) 3 and Na x VCo(PO 4 ) 3 . The calculated quaternary phase diagrams of the Na-P-O-Co and Na-P-O-Ni chemical systems explain the origin of the suspected instability of Ni and Co-based NaSICON compositions. From our analysis, we are also able to rationalize anomalies in previously reported experimental data in this diverse and important chemical space.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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".