Bildung und Stabilität von anodischen Deckschichten auf Eisen-Silizium-Legierungen

In den letzten Jahren wurde umfangreich über Wirkprinzipien berichtet, die zur Verbesserung der Passivschichtstabilität von Fe-Basislegierungen durch Legierungsbestandteile, wie Cr [1-3] und Al [4-6] beitragen. Die Zahl der Arbeiten zu Untersuchungen mit dem Legierungsbestandteil Si sind dagegen gering und lassen noch keine endgültige Schlußfolgerung zu. Insbesondere sind verschiedene Wirkmechanismen bei unterschiedlichen ph-Werten zu erwarten

State-of-Health Diagnosis of Lithium-Ion Batteries Using Nonlinear Frequency Response Analysis

Estimation of the State-of-Health (SOH) of Lithium-ion Batteries (LIBs) is commonly conducted using in-situ measurement methods, such as Incremental Capacity Analysis (ICA) and Differential Voltage Analysis (DVA) as well as impedance based techniques. In this study, we present an alternative method for SOH estimation: The nonlinear dynamic measurement method Nonlinear Frequency Response Analysis (NFRA) is shown to be able to estimate capacity fade of LIBs due to loss of active material. Capacity loss correlates with the quotient of the root mean square of the second and the third harmonic response for different excitation amplitudes in the frequency range sensitive to electrochemical reactions at approximately 1 Hz. The results of the experimental cycle-aging study are validated and further analyzed by using a reaction model containing Butler-Volmer kinetics with a dynamic charge balance of the electrode. Simulations show that the NFR quotient and capacity fade due to loss of specific surface area correlate exactly. We identify the NFR quotient as a reliable, easily measurable parameter for the diagnosis of the SOH of LIBs. Therefore, this study reveals a novel approach for SOH estimation of LIBs based on dynamic analysis with NFRA

Identifying Anode and Cathode Contributions in Li-Ion Full-Cell Impedance Spectra

Measured impedance spectra of Li-ion battery cells are often reproduced with equivalent circuits or physical models to determine losses due to charge transfer processes at the electrodes. The identified model parameters can usually not readily or unambiguously be assigned to the anode and the cathode. A new measurement method is presented that enables the assignment of features of impedance spectra of full cells to single electrodes. To this end, temperature gradients are imprinted perpendicular to the electrode layers of a single-layered Li-ion battery cell while impedance spectra are measured. The method exploits different dependences of the charge transfer processes at the electrodes on temperature. An equivalent circuit model of RC-elements and the effect of temperature on the related electrode properties is discussed to demonstrate the feasibility of the method. A reliable assignment of the change of impedance spectra to the electrode processes is shown to be possible. The assignment can be used to identify if changes in an impedance spectrum originate from the anode or the cathode

ПРОБЛЕМИ ФОРМУВАННЯ ЕФЕКТИВНОГО ІМІДЖУ В СУЧАСНІЙ ПОЛІТОЛОГІЇ

Проаналізовані поняття та структура політичного іміджу, визначені компоненти ефективного іміджу політика, виділені складові елементи та закономірності побудови сучасної іміджевої стратегії.Analyzed the concept and structure of political image, the image identified components of effective policy, special elements and patterns of modern image strategy

Electrodeposition of manganese layers from sustainable sulfate based electrolytes

Functional manganese-(Mn)-containing layers are becoming increasingly important in the fields of sacrificial corrosion protection, biodegradable medical devices or electrochemical energy conversion systems. Electrodeposition can be a low cost and time-efficient production route, but the very electronegative nature of Mn makes this reduction process quite challenging. In this paper, electrolytic potentiostatic deposition of metallic Mn layers from environmentally friendly aqueous manganese sulfate electrolytes with pH 3 is successfully demonstrated. A continuous electrolyte flow in the cathodic compartment of the electrochemical cell for controlling the pH value during deposition was found to be essential for achieving good layer qualities. Based on cyclic voltammetry analysis in combination with quartz crystal microbalance measurements a suitable deposition potential range was identified. The obtained electrodeposited layers were characterized by means of SEM, XRD, GD-OES and XPS. The shift of the deposition potential from − 2.4 VMSE to − 2.6 VMSE (deposition time 60 min) yields a thickness increase of the metallic α-Mn deposits from < 500 nm to ~ 2 μm. Only thin additional surface regions of Mn-oxides/-hydroxides were identified. The important role of (NH4)2SO4 as complex-forming electrolyte additive is discussed and an impact of the salt concentration on the deposit properties is revealed. This is a promising starting point for further Mn alloy deposition analysis

State-of-Health Identification of Lithium-Ion Batteries Based on Nonlinear Frequency Response Analysis: First Steps with Machine Learning

In this study, we show an effective data-driven identification of the State-of-Health of Lithium-ion batteries by Nonlinear Frequency Response Analysis. A degradation model based on support vector regression is derived from highly informative Nonlinear Frequency Response Analysis data sets. First, an ageing test of a Lithium-ion battery at 25 C is presented and the impact of relevant ageing mechanisms on the nonlinear dynamics of the cells is analysed. A correlation measure is used to identify the most sensitive frequency range for ageing tests. Here, the mid-frequency range from 1 Hz to 100 Hz shows the strongest correlation to Lithium-ion battery degradation. The focus on the mid-frequency range leads to a dramatic reduction in measurement time of up to 92% compared to standard measurement protocols. Next, informative features are extracted and used to parametrise the support vector regression model for the State of Health degradation. The performance of the degradation model is validated with additional cells and validation data sets, respectively. We show that the degradation model accurately predicts the State of Health values. Validation data demonstrate the usefulness of the Nonlinear Frequency Response Analysis as an effective and fast State of Health identification method and as a versatile tool in the diagnosis of ageing of Lithium-ion batteries in general

State-of-Health Identification of Lithium-Ion Batteries Based on Nonlinear Frequency Response Analysis: First Steps with Machine Learning

In this study, we show an effective data-driven identification of the State-of-Health of Lithium-ion batteries by Nonlinear Frequency Response Analysis. A degradation model based on support vector regression is derived from highly informative Nonlinear Frequency Response Analysis data sets. First, an ageing test of a Lithium-ion battery at 25 C is presented and the impact of relevant ageing mechanisms on the nonlinear dynamics of the cells is analysed. A correlation measure is used to identify the most sensitive frequency range for ageing tests. Here, the mid-frequency range from 1 Hz to 100 Hz shows the strongest correlation to Lithium-ion battery degradation. The focus on the mid-frequency range leads to a dramatic reduction in measurement time of up to 92% compared to standard measurement protocols. Next, informative features are extracted and used to parametrise the support vector regression model for the State of Health degradation. The performance of the degradation model is validated with additional cells and validation data sets, respectively. We show that the degradation model accurately predicts the State of Health values. Validation data demonstrate the usefulness of the Nonlinear Frequency Response Analysis as an effective and fast State of Health identification method and as a versatile tool in the diagnosis of ageing of Lithium-ion batteries in general

Використання кавітаційних пристроїв в харчовій промисловості

We present a direct experimental investigation of the thermal ordering in an artificial analogue of an asymmetric two-dimensional Ising system composed of a rectangular array of nano-fabricated magnetostatically interacting islands. During fabrication and below a critical thickness of the magnetic material the islands are thermally fluctuating and thus the system is able to explore its phase space. Above the critical thickness the islands freeze-in resulting in an arrested thermalized state for the array. Determining the magnetic state we demonstrate a genuine artificial two-dimensional Ising system which can be analyzed in the context of nearest neighbor interactions