(Invited) Electrochemical Stability of Prussian Blue Analogs and Implications for Energy Storage and Water Desalination Applications
Bibliographic record
Abstract
The increasing share of renewables in our electricity grid requires affordable and scalable battery technology that uses sustainable materials and has long lifetime. At the same time, growing water scarcity necessitates new desalination technologies with a similar requirement for sustainability and lifetime. Prussian blue analogs (PBAs), e.g., manganese hexacyanoferrate, MnFe(CN) 6 , or nickel hexacyanoferrate, NiFe(CN) 6 , are promising intercalation materials for secondary batteries. 1–4 Recently, this material class has been shown to be suitable for a novel energy-efficient water desalination approach, based on sodium intercalation into PBAs, sometimes referred to as battery desalination. 5–8 Hence, PBAs are potentially suitable electrode materials for, both, energy storage and water desalination, but it is important to investigate the materials’ stability to processing conditions and repeated ion intercalation. Prussian blue analogs like Na x MnFe(CN) 6 , 0<x<2, show high sensitivity towards moisture, which results in structural change and loss of sodium from the structure. 9,10 Different approaches can be utilized to mitigate this issue, such as surface coating or handling of the material under strictly inert conditions, starting from the storage of powder to slurry preparation and electrode processing. 9,11 In this study, we investigated the changes in surface chemistry during ambient storage, water exposure and subsequent heating of stored PBAs. Infrared spectroscopy (ATR-FTIR) and thermal analysis (TGA-MS) of materials stored for different times ranging from one hour to one week show a sharp increase in the moisture content of the active material. X-ray diffraction of exposed materials shows a clear trend between hydration state and crystal structure. Furthermore, surface hydroxides and carbonates are found by ATR-FTIR. A reheating step at relatively low temperature shows the release of adsorbed and interstitial water, but hydroxides and carbonates remain on the surface of the active material. The moisture stability of PBAs has important implications for aqueous electrode processing in energy storage applications and water-based device operation in desalination applications. We demonstrate effective drying strategies of electrodes made with aqueous slurries and appropriate binders. The as-prepared water-based electrodes show similar cycling stability as their non-aqueous counterparts. In the desalination context, exposure of the active material to water is unavoidable and may reduce performance and lifetime since the material remains hydrated during operation. We quantify performance and lifetime metrics of the novel battery desalination cells that employ NiFe(CN) 6 electrodes at opposite state-of-charge separated by an anion exchange membrane. 12 Using objective metrics like retention of specific capacity (mAh/g), energy consumption (Wh/l) and productivity (l/h/m 2 ) we show that these cells achieve vastly different performance for removal of monovalent and divalent ions. Stable charge/discharge cycling can be achieved for over 500 cycles with NaCl feed water, but rapid aging is observed with CaCl 2 feeds. Synchrotron-based characterization of NiFe(CN) 6 electrodes from the battery desalination cells is used to elucidate the reason for capacity fade (see Figure 1). X-ray absorption spectroscopy and X-ray fluorescence spectroscopy reveal Fe dissolution from the NiFe(CN) 6 active material as a primary aging mode with CaCl 2 water feeds. Based on the performance of Prussian blue analogs in, both, energy storage and water desalination, we will discuss potential synergies between these fields and strategies for efficient device design. 13 References C. D. Wessells, S. V. Peddada, R. A. Huggins, and Y. Cui, Nano Lett. , 11 , 5421–5425 (2011). C. D. Wessells et al., ACS Nano , 6 , 1688–1694 (2012). M. Pasta et al., Nat. Commun. , 5 , 1–9 (2014). A. Firouzi et al., Nat. Commun. , 9 (2018). M. Pasta, C. D. Wessells, Y. Cui, and F. La Mantia, Nano Lett. , 12 , 839–843 (2012). J. Lee, S. Kim, and J. Yoon, ACS Omega , 2 , 1653–1659 (2017). T. Kim, C. A. Gorski, and B. E. Logan, Environ. Sci. Technol. Lett. , 4 , 444–449 (2017). S. Porada, A. Shrivastava, P. Bukowska, P. M. Biesheuvel, and K. C. Smith, Electrochim. Acta , 255 , 369–378 (2017). D. O. Ojwang et al., ACS Appl. Mater. Interfaces , 13 , 10054–10063 (2021). J. Song et al., J. Am. Chem. Soc. , 137 , 2658–2664 (2015). L. Yang et al., J. Power Sources , 448 , 227421 (2020). M. M. Besli et al., Desalination , 517 , 115218 (2021). M. Metzger et al., Energy Environ. Sci. , 13 , 1544–1560 (2020). Figure 1 . (a) Three-electrode cells for evaluation of electrochemical stability of NiFe(CN) 6 towards mono- and divalent ion intercalation. (b) Specific capacity versus cycle number for 1C cycling with high and low concentrations of NaCl or CaCl 2 . (c) Visual inspection of electrolyte, revealing strong discoloration and brown precipitate after 300 cycles with 1 M CaCl 2 , and no discoloration after 400 cycles with 1 M NaCl. Figure 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".