Long-term Degradation Research Articles

Ultra-long-term spatiotemporal feature fusion network for bearing remaining life prediction under strong interference

Under high noise conditions and random impacts, which constitute strong interference, models often exhibit limited capability in capturing long-term dependencies, leading to lower accuracy in predicting the remaining useful life (RUL) of bearings. To address this issue, a spatiotemporal fusion network capable of ultra-long-term feature analysis is proposed to enhance the accuracy of bearing RUL prediction under substantial interference. This network utilizes a dilated convolution-based lightweight vision transformer encoder to extract spatial features reflecting the short-term degradation state of the bearing. Then, these features are sequentially fed into an adaptive tiered memory unit, based on the multiple attention mechanism and the neuron layering mechanism, to analyze temporal features indicative of long-term degradation. Subsequently, short-term spatial and long-term temporal features are fused for RUL prediction. To validate the robustness and predictive accuracy of the proposed approach under strong interference, a gearbox-rolling bearing accelerated platform is constructed, simulating high noise and random impact conditions. Experiments confirm the high robustness and predictive accuracy of the proposed method under strong interference conditions.

Long-term temporal attention neural network with adaptive stage division for remaining useful life prediction of rolling bearings

Accurate rolling bearing remaining useful life (RUL) prediction, an effective assurance of the rotating machinery's safety and reliability, is one of the essential procedures in equipment maintenance. Current RUL prediction methods mostly adopt direct prediction methods, but it is difficult for them to guarantee prediction accuracy under the influence of longlife cycles and variable production environments. Therefore, a long-term temporal attention neural network with adaptive stage division (AD-LTAN) is proposed to predict the RUL of rolling bearings. Aiming at the large fluctuation range of the degradation starting point, an adaptive stage division model with the augmentation of early features is proposed to analyze the long-sequence signals, and then the life cycle of the bearings will be divided into different health stages. Aiming at the network memory decline under the longlife cycle of degradation, a long-term temporal attention neural network is designed to retain the long-term degradation characteristics of bearings by leveraging multilevel expansion convolution and integrating attention mechanisms to extract the fault signal features to realize the RUL prediction. The experimental results conducted on the PHM2012 and XJTU-SY datasets demonstrate that the proposed method outperforms the compared methods in terms of prediction loss (35.4 % less than their best).

Reshaping energy horizon of Iran: investigating economic sanctions, export diversification, and environmental resilience.

Employing robust methodologies, including principal component analysis, autoregressive moving average, Fourier bootstrap dynamic autoregressive distributed lag, error correction model, and the Breitung-Candelon spectral Granger causality test, this study scrutinizes the impact of export diversification (EXD) on Iran's ecological footprint (EF) from 1997 to 2020, considering economic sanctions (ESI), trade openness (TOP), energy consumption per capita (ECpc), globalization (KOF), and real GDP per capita (RGDPpc). Findings consistently affirm a positive environmental impact of EXD, revealing a nuanced temporal pattern. Notably, the short-term impact (- 0.645) is more pronounced than its long-term counterpart (- .020). Increased industrial activities due to globalization (10% rise) lead to 4.26% and 1.64% EF degradation in the long and short term. Conversely, due to Iran's heavy reliance on fossil fuels, a 10% rise in ECpc correlates with 1.63% and 3.81% long- and short-term environmental quality reduction. ESI demonstrates a dual impact, improving short-term environmental quality but contributing to long-term degradation. Frequency-domain causality analysis highlights EXD and KOF as short- and long-term causes of EF, ESI, and TOP as medium- to long-term causes and RGDPpc as a long-term cause. These findings emphasize the need for sustainable policies, stringent environmental standards, and a balanced approach to fostering economic growth while preserving the environment.

Directional Formation of Reactive Oxygen Species Via a Non-Redox Catalysis Strategy That Bypasses Electron Transfer Process.

A broad range of chemical transformations driven by catalytic processes necessitates the electron transfer between catalyst and substrate. The redox cycle limitation arising from the inequivalent electron donation and acceptance of the involved catalysts, however, generally leads to their deactivation, causing substantial economic losses and environmental risks. Here, a "non-redox catalysis" strategy is provided, wherein the catalytic units are constructed by atomic Fe and B as dual active sites to create tensile force and electric field, which allows directional self-decomposition of peroxymonosulfate (PMS) molecules through internal electron transfer to form singlet oxygen, bypassing the need of electron transfer between catalyst and PMS. The proposed catalytic approach with non-redox cycling of catalyst contributes to excellent stability of the active centers while the generated reactive oxygen species find high efficiency in long-term catalytic pollutant degradation and selective organic oxidation synthesis in aqueous phase. This work offers a new avenue for directional substrate conversion, which holds promise to advance the design of alternative catalytic pathways for sustainable energy conversion and valuable chemical production.

Controllable hydrophobization of sands with self-healing polymeric microcapsules

Hydrophobized soils have functional hydrophobic coatings to delay or restrict water infiltration and thus prevent infrastructure failure and long-term degradation. Over time, hydrophobized soils will be subjected to degradation under the action of external stresses, leading to the loss of its functional properties. Microencapsulation approaches, initially developed for self-healing applications emerge as a potential solution to enhance, switch (from hydrophilic) or prolong the longevity of hydrophobized soils. The aim of this study is to produce and investigate the effectiveness of microencapsulation to impart hydrophobicity in granular materials in response to external stimuli. In this research, polydimethylsiloxane (PDMS), with hydrophobic properties, is encapsulated in calcium alginate microcapsules with the ionic gelation method. The effectiveness of the microcapsules to induce hydrophobicity is investigated by mixing sand with microcapsules and quantifying the change of the contact angle and water drop penetration time (measures of hydrophobicity) under an external trigger, i.e., under drying and consecutive wetting–drying cycles. The results show that microcapsules release the hydrophobic cargo (PDMS) during shrinkage. After drying, the PDMS content in sand increased to 0.1–0.8% by mass of sand. The released hydrophobic cargo (PDMS) induced hydrophobicity in sands, reflected by a contact angle increase from 29.7° to at least 87.7°. The amount of polydimethylsiloxane encapsulated is a key parameter controlling the release of hydrophobic cargo. In addition, 4% capsule content in sands is identified as an effective microcapsule content in inducing hydrophobicity.

Assessment of residual prestress in existing concrete bridges: The Kalix bridge

The direct socio-economic consequences of the deterioration of aging infrastructure systems have triggered a continuous process of revising and updating current design standards and guidelines for critical network components. Specifically, long-term degradation processes demand the analysis and evaluation of vital structural assets such as prestressed concrete bridges. It is crucial to develop theoretically consistent, user-friendly, and non-destructive methodologies that engineering professionals can employ to prevent and mitigate potential catastrophic outcomes during the service life of these bridges. This study provides a thorough review of the available testing methods employed over the years for prestressed concrete bridges and introduces a comprehensive framework for evaluating existing methods for residual prestress force assessment. Through a multi-criteria selection process, the three most feasible tests were designed and carried out on an existing 66-year-old balanced cantilever box girder bridge exposed to freezing temperatures that affected the instrumentation plan and test execution. Finally, predictive models compliant with standard codes were calibrated based on the experimental results and the life cycle loss of prestress forces was evaluated to assess relevant bounding intervals. Findings reveal limited on-site testing and discrepancies between calculated residual forces and predictions by standard codes. The saw cut method showed a 18% difference from the initial applied prestress according to the prestress protocol, suggesting the use of a cover meter and concrete modulus evaluation for improved accuracy. The strand cutting method resulted in a 14% difference, emphasizing the need for stress redistribution assessment. The second-order deflection method showed a 6% difference, indicating a focus on enhanced boundary conditions and thorough sensitivity analysis for future investigations.

Data-driven diagnosis of high temperature PEM fuel cells based on the electrochemical impedance spectroscopy: Robustness improvement and evaluation

Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a critical and challenging task in real application. To enhance the robustness of diagnosis and achieve a more thorough evaluation of diagnostic performance, a robust diagnostic procedure based on electrochemical impedance spectroscopy (EIS) and a new method for evaluation of the diagnosis robustness was proposed and investigated in this work. To improve the diagnosis robustness: (1) the degradation mechanism of different faults in the high temperature PEM fuel cell was first analyzed via the distribution of relaxation time of EIS to determine the equivalent circuit model (ECM) with better interpretability, simplicity and accuracy; (2) the feature extraction was implemented on the identified parameters of the ECM and extra attention was paid to distinguishing between the long-term normal degradation and other faults; (3) a Siamese Network was adopted to get features with higher robustness in a new embedding. The diagnosis was conducted using 6 classic classification algorithms—support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), and Naive Bayes employing a dataset comprising a total of 1935 collected EIS. To evaluate the robustness of trained models: (1) different levels of errors were added to the features for performance evaluation; (2) a robustness coefficient (Roubust_C) was defined for a quantified and explicit evaluation of the diagnosis robustness. The diagnostic models employing the proposed feature extraction method can not only achieve the higher performance of around 100% but also higher robustness for diagnosis models. Despite the initial performance being similar, the KNN demonstrated a superior robustness after feature selection and re-embedding by triplet-loss method, which suggests the necessity of robustness evaluation for the machine learning models and the effectiveness of the defined robustness coefficient. This work hopes to give new insights to the robust diagnosis of high temperature PEM fuel cells and more comprehensive performance evaluation of the data-driven method for diagnostic application.

Injectable and Dynamically Crosslinked Zwitterionic Hydrogels for Anti-Fouling and Tissue Regeneration Applications.

A zwitterionic injectable and degradable hydrogel based on hydrazide and aldehyde-functionalized [2-(methacryloyloxy)ethyl] dimethyl-(3-sulfopropyl)ammonium hydroxide (DMAPS) precursor polymers that can address practical in vivo needs is reported. Zwitterion fusion interactions between the zwitterionic precursor polymers create a secondary physically crosslinked network to enable much more rapid gelation than previously reported with other synthetic polymers, facilitating rapid gelation at much lower polymer concentrations or degrees of functionalization than previously accessible in addition to promoting zero swelling and long-term degradation responses and significantly stiffer mechanics than are typically accessed with previously reported low-viscosity precursor gelation systems. The hydrogels maintain the highly anti-fouling properties of conventional zwitterionic hydrogels against proteins, mammalian cells, and bacteria while also promoting anti-fibrotic tissue responses in vivo. Furthermore, the use of the hydrogels for effective delivery and subsequent controlled release of viable cells with tunable profiles both in vitro and in vivo is demonstrated, including the delivery of myoblasts in a mouse skeletal muscle defect model for reducing the time between injury and functional mobility recovery. The combination of the injectability, degradability, and tissue compatibility achieved offers the potential to expand the utility of zwitterionic hydrogels in minimally invasive therapeutic applications.

Reversal of chronic surface degradation of Sr(Ti,Fe)O3 perovskite-based fuel cell cathodes by surface acid/base engineering

The major barrier to commercialization of solid oxide fuel cells (SOFCs) is to overcome long-term surface degradation during operation. Sr/Co-based perovskite cathodes have been recognized to suffer from two major sources of degradation: inherent Sr segregation and extrinsic Cr-impurities at the surface, respectively. Here, we demonstrate the ability to simultaneously reverse both surface degradation modes in SrTi0.65Fe0.35O3-δ (STF35), a model high performance perovskite cathode, via surface acid/base engineering via infiltration. The acidic nature of Cr-infiltration is found to induce surface electron depletion, while enhancing Sr segregation to the surface, leading to ∼40% drop in SOFC peak power. Subsequent infiltration of basic Ca-additives is demonstrated to not only neutralize these detrimental effects, but ultimately lead to 160% enhancement in peak power, thereby enabling Co-free operation. This approach holds great promise for extending the operating lives, more generally, of materials and devices for which the catalytic oxygen/solid interface reaction is central.

A novel transformer-based DL model enhanced by position-sensitive attention and gated hierarchical LSTM for aero-engine RUL prediction

Accurate prediction of remaining useful life (RUL) for aircraft engines is essential for proactive maintenance and safety assurance. However, existing methods such as physics-based models, classical recurrent neural networks, and convolutional neural networks face limitations in capturing long-term dependencies and modeling complex degradation patterns. In this study, we propose a novel deep-learning model based on the Transformer architecture to address these limitations. Specifically, to address the issue of insensitivity to local context in the attention mechanism employed by the Transformer encoder, we introduce a position-sensitive self-attention (PSA) unit to enhance the model's ability to incorporate local context by attending to the positional relationships of the input data at each time step. Additionally, a gated hierarchical long short-term memory network (GHLSTM) is designed to perform regression prediction at different time scales on the latent features, thereby improving the accuracy of RUL estimation for mechanical equipment. Experiments on the C-MAPSS dataset demonstrate that the proposed model outperforms existing methods in RUL prediction, showcasing its effectiveness in modeling complex degradation patterns and long-term dependencies.

Predictive analysis of power degradation rate in solar PV systems emphasizing hot spots and visual effects-based failure modes

A definite and exclusive long-term PV degradation projection is required to reduce financial vulnerability in the solar photovoltaic plant-based energy transition markets. So, in this paper beside a traditional linear rate approach, a non-linear design tool for quantifying long-term failure mode-based power degradation rate (Rd), is proposed. An on-field experimental investigation incorporating visual effects-based Solar I–V characterization and thermal imaging for a duration of 1 year (February 2022 to January 2023) is carried out on a realistic solar PV system exposed to field conditions for 5–6 years. The experimentally evaluated Rd is 7.7 %/year. Two non-linear failure mode-based Rd models employing machine learning approaches, with differences in inputs are developed and are proven economically useable against the reported assessment techniques. The root mean square error (RMSE) for model 1 and model 2 during testing is 0.0019 and 0.0020 respectively (LSTM based approach) whereas testing error for model 1 and model 2 using back propagation based approach is 0.00182 and 0.00132 respectively. The proposed tool is also validated for a realistic PV plant in Telangana. A closeness of prediction against the actual of 9.0 % is observed for model 1 during validation, whereas the same for model 2 is 9.6 %.

Analyses of long-term fungal degradation of spruce bark reveals varying potential for catabolism of polysaccharides and extractive compounds

The bark represents the outer protective layer of trees. It contains high concentrations of antimicrobial extractives, in addition to regular wood polymers. It represents a huge underutilized side stream in forestry, but biotechnological valorization is hampered by a lack of knowledge on microbial bark degradation. Many fungi are efficient lignocellulose degraders, and here, spruce bark degradation by five species, Dichomitus squalens, Rhodonia placenta, Penicillium crustosum, Trichoderma sp. B1, and Trichoderma reesei, was mapped, by continuously analyzing chemical changes in the bark over six months. The study reveals how fungi from different phyla degrade bark using diverse strategies, regarding both wood polymers and extractives, where toxic resin acids were degraded by Basidiomycetes but unmodified/tolerated by Ascomycetes. Proteome analyses of the white-rot D. squalens revealed several proteins, with both known and unknown functions, that were specifically upregulated during growth on bark. This knowledge can accelerate improved utilization of an abundant renewable resource.

Development of porous hydroxyapatite/PVA/gelatin/alginate hybrid flexible scaffolds with improved mechanical properties for bone tissue engineering

Porous scaffolds based on gelatin and hydroxyapatite particles (HAp) are promising for applications in bone tissue engineering and regenerative medicine. However, they are not ideal for stress and shock protection due to low compressive strength, brittleness, and poor toughness. Herein, we developed porous hybrid scaffolds by combining multiple components into a single bone scaffold, including hydroxyapatite nanoparticles (HAp NPs), alginate, polyvinyl alcohol (PVA), and gelatin with different gelatin/PVA composition ratios. HAp NPs were synthesized in situ in PVA solution and scaffolds were fabricated using a freeze-drying method. The results showed good physicochemical properties of the scaffolds: formation of pure HAp NPs phase, high porosity, large pore sizes, large swelling capacity depending on varying gelatin/PVA ratios, as well as a long-term degradation rate up to 28 days. The porous scaffolds exhibited compressive strength close to the cancellous bone with stress-strain behavior exhibiting three-stage flexible behavior indicating improved fracture resistance with an energy absorbing capability up to 1.9 MJ/m3. The scaffolds have a yield strength of 70–403 kPa, a compressive strength at 65 % strain of 0.96–1.80 MPa, a nonporous elastic modulus of 10.44–12.40 GPa, and a densification strain of approximately 0.92 %. This work develops three-dimensional (3D) porous hybrid bone scaffolds with mechanical strength within the minimum compressive strength of cancellous bone and mechanical energy absorption capacity favor for cancellous bone repair, bone tissue engineering, and regenerative medicine applications.

The Influence of Solar Ageing on the Compositions of Epoxy Resin with Natural Polyphenol Quercetin.

Epoxy resin compositions are used in modern railways, replacing other materials. However, epoxy composites in public transport are subject to many requirements, including that they should be flame retardant and resistant to weather conditions. The aim of the research was to analyse the resistance to solar ageing of epoxy resin composites containing flame retardants and the addition of the natural stabilising substance-quercetin. The homogeneity of the samples (optical microscopy and FTIR spectroscopy) and their thermal stability (TGA thermogravimetry) were analysed. The T5 temperature, which is the initial temperature of thermal decomposition of the samples, was 7 °C higher for the epoxy resin containing quercetin, so the material with polyphenol was characterised by better thermal resistance. Changes in material properties (hardness, surface energy, carbonyl index, colour) after 800 h solar ageing were investigated. The tensile tests on materials were executed for three different directions before and after ageing effect. The samples showed good resistance to degradation factors, i.e., they retained the functional properties (hardness and mechanical properties). However, analysis of carbonyl indices and surface energies showed that changes appeared in the composites after solar ageing, suggesting the beginning of material degradation. An approximately 3-fold increase in the polar component in epoxy resin compositions (from approximately 3 mN/m to approximately 11 mN/m) is associated with an increase in their hydrophilicity and the progress of ageing of the materials' surface. The obtained results are an introduction to further research on the long-term degradation processes of epoxy resins with plant stabilisers.

Using a novel vector length stability index (VLSI) to evaluate soil aggregate stability: A preliminary study

Soil aggregate stability is an important soil characteristic which can represent the improvement of soil structure after revegetation. Due to the high heterogeneity of soil, large-scale sampling is usually necessary to estimate the spatial variation of aggregate stability across landscapes. Although there are many methods and corresponding indices for analysis of aggregate stability, many of them are not suitable for investigations with a large number of samples due to time-consuming experimental practices. To avoid unnecessary costs of time and labor in the long-term land degradation and restoration monitoring across large regional scales, simple indices of aggregate stability would help monitoring across large regional scales. For this reason, we introduced a new, simple, and normalized index, the vector length stability index (VLSI) and its weighted form (wVLSI) to describe soil aggregate stability. The VLSI shares key information of aggregate size distribution with mean weight diameter (MWD), but it is naturally normalized and dimensionless. This new index was tentatively tested to describe soil aggregate stability of the 0–30 cm soil layers under artificial plantations, natural grassland, abandoned farmland, and farmland in production in a small watershed located in the Loess Plateau of China. The results showed that the aggregate stability of the 0–20 cm layers in abandoned farmland could be recovered near to the condition under artificial plantations just after 2-year abandonment, but the aggregate stability hardly changed in the 20–30 cm layer. The aggregate stability of the 0–10 cm layer in natural recovered grassland is close to that in top soil layers under artificial plantations, but the stability in the 10–30 layers appeared as low as that in farmland. Because of its simple form and low data requirements, the VLSI could be an alternative efficient tool for soil aggregate stability evaluation when a large number of samples are needed.

Physics-informed machine learning for battery degradation diagnostics: A comparison of state-of-the-art methods

Monitoring the health of lithium-ion batteries’ internal components as they age is crucial for optimizing cell design and usage control strategies. However, quantifying component-level degradation typically involves aging many cells and destructively analyzing them throughout the aging test, limiting the scope of quantifiable degradation to the test conditions and duration. Fortunately, recent advances in physics-informed machine learning (PIML) for modeling and predicting the battery state of health demonstrate the feasibility of building models to predict the long-term degradation of a lithium-ion battery cell’s major components using only short-term aging test data by leveraging physics. In this paper, we present four approaches for building physics-informed machine learning models and comprehensively compare them, considering accuracy, complexity, ease-of-implementation, and their ability to extrapolate to untested conditions. We delve into the details of each physics-informed machine learning method, providing insights specific to implementing them on small battery aging datasets. Our study utilizes long-term cycle aging data from 24 implantable-grade lithium-ion cells subjected to varying temperatures and C-rates over four years. This paper aims to facilitate the selection of an appropriate physics-informed machine learning method for predicting long-term degradation in lithium-ion batteries, using short-term aging data while also providing insights about when to choose which method for general predictive purposes.

Mn-regulated CuFe2O4 spinel based active component continuous exsolution catalyst for long-term degradation of tetracycline

The development of catalysts with fast reaction rates and long service life remains a significant challenge in advanced oxidation processes. The research applied the property that Mn could occupy both tetrahedral and octahedral sites in the spinel structure. By adjusting Mn composition engineering in CuFe2O4, it could regulate the proportion of active components in spinel constituents and form the hybrid catalyst of Mn-doped CuFe2O4/CuO. CuFe2O4/CuO biphasic interface caused lattice distortion, effectively enhancing the interface interaction, which was conducive to the gradual exsolution and redeposition of the active component CuFe2O4 during the catalytic reaction process, thereby prolonging the service life of the catalyst. Within 30 min, the removal rate of tetracycline hydrochloride was 97.3%, while peroxymonosulfate (PMS) stoichiometric efficiency was 0.12. The degradation effect of the powder catalyst only decreased by 7.3% after 6 cycles. In the self-made fixed bed, the mass ratio of sodium alginate solidified catalyst to sewage treatment capacity could reach 1:33000. These results indicated that Mn doping could effectively improve the catalytic degradation performance of CuFe2O4 and prolong the service life of the catalyst by forming a two-phase interface. Furthermore, sodium alginate curing catalysts could provide new insights into the industrial application of catalysts.

Electrochemical–mechanical coupled model for computationally efficient prediction of long-term capacity fade of lithium-ion batteries

This paper proposes a novel physics-based (electrochemical–mechanical coupled) capacity fade model called the inhomogeneous stress-induced fracture (ISIF) model based on electrochemical and mechanical degradation mechanisms. The ISIF model models the mechanical fatigue fracture of cathode particles, which is a key aging mechanism that contributes to nonlinear aging observed in various studies. Through this approach, the ISIF model can accurately predict long-term capacity degradation, including the region of capacity loss below 80% of the initial capacity, without the need for time-consuming calculations, unlike electrochemical-based models. Additionally, the ISIF model can predict the knee point phenomenon, where there is a sudden decrease in capacity in the later-stages of battery life. We also propose a hybrid model called SVD-ISIF model that combines the ISIF model with a data-driven method called sparse variational dropout Bayesian neural network (SVDBNN) to improve accuracy and data efficiency especially when experimental data and computational resources are abundant, which helps users flexibly choose the optimal method for various situations. The proposed method was validated using experimental long-term capacity fading data from commercial 2170 NMC cells over a wide range of cycles (3000 to 20,000) that have not been studied before, because of the difficulties of conducting long-term aging cycle experiments and reproducing the knee point, to the knowledge of the authors. Both the ISIF model and the SVD-ISIF model accurately predict the long-term capacity reduction trend, including the knee point, with a mean absolute error is around or less than 2% for all data.

Prediction of electrode microstructure evolutions with physically constrained unsupervised image-to-image translation networks

Microstructure of electrodes determines the performance of electrochemical devices such as fuel cells and batteries. The efficiency and economic feasibility of these technologies depend on the stability of the microstructures throughout their lifetime. Although modeling techniques were proposed for determining electrode performance from 2- or 3-dimensional microstructural data, it is still extremely challenging to predict long-term structural degradation by means of numerical simulations. One of the major challenges is to overcome the difficulties in obtaining experimental data of an identical sample through the degradation process. In this work, a machine learning-based framework for predicting microstructural evolutions with limited amount of un-paired training data is proposed. Physically-constrained unsupervised image-to-image translation (UNIT) network is incorporated to predict nickel oxide reduction process in solid oxide fuel cell anode. The proposed framework is firstly validated by simplified toy-problems. Secondly, the UNIT network is applied to real microstructures of solid oxide fuel cells, which results in excellent visual and statistical agreements between real and artificially reduced samples. The proposed network can predict evolutions in new microstructures, which have not been used during training. Furthermore, a conditional UNIT network (C-UNIT) was demonstrated, which can predict the microstructure evolutions based on process conditions as well as continuous time series of microstructural changes.

An efficient and robust Co-free BaFeO3-based oxygen electrode for protonic ceramic electrochemical cells

Protonic ceramic electrochemical cells (PCECs) are promising devices for sustainable energy conversion and storage with high efficiency and low cost. However, the application of PCECs is limited by the scarcity of oxygen electrode materials with excellent oxygen reduction/evolution reaction (ORR/OER) activity and robust durability. In this investigation, perovskite oxide with specific non-stoichiometric ratios, namely Ba0.9Sr0.05La0.05Fe0.8Zn0.1Y0.1O3 (BSLFZY) are synthesized using a sol-gel method, and electrochemically evaluated as oxygen electrodes for PCECs. The structural and chemical stability of the synthesized material is explored and analyzed through X-ray diffraction (XRD) and transmission electron microscopy (TEM) characterizations. Surface catalytic activity is assessed using X-ray photoelectron spectroscopy (XPS), thermogravimetry (TG), and oxygen temperature-programmed desorption techniques (O2-TPD). Additionally, the electronic conductivity results demonstrate the suitability of BSLFZY for application in PCECs. The electrochemical performance of the material is estimated using a homemade Ni-BZCYYb/BZCYYb cell with a BSLFZY oxygen electrode. Electrochemical impedance spectra within a specific temperature range are analyzed using the distribution of relaxation times (DRT) method. The results offer valuable insights into the underlying reasons for long-term electrochemical performance degradation in fuel cell (FC) mode. These results present the potential of BSLFZY for practical applications in PCECs, highlighting its excellent electrochemical performance and durability.