Despite the multitude of analytical methods available to characterize battery cathode materials, identifying the factors responsible for material aging is still challenging. We present the first investigation of transient redox processes in a spinel cathode during electrochemical cycling of a lithium ion battery by in operando electron paramagnetic resonance (EPR). The battery contains a LiNiMnO (LNMO) spinel cathode, which is a material whose magnetic interactions are well understood. The evolution of the EPR signal in combination with electrochemical measurements shows the impact of Mn on the Li motion inside the spinel. Moreover, state of charge dependent linewidth variations confirm the formation of a solid solution for slow cycling, which is taken over by mixed models of solid solution and two-phase formation for fast cycling due to kinetic restrictions and overpotentials. Long-term measurements for 480 h showed the stability of the investigated LNMO, but also small amounts of cathode degradation products became visible. The results point out how local, exchange mediated magnetic interactions in cathode materials are linked with battery performance and can be used for material characterization.

No abstract

Electrochemical Impedance Spectroscopy (EIS) has become an important tool for the analysis of batteries and fuel cells. Thus, the interest in the ongoing development of this technique is high. The major drawback of EIS is the ambiguity caused by the necessity to select a particular model for fitting the experimental data. One approach to mitigate this problem is the analysis of impedance data by the distribution of relaxation times (DRT) of the underlying physico-chemical processes, which might be the reason for its recent popularity [1-3]. The DRT can be calculated by a variety of methods, such as Fourier filtering [4], maximum-entropy deconvolution [5], least-squares deconvolution [6], evolutionary programming [7] or regularization [8]. The advantage of regularization is its usability without any a priori knowledge of the system. In this work, an evolution of the widely applied Tikhonov regularization in standard form [9] with an RC kernel is presented. The algorithm is using a uniform penalty. It has the advantage that narrow, high amplitude features are less broadened than low amplitude features in the same DRT spectrum and that it is less susceptible to unphysical oscillations [10]. This allows us to avoid a non-negativity constraint or similar penalties, therefore inductive and capacitive features can be unraveled in the spectrum by their opposite sign. In addition, by performing the regularization on two-dimensional (2D) data even if only one dimension is inverted, an improved resolution in the DRT dimension and smoother features in the non-inverted dimension are achieved. With peak-fitting algorithms each identified process is quantified and subsequently back-transformed into the frequency domain. By this, the contributions of the different processes to the overall impedance are visualized. Regularization is, in general, vulnerable to artifacts [11]. Hence checking the validity of the inverted data to prevent false interpretations is necessary, for which a procedure is described. 2D-DRT is used for the analysis of impedance data obtained from measurements of solid oxide fuel cells (SOFC) and lithium-ion batteries (LIB). For instance, for the SOFC the impedance measurements were performed for varying temperatures between 700°C and 900°C and potentials between 1200 mV and 700 mV. With this approach five different physico-chemical processes could be quantified in the frequency range between 100 mHz and 100 kHz which is in accordance with the literature [12]. References [1] P. Büschel, T. Gunther, O. Kanoun, “Distribution of relaxation times for effect identification and modeling of impedance spectra”, 2014 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings (2014) 901-904 [2] M. Saccoccio, T. H. Wan, C. Chen, F. Ciucci, “Optimal regularization in distribution of relaxation times applied to electrochemical impedance spectroscopy: Ridge and lasso regression methods - a theoretical and experimental study”, Electrochimica Acta 147 (2014) 470-482 [3] Y. Zhang, Y. Chen, M. Yan, F. Chen, “Reconstruction of relaxation time distribution from linear electrochemical impedance spectroscopy”, Journal of Power Sources 283 (2015) 464-477 [4] H. Schichlein, A. Müller, M. Voigts, A. Krügel, E. Ivers-Tiffée, “Deconvolution of electrochemical impedance spectra for the identification of electrode reaction mechanisms in solid oxide fuel cells”, Journal of Applied Electrochemistry 32 (8) (2002) 875-882 [5] T. VanderNoot, “Maximum entropy deconvolution of dielectric and impedance data”, Journal of Electroanalytical Chemistry 386 (1-2) (1995) 57-63 [6] E. Tuncer, J. R. Macdonald, “Comparison of methods for estimating continuous distributions of relaxation times”, Journal of Applied Physics 99 (7) (2006) 074106/1-4 [7] A. Oz, S. Hershkovitz, Y. Tsur, “Electrochemical impedance spectroscopy of supercapacitors: A novel analysis approach using evolutionary programming.”, AIP Conference Proceedings 1627 (1) (2014) 76-80 [8] F. Dion, A. Lasia, “The use of regularization methods in the deconvolution of underlying distributions in electrochemical processes”, Journal of Electroanalytical Chemistry 475 (1) (1999) 28-37 [9] S. W. Provencher, “A constrained regularization method for inverting data represented by linear algebraic or integral equations”, Computer Physics Communications 27 (3) (1982) 213-227 [10] J. Granwehr, P. J. Roberts, “Inverse Laplace transform of multidimensional relaxation data without non-negativity constraint”, Journal of Chemical Theory and Computation 8 (10) (2012) 3473-3482 [11] R. L. Parker, Y.-Q. Song, “Assigning uncertainties in the inversion of NMR relaxation data”, Journal of Magnetic Resonance 174 (2) (2005) 314-324 [12] M. J. Jørgensen, M. Mogensen, „Impedance of solid oxide fuel cell LSM/YSZ composite cathodes“, Journal of Electrochemical Society 148 (2001) A433-A442 Figure Caption: Two-dimensional DRT analysis of SOFC impedance data measured at a potential of 1000 mV and temperatures between 700°C and 900°C in the frequency range between 100 mHz and 100 kHz. Figure 1

No abstract

One focus of the present research of Lithium-Ion Batteries is the development of higher energy and higher power density materials. Spinels like LiMn2O4 exhibit a potential of 4.1 V v.s. Li/Li+ that can be further increased by substitution of Mn by transition metals like Ni. Those substituted spinels like LiMn1.5Ni0.5O4 are highly interesting cathode-materials for the high-energy range up to 5 V. The synthesis of spinels via a solid state route enables the production of high amounts up to 100 g with inexpensive starting materials and a combined step for milling and homogeni­zation of the raw materials. However, the solid state synthesis is a challenging approach regarding an adequate homogenization and a potential eva­po­ra­tion of Li during calcination[1]. The present work aims the characterization of calcined cathode-materials to control the processing parameters in order to prepare single-phase spinel materials. Therefore, different compositions of LixMn2O4 have been synthesized from Li2CO3 and MnO2 as starting materials, applying different milling parameters and calcination conditions. The calcined powders were then characterized by powder diffraction (XRD) and electron-paramagnetic-resonance spectroscopy (EPR). It is known that Li2MnO3 might be formed during the calcination process as an undesirable lithium-rich secondary phase[2]. Thus, particular attention has been paid on this phase. The formation of Li2MnO3 may occur by non-homogeneous mixing or remaining coarse particles of Li2CO3 in the powder mixture. The EPR is a highly sensitive method to obtain even small amounts of Li2MnO3 besides LiMn2O4in the calcined product and is therefore highly interesting for this application. [1] Y. Lee, The Effects of Lithium and Oxygen Contents Inducing Capacity Loss of the LiMn2O4    Obtained at High Synthetic Temperature, Journal of Electroceramics, 9, 209 – 214, 2002. [2] V. Massarotti et al., Stoichiometry of Li2MnO3 and LiMn2O4 Coexisting Phases: XRD and EPR Characterization, Journal of Solid State Chemistry, 128, 80 – 86, 1997.