Crystallite Size Control of Prussian White Analogues for Nonaqueous Potassium-Ion Batteries
Bibliographic record
Abstract
Nonaqueous potassium-ion batteries have emerged as possible low-cost alternatives to Li-ion batteries for large-scale energy storage, owing to their ability to use graphitic carbon as the negative electrode. Positive electrode materials remain a challenge. Here, we report control of the crystal dimensions of the Prussian white hexacyanoferrate (HCF), K 1.7 Fe[Fe(CN) 6 ] 0.9, using solution chemistry to obtain either nano, submicron, or micron crystallites. We observe a very strong effect of crystallite size on electrochemical behavior. The optimal cathode material comprised of 20 nm crystallites delivers a close-to-theoretical reversible capacity of 140 mAh g –1 with two well-defined plateaus at 4.0 and 3.2 V vs K/K + upon discharge. Slightly inferior electrochemical behavior is observed for crystallites up to ∼160–200 nm in diameter, but unlike the analogous Na HCFs, micron-sized crystals show very limited capacity. For the nanosized crystallites, however, the energy density of ∼500 Wh kg –1 is comparable to that of the best Na HCF cathode materials. At a relatively high current density of 100 mA g –1, half-cells cycled with ethylene carbonate/diethyl carbonate (EC/DEC) and 5% fluoroethylene carbonate (FEC) demonstrate an initial discharge capacity of 120 mAh g –1 with a capacity retention of 85% after 100 cycles and 65% after 300 cycles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".