(Invited) Electrochemical Stability of Prussian Blue Analogs and Implications for Energy Storage and Water Desalination Applications
Notice bibliographique
Résumé
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 NaxMnFe(CN)6, 0 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/m2) 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 CaCl2 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 CaCl2 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 CaCl2. (c) Visual inspection of electrolyte, revealing strong discoloration and brown precipitate after 300 cycles with 1 M CaCl2, and no discoloration after 400 cycles with 1 M NaCl. Figure 1
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,014 | 0,004 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».