Reversing the Chemical and Structural Changes of Prussian White After Exposure to Humidity to Enable Aqueous Electrode Processing for Sodium-ion Batteries
Bibliographic record
Abstract
Prussian White is a promising active material for the positive electrode of sodium-ion batteries as it is comprised of Na, Mn, Fe, C, and N and thus offers high sustainability and low cost. However, exposure of Prussian White to moisture results in chemical changes due to the formation of surface contaminants, as well as structural changes due to the absorption of water into the bulk crystal structure. Here we report an analysis of the formation rate of surface contaminants and bulk water absorption by weight tracking, infrared spectroscopy, and X-ray diffraction over extended periods of storage in high relative humidity air for fully sodiated Na1.8Mn0.8Fe0.2[Fe(CN)6]0.9 and partially sodiated Na1.3Mn0.8Fe0.2[Fe(CN)6]0.9. Fully sodiated Prussian White gains almost 20% in mass due to the formation of interstitial water during 20 h of storage in 100% relative humidity at 25 °C. Surface hydroxides and carbonates are found after storage and a structural change from the rhombohedral to a monoclinic crystal structure is observed. It is found that vacuum drying of Prussian White powder or electrodes at 150 °C can remove the majority of interstitial water and restore the rhombohedral crystal structure, but not remove surface contaminants. Prussian White immersed in water during aqueous electrode processing also shows interstitial water and a monoclinic crystal structure, but no surface contaminants. This suggests that aqueous electrode processing of Prussian White is feasible when effective drying strategies are employed. Indeed, Prussian White electrodes made from H2O-based slurries with CMC/NaPAA binder vacuum-dried at 150 °C show higher specific capacity and similar capacity retention in full cells as Prussian White electrode made from NMP-based slurries with PVDF binder.
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 | 0.001 |
| 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".