Multiple Degradation Mechanisms Research Articles

A multi-source data fusion driven power field effect transistor health state assessment and remaining useful life prediction method

Abstract The metal oxide semiconductor field effect transistor (MOSFET) is subjected to various stresses due to the external and internal operating environments, leading to ageing and failure of the device. With multiple degradation mechanisms, a single piece of information can no longer fully reflect the health state of MOSFETs, so how to use multi-source data to characterise the state of the device and predict the remaining useful life (RUL) is an issue worth exploring. To address this problem, a method for constructing health indicators (HI) as well as predicting RUL using multi-source data is proposed. In this method, firstly, the features are computed by selecting the appropriate ageing signal from the ageing mechanism. Secondly, the extracted features are filtered using Pearson’s algorithm to find the features that are strongly correlated with longevity. Then, the filtered features are merged by dimensionality reduction using the kernel principal component analysis algorithm and the HI is constructed from the reduced result. Finally, an unsupervised clustering algorithm is used to classify the states based on the data distribution in HI, and the filtered features are used as input to the grey wolf optimisation bidirectional long short-term memory neural network to predict the RUL of the device. In this paper, the proposed method is validated using data from the MOSFET Accelerated Aging Experiment at the NASA Ames Centre of Excellence for Prediction. The results show that the method is able to achieve good results in health state assessment and RUL prediction of MOSFETs. The proposed method is an effective and comprehensive strategy that is more helpful for the operation and maintenance of electronics.

Seismic time-dependent resilience assessment of aging highway bridges incorporating the effect of retrofitting

Bridges are one of the most critical components of transportation networks, which are highly vulnerable due to exposure to natural hazards. In addition, multiple degradation mechanisms can considerably affect the functionality of highway bridges during their service life. Therefore, accumulated damage could also arise continuously because of seismic loading and structural deterioration, which consequently can change system resilience over its life-cycle. This study presents a probabilistic methodology to systematically evaluate and develop the time-dependent seismic resilience curves for Reinforced Concrete (RC) bridges under chloride attacks. In this regard, different jacketing materials with varying thicknesses are chosen for retrofitting purposes. In each case, the time-dependent seismic fragility curves (four damage states) for calculating the time-dependent seismic resilience curves are established through Probabilistic Seismic Demand Models (PSDMs) by performing Incremental Dynamic Analysis (IDA). Finally, the time-dependent seismic resilience curves are evaluated based on the time-dependent seismic fragility curves using the restoration function for all the retrofitting strategies by considering the effect of structural deterioration over time. The results indicate that aging, earthquake intensities, and retrofitting strategies undeniably affect the time-variant seismic resilience of bridges, which is helpful for the long-term safety and reliability of bridges.

Adaptive Model-Based Reinforcement Learning for Fast-Charging Optimization of Lithium-Ion Batteries

Fast charging problem of lithium-ion batteries with minimum-charging time while limiting battery degradation, has been receiving increasing attention and is a critical challenge to battery community. Difficulties in this optimization lie in that: (i) The parameter space of charging strategies is high dimensional while the budget of the experimental cost is often limited; (ii) The evaluation of charging strategies' performance is expensive, and (iii) the degradation process of battery is strongly nonlinear and multiple degradation mechanisms occur simultaneously leading to difficulties for establishing accurate first-principles models. Current methods to address these difficulties are mainly electrochemical model-based optimization and grid search, which are rarely adaptive to battery degradation and/or are of low sample efficiency. In this work, we propose an adaptive model-based reinforcement learning (RL) approach for fast charging optimization while limiting battery degradation, in which a probabilistic surrogate model of differential Gaussian process (GP) is adopted to adaptively describe the degradation of cells. The effectiveness of the proposed approach is demonstrated on PETLION, a high-performance PET-based battery simulator. The results show that (i) compared with the model-free RL method, the proposed adaptive GP-based RL approach possesses superior charging performance and high sample efficiency, and (ii) the proposed method performs well in the handling of degradation constraints on voltage and temperature for dynamically aging batteries with its adaptability to the variations of environment.

Irradiation-induced age-related degradation mechanisms effect on integrity of baffle former bolts

For long-term operation of nuclear power plants, integrity of structures, systems and components should be ensured rigorously. In particular, since the complicated reactor vessel internals (RVIs) have been exposed to high temperature and neutron irradiation environment, materials reliability programs on the RVIs to resolve anticipated issues were conducted by several research institutes. The objectives of this research are to examine evaluation procedures derived from the previous works and to apply them for RVIs in two operating reactors with baffle former assembly from different electricity powers. Finite element (FE) analyses were performed employing five user-subroutines developed by the authors for reflection of multiple age-related degradation mechanisms (ARDMs) as well as pre- and post-processing. Temperature distributions due to gamma heatings, fuel cycles, typical normal operating temperature and pressure histories were considered with relevantly varying material properties. As results, effects of irradiation were quantified in terms of stresses, strains and irradiation assisted crack sensitivity parameters. Further assessment was carried out through not only investigating contribution of each ARDM but also comparing the present FE analysis results with those obtained from a different reactor type in detail.

Inhomogeneous Degradation in Lithium-Ion Batteries: The Effect of Thermal Gradients

Understanding lithium ion battery degradation is a consequence of multiple, tightly coupled and non-linear degradation mechanisms. The interplay between these degradation mechanisms, and how the cell is used, gives rise to path dependent behaviour, and hence data driven approaches to model behaviour and predict lifetime rapidly become prohibitively expensive. Therefore, a physics based approach to model degradation is desirable.In addition, all of the degradation mechanisms are a function of temperature, which adds another variable to the path dependency. To make matters worse, all cells experience internal thermal gradients during operation, which means the path dependency for different regions within a battery will be different. This leads to inhomogeneous degradation. After a few cycles, the degradation pathway of each region will then be affected not just by the thermal gradients and local current density but also by the state-of-health of that region, leading to complicated emergent behaviour.Solving a physics based model with multiple degradation mechanisms, in a detailed 3-dimensional thermally coupled discretised model is also prohibitively expensive. Therefore, in this paper we present a thermally coupled distributed equivalent circuit model, with degradation functions built in, so each node can degrade at different rates, as a function of the operating conditions at that node. We have reproduced experimental data previously published, showing the effect of thermal gradients on degradation for two different cooling strategies for a tab and surface cooled pouch cell. For the first time reproducing the positive feedback mechanism responsible for accelerated degradation for surface cooling.The results of this work show that thermal gradients cannot and should not be ignored, and degradation models that do not take them into account have a significant risk of overfitting and leading to incorrect conclusions.

Modeling and Analysis of Coupled Thermal and Mechanical Capacity Degradation in Silicon-Based Lithium-Ion Batteries

Large-format cells are gaining popularity for battery electric vehicles (BEVs) to maximize volumetric and gravimetric energy densities but are associated with challenges in ensuring adequate thermal management. Spatial current density and temperature variations negatively affect overall cell utilization and lifetime.In addition, the mechanical stresses generated within the silicon-based battery electrodes also cause aging and resultant capacity loss. The coupled effects of the thermal and mechanical factors could thus particularly exacerbate cell capacity degradation. Rigorous mechano-thermo-electrochemical models for battery degradation can be a powerful complementary tool to understand these phenomena better. Simulating performance scenarios can significantly improve cell design and experimentation while reducing time and cost commitments. Model-based design approaches thus have the potential to yield substantial economic benefits.This work shall serve as an extension to the previously developed thermal degradation model for non-uniform aging in lithium-ion batteries by coupling with mechanical models predicting the effects of volume change within the batteries over cycling. The observed capacity fade trend for a given battery is ultimately the result of an interplay between multiple degradation mechanisms, which depend on a range of design and operating parameters. The errors between the model predictions and experimental data can provide information on the suitability of a given model and the relative prevalence of a given degradation mechanism, with functional and practical benefits, as there exists significant variation in the parameters, mechanisms, and predictive capability of models in the literature. In addition, a rigorous model of volume changes increases the accuracy of predictions of dynamic stress-strain profiles, which in turn helps improve predictions of associated mechanical degradation phenomena. As a first step towards including mechanical effects, the current model builds on the previously reported studies for modeling volume change in porous electrodes due to intercalation (deintercalation) during lithiation (delithiation). This volume change leads to dimensional and porosity changes in the porous electrodes. Furthermore, these mechanical models will help predict generated stresses and strain profiles within the battery and will be related to models of resultant degradation, especially in the case of silicon/graphite electrodes characterized by substantial volume changes.

(Digital Presentation) Data-Driven Prognosis of Lithium-Ion Batteries Thermal Runaway Early Warning and Detection

Li-ion batteries (LIBs) are becoming ubiquitous in portable, stationary, and electrical energy storage applications that power various devices, from cell phones to the zero-emission plane. Market research suggests the $26.9 billion-a-year (2018) worldwide LIB market could reach $107 billion-a-year by 2025. Taking electric vehicles (EVs) for example, according to the Electric Drive Transportation Association (EDTA), the number of EV sales in the United States market has increased from 345 vehicles in 2010 to 331,617 in 2019, with a total cumulative number of 1.44 million vehicles over the ten-year sales period. This trend has also been observed in the global market as EVs' sales reached 2.1 million worldwide in 2019, boosting the stock to 7.2 million. Although the COVID-19 pandemic dramatically affected the global EV market and prompted the market to plummet over the year 2020 relative to 2019. The global EVs stock projections are expected to increase from 7.2 million to 14 million in 2025 and 25 million in 2030, accounting for 10% of global passenger vehicle sales in 2025 and 28% in 2030. Despite rapidly skidding into the EV era - a technical fait accompli- significant obstacles to vehicle electrification and adoption, especially the safety of their batteries, continue to threaten that future. The risk of anomaly generation and evolution (e.g., thermal runaway and exploration) is currently a major safety concern and challenge in applications powered by LIBs.The traditional first-principle simulations and comprehensive experiments are time-consuming or less effective in detecting and predicting the above problems induced by a synergistic effect of multiple degradation mechanisms. The above challenge eventually necessitates predictive or prognostic capability for Prognostics and Health Monitoring (PHM) under a complexly hostile working environment.This proposal will break away from existing practices, and instead, for the first time, the proposed data-driven prognosis (DDP) requires the assumption of the existence of a conservation principle but does not require a priori knowledge of the key mechanisms and boundary conditions. Alternatively, the exact form of the conservation functional is only specified locally, in the neighborhood of each observation point (and is assumed to be piecewise quadratic in the current work), whereas its global form remains unknown. The conservation principle is then defined as the minimization of system curvature at each observation point. The DDP methodology is based on the local curvature of the mathematical manifold representing the Euler-Lagrange governing equation of a system. The curvature represents a derivative of the total energy with respect to sensed variables, and instability is anticipated whenever the curvature exceeds a critical threshold, denoted by the inverse of the local length-scale of the manifold (or its extrinsic curvature). The length-scale, in turn, is calculated by solving the first moment of the state-space description (conservation of linear momentum) based on two consecutive time instants of measurements. In another sense, the DDP technique can estimate a mass normalized Energy Exchange Amplitude (EEA) that also represents a scaled “entropy rise” of the system. After acquiring the input data from the literatures, they were fed into DDP in a continuous order separated by each time step. At each time step, two consecutive time instances were considered that are sufficient to make the prognostication. The results of the simulation using the DDP are shown below. First the progression of PDI which shows the percentage of points that have reached different path dependence index (PDI) markers as shown in Figure 1 (a). It signifies the instants when the battery shows local instabilities. As shown in the figure, battery starts to show local instability from time instant 20 and beyond, highlighting the instability's progression. The chain length (CL) values and residual curvature information were analyzed as well and shown in Figure 1 (b) along with other criterias. As illustrated in the Figure, at time instant 10, the system crosses the critical chain length threshold and intensifies around time instance 20. Furthermore, the residual curvature (RC) value exceeds the critical threshold at time instance 19 and 21. The moment when all three criterias were met, the battery is expected to fail which is time instance 21 as shown in the Figure. Figure 1

Disruption of Dcp1 leads to a Dcp2-dependent aberrant ribosome profiles in Aspergillus nidulans.

There are multiple RNA degradation mechanisms in eukaryotes, key among these is mRNA decapping, which requires the Dcp1-Dcp2 complex. Decapping is involved in various processes including nonsense-mediated decay (NMD), a process by which aberrant transcripts with a premature termination codon are targeted for translational repression and rapid decay. NMD is ubiquitous throughout eukaryotes and the key factors involved are highly conserved, although many differences have evolved. We investigated the role of Aspergillus nidulans decapping factors in NMD and found that they are not required, unlike Saccharomyces cerevisiae. Intriguingly, we also observed that the disruption of one of the decapping factors, Dcp1, leads to an aberrant ribosome profile. Importantly this was not shared by mutations disrupting Dcp2, the catalytic component of the decapping complex. The aberrant profile is associated with the accumulation of a high proportion of 25S rRNA degradation intermediates. We identified the location of three rRNA cleavage sites and show that a mutation targeted to disrupt the catalytic domain of Dcp2 partially suppresses the aberrant profile of Δdcp1 strains. This suggests that in the absence of Dcp1, cleaved ribosomal components accumulate and Dcp2 may be directly involved in mediating these cleavage events. We discuss the implications of this.

Least squares smoothed k-nearest neighbors online prediction of the remaining useful life of a NASA turbofan

An accurate prediction of the Remaining Useful Life (RUL) of aircraft engines plays a fundamental role in the aerospace field since it is both mission and safety critical. In fact, a reliable estimate of the RUL can effectively reduce the maintenance costs while fostering safety. This paper proposes a novel data-driven method to increase accuracy of the RUL prediction for real-time prognostic systems, considering multiple degradation mechanisms and making the model easy to implement. The proposed method exploits a novel modified k-Nearest Neighbors Interpolation (kNNI) with an a posteriori Least Square Smoothing (LSS) automatically optimized to obtain the minimum prediction error. The LSS novel formulation was also generalized and proved to be equivalent to a Cumulative and Moving Average (CMA) mixture filter, which can be easily implemented online. The method was developed and validated based on a new NASA dataset generated by the dynamic model Commercial Modular Aero-Propulsion System Simulation (N-CMAPSS) with run-to-failure data related to a small fleet of aircraft engines under realistic flight conditions. Finally, a reference kNN-based method already known in the literature was compared to the novel proposed one to demonstrate the goodness of the results and the performance improvements.

Long-term PV system modelling and degradation using neural networks

The power production of photovoltaic plants can be affected throughout its operational lifetime by multiple losses and degradation mechanisms. Although long-term degradation has been widely studied, most methodologies assume a specific degradation behaviour and require detailed metadata. This paper presents a methodology for the calculation of long-term degradation of a photovoltaic plant based on neural networks. The goal of the neural network is to model the photovoltaic plant's power production as a function of environmental conditions and time elapsed since the plant started operating. A big advantage of this method with respect to others is that it is completely data-driven, requires no additional information, and makes no assumptions related to degradation behaviour. Results show that the model can derive a long-term degradation trend without overfitting to shorter-term effects or abrupt changes in year-to-year operation.

Investigation of Multiple Degradation Mechanisms of a Proton Exchange Membrane Fuel Cell under Dynamic Operation

In this paper, a new voltage aging model for the polymer electrolyte membrane fuel cell (PEMFC), which includes multiple degradation mechanisms for proton exchange membrane fuel cells, is proposed. The model parameters are identified using a curve-fitting procedure based on long-term experimental data for the modular stack under the New European Driving Cycle (NEDC). A good fit was found between the model and experimental data, with R-squared values greater than 0.99 for all simulation cases. Moreover, according to the model sensitivity analysis, the voltage degradation model is most sensitive to load current, followed by time. The effect of operating temperature on performance, voltage degradation, and lifetime is investigated. After 300 h, significant performance loss was detected. When the temperature is raised to 75 °C, voltage degradation becomes worse. Based on the simulated voltage degradation profiles at 55 °C and 75 °C, PEMFCs have reached the end of their useful lives at 1100 h and 600 h, respectively. The simulation model indicates that the model is capable of forecasting how long the fuel cell will last under specified operational conditions and drive cycles.

Dynamic rearrangement and autophagic degradation of mitochondria during spermiogenesis in the liverwort Marchantia polymorpha.

Mitochondria change their morphology in response to developmental and environmental cues. During sexual reproduction, bryophytes produce spermatozoids with two mitochondria in the cell body. Although intensive morphological analyses have been conducted, how this fixed number of mitochondria is realized remains poorly understood. Here, we investigate how mitochondria are reorganized during spermiogenesis in Marchantia polymorpha. We find that the mitochondrial number is reduced to one through fission followed by autophagic degradation during early spermiogenesis, and then the posterior mitochondrion arises by fission of the anterior mitochondrion. Autophagy is also responsible for the removal of other organelles, including peroxisomes, but these other organelles are removed at distinct developmental stages from mitochondrial degradation. We also find that spermiogenesis involves nonautophagic organelle degradation. Our findings highlight the dynamic reorganization of mitochondria, which is regulated distinctly from that of other organelles, and multiple degradation mechanisms operate in organelle remodeling during spermiogenesis in M.polymorpha.

Quantitative measurement of solid electrolyte interphase growth on Si-based anode materials

Symmetric cells have been developed to evaluate electrode degradation directly associated with solid electrolyte interphase (SEI) growth in Li-ion cells. This limits their applications to only certain anodes, such as graphite and Li4Ti5O12. In this work, symmetric cells have been applied to investigate the degradation of high energy density anodes, such as Si-alloys, which undergo multiple degradation mechanisms, including mechanical failure and SEI growth. A Li inventory model has been developed to correlate the symmetric cell capacity fade and the multiple anode degradation mechanisms. Using symmetric cells with a pseudo-reference electrode, it was found that irreversible capacity caused by anode degradation can be partially compensated by a positive shift in electrode upper endpoint potential, which is key to interpreting degradation mechanisms. Importantly, a feasible method to measure Li consumption due to SEI growth on Si-alloys has been established using symmetric cells. This method could help battery researchers develop technologies to lower the SEI growth rate on Si-alloys.

Green nickel/nickel oxide nanoparticles for prospective antibacterial and environmental remediation applications

The extensive use of broad-spectrum antibiotics has resulted in antibiotic resistance for many human pathogenic bacteria making multi-drug resistance an increasing issue in the management of various infectious diseases. The current research focused on the green synthesis of nickel/nickel oxide nanoparticles (Nio/NiO nanoparticles) using seeds extract of Lactuca Serriola, bactericidal effect on human pathogenic bacteria and the photocatalytic activity. Highly crystalline nature of Nio/NiO nanoparticles was confirmed by X-ray diffraction (XRD). Infrared spectra of seeds extract of Lactuca Serriola (LS) evidenced the presence of many functional groups of phytochemicals acting as reducing or capping agents. From field emission scanning electron microscopic (FESEM) images of Nio/NiO nanoparticles, it was clearly observed that the particles were slightly spherical in shape with size <100 nm. The Nio/NiO nanoparticles were also tested against eight pathogenic bacteria (Pseudomonas aeruginosa, Staphylococcus aureus, Staphylococcus epidermidis, Basilus subtilis, Basilus pumilus, Micrococcus luteus, E. coli and Bordetella bronchiseptica) which displayed significant antibacterial activity at low doses and almost complete inhibition at optimized concentration. From the bandgap study, the reduced bandgap energy value of 1.57 eV indicated its potential semiconductor photocatalytic behavior. Higher degradation efficiency against the model contaminant crystal violet dye, possibility of multiple degradation mechanisms and simple recovery suggested that the green synthesized Nio/NiO nanoparticles might be best suitable candidates for environmental remediation applications.

A Model for Temperature-Dependent Degradation in Lithium-Ion Batteries: Correlating Electrochemical Phenomena with Cell-Level Performance Parameters

Significant progress has been made in developing lithium-ion battery models that incorporate transport phenomena, electrochemical kinetics, and thermodynamics. While these models have been used to produce reliable results for initial performance, they lack the ability to predict capacity degradation during cycling. The prediction of key performance parameters like battery lifetime and capacity is vital for the design and thermal management of the batteries. There is often a complex interplay of simultaneous degradation mechanisms with the electrochemical dynamics of the cell. These phenomena in turn influence trends in cell-level parameters of interest, namely cell capacity and voltage response. Degradation modeling is an active research area where several works have attempted to augment state-of-the-art electrochemical models with those of various degradation mechanisms.1 In this work, we aim to develop a detailed and efficient physics-based model which couples temperature gradients within the battery with the standard models for electrochemical SEI formation.2 The model inputs for temperature can be obtained experimentally or using a detailed thermal model for the battery. There have been past attempts to combine one or multiple degradation mechanisms.3 While these works provide valuable insight, a more comprehensive picture requires consideration of electrolyte dynamics and temperature variations within the battery. The poor predictions for battery degradation are related to spatial non-uniformities and difficulties in uniform thermal management, whether due to cost or performance constraints. There is also a significant variation in parameters, mechanisms, and predictive capability of the models in the literature. The present model will consider simultaneous battery degradation due to the SEI layer growth with temperature effects and will be integrated into state-of-the-art electrochemical models like the classic pseudo-two-dimensional (p2D) model4 to be able to present a comprehensive analysis of the battery performance parameters. In order to check for the computational challenges, the integration of fade and thermal models will be performed in two different solver environments to ensure accuracy and mitigate any convergence issues.The ultimate goal is a simple and efficient numerical framework for fast and robust simulations such that the developed model would be able to generate cell-level signatures of degradation over a wide range of operating conditions and temperatures. This tailor-made model can also be combined with available experimental data for an improved qualitative understanding, as well as quantitative prediction of the different stages of battery degradation. References Edge, J. S., O'Kane, S., Prosser, R., Kirkaldy, N. D., Patel, A. N., Hales, A., Ghosh, A., Ai, W., Chen, J., Jiang, J. and Li, S., Chem. Chem. Phys. (2021) DOI: 10.1039/D1CP00359C.Safari, M., Morcrette, M., Teyssot, A., and Delacourt, C., Electrochem. Soc., 156, A145 (2009).Reniers, J. M., Mulder, G. and Howey, D. A., Electrochem. Soc, 166(14), A3189 (2019).Fuller, T. F., Doyle, M., and Newman, J., J. Electrochem. Soc., 141, 1 (1994).

Lifetime Prediction and Rapid Design Evaluation of SteelCell® Stacks Using Advanced Multiphysics Modeling

A major challenge in the development of SOC stacks designed for long operating lives of the order of 50-100kh is predicting through-life performance without running durability tests under a particular set of operating conditions for 5-10 years. Whilst this is ultimately necessary it is not practical for system design purposes. It is also very costly to have to repeat experimental validation for a change in operating conditions or a new generation of stack technology.To address this issue Ceres Power has invested heavily in Multiphysics simulation capability for stacks using COMSOL Multiphysics, incorporating fluid dynamics, heat transfer, electrochemical and heterogeneous chemical reactions.The multiphysics model has the capability to simulate multiple scales from a single cell to a full-size 5kW stack. Start-of-life cell performance as a function of temperature and fuel composition is calibrated from extensive high-resolution AC impedance measurements on button cells, with the predicted stack performance experimentally validated using highly instrumented stacks.A further challenge in stack simulation is to simulate through life performance changes due to multiple cell degradation mechanisms, to predict stack life and through-life performance and efficiency. To address this issue, Ceres Power has invested significant effort into understanding mechanisms of performance degradation at the fundamental cell level, generating mathematical models of degradation as a function of time, temperature and current density. Most degradation mechanisms observed are common to all SOFCs cells, such as poisoning of both electrodes and increases in ohmic resistance due to oxide scale growth.A pseudo-dynamic solver has been applied to the multiphysics model applying the models of cell degradation to be applied to a full stack, including a control loop changing the thermal boundary conditions through life to maintain a constant nominal stack temperature. To maintain tractable simulation times, most of the physics of the stack is treated as steady-state, with only those mechanisms changing over a timescale of hundreds to thousands of hours solved dynamically. Using this approach, tens of thousands of hours stack operation can be simulated in a few tens of hours of CPU time on a high-performance server even at the full 5kW stack scale. The dynamic multiphysics degradation model has been validated against through-life performance tests. This is a powerful tool which allows rapid evaluation of the likely impact of operating a stack in a condition other than ones in which extensive test data already exist.

Bias-Dependent Dynamics of Degradation and Recovery in Perovskite Solar Cells

Degradation of perovskite solar cells (PSCs) is often found to be partially or fully reversible when the cells are allowed to recover in the dark. Unlike the dynamics of degradation, knowledge about the dynamics of PSC cell recovery is very limited. Here, we demonstrate that the PSC recovery strongly depends on the electrical bias conditions during the light-induced degradation and that it can be manipulated by applying an external electrical bias during the recovery phase. Investigation of the recovery dynamics allows us to analyze the degradation mechanisms in detail. More specifically, we aged a mixed-cation mixed-halide PSC with a n-i-p structure under illumination in open-circuit (OC) or short-circuit (SC) conditions, and periodically measured their characteristics during the recovery. PSCs aged in SC degrade faster and fully recover after the light is switched off, while the performance of the cells aged in OC does not recover but instead further decreases after the light is switched off (“drop-in-dark” effect). With the use of transient photoluminescence, secondary ion mass spectrometry, and drift-diffusion-based simulations, we hypothesize that extrinsic ion migration causes the drop-in-dark effect, by forming an electron extraction barrier at the metal oxide electron transport layer. The applied bias alleviates this effect. Our results are relevant for gaining a deeper understanding of the multiple degradation mechanisms present in perovskite solar cells, and for finding a practical way to assist their recovery.

Capacity Fade in Ni-Rich Cathodes: Additive Effects and Monitoring Surface Composition Changes

Ni-rich cathodes used in lithium-ion batteries can provide a desirable increase in energy density due to their high specific capacity, such as NMC622 (LiNi0.6Co0.2Mn0.2O2) and NMC811 (LiNi0.8Co0.1Mn0.1O2). However, the higher capacity of Ni-rich cathodes comes with a downside: faster capacity fade and lower cycling stability. The source of capacity fade for these cathodes has been attributed to multiple degradation mechanisms including particle cracking[1], irreversible structural changes[2], and side reactions that catalyze electrolyte decomposition[3]. Previous studies have shown evidence that electrolyte additives that influence the cathode surface can inhibit cycling-induced degradation with high-nickel cathodes[4,5]. In this study, we investigate the cycling stability of multi-layer pouch cells with NMC622 or NMC811 cathodes and graphite anodes, and the effects of different additives on cycling performance and impedance.Our study shows that in batteries with Ni-rich cathodes, organosilicon additives significantly improve cycling stability, as well as reduce gas generation during cycling. We use XPS to study the electrode surface compositions after different stages of cycling, and specific surface features are identified that correlate to performance and impedance over the course of cycling. NMR is used to monitor the consumption of additives from the liquid electrolyte. Through depth profiling and XPS signal attenuation, the surface layer thicknesses on the anodes and cathodes are estimated, and compared across different cycling protocols and different additives. It is proven that organosilicon additives alter the cathode and anode surface compositions, and the effect of changing the additive concentration on cycling stability and the electrode surfaces is also demonstrated. Varying structural features on the organosilicon additives, such as the degree of fluorination and type of functional group, are shown to impact the electrode interaction, impedance, and cycling stability. The impact of additives on reducing gas generation and affecting the gas composition is also quantified by the Archimedes method and gas chromatography. Overall, through this study we aim to provide enhanced understanding of the role of surface chemistry as an agent and an indicator of battery performance, and the effects of additives on surface chemistry and cycling stability. S. Oswald, D. Pritzl, M. Wetjen, H. A. Gasteiger, J. Electrochem. Soc., 167 (2020) 100511.C. Xu, K. Märker, J. Lee, A. Mahadevegowda, P. J. Reeves, S. J. Day, M. F. Groh, S. Emge, C. Ducati, B. L. Mehdi, C. C. Tang, C. P. Grey, Nat. Mater. (2020).A. T. S. Freiberg, M. K. Roos, J. Wandt, R. de Vivie-Riedle, H. A. Gasteiger, J. Phys. Chem. A, 122 (2018) 8828.T. Yim, K. S. Kang, J. Mun, S. H. Lim, S-G. Woo, K. J. Kim, M-S. Park, W. Cho, J. H. Song, Y-K. Han, J-S. Yu, Y-J. Kim, J. Power Sources, 302 (2016) 431.K. Kim, Y. Kim, S. Park, H. J. Yang, S. J. Park, K. Shin, J-J. Woo, S. Kim, S. Y. Hong, N-S. Choi, J. Power Sources, 396 (2018) 276.

Characterizing Materials and Electrochemical Changes in a Range of 18650 Li-Ion Cells Cycled to 80% Initial Capacity

Understanding systematic trends in degradation of Li-ion batteries through a broad range of operating conditions is critical to improving their performance. Over the course of the last few years, researchers at Sandia National Labs have been conducting a systematic study of battery cycling behavior. In this study, dozens of commercial NCA (LiNixCoyAl1-x-yO2) and NMC (LiNixMnyCo1-x-yO2) cells were cycled to 80% capacity across a range of temperatures, discharge rates, and depth of discharge windows. Analysis of both in situ electrochemical data and postmortem materials degradation was done and correlated to determine root causes of rapid capacity fade.During cycling, Electrochemical Impedance Spectroscopy (EIS) and Incremental Capacity Analysis (ICA) experiments were conducted periodically. The data from these experiments were used to understand the state of health (SoH) of cycling cells, including SEI formation and Li plating, as well as general mass transport and kinetic properties.X-ray Computed Tomography (XCT) scans completed before cell disassembly showed large scale deformation in the cells’ jelly rolls. For the postmortem analysis, cells in pristine condition and 80% capacity were disassembled, and their electrodes were analyzed with X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), and Differential Scanning Calorimetry (DSC). These techniques analyzed different aspects of the electrodes’ physical and chemical properties, including Li plating, micro-cracking, SEI layer formation, and phase changes in cathode materials. DSC measurements also provided insight into changes in the degree to which aged electrodes undergo thermal runaway events.Correlation of the postmortem materials analysis with the in situ electrochemical analysis showed that cells with the most rapid rate of capacity fade had multiple degradation mechanisms active during cycling. The scale of this electrochemical and materials evaluation fills in gaps in the literature about commercial Li-ion battery performance.Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Enhanced photo-Fenton activity over a sunlight-driven ignition synthesized α-Fe2O3-Fe3O4/CeO2 heterojunction catalyst enriched with oxygen vacancies

The use of catalysts with several degradation mechanisms has emerged as a promising technique for advanced water treatment. Herein, a ternary α-Fe2O3-Fe3O4/CeO2 nanocomposite is synthesized using a sunlight-driven ignition method for the photo-Fenton degradation of organic dyes under sunlight irradiation. The fabrication of the nanocomposite using this method facilitates the introduction of oxygen vacancies on its surface, which enables the occurrence of multiple degradation mechanisms. The oxygen vacancies prolong the photogenerated charge recombination and expedite the redox cycle between Fe3+/Ce4+ and Fe2+/Ce3+. The effective construction of the α-Fe2O3-Fe3O4/CeO2 heterojunction catalyst enriched with oxygen vacancies is confirmed using different powerful analytical techniques. α-Fe2O3-Fe3O4/CeO2 shows higher photocatalytic activity toward the degradation of methylene blue (MB) than CeO2, α-Fe2O3, and α-Fe2O3-Fe3O4, which can be attributed to the enhanced light absorption in the visible region along with the improved charge separation among the components of the heterojunction through the oxygen vacancies. An inspection of the sample in the presence of H2O2 reveals that the degradation of MB dye is enhanced by 2.6 times, confirming the photo-Fenton catalytic process. Photoluminescence and active species trapping experiments evidence the role of •OH, •O2−, and 1O2 in the degradation of the dye. A synergistic mechanical pathway is proposed to explain the improved photo-Fenton reaction of the synthesized heterojunction composites.