Bibliographic record
Abstract
In the search for better performing battery materials, high-throughput approaches have the potential to screen large composition spaces in a short period of time and thereby greatly accelerate the development of advanced materials. The challenges are numerous both in terms of synthesis which is often done at much smaller scale than in commercial settings (e.g. milligrams) and in terms of characterization. Herein, we develop high-throughput synthesis of Na-ion cathodes in the Na-Mn-Fe-O pseudo-ternary system. The sol-gel approach is adapted to high-throughput and shows that single-phase materials are made whereas co-precipitation does not yield sufficiently intimate mixing of the cations and phase separation occurs. The samples made by sol-gel are further characterized using both X-ray diffraction and cyclic voltammetry (both in high-throughput) and yield results that match up very well with those obtained by making bulk amounts of the samples. This demonstrates scale-up of the combinatorial results will be possible. Preliminary data on the entire Na-Mn-Fe-O pseudoternary system will be shown for the first time revealing the structure-property relations taking place in this important system. This work will serve as both a screening tool and also help guide future research in the design of Na-ion cathodes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".