Prussian blue analogues are considered as promising candidates for aqueous sodium-ion batteries providing a decently high energy density for stationary energy storage. However, suppose the operation of such materials under high-power conditions could be facilitated. In that case, their application might involve fast-response power grid stabilization and enable short-distance urban mobility due to fast re-charging. In this work, sodium nickel hexacyanoferrate thin-film electrodes are synthesized via a facile electrochemical deposition approach to form a model system for a robust investigation. Their fast-charging capability is systematically elaborated with regard to the electroactive material thickness in comparison to a ″traditional″ composite-type electrode. It is found that quasi-equilibrium kinetics allow extremely fast (dis)charging within a few seconds for sub-micron film thicknesses. Specifically, for a thickness below ≈ 500 nm, 90% of the capacity can be retained at a rate of 60C (1 min for full (dis)charge). A transition toward mass transport control is observed when further increasing the rate, with thicker films being dominated by this mode earlier than thinner films. This can be entirely attributed to the limiting effects of solid-state diffusion of Na+ within the electrode material. By presenting a PBA model cell yielding 25 Wh kg–1 at up to 10 kW kg–1, this work highlights a possible pathway toward the guided design of hybrid battery–supercapacitor systems. Furthermore, open challenges associated with thin-film electrodes are discussed, such as the role of parasitic side reactions, as well as increasing the mass loading.

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Electrochemical Impedance Spectroscopy (EIS) has been widely applied for Li-ion battery research because EIS can reflect the physical characteristics. The full impedance spectrum sweep generally takes several minutes. Thus, it is impossible to implement a full spectrum sweep for real-time investigations. In this paper, machine learning approach is proposed to address the issue. The proposed approach is based on multi-sine signal sweep technique, where the impedances at corresponding frequencies are derived with a fast Fourier transform. The full impedance spectrum is obtained via machine learning approach. The results are compared with three alternative techniques namely, the piecewise cubic Hermite interpolation polynomial, modified Akima piecewise cubic Hermite interpolation, and Spline. The results demonstrate that the proposed machine learning approach has the best performance.

Niobium belongs to the group of refractory metals, and shows advanced properties such as a high melting point and a good hardness, due to the strong interatomic bonds [1,2]. Furthermore, niobium exhibits excellent chemical and corrosion resistance [3]. This makes niobium coatings very attractive for a broad spectrum of applications [4]. Due to the low standard potential, it is not possible to electrodeposit niobium from aqueous solutions. Ionic liquids (ILs) are a good alternative, and first studies have demonstrated the general feasibility of Nb metal deposition from NbF5 containing ILs [5-7]. However, layers deposited from standard ionic liquids like 1-butyl-1-methylpyrrolidinium bistrifluoromethylsulfonyl imide (BMP TFSI) often still are not dense, show cracks, have residual ionic liquids incorporated, and contain Nb subhalides [5]. The use of different precursors like NbCl5 and other ionic liquids, particularly those based on a triflate anion, has been reported [8-10]. In our own research, we recently studied in detail the electrochemical reduction of NbCl5 in TFSI-based ionic liquids [11]. In this work, the electrochemical behavior of NbF5 in different ionic liquids was studied with the electrochemical quartz crystal microbalance technique (EQCM). A network analyzer was used to measure the admittance between the two Au electrodes (one serving as working electrode) of 10 MHz quartz resonators in parallel to the electrochemical measurements. This permitted to extract both changes in resonance frequency and damping of the quartz. The latter is particularly important in ionic liquids, as local changes in the electrolyte composition due to a reduction process are often associated with damping changes. Experiments were carried out at different temperatures, using pulsed electrodeposition and pulsed ultrasound techniques. The application of ultrasound can significantly enhance mass transport and thereby prevent precursor depletion and fluoride enrichment at the surface. On the downside, there is a risk of accelerated electrolyte degradation [11, 12]. Figure 1a shows an example for a successful deposition from a 0.25 M NbF5 solution in an alkylammonium IL electrolyte. A reasonably well adhering layer was obtained even at room temperature that however still contained some fluoride ions at the surface, and was very rough. According to the XPS measurements in Figure 1b and c, an average composition of NbF0.6 was obtained at the surface. Figure 1. a) Photograph of layer deposited from 0.25 M NbF5 b) XPS spectrum (F1s) c) XPS spectrum (Nb 3p5/2,3/2) References [1] S. Zein El Abedin, H.K. Farag, E.M. Moustafa, U. Welz-Biermann, F. Endres. Phys. Chem. Chem. Phys., 7, 2333 (2005). [2] N. Borisenko, A. Ispas, E. Zschippang, Q. Liu, S. Zein El Abedin, A. Bund, F. Endres, Electrochim. Acta, 54, 1519 (2009). [3] V.-H. Pham, S.-H. Lee, Y. Li, H.-E. Kim, K.-H. Shin, Y.-H. Koh, Thin Solid Films, 536, 269 (2013). [4] F. Cardarelli, Int. J. of Refractory Metals & Hard Materials, 14, 3655 (1996). [5] P. Giridhar, S. Zein El Abedin, A. Bund, A. Ispas, F. Endres, Electrochim. Acta, 129, 312 (2014). [6] S. Krischok, A. Ispas, A. Zühlsdorff, A. Ulbrich, A. Bund, F. Endres, ECS Trans., 50(11), 229 (2012). [7] A. Vacca, M. Mascia, L. Mais, S. Rizzardini, F. Delogu, S. Palmas, Electrocatalysis, 5, 16 (2014). [8] E. Freydina, and J. G. Abbott, ECS Trans., 75 (15), 639 (2016). [9] O. B. Babushkina, E. O. Lomako, and W. Freyland, Electrochim. Acta, 62, 234 (2012). [10] Endrikat, N. Borisenko, A. Ispas, R. Peipmann, F. Endres, A. Bund; under revision. [11] L. Seidl, L. Asen, G. Yesilbas, P. Fischer, F. Kühn, and O. Schneider, ECS Trans., 86 (14), 3 (2018). [12] J. D. Oxley, T. Prozorov, and K. S. Suslick, J. Am. Chem. Soc., 125 (37), 11138 (2003). Figure 1