Singular Components Research Articles

On differentiability and mass distributions of typical bivariate copulas

Despite the fact that copulas are commonly considered as analytically smooth/regular objects, derivatives of copulas have to be handled with care. Triggered by a recently published result characterizing multivariate copulas via (d−1)-increasingness of their partial derivative we study the bivariate setting in detail and show that the set of non-differentiability points of a copula may be quite large. We first construct examples of copulas C whose first partial derivative ∂1C(x,y) is pathological in the sense that for almost every x∈(0,1) it does not exist on a dense subset of y∈(0,1), and then show that the family of these copulas is dense. Since in commonly considered subfamilies more regularity might be typical, we then focus on bivariate Extreme Value copulas (EVCs) and show that a topologically typical EVC is not absolutely continuous but has degenerated discrete component, implying that in this class typically ∂1C(x,y) exists in full (0,1)2.Considering that regularity of copulas is closely related to their mass distributions we then study mass distributions of topologically typical copulas and prove the surprising fact that topologically typical bivariate copulas are mutually completely dependent with full support. Furthermore, we use the characterization of EVCs in terms of their associated Pickands dependence measures ϑ on [0,1], show that regularity of ϑ carries over to the corresponding EVC and prove that the subfamily of all EVCs whose absolutely continuous, discrete and singular component has full support is dense in the class of all EVCs.

Kinematics of Supernova Remnants Using Multiepoch Maximum Likelihood Estimation: Chandra Observation of Cassiopeia A as an Example

Decadal changes in a nearby supernova remnant (SNR) were analyzed using a multiepoch maximum likelihood estimation (MLE) approach. To achieve greater accuracy in capturing the dynamics of SNRs, kinematic features and point-spread function effects were integrated into the MLE framework. Using Cassiopeia A as a representative example, data obtained by the Chandra X-ray Observatory in 2000, 2009, and 2019 were utilized. The proposed multiepoch MLE was qualitatively and quantitatively demonstrated to provide accurate estimates of various motions, including shock waves and faint features, across all regions. To investigate asymmetric structures, such as singular components that deviate from the direction of expansion, the MLE method was extended to combine multiple computational domains and classify kinematic properties using the k-means algorithm. This approach allowed for the mapping of different physical states onto the image, and one classified component was suggested to interact with circumstellar material by comparison with infrared observations from the James Webb Space Telescope. Thus, this technique will help quantify the dynamics of SNRs and discover their unique evolution.

SINGULAR SPECTRAL ANALYSIS OF TEMPERATURE MULTIDIMENSIONAL TIME SERIES

In the paper the algorithm of singular structural analysis and forecasting of a multidimensional series by the MSSA method is used. The program was developed, in which the steps of the method for the selection of singular decomposition components were implemented, the analysis and forecast of real time series was carried out. Singular spectrum analysis (SSA) is increasingly used in the study of time series. Unlike other methods of statistical research of time series, this method is used to study the structure, selection of individual components and forecast of both stationary and non-stationary time series. It does not require an analytical model of the series. In fact, this approach is based on the method of principal components. It is based on the transformation of a series into a matrix and its singular decomposition. After identifying the components of the singular decomposition, their grouping takes place, which leads to the decomposition of the original series into additive components, such as trend, oscillations (periodics), and noise. The SSA method allows you to continue the structure of the time series, thus building a forecast (continuation). An important direction of the development of the SSA method as a method of time series analysis is its generalization for the analysis of multidimensional time series. The method is known as MSSA (Multi-Channel SSA) or E-EOFs (Extended Empirical Orthogonal Functions). In this case, the expected result is the simultaneous decomposition of several series into interpreted components. However, a sufficiently complete theory for MSSA does not exist.

Tuning the Flavin Core via Donor Appendage for Selective Subcellular Bioimaging and PDT Application.

Herein, we report a novel flavin analogue as singular chemical component for lysosome bioimaging, and inherited photosensitizer capability of the flavin core was demonstrated as a promising candidate for photodynamic therapy (PDT) application. Fine-tuning the flavin core with the incorporation of methoxy naphthyl appendage provides an appropriate chemical design, thereby offering photostability, selectivity, and lysosomal colocalization, along with the aggregation-induced emissive nature, making it suitable for lysosomal bioimaging applications. Additionally, photosensitization capability of the flavin core with photostable nature of the synthesized analogue has shown remarkable capacity for generating reactive oxygen species (ROS) within cells, making it a promising candidate for photodynamic therapy (PDT) application.

Thermalization and Hydrodynamics in an Interacting Integrable System: The Case of Hard Rods

We consider the relaxation of an initial non-equilibrium state in a one-dimensional fluid of hard rods. Since it is an interacting integrable system, we expect it to reach the Generalized Gibbs Ensemble (GGE) at long times for generic initial conditions. Here we show that there exist initial conditions for which the system does not reach GGE even at very long times and in the thermodynamic limit. In particular, we consider an initial condition of uniformly distributed hard-rods in a box with the left half having particles with a singular velocity distribution (all moving with unit velocity) and the right half particles in thermal equilibrium. We find that the density profile for the singular component does not spread to the full extent of the box and keeps moving with a fixed effective speed at long times. We show that such density profiles can be well described by the solution of the Euler equations almost everywhere except at the location of the shocks, where we observe slight discrepancies due to dissipation arising from the initial fluctuations of the thermal background. To demonstrate this effect of dissipation analytically, we consider a second initial condition with a single particle at the origin with unit velocity in a thermal background. We find that the probability distribution of the position of the unit velocity quasi-particle has diffusive spreading which can be understood from the solution of the Navier–Stokes (NS) equation of the hard rods. Finally, we consider an initial condition with a spread in velocity distribution for which we show convergence to GGE. Our conclusions are based on molecular dynamics simulations supported by analytical arguments.

High-Speed Electro-Optic Modulators Based on Thin-Film Lithium Niobate.

Electro-optic modulators (EOMs) are pivotal in bridging electrical and optical domains, essential for diverse applications including optical communication, microwave signal processing, sensing, and quantum technologies. However, achieving the trifecta of high-density integration, cost-effectiveness, and superior performance remains challenging within established integrated photonics platforms. Enter thin-film lithium niobate (LN), a recent standout with its inherent electro-optic (EO) efficiency, proven industrial performance, durability, and rapid fabrication advancements. This platform inherits material advantages from traditional bulk LN devices while offering a reduced footprint, wider bandwidths, and lower power requirements. Despite its recent introduction, commercial thin-film LN wafers already rival or surpass established alternatives like silicon and indium phosphide, benefitting from decades of research. In this review, we delve into the foundational principles and technical innovations driving state-of-the-art LN modulator demonstrations, exploring various methodologies, their strengths, and challenges. Furthermore, we outline pathways for further enhancing LN modulators and anticipate exciting prospects for larger-scale LN EO circuits beyond singular components. By elucidating the current landscape and future directions, we highlight the transformative potential of thin-film LN technology in advancing electro-optic modulation and integrated photonics.

Enhanced paracetamol photodegradation over synthesized TiO2/g-C3N4 nanocomposites: Effect of g-C3N4 loading on the properties and performance

Water pollution has been a consistent issue faced by mankind worldwide. With the increase in world population and rapid urbanization, it has become even more concerning, especially with the detection of new pollutants such as pharmaceutical and personal care products. These compounds must be removed to minimize the adverse effects on all living beings, and one effective method for their elimination is through photocatalytic degradation. In this study, titanium dioxide nanoparticles (TiO2 NPs) was synthesized and nanocomposites were formed with addition of graphitic carbon nitride (GCN) where its loading was varied. These materials were then characterized and used as catalysts to degrade paracetamol, a pharmaceutical product. Structural analysis showed that GCN is present in the nanocomposites indicating the successful modification. Nanocomposites exhibited excellent photocatalytic activity with the highest degradation rate of 0.0364 min−1 was obtained using 2 wt% GCN loading with experimental conditions of an initial paracetamol concentration of 10 ppm, a catalyst dosage of 0.2 g and a pH 5. The synergistic effect that occurred between the two components in the nanocomposites contributed to the high photocatalytic activity, compared to its singular component which is lacking this factor. Overall, nanocomposites have good potential to be used to remove pharmaceutical products from wastewater.

Complementation and Lebesgue‐type decompositions of linear operators and relations

AbstractIn this paper, a new general approach is developed to construct and study Lebesgue‐type decompositions of linear operators or relations in the Hilbert space setting. The new approach allows to introduce an essentially wider class of Lebesgue‐type decompositions than what has been studied in the literature so far. The key point is that it allows a nontrivial interaction between the closable and the singular components of . The motivation to study such decompositions comes from the fact that they naturally occur in the corresponding Lebesgue‐type decomposition for pairs of quadratic forms. The approach built in this paper uses so‐called complementation in Hilbert spaces, a notion going back to de Branges and Rovnyak.

“Web Services Performance Prediction with Confusion Matrix and K-Fold Cross Validation to Provide Prior Service Quality Characteristics”

The Information Technology sector has experienced substantial growth, especially in the realm of application development, over the last few decades. This ongoing evolution highlights the complexity of contemporary IT applications, which no longer rely on a singular component. Developers now integrate a multitude of components from diverse sources, including users and vendors. Thoroughly examining the quality of external software components before integration into an application is imperative for ensuring optimal service performance. Numerous methodologies and approaches are available for appraising the quality of software components, including the Software as a Service Model (SaaS), Quality of Experience (QoE), and alternative models for identifying quality. These models employ traditional techniques to assess factors like availability, integrity, accessibility, security, performance, and reliability, collectively contributing to the measurement of Quality of Service (QoS). The suggested method enables thorough scrutiny of software components through a 360-degree evaluation, utilizing the Confusion Matrix for predicting performance. This evaluation method ranks web services based on throughput and response time, providing tangible values for decision-making by service users. The classification mechanism aids in categorizing standards within a benchmark web service dataset. By utilizing this performance measuring method, one can determine service quality through the confusion matrix, aiding in the identification of the best web services and contributing to the optimization of application performance.

Where do we intervene to optimize sports systems? Leverage Points the way

ABSTRACT Sport and sports research are inherently complex systems. This appears to be somewhat at odds with the current research paradigm in sport in which interventions are aimed are fixing or solving singular broken components within the system. In any complex system, such as sport, there are places where we can intervene to change behaviour and, ideally, system outcomes. Meadows influential work describes 12 different points with which to intervene in complex systems (termed “Leverage Points”), which are ordered from shallow to deeper based on their potential effectiveness to influence transformational change. Whether research in sport is aimed at shallow or deeper Leverage Points is unknown. This study aimed to assess highly impactful research in sports science, sports nutrition/metabolism, sports medicine, sport and exercise psychology, sports management, motor control, sports biomechanics and sports policy/law through a Leverage Points lens. The 10 most highly cited original-research manuscripts from each journal representing these fields were analysed for the Leverage Point with which the intervention described in the manuscript was focused. The results indicate that highly impactful research in sports science, sports nutrition/metabolism, sports biomechanics and sports medicine is predominantly focused at the shallow end of the Leverage Points hierarchy. Conversely, the interventions drawn from journals representing sports management and sports policy/law were focused on the deeper end. Other journals analysed had a mixed profile. Explanations for these findings include the dual practitioner/academic needing to “think fast” to solve immediate questions in sports science/medicine/nutrition, limited engagement with “working slow” systems and method experts and differences in incremental vs. non-incremental research strategies

Approach to the "Missing" Diarylsilylene: Formation, Characterization, and Intramolecular C-H Bond Activation of Blue Diarylsilylenes with Bulky Rind Groups.

The treatment of the bulky Rind-based dibromosilanes, (Rind)2SiBr2 (2) [Rind = 1,1,7,7-tetra-R1-3,3,5,5-tetra-R2-s-hydrindacen-4-yl: EMind (a: R1 = Et, R2 = Me) and Eind (b: R1 = R2 = Et)], with two equivalents of tBuLi in Et2O at low temperatures resulted in the formation of blue solutions derived from the diarylsilylenes, (Rind)2Si: (3). Upon warming the solutions above -20 °C, the blue color gradually faded, accompanying the decomposition of 3 and yielding cyclic hydrosilanes (4) via intramolecular C-H bond insertion at the Si(II) center. The molecular structures of the bulky Eind-based 3b and 4b were confirmed by X-ray crystallography. Thus, at -20 °C, blue crystals were formed (Crystal-A), which were identified as mixed crystals of 3b and 4b. Additionally, colorless crystals of 4b as a singular component were isolated (Crystal-B), whose structure was also determined by an X-ray diffraction analysis. Although the isolation of 3 was difficult due to their thermally labile nature, their structural characteristics and electronic properties were discussed based on the experimental findings complemented by computational results. We also examined the hydrolysis of 3b to afford the silanol, (Eind)2SiH(OH) (5b).

Symmetric circulant matrix decomposition-based multivariable group sparse coding for rolling bearing fault diagnosis

Singular value decomposition technique proves its effectiveness in mechanical signal analysis by decomposing the test signal into a series of singular spectral components of different frequency bands. Nevertheless, how to adapt this technology to the needs of cyclo-nonstationary signal and how to set the decomposition number while maintaining detailed features to obtain the optimal component containing the most fault information, remains an important issue that needs to be addressed in the field of mechanical fault diagnosis. To overcome these disadvantages, the symmetric circulant matrix decomposition (SCMD) is presented. Two main ideas structure the present technique. Firstly, symmetric circulant matrix is used to generate eigenvectors, which will better adapt to the cyclo-nonstationary signal associated with the structural symmetry of rotating machinery. Then, an impulse fluctuation measure is established to adaptively search for the decomposition number and extract the optimal component. Moreover, to better improve the impulse extraction effect of SCMD, the multivariate group sparse coding based on the multivariate correlation characteristics and intra group sparsity characteristics of impulse signals is proposed, which can enhance impulse features while preserving fault details as much as possible. The reliability and feasibility of the proposed method are verified by the experimental signals. The comparison with several classic methods shows that this method is more effectiveness in weak feature extraction.

A novel method of rolling bearings fault diagnosis based on singular spectrum decomposition and optimized stochastic configuration network

Fault diagnosis of rolling bearings is essential for the safe operation of rotating machinery. However, in the production process, the rolling bearings have a complex working environment embedded with weak fault signals and a large number of interfering signals, which is a considerable challenge to automatically and accurately detect bearing fault type from the actual vibration signal. Therefore, a novel fault diagnosis scheme is proposed based on singular spectrum decomposition (SSD) and an optimized stochastic configuration network (SCN). Firstly, SSD is used to pre-process the original rolling bearings vibration signal to obtain several singular spectral components (SSCs) and the practical component is selected according to the maximum correlation coefficient for signal reconstruction. Furthermore, time domain and power spectrum entropy (PSE) features of the reconstructed signal are extracted to obtain a fault information-rich feature sets. In addition, the parameters of the SCN are optimized by marine predators algorithm (MPA) to enhance the learning ability and generalization performance of the SCN. Finally, the feature sets are input into MPA-SCN to achieve fault classification. Experimental results exhibit that the proposed method has higher accuracy in rolling bearings fault diagnosis compared with other methods, which provides a high-efficiency solution for rolling bearings fault diagnosis.

Generation a solution to the equations of elasticity theory for a layered strip basing on the principle of compressed mappings

A systematic presentation of the modified classical semi-inverse SaintVenant method as an iterative one is given on the example of generating a solution to the differential equations of elasticity theory for a long layered strip. The firstorder differential equations of the plane problem are reduced to the dimensionless form and replaced by integral equations with respect to the transverse coordinate, just as it is done in the Picard method of simple iterations. In this case, a small parameter appears in the integral equations before the integral sign as a multiplying factor, which is used to ensure convergence of solutions in accordance with the Banach’s principle of compressed mappings. The equations and elasticity relations are converted to a form that enables to calculate the unknowns consecutively, so that the unknowns being calculated in one equation are the inputs for the next equation, and etc. Fulfillment of the boundary conditions at the long edges leads to ordinary differential equations for slowly and rapidly changing singular components of the solution with sixteen effective stiffness coefficients that are defined by integrals from the given ones as a stepped function of Young's moduli for each layer. Integrating of these ordinary differential equations makes it possible to obtain the formulas for all the required unknowns of the problem, including transverse stresses that are not defined in the classical theory of the beam and solutions of the edge effect type, and to fulfill all the boundary conditions for the elasticity theory problem. The solution of three boundary value problems of the strip elasticity theory is provided such as for a two-layer strip with layers of the same thickness and different thicknesses, and a strip with an arbitrary number of layers. Formulas for all unknowns of the problem are obtained.

An EM algorithm for absolutely continuous Marshall-Olkin bivariate Pareto distribution with location and scale

Block and Basu bivariate exponential distribution is one of the most popular absolutely continuous bivariate distributions. In this article, we have considered a Block–Basu type bivariate Pareto distribution. Here in the standard manner, first Marshall-Olkin type singular bivariate distribution has been constructed, and then by taking away the singular component similar to the Block and Basu model, an absolute continuous BB-BVPA model has been constructed. Further, the location and scale parameters also have been introduced. Therefore, the model has seven parameters. We drive different properties of this absolutely continuous distribution. Since the maximum likelihood estimators of the parameters cannot be expressed in a closed form, we propose to use an EM algorithm to compute the estimators of the model parameters. Some simulation experiments have been performed for illustrative purposes. We fit the model to rainfall data in the context of landslide risk estimation. The methods are implemented in the publicly available R package bvpa.

Iterative singular value decomposition-based in-band denoising approach with envelope order analysis for sun gear fault diagnosis of planetary system under varying speed

The vibration signal of the planetary gear train is easily disturbed by the background noise, so the measured signal is complicated. In order to accurately extract the fault features, the resonance method has been widely used. However, even if the optimal resonance frequency band is found, the in-band noise still exists, so it is necessary to study an effective in-band denoising method. In this paper, an in-band denoising method based on Iterative Singular Value Decomposition (ISVD) is proposed for the fault diagnosis of the secondary sun gear of a planetary gear train, combined with the envelope order spectrum analysis. This method uses the enhanced Wavelet Packet Transform Spectral Kurtosis (WPTSK) to determine the best frequency band for the signal, and uses the ISVD method to realize the signal denoising. It sets a threshold to avoid the destruction of the useful information caused by the excessive iteration, and uses the relationship between the singular values and frequency components to reconstruct the denoised signal. Finally, the signal is converted to the fault characteristic order domain by resampling to identify the fault type of the sun gear from the envelope order spectrum. The simulation and experimental results show that compared with the Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD), the ISVD can effectively suppress the in-band noise and more accurately extract the fault characteristic order of the secondary sun gear under varying speed.

Reticulated platelets predict cardiovascular death and adverse events in coronary artery disease

Abstract Background/Introduction The pro-thrombotic immature or reticulated platelets (RPs) are known to be elevated in high risk patients and in different pathological settings. Several studies have shown that RPs correlate with an insufficient antiplatelet response to antiplatelet agents. In addition, RPs are emerging novel biomarkers for the prediction of adverse cardiovascular events in cardiovascular disease. Purpose The current study, using the totality of existing evidence, evaluated the prognostic role of RPs in patients with coronary artery disease. Methods We performed a systematic review and meta-analysis including trials of acute and chronic coronary syndrome reporting clinical outcomes according to RPs levels in the peripheral blood. Patients with elevated RPs were defined as RPshigh according to the single study definitions and compared to patients without elevated RPs (RPslow). Odds ratios (ORs) and 95% CIs were used as metric of choice for treatment effects with random-effects models. The primary endpoint was major adverse cardiovascular and cerebrovascular events (MACCE). Secondary endpoints were cardiovascular death, myocardial infarction, ischemic stroke, urgent coronary revascularization and any bleeding. Prospero Registration number: CRD42022381282. Results A total of 7 studies, including 2213 patients, were included. The risk for MACCE was significantly higher in RPshigh compared to RPslow patients (OR 2.67 [1.87; 3.81], I2=43.8%, Figure 1). RPshigh were significantly associated with cardiovascular death (OR 2.09 [1.36; 3.22], I2=40.4%). No significant associations for RPshigh were detected with the other singular components of MACCE: myocardial infarction (OR 1.73 [0.89; 3.38] I2=60.5%) and stroke (OR 1.72 [0.59; 4.96] I2=21%). Urgent revascularization was not significantly associated with elevated RPs (OR 2.09 [0.90; 4.86] I2=55.5%). The risk of bleeding did not significantly differ between groups(OR 0.58 [0.15; 2.22] I2=86.1%). Conclusion Elevated RPs are significantly associated with increased risk of cardiovascular events and cardiovascular death in patients with coronary artery disease and should be considered a useful prognostic biomarker in this setting.Figure 1

An Automated Electronic System in a Motorized Wheelchair for Telemonitoring: Mixed Methods Study Based on Internet of Things.

Wheelchair positioning systems can prevent postural deficits and pressure injuries. However, a more effective professional follow-up is needed to assess and monitor positioning according to the specificities and clinical conditions of each user. This study aims to present the concept of an electronic system embedded in a motorized wheelchair, based on the Internet of Things (IoT), for automated positioning as part of a study on wheelchairs and telemonitoring. We conducted a mixed methods study with a user-centered design approach, interviews with 16 wheelchair users and 66 professionals for the development of system functions, and a formative assessment of 5 participants with descriptive analysis to design system concepts. We presented a new wheelchair system with hardware and software components developed based on coparticipation with singular components in an IoT architecture. In an IoT solution, the incorporation of sensors from the inertial measurement unit was crucial. These sensors were vital for offering alternative methods to monitor and control the tilt and recline functions of a wheelchair. This monitoring and control could be achieved autonomously through a smartphone app. In addition, this capability addressed the requirements of real users. The technologies presented in this system can benefit telemonitoring and favor real feedback, allowing quality provision of health services to wheelchair users. User-centered development favored development with specific functions to meet the real demands of users. We emphasize the importance of future studies on the correlation between diagnoses and the use of the system in a real environment to help professionals in treatment.

Effects of Iron Species on Low Temperature CO2 Electrolyzers.

Electrochemical energy conversion devices are considered key in reducing CO2 emissions and significant efforts are being applied to accelerate device development. Unlike other technologies, low temperature electrolyzers have the ability to directly convert CO2 into a range of value-added chemicals. To make them commercially viable, however, device efficiency and durability must be increased. Although their design is similar to more mature water electrolyzers and fuel cells, new cell concepts and components are needed. Due to the complexity of the system, singular component optimization is common. As a result, the component interplay is often overlooked. The influence of Fe-species clearly shows that the cell must be considered holistically during optimization, to avoid future issues due to component interference or cross-contamination. Fe-impurities are ubiquitous, and their influence on single components is well-researched. The activity of non-noble anodes has been increased through the deliberate addition of iron. At the same time, however, Fe-species accelerate cathode and membrane degradation. Here, we interpret literature on single components to gain an understanding of how Fe-species influence low temperature CO2 electrolyzers holistically. The role of Fe-species serves to highlight the need for considerations regarding component interplay in general.

Einfluss von Eisenionen auf die Niedertemperatur CO2 Elektrolyse

AbstractDa die elektrochemische Umwandlung von CO2 als Schlüsseltechnologie im Kampf gegen den Klimawandel betrachtet wird, werden erhebliche Maßnahmen eingesetzt um ihre Entwicklung zu beschleunigen. Der Vorteil der CO2 Elektrolyse bei niedrigen Temperaturen im Gegensatz zu Hochtemperatur‐elektrolyseuren liegt in der Möglichkeit zur direkten Umsetzung von CO2 zu technisch relevanten Chemikalien. Die Niedertemperatur‐CO2‐Elektrolyse ist jedoch nur bei hoher Effizienz und Stabilität wirtschaftlich rentabel. Obwohl die verwendeten Zelldesigns Ähnlichkeiten mit den etablierten Brennstoff‐ oder auch den weiter entwickelten Wasser‐Elektrolyse‐Zellen aufweisen, müssen verschiedene Designaspekte und einzelne Komponenten adaptiert werden. Aufgrund der Komplexität des Gesamtsystems werden in der Entwicklung oftmals nur einzelne Zellkomponenten optimiert, ohne dass etwaige Einflussfaktoren, die im Zusammenspiel mit anderen Bestandteilen auftreten, berücksichtigt werden. Dass eine ganzheitliche Systembetrachtung bei der Optimierung von Niedertemperatur‐CO2‐Elektrolyseuren notwendig ist, zeigt die Betrachtung des Einflusses von Eisenionen auf die Gesamtzelle deutlich, da nur so bspw. Kreuzkontaminierung gemieden werden kann. Da Eisenionen (Fe) als Verunreinigung omnipräsent sind, wurde ihr Einfluss auf einzelne Zellkomponenten schon vielfach untersucht. Die Zugabe von Eisen erhöht die Aktivität von Nichtedelmetallanoden, gleichzeitig aber tritt eine Verschlechterung der Kathodenaktivität ein und der Membranabbau wird katalysiert. Im Folgenden stellen wir anhand bestehender Literatur, die sich meist auf einzelne Komponenten fokussiert, den Einfluss von Fe‐Spezien in der CO2 Elektrolyse‐Zelle ganzheitlich dar. Hierbei wird ersichtlich, dass das Zusammenspiel zwischen den einzelnen Bestandteilen bei der Zelloptimierung berücksichtigt werden muss.