Complex Degradation Process Research Articles

Mechanistic modeling for coupled chloride-sulfate attack in cement-based materials

Concrete infrastructures in marine and saline environments are vulnerable to simultaneous chloride and sulfate attacks, compounded by calcium leaching. To address this complex degradation process, we developed a multi-ion transport-chemo-thermo-damage (TCTD) model. This model captures the intricate interactions among multi-ion diffusion, chemical reactions, pore evolution, thermodynamic effects, mechanical damage, and calcium leaching. Validation against multiple independent third-party experimental data confirms the model's reliability and accuracy. Based on this validated model, we analyzed the instantaneous spatiotemporal variations in phase concentrations and porosity, quantifying the primary factors affecting ion transport and concrete degradation. This analysis provides a clear understanding of the individual and combined impacts of these factors. The results indicate that coupled chloride-sulfate attack mitigates individual sulfate and chloride attacks in the short term, while calcium leaching promotes significant gypsum and ettringite formation near the concrete surface. Higher water-to-cement ratios, increased aluminate content, and elevated temperatures are found to exacerbate degradation by accelerating diffusion and reaction rates. Calcium leaching and pore evolution have a much greater effect on coupled chloride sulfate attack than chemical activity coefficients. This research enhances the understanding of coupled ion attacks and aids in optimizing the durability design and predicting the longevity of concrete structures in aggressive environments.

Reliability analysis for binary Lévy degradation processes using characteristic functions

AbstractMany engineering systems experience complex degradation processes along with random shocks that often pose a risk to the safe and reliable operations of the system. The analysis of dependence among multiple degradation processes has been a challenging task in the field of reliability engineering. To study dependence behaviors and jump uncertainties among degradation processes, one of the effective approaches is to model these degradation processes using multidimensional Lévy subordinators. However, a critical obstacle arises in determining the convergent mathematical form of the reliability function under Lévy measures and distributions. To obtain a closed form of reliability function, this study investigates the Lévy measure and the characteristic function of multiple dependent degradation processes. Each degradation process is modeled by a one‐dimensional Lévy subordinator utilizing the marginal Lévy measure and characteristic function. The dependence among all dimensions is described by the Lévy copula and the associated multidimensional Lévy measure. For a two‐dimensional case, we derive the reliability function and probability density function (PDF) from the characteristic function and the inverse Fourier transform. Numerical examples are provided to demonstrate the proposed models for reliability and lifetime analysis of multidimensional degradation processes in engineering systems.

Machine Learning Prediction of Fuel Cell Remaining Life Enhanced by Variational Mode Decomposition and Improved Whale Optimization Algorithm

In predicting the remaining lifespan of Proton Exchange Membrane Fuel Cells (PEMFC), it is crucial to accurately capture the multi-scale variations in cell performance. This study employs Variational Mode Decomposition (VMD) to decompose performance data into intrinsic modes, elucidating critical multi-scale dynamics vital for understanding the complex degradation processes in fuel cells. In addition to VMD, this research utilizes an Improved Whale Optimization Algorithm (IWOA) to optimize a Back Propagation (BP) Neural Network. The IWOA focuses on precise adjustments of weights and biases, enabling the BP network to effectively interpret complex nonlinear relationships within the dataset. This optimization enhances the predictive model’s reliability and stability. Extensive experimental evaluations demonstrate that the integration of VMD, and the learning capabilities of the IWOA-optimized BP network significantly improves the model’s accuracy and stability across multiple predictions, thereby increasing the reliability of lifespan predictions for PEMFCs. This methodology offers a robust framework for extending the operational life and efficiency of fuel cells.

Radiation effects modeling of InP-based HEMT based on neural networks

This paper has proposed a novel radiation effects modeling methodology based on neural networks for InP-based high-electron-mobility transistors (HEMTs). 2 MeV proton radiation has been performed with dose of 1 × 1012 H+/cm2, 5 × 1012 H+/cm2, 1 × 1013 H+/cm2, 5 × 1013 H+/cm2, 1 × 1014 H+/cm2. The radiation neural network models were comparatively constructed based on Feedforward Neural Network (FNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM). Results indicate that the LSTM network outperforms the FNN and RNN networks in the modeling for both drain-source current (IDS) and S-parameters, which demonstrates superior prediction accuracy with smaller fitting error. The proposed modeling approach offers an accurate characterization for the radiation effects of InP-based HEMT devices, without the need to consider the complex degradation process associated with radiation, thus providing practical guidelines for the space applications of such devices.

Polysorbate stability: Effects of packaging materials, buffers, counterions, and pH

Polysorbates, widely used excipients in drug formulations, present a stability challenge due to complex degradation processes. This study investigates the hydrolysis of polysorbate (PS) under temperature stress (50 °C), focusing on the impact of primary packaging materials (glass vs. plastic vials), buffers (histidine and acetic acid), counterions (chloride vs. malate), and pH (4–7). Our findings reveal that leachables from plastic vials inhibit PS degradation in both histidine and acetic acid buffers. Kinetic parameters derived from sigmoidal fitting suggest distinct degradation mechanisms for each buffer. Furthermore, the malate counterion with histidine displays inhibitory effects on PS hydrolysis. Principal component analysis was employed to identify key factors. These results highlight the critical role of excipients and packaging in PS stability, providing valuable insights for biopharmaceutical formulation development and a deeper understanding of PS degradation complexities.

A condition‐based maintenance optimization method with oscillating uncertain degradation process

AbstractCondition‐based maintenance (CBM) has gradually gained more attention, and the degradation process has been increasingly applied to maintenance optimization models. The insufficient data and the complex degradation process of the equipment conditions will contribute to epistemic uncertainty. Besides, the implementation of maintenance introduces oscillatory features into the equipment degradation process, deviating from a monotonically decreasing trend, complicating the optimization of CBM. In this article, to simultaneously address the problem of epistemic uncertainty and consider the influence of inspection and maintenance, we establish a new type of degradation model based on uncertainty theory to deal with epistemic uncertainty. Then an uncertain maintenance optimization model is proposed to give an optimal CBM strategy. Finally, a case study is provided to illustrate the proposed CBM optimization method.

Change point detection of health stage and remaining useful lifetime prediction for electro-mechanical brake unit on trains

Electro-mechanical brake units (EMBUs) have complex degradation process. There are challenges such as component fault coupling, unclear degradation characteristics, random disturbance and so on. A remaining useful life (RUL) framework for EMBUs is developed in this paper. To eliminate the issue of time lag, a data pre-processing method based on zero-phase filtering is proposed for health indicator (HI) construction. Then, the 3 health stages (HSs) characteristics of degradation process is proposed, and is modeled using the Wiener process. To overcome perturbation of large diffusion coefficient and random fluctuation, the change point (CP) detection methods based on the variance ratio hypothesis test and maximal overlap discrete wavelet transform — multi-resolution analysis (MODWT-MRA) are developed. Besides, a consecutive anomaly window detection strategy using sliding windows is employed. Finally, the MODWT-MRA-based RUL prediction methods is proposed. The proposed methods are verified by the durability test data. The proposed data pre-processing method can avoid 10% time error compared to the common difference method. The proposed MODWT-MRA-based CP detection and RUL prediction method can overcome the interference of large random fluctuations in the non-healthy state of EMBU.

SwinIBSR: Towards real-world infrared image super-resolution

Traditional infrared image super-resolution (SR) methods often fall short in real-world scenarios, particularly when dealing with images degraded by various forms of noise and artifacts. Addressing this gap, this paper presents SwinIBSR, a blind SR approach optimized for real-world infrared imaging conditions. SwinIBSR introduces a practical degradation model, specifically designed to simulate the complex degradation processes encountered in real-world infrared imaging. Additionally, a mixed training strategy using both infrared and visible light datasets greatly enhances the network’s generalization capabilities, leading to the production of high-quality SR images. Building upon the Transformer-based architecture of SwinIR, SwinIBSR is adept at learning intricate mappings from low-resolution to high-resolution images. Our comprehensive experiments validate the effectiveness of SwinIBSR, showcasing its outstanding performance in processing real-world infrared images and its notable superiority over existing methods regarding visual quality. This work contributes to the field of infrared image SR, offering insights in enhancing SR techniques for practical applications.

Fungal and bacterial species richness in biodeteriorated seventeenth century Venetian manuscripts

Historical paper documents are susceptible to complex degradation processes, including biodeterioration, which can progressively compromise their aesthetic and structural integrity. This study analyses seventeenth century handwritten historical letters stored at the Correr Museum Library in Venice, Italy, exhibiting pronounced signs of biodeterioration. The techniques used encompassed traditional colony isolation on agar plates and proteomics analyses, employing nanoscale liquid chromatography coupled with high-resolution mass spectrometry (nano-LC–MS). Fluorescence microscopy was used for the first time in the historical paper biodeterioration context to supplement the conventional stereoscopic, optical, and scanning electron microscopic imaging techniques. This method enables the visualisation of microorganisms beyond and beneath the paper’s surface through their natural intrinsic autofluorescence in a non-invasive and non-destructive way. The results demonstrate a diverse, complex, and abundant microbiota composed of coexisting fungal and bacterial species (Ascomycota, Mucoromycota, Basidiomycota, Proteobacteria, and Actinobacteria), along with mite carcasses, insects, parasites, and possibly protists. Furthermore, this study reveals certain species that were not previously documented in the biodeterioration of historical paper, including human pathogens, such as Histoplasma capsulatum, Brucella, Candida albicans, and species of Aspergillus (A. flavus, A. fumigatus, A. oryzae, A. terreus, A. niger) known to cause infections or produce mycotoxins, posing substantial risk to both artefacts and humans.

Prediction of Operational Lifetime of Perovskite Light Emitting Diodes by Machine Learning

Perovskite light‐emitting diodes (LEDs) with advantages of high electroluminescence efficiency at high brightness, good color purity, and tunable bandgap, are believed to have potential applications in the next generation display and lighting technologies. Due to the complex degradation process, mathematic models to describe the degradation process of perovskite LEDs are absent. In this work, it is found that the mathematical fitting methods which have been widely used to describe the decay trend of organic LEDs and quantum‐dot LEDs, are unable to accurately predict the lifetime of perovskite LEDs. Then an ensemble machine learning model is developed, which utilizes data augmentation technique to predict T50 of perovskite LEDs based on features before T80, achieving an accuracy of 0.995. Furthermore, the model can also accurately predict the T90 lifetime of quantum‐dot LEDs (QLEDs) using features before T98, suggesting it is a useful tool to efficiently evaluate LED lifetimes.

Case Study: Providing Tens and Retrowalking on Knee Osteoarthritis Functional Activities

Osteoarthritis is a degenerative joint disease associated with cartilage damage in the joints, involving a complex interactive process of joint degradation, consisting of repair processes in the cartilage, bone, and synovium followed by secondary components of inflammation. This disease is commonly found in patients over the age of 50. This study aims to investigate the effects of TENS (Transcutaneous Electrical Nerve Stimulation) and Retrowalking on functional activity in osteoarthritis knee in the year 2023. The research design employed is a quasi-experimental one-group pretest-posttest design. The population in this study was 232 individuals, with a sample size of 10 respondents. Sampling technique was done through purposive sampling. Bivariate analysis results indicate the impact of TENS and Retrowalking on functional activity in osteoarthritis knee, with an average increase in functional activity of 23.270 and a significance level (α) of 0.005. It can be concluded that TENS and Retrowalking have been shown to affect the increase in functional activity in osteoarthritis knee. Therefore, it is hoped that hospitals can implement TENS and Retrowalking interventions for osteoarthritis knee patients.

Photocatalytic degradation of enrofloxacin with CoAl-LDH mediated persulfate system: Efficiency evaluations and reaction pathways

Residual enrofloxacin (ENR) exposed in aqueous environments is challenging to the ecosphere. In this work, a layered double hydroxide CoAl-LDH was used to activate the common oxidizing agent persulfate (PS) for photodegradation of ENR, and the degradation pathways of ENR were scrutinized and elucidated. The results indicated that, under the optimal conditions obtained through orthogonal experiments, even though the degradation rate of ENR was as high as 97.72%, the removal of total organic carbon (TOC) from the system was only about 30%. Eleven probable reaction pathways were categorized, and thirty-one types of intermediates were identified in participating in the complex degradation process. The major products of ENR were P4 (C17H20FN3O3), P22 (C19H22FN3O4), P19 (C17H18FN3O3), which are mainly derived from the cleavage of the piperazine groups and quinolone rings. Density functional theory (DFT) calculations of the Fukui index for ENR revealed that the two N atoms in the piperazine ring were the core reactive sites in triggering the degradation chains, which were sensitive for electrophilic attack by the dominant radicals (•OH and SO4•-) generated from the composite PS-UV-CoAl-LDH system.

Frequency-Aware Degradation Modeling for Real-World Thermal Image Super-Resolution.

The supervised super-resolution (SR) methods based on simple degradation assumptions (e.g., bicubic downsampling) have unsatisfactory generalization ability on real-world thermal images. To enhance the SR effect of real-world sceneries, we introduce an unsupervised SR framework for thermal images, incorporating degradation modeling and corresponding SR. Inspired by the physical prior that high frequency affects details and low frequency affects thermal contrast, we propose a frequency-aware degradation model, named TFADGAN. The model achieves image quality migration between thermal detectors of different resolutions by degrading different frequency components of the image from high-resolution (HR) to low-resolution (LR). Specifically, by adversarial learning with unpaired LR thermal images, the complex degradation processes of HR thermal images at low and high frequencies are modeled separately. Benefiting from the thermal characteristics mined from real-world images, the degraded images generated by TFADGAN are similar to LR thermal ones in terms of detail and contrast. Then, the SR model is trained based on the pseudo-paired data consisting of degraded images and HR images. Extensive experimental results demonstrate that the degraded images generated by TFADGAN provide reliable alternatives to real-world LR thermal images. In real-world thermal image experiments, the proposed SR framework can improve the peak signal-to-noise ratio (PSNR) and structural similarity degree (SSIM) by 1.28 dB and 0.02, respectively.

Biopolymer and polymer precursor production by microorganisms: applications and future prospects

AbstractPolymers have been used in various industries over the past few decades due to their tremendous applications. Among these, polyhydroxyalkanoates and poly(lactic acid) are easily biodegradable biopolymers derived from bacteria, including recombinant Escherichia coli, Alcaligenes eutrophus, Alcaligenes latus, Azotobacter vinelandii, methylotrophs and Pseudomonas. Conventional petroleum‐derived polymers have become potentially harmful to the environment due to their complex degradation process. The nonbiodegradability of synthetic polymers has become a global issue of concern. There is an urgent need for a substitute to tackle the increasing environmental stress. Microorganisms are small factories for producing different types of polymers during their growth cycle. Various features like biodegradability, biocompatibility, nontoxicity and wide substrate spectrum make such microbial polymers highly reliable. Biopolymers such as alginate, cellulose, cyanophycin, levan, polyhydroxyalkanoates, xanthan, poly(lactic acid) and poly(γ‐glutamic acid) can be obtained from different microorganisms like Aureobasdium pullulans, Acetobacter xylinum, Bacillus thermoamylovorans and Cupriavidusnecator. These are extensively used in various fields like food, medicine, wastewater treatment, biofuel production, packaging and cosmetics. Despite being advantageous in several ways, the biopolymer market still faces several hurdles. This review mainly emphasizes the different types of biopolymers, production by microorganisms and various applications of these biopolymers in different fields. The main drawback limiting the development of these polymers is the high production cost and low efficiency of the microbial strains. Genetic recombination is an efficient technique to enhance the microbial yield and to expand the biopolymer market size. © 2023 Society of Chemical Industry (SCI).

Real-infraredSR: real-world infrared image super-resolution via thermal imager.

Infrared image super-resolution technology aims to overcome the pixel size limitation of the infrared focal plane array for higher resolution images. Due to the real-world images with different resolutions having more complex degradation processes than mathematical calculation, most existing super-resolution methods using the synthetic data obtained by bicubic interpolation achieve unsatisfactory reconstruction performance in real-world scenes. To solve this, this paper innovatively proposes an infrared real-world dataset with different resolutions based on a refrigerated thermal detector and the infrared zoom lens, enabling the network to acquire more realistic details. We obtain images under different fields of view by adjusting the infrared zoom lens and then achieve the scale and luminance alignment of high and low-resolution (HR-LR) images. This dataset can be used for infrared image super-resolution, with an up-sampling scale of two. In order to learn complex features of infrared images efficiently, an asymmetric residual block structure is proposed to effectively reduce the number of parameters and improve the performance of the network. Finally, to solve the slight misalignment problem in the pre-processing stage, contextual loss and perceptual loss are introduced to improve the visual performance. Experiments show that our method achieves superior results both in reconstruction effect and practical value for single infrared image super-resolution in real scenarios.

Evidence through Thermal Analysis of Retro Diels-Alder Reaction in Model Networks Based on Anthracene Modified Polyester Resins.

The present work is focused on polyester resins obtained from the diglycidyl ether of bisphenol A and anthracene modified 5-maleimidoisophthalic acid. Because the maleimide-anthracene Diels-Alder (DA) adduct is stable at high temperatures, it is considered a good option for high performance polymers. However, the information related to the retroDA reaction for this type of adduct is sometimes incoherent. A detailed thermal study (conventional TGA, HiRes TGA, MTGA, DSC, MDSC) was performed in order to establish whether the rDA reaction can be revealed for this type of anthracene modified polyester resins. The TGA method confirmed the cleavage of the anthracene-maleimide DA adduct, while the DSC demonstrated the presence of anthracene in the system. At high temperatures, unprotected maleimide homopolymerizes and/or reacts with allyl groups according to the -ene reaction. Therefore, the thermal DA reaction is not displayed anymore upon the subsequent cooling, and the glass transition region is registered at a higher temperature range during the second heating. The use of sample-controlled thermal analysis (HiRes TGA) and MTGA improved the TGA result; however, it was not possible to separate the very complex degradation processes that are interconnected.

Remaining Useful Life Prediction of Lithium-Ion Batteries Based on a Cubic Polynomial Degradation Model and Envelope Extraction

Remaining useful life (RUL) prediction has become one of the key technologies for reducing costs and improving safety of lithium-ion batteries. To our knowledge, it is difficult for existing nonlinear degradation models of the Wiener process to describe the complex degradation process of lithium-ion batteries, and there is a problem with low precision in parameter estimation. Therefore, this paper proposes a method for predicting the RUL of lithium-ion batteries based on a cubic polynomial degradation model and envelope extraction. Firstly, based on the degradation characteristics of lithium-ion batteries, a cubic polynomial function is used to fit the degradation trajectory and compared with other nonlinear degradation models for verification. Secondly, a subjective parameter estimation method based on envelope extraction is proposed that estimates the actual degradation trajectory by using the average of the upper and lower envelope curves of the degradation data of lithium-ion batteries and uses the maximum likelihood estimation (MLE) method to estimate the unknown model parameters in two steps. Finally, for comparison with several typical nonlinear models, experiments are carried out based on the practical degradation data of lithium-ion batteries. The effectiveness of the proposed method to improve the accuracy of RUL prediction for lithium-ion batteries was demonstrated in terms of the mean square error (MSE) of the model and MSE of RUL prediction.

Intertwined synergistic abiotic and biotic degradation of polypropylene pellets in marine mesocosms

The accumulation of plastic waste in the oceans has caused growing concern for its effects on marine life. The interactions of plastics with environmental factors have been linked to fragmentation to micro- and nanoparticles with different properties and consequences, but the mechanism of fragmentation has not been fully understood yet. In this work, we investigate the combined effect of marine communities and ultraviolet (UV) radiation towards the degradation of virgin and artificially weathered polypropylene (PP) pellets after a long-term incubation period in marine mesocosms. The surface chemical alterations and deterioration of the polymer, in conjunction with the attachment and evolution of marine bacterial communities, the development of biofilm and exopolymeric substances (EPS), as well as the colloidal properties (zeta-potential and hydrodynamic diameter) of the mesocosms were studied. The surface area of both types of pellets decreased over time, despite no concrete weight change being observed. Cell growth, EPS production and colloid particle size were correlated to the loss of area. Therefore, we propose that surface area could be effectively monitored, instead of weight loss, as an alternative indicator of polymer degradation in biodegradation experiments. Changes in the chemical structure of the polymer, in addition to the evolution of the biological factors, implied that a complex degradation process alternated between two phases: an abiotic phase, when UV irradiation contributes to the deterioration of the polymer surface layers and a biotic phase, when marine communities degrade the weathered polymer surface to reveal the underlying layer of virgin polymer. Finally, microscopic particles, produced as a result of the decrease in pellet area, promoted the aggregation of colloidal particles. The role and impacts of these colloidal particles in marine ecosystems are yet as unidentified as that of micro- and nano-sized plastic particles and call for further investigation.

The coupling effects of seawater aging and fatigue micro-crack on mechanical degradation of carbon fiber/epoxy plain woven composites

Carbon fiber reinforced plastics applied in the marine industry are often exposed to conditions of seawater and alternating loads for extended periods, which can cause the complex degradation process of the composites. In this work, the coupling effects of micro-cracks and the seawater aging on the carbon fiber/epoxy resin-based plain woven composites were experimentally investigated. The micro-cracks were introduced into the composites through the three-point bending fatigue load. The results indicate that the residual stress generated by different expansions between carbon fiber and epoxy resin that reduces the interfacial properties of the composite is the primary reason for seawater aging. Besides, seawater aging causes the bending strength of the specimens with fatigue damage to decrease more seriously than that of the undamaged specimens. This is because micro-cracks increase the specific surface area of the composites in contact with seawater to provide channels for seawater diffusion. The coupling effects suggest the maintenance cycle of the composite parts should be gradually reduced if micro-cracks occur in some composites.

Autophagy and podocytopathy.

Autophagy is a complex process of lysosomal-dependent degradation of unwanted cellular material. In response to endogenous or exogenous stimuli, autophagy is induced and regulated by two kinases: the AMP activated kinase and the mammalian target of rapamycin (mTOR). Cells activated by Unc-51-like kinase 1 form a double membrane complex that sequesters the cargo (phagophore) and elongates producing spherical vesicles (autophagosomes). These reach and fuse with lysosomes, which degrade the cargo (autolysosomes). The resulting macromolecules are released back and recycled in the cytosol for reuse. In the podocyte, autophagy is a homeostatic mechanism that contributes to the formation and preservation of the morphological and functional integrity of actin cytoskeleton. Podocytes, fenestrated endothelial cells and glomerular basement membrane compose the glomerular filtration barrier. Podocyte damage may cause dysfunction of the glomerular barrier, proteinuria and glomerulosclerosis in different glomerular diseases and particularly in so-called podocytopathies, namely minimal change disease and focal segmental glomerulosclerosis. Several drugs and molecules may activate autophagic function in murine models. Among them, aldosterone inhibitors, mineralocorticoid inhibitors and vitamin D3 were proven to protect podocyte from injury and reduce proteinuria in clinical studies. However, no clinical trial with autophagy regulators in podocytopathies has been conducted. Caution is needed with other autophagy activators, such as mTOR inhibitors and metformin, because of potential adverse events.