Complexity Of Process Research Articles

Evaluating osteogenic potential of a 3D-printed bioceramic-based scaffold for critical-sized defect treatment: an in vivo and in vitro investigation.

The integration of precision medicine principles into bone tissue engineering has ignited a wave of research focused on customizing intricate scaffolds through advanced 3D printing techniques. Bioceramics, known for their exceptional biocompatibility and osteoconductivity, have emerged as a promising material in this field. This article aims to evaluate the regenerative capabilities of a composite scaffold composed of 3D-printed gelatin combined with hydroxyapatite/tricalcium phosphate bioceramics (G/HA/TCP), incorporating human dental pulp-derived stem cells (hDPSCs). Using 3D powder printing, we created cross-shaped biphasic calcium phosphate scaffolds with a gelatin layer. The bone-regenerating potential of these scaffolds, along with hDPSCs, was assessed through in vitro analyses and in vivo studies with 60 rats and critical-sized calvarial defects. The assessment included analyzing cellular proliferation, differentiation, and alkaline phosphatase activity (ALP), and concluded with a detailed histological evaluation of bone regeneration. Our study revealed a highly favorable scenario, displaying not only desirable cellular attachment and proliferation on the scaffolds but also a notable enhancement in the ALP activity of hDPSCs, underscoring their pivotal role in bone regeneration. However, the histological examination of calvarial defects at the 12-wk mark yielded a rather modest level of bone regeneration across all experimental groups. The test and cell group exhibited significant bone formation compared to all other groups except the control and cell group. This underscores the complexity of the regenerative process and paves the way for further in-depth investigations aimed at improving the potential of the composite scaffolds.

Enhancing Soil Moisture Forecasting Accuracy with REDF-LSTM: Integrating Residual En-Decoding and Feature Attention Mechanisms

This study introduces an innovative deep learning model, Residual-EnDecode-Feedforward Attention Mechanism-Long Short-Term Memory (REDF-LSTM), designed to overcome the high uncertainty challenges faced by traditional soil moisture prediction methods. The REDF-LSTM model, by integrating a residual learning encoder–decoder LSTM layer, enhanced LSTM layers, and feedforward attention, not only captures the deep features of time series data but also optimizes the model’s ability to identify key influencing factors, including land surface features, atmospheric conditions, and other static environmental variables. Unlike existing methods, the innovation of this model lies in its first-time combination of the residual learning encoder–decoder and feedforward attention mechanisms in the soil moisture prediction field. It delves into the complex patterns of time series through the encoder–decoder structure and accurately locates key influencing factors through the feedforward attention mechanism, significantly improving predictive performance. The choice to combine the feedforward attention mechanism and encoder–decoder with the LSTM model is to fully leverage their advantages in processing complex data sequences and enhancing the model’s focus on important features, aiming for more accurate soil moisture prediction. After comparison with current advanced models such as EDLSTM, FAMLSTM, and GANBiLSTM, our REDF-LSTM demonstrated the best performance. Compared to traditional LSTM models, it achieved an average improvement of 13.07% in R2, 20.98% in RMSE, 24.86% in BIAS, and 11.1% in KGE key performance indicators, fully proving its superior predictive capability and potential application value in precision agriculture and ecosystem management.

Dual‐Polarity Response in Self‐Powered Infrared SnTe Photodetector with Double Symmetric Schottky Junctions

AbstractSelf‐powered photodetectors with dual‐polarity response have great advantages in processing complex light information that is proven to be applied in switchable light imaging, optical communication, and spectral band distinction. Here, a novel strategy for designing self‐powered photodetectors by introducing the double symmetric Schottky junctions at the semiconductor‐metal interfaces is proposed, which guarantees the linear response characteristic and dominates the transfer of photogenerated carriers without external bias. Narrow bandgap semiconductor SnTe is employed as the photosensitive material. The bandgap is characterized to be 126 meV, showing great potential for wide spectrum response. As the consequence of selective excitation of unilateral Schottky junction and switching of electric field polarity, the device obtains a dual‐polarity photoresponse, which can be tuned from −14.8 to 13.5 µV. Moreover, an ultra‐broadband photoresponse from near‐infrared 808 nm to far infrared 10.6 µm is achieved with deep exploration of the working mechanism. The synergistic effect of photo thermoelectricity and photoconductivity created a high photoresponsivity of 0.35 mV W−1 excited by 10.6 µm at room temperature. This work provides a novel design strategy for self‐powered photodetectors with a dual‐polarity powered principle, which can take full advantage of the directivity of the photoresponse to identify optical information.

Achieving Enhanced Dielectric and Energy Storage Performance in Poly(vinyl chloride-glycidyl methacrylate) through Tuning Interchain Interactions.

Glassy polymer dielectrics exhibit significant advantages in energy storage density and discharge efficiency; however, their potential application in thin-film capacitors is limited by the complexity of the production process, rising costs, and processing challenges arising from the brittleness of the material. In this study, a small amount of the polar monomer glycidyl methacrylate (GMA) was copolymerized with vinyl chloride (VC) using a highly integrated and precisely controlled process. This effectively facilitated the bulk synthesis of P(VC-GMA) copolymers, aimed at enhancing the dielectric properties and energy storage capabilities of the copolymer. Moreover, the incorporation of GMA into PVC induces significant alterations in the structural sequence of the copolymer, resulting in an enhancement of interchain interactions that ultimately contribute to an increase in the modulus and improved breakdown strength. With a GMA content of 2.4 mol %, P(VC-GMA) exhibits a significant enhancement in discharge energy density, surpassing that of a pure PVC copolymer, while maintaining high discharge efficiency and stability. The finding of this study paves the way for future advancements in high-energy-storage polymer dielectrics, thereby expanding the scope of advanced dielectric materials.

Investigation on the strategy of business administration level of enterprises in the new economic environment.

In the face of the new economic environment, enterprises must continuously enhance their capabilities to achieve long-term development. In the current market scenario, business management relies on economic principles and legal accounting. Considering the current market situation, the article analyzed enterprises system reform and production planning, proposing corresponding countermeasures. Therefore, in order to achieve rapid development, it was necessary to strengthen the management of enterprises. In this paper, the current problems faced by enterprises, solutions and the significance of enterprises needed to improve their management level were explained, and the situation of enterprises was analyzed through the enterprise strategic management model. Comparing with the traditional management model in terms of the complexity of enterprise management processes, efficiency, management level score, and quarterly profit,findings reveal that the management model in the new economic environment has reduced the complexity of the enterprise process by 0.17 points. The management efficiency has increased by 0.15 points, the management score has increased by 14 points, and the quarterly profit of the company has increased by 30,000 yuan. Furthermore, it is elucidated that, in the new economy, enhancing the management level is essential for enabling enterprises to attain long-term development.

An insight into viscosity and conductivity in the formulation of co-axial electrospun Carica papaya leaf extract

Objective: This research aimed to investigate the application of the coaxial electrospun method for the production of natural extracts (papaya leaf extract) fibre films. This was achieved through utilising different polymers and with a focus on the conductivity and the viscosity of polymer solutions as critical parameters to generate successful fibres.Significance: Electrospinning is a promising trending manufacturing method for incorporating thermolabile herbal extracts using coaxial electrospun features. However, the complexity of the electrospinning process and the feasibility of the product required precise scrutiny.Methods: The electrospinning solution parameters (conductivity and viscosity) were evaluated by employing various ratios of Eudragit L100 (EL100) and Eudragit L100-55 (EL100-55) pre-spinning polymeric blend solutions. The electrospinning process and ambient parameters were optimised. Following that, the in-silico physicochemical properties of phytochemical marker, rutin, were illustrated using SwissADME web tool. Both freeze-dried Carica papaya leaf extract and its produced films were characterised using Scanning Electron Microscopy (SEM), Attenuated Total Reflectance Fourier Transform Infrared (ATR-FTIR), polarised light microscopy, and X-ray Powder Diffraction (XRPD).Results: The optimal values of conductivity (≈40–44 × 10−4 S/m) and viscosity (≈32–42 × 10−3 Pa·s) were determined for producing evenly distributed and small fibre diameters in SEM images. These parameters significance was highlighted in acquiring and maintaining adequate tangential stress for fibre elongation, which would consequently affect the morphology and diameter of the fibres formed.Conclusion: In conclusion, the solution, process, and ambient parameters are significant in developing natural extracts into films via electrospinning technology, and this includes the promising Carica papaya leaf extract films produced by coaxial electrospinning.

The use of sequential window acquisition of all theoretical fragment ion spectra (SWATH), a data-independent acquisition high-resolution mass spectrometry approach, in forensic toxicological regimes: A review.

Sequential window acquisition of all theoretical fragment ion spectra (SWATH) is a type of high-resolution mass spectrometry that uses data-independent acquisition. Compared with more targeted acquisition schemes, the power behind this data-independent acquisition technique comes from its ability to mitigate interferences via the use of SWATH acquisition windows (Q1 quadrupole isolation windows) while still obtaining all accurate mass information. However, consistent with high-resolution mass spectrometry techniques, its routine and high throughput implementation in forensic toxicology is limited due to the complex processing power required to effectively manage the large amount of acquired data. It is therefore pivotal to create an efficient and validated identification criterion that confidently reports suspected positive detections as a confirmational technique for final reporting. This review examines all publications that implemented SWATH in a forensic toxicological framework with suggestive best practices and commonly used criteria. Seventeen publications were reviewed for extraction, liquid chromatography and mass spectrometry parameters, and more specifically for all SWATH applicable characteristics including spray voltages, collision energies and spreads, mass error, isotopic ratio difference, retention time error, and library score thresholds. Notwithstanding the challenges SWATH implementation faces for a laboratory, the technique demonstrates its potential to be utilized in routine forensic toxicology testing regimes and aids in the detection of both common and emerging novel drugs simultaneously.

Building a bridge with others: Alternative patterns of the acculturation process through the eyes of immigrants and members of the receiving culture

Our study investigated the acculturation process in a new culture from the perspectives of immigrants (interviewees) and members of the receiving culture (interviewers). One hundred and seven semi-structured interviews were carried out with foreigners from 38 countries who lived in Hungary for at least a year. Using a grounded theory approach, the perspectives and feelings of the interviewers were also analysed in addition to the interview texts. The results reveal that the receiving culture shows what we call a ‘two-stage receptive attitude’, which comprises at first, closedness and later, openness towards foreigners. A binary set of attitudes as ‘traveller or guest’ was observed in immigrants; the ‘guest’ makes efforts to take the host as a complementary actor into consideration, integrating the depths of the foreign culture, while the ‘traveller’ tastes the surface of a foreign culture and collects experiences. Based on our analysis, the dual modes of the traveller and the guest are interpreted as two ideal typical role conceptions or role realisations. Our study increases awareness of the complexity of the acculturation process, as the unique design provided insights into the importance of analysing the perspectives – in addition to those of the interviewees – of the reports from members of the receiving society to add to our understanding of cross-cultural encounters. In addition to its methodological novelty, the present study contributes to the literature by providing an in-depth understanding of the “traveller versus guest” typology and, accordingly, proposing ways to investigate how these two types might function in an organisation.

Rolling Bearing Remaining Useful Life Prediction Based on CNN-VAE-MBiLSTM

Ensuring precise prediction of the remaining useful life (RUL) for bearings in rolling machinery is crucial for preventing sudden machine failures and optimizing equipment maintenance strategies. Since the significant interference encountered in real industrial environments and the high complexity of the machining process, accurate and robust RUL prediction of rolling bearings is of tremendous research importance. Hence, a novel RUL prediction model called CNN-VAE-MBiLSTM is proposed in this paper by integrating advantages of convolutional neural network (CNN), variational autoencoder (VAE), and multiple bi-directional long short-term memory (MBiLSTM). The proposed approach includes a CNN-VAE model and a MBiLSTM model. The CNN-VAE model performs well for automatically extracting low-dimensional features from time–frequency spectrum of multi-axis signals, which simplifies the construction of features and minimizes the subjective bias of designers. Based on these features, the MBiLSTM model achieves a commendable performance in the prediction of RUL for bearings, which independently captures sequential characteristics of features in each axis and further obtains differences among multi-axis features. The performance of the proposed approach is validated through an industrial case, and the result indicates that it exhibits a higher accuracy and a better anti-noise capacity in RUL predictions than comparable methods.

Research on Deformation Prediction of VMD-GRU Deep Foundation Pit Based on PSO Optimization Parameters

As a key guarantee and cornerstone of building quality, the importance of deformation prediction for deep foundation pits cannot be ignored. However, the deformation data of deep foundation pits have the characteristics of nonlinearity and instability, which will increase the difficulty of deformation prediction. In response to this characteristic and the difficulty of traditional deformation prediction methods to excavate the correlation between data of different time spans, the advantages of variational mode decomposition (VMD) in processing non-stationary series and a gated cycle unit (GRU) in processing complex time series data are considered. A predictive model combining particle swarm optimization (PSO), variational mode decomposition, and a gated cyclic unit is proposed. Firstly, the VMD optimized by the PSO algorithm was used to decompose the original data and obtain the Internet Message Format (IMF). Secondly, the GRU model optimized by PSO was used to predict each IMF. Finally, the predicted value of each component was summed with equal weight to obtain the final predicted value. The case study results show that the average absolute errors of the PSO-GRU prediction model on the original sequence, EMD decomposition, and VMD decomposition data are 0.502 mm, 0.462 mm, and 0.127 mm, respectively. Compared with the prediction mean square errors of the LSTM, GRU, and PSO-LSTM prediction models, the PSO-GRU on the PTB0 data of VMD decomposition decreased by 62.76%, 75.99%, and 53.14%, respectively. The PTB04 data decreased by 70%, 85.17%, and 69.36%, respectively. In addition, compared to the PSO-LSTM model, it decreased by 8.57% in terms of the model time. When the prediction step size increased from three stages to five stages, the mean errors of the four prediction models on the original data, EMD decomposed data, and VMD decomposed data increased by 28.17%, 3.44%, and 14.24%, respectively. The data decomposed by VMD are more conducive to model prediction and can effectively improve the accuracy of model prediction. An increase in the prediction step size will reduce the accuracy of the deformation prediction. The PSO-VMD-GRU model constructed has the advantages of reliable accuracy and a wide application range, and can effectively guide the construction of foundation pit engineering.

Customers' political ideology and Self‐Service Technologies: Do political leanings predict usage of Self‐Service Technologies?

AbstractSelf‐service technologies are widely used in business, and retailers and service firms invest significant resources to obtain and improve their Self‐Service Technology capabilities. To allocate resources efficiently, it is crucial for firms to predict Self‐Service Technology usage by their customers. However, predictors in the extant literature (e.g., customers' perceptions and personality traits) are not easy to objectively measure or obtain secondary data about. This research proposes and examines political ideology, for which fairly accurate and objective data can be obtained, as a novel predictor of customer Self‐Service Technology usage. In four studies in different contexts, the authors consistently find that political ideology is significantly related to customers' intention to use and actual use of Self‐Service Technologies; Liberals, on average, are found to be significantly more likely to use Self‐Service Technologies compared to conservatives. Moreover, process complexity is identified as a moderator of this effect. In addition, two mediators, customers' need for interaction and customers' perceived control, through which political ideology affects intention to use Self‐Service Technologies are uncovered. The manuscript concludes with a discussion of contributions and practical implications for managers and practitioners as well as avenues for future research.

EARTHQUAKE PREDICTION USING ARTIFICIAL INTELLIGENCE IN THE FERGHANA DEPRESSION (UZBEKISTAN)

Solving the problem of predicting earthquakes faces difficulties of both theoretical and practical nature. The reason is that the occurrence of earthquakes depends on many factors, which give rise to various anomalies that are used as precursors. However, because of the complexity of the earthquake process and the unavailability of much information about the detailed structure of the Earth's crust, a small number of them can accurately indicate future seismic events. The results of the application of machine learning and deep learning give hope for the possibility of obtaining more accurate information about future strong earthquakes if disparate factors are combined. To determine the most important signs of an earthquake and determine the spatial location of strong earthquakes in a specific seismically active territory of Uzbekistan, namely, in the Fergana depression, the Cora 3, Cora 4, random forest algorithms of machine learning and LSTM, ANN architectures of deep learning were implemented.

Structural design of CA/SiO2 hybrid nanofiber membranes with semi-interpenetrating structure and switching wettability

The development of a stable structure separation membrane with adaptive switching wettability has positive significance for the expansion of membrane applications, especially in complex processing scenarios. Herein, a kind of core-shell cellulose acetate/silicon dioxide (CA/SiO2) hybrid nanofiber membrane with a semi-interpenetrating structure and switching wettability surface property was prepared via electrospinning process and subsequently in-situ hydrolysis, which integrated a superamphiphilicity SiO2 shell onto the porous CA nanofibers core to form an anchor embedding structure by vacuum forcing the silica precursor into the pores of CA nanofibers. The obtained CA/SiO2 hybrid nanofiber membrane exhibited superamphiphilicity in air with adaptive switching wettability, appearing non-stick to oil underwater or to water underoil, corresponded to superoleophobicity underwater and superhydrophobicity underoil, respectively. The CA/SiO2 hybrid nanofiber membrane with the hydrolysis time of 5 h demonstrated excellent properties. It achieved permeation fluxes of 19771 and 9104 L/m2·h with a separation efficiency exceeding 99.99% for immiscible light/heavy oil/water mixtures when pre-wetted by water and oil and subjected to gravity. Moreover, the semi-interpenetrating structure of SiO2 nanoparticles to CA nanofibers ensured the structural stability of the CA/SiO2 hybrid nanofiber membranes, which endowed it with excellent reusable performance.

Quality of care transition, patient safety incidents, and patients’ health status: a structural equation model on the complexity of the discharge process

BackgroundThe transition of patients between care contexts poses patient safety risks. Discharges to home from inpatient care can be associated with adverse patient outcomes. Quality in discharge processes is essential in ensuring safe transitions for patients. Current evidence relies on bivariate analyses and neglects contextual factors such as treatment and patient characteristics and the interactions of potential outcomes. This study aimed to investigate the associations between the quality and safety of the discharge process, patient safety incidents, and health-related outcomes after discharge, considering the treatments’ and patients’ contextual factors in one comprehensive model.MethodsPatients at least 18 years old and discharged home after at least three days of inpatient treatment received a self-report questionnaire. A total of N = 825 patients participated. The assessment contained items to assess the quality and safety of the discharge process from the patient’s perspective with the care transitions measure (CTM), a self-report on the incidence of unplanned readmissions and medication complications, health status, and sociodemographic and treatment-related characteristics. Statistical analyses included structural equation modeling (SEM) and additional analyses using logistic regressions.ResultsHigher quality of care transition was related to a lower incidence of medication complications (B = -0.35, p < 0.01) and better health status (B = 0.74, p < 0.001), but not with lower incidence of readmissions (B = -0.01, p = 0.39). These effects were controlled for the influences of various sociodemographic and treatment-related characteristics in SEM. Additional analyses showed that these associations were only constant when all subscales of the CTM were included.ConclusionsQuality and safety in the discharge process are critical to safe patient transitions to home care. This study contributes to a better understanding of the complex discharge process by applying a model in which various contextual factors and interactions were considered. The findings revealed that high quality discharge processes are associated with a lower likelihood of patient safety incidents and better health status at home even, when sociodemographic and treatment-related characteristics are taken into account. This study supports the call for developing individualized, patient-centered discharge processes to strengthen patient safety in care transitions.

Update of the World Health Organization's Mental Health Gap Action Programme Guideline for Psychoses (Including Schizophrenia).

The World Health Organization's (WHOs) Mental Health Gap Action Programme (mhGAP) aims to improve healthcare for mental, neurological, and substance use disorders in nonspecialized settings, with a focus on low- and middle-income countries (LMICs). mhGAP includes guidelines for the treatment of psychoses (including schizophrenia), which were recently updated in 2023. The complexity of the WHO guideline update process and the updated recommendations on psychoses are presented. The WHO guideline development process is outlined as well as the evidence appraisal and the translation of the evidence into recommendations following the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology. The guideline update process includes a review of the literature, a compilation of systematic reviews, and extracting data related to critical and important outcomes. The updated recommendations and the justifying evidence are discussed. The WHO mhGAP guidelines for psychoses are adapted to LMICs, and consist of 13 recommendations in 2023, whereof 5 were updated, and 1 recommendation was newly developed. Background information on how these recommendations were obtained, and significant changes since the previous guideline update in 2015 are provided. Unlike other guidelines, the WHO must consider various countries, contextual factors, and the WHO Model Lists of Essential Medicines when developing its guidelines. A transformation of the WHO guideline for psychoses into a living guideline would ensure always up-to-date recommendations and facilitate shared decision-making.

PointAS: an attention based sampling neural network for visual perception

Harnessing the remarkable ability of the human brain to recognize and process complex data is a significant challenge for researchers, particularly in the domain of point cloud classification—a technology that aims to replicate the neural structure of the brain for spatial recognition. The initial 3D point cloud data often suffers from noise, sparsity, and disorder, making accurate classification a formidable task, especially when extracting local information features. Therefore, in this study, we propose a novel attention-based end-to-end point cloud downsampling classification method, termed as PointAS, which is an experimental algorithm designed to be adaptable to various downstream tasks. PointAS consists of two primary modules: the adaptive sampling module and the attention module. Specifically, the attention module aggregates global features with the input point cloud data, while the adaptive module extracts local features. In the point cloud classification task, our method surpasses existing downsampling methods by a significant margin, allowing for more precise extraction of edge data points to capture overall contour features accurately. The classification accuracy of PointAS consistently exceeds 80% across various sampling ratios, with a remarkable accuracy of 75.37% even at ultra-high sampling ratios. Moreover, our method exhibits robustness in experiments, maintaining classification accuracies of 72.50% or higher under different noise disturbances. Both qualitative and quantitative experiments affirm the efficacy of our approach in the sampling classification task, providing researchers with a more accurate method to identify and classify neurons, synapses, and other structures, thereby promoting a deeper understanding of the nervous system.

In-situ growth of Cs5Cu3Cl6I2 nanocrystals within AAO arrays for X-ray imaging

Lead-free halide perovskites have shown promising application for indirect X-ray detection as fascinating scintillator. The fabrication of pixelated scintillator films leveraging directional confinement allows high-resolution imaging, yet process complexity and the potential crystal structure damage pose challenges that compromises the corresponding scintillator performance. In this work, a facile in-situ strategy for growing scintillation perovskite of Cs5Cu3Cl6I2 within anodic aluminum oxide (AAO) membrane is developed as scintillator Cs5Cu3Cl6I2@AAO film. The as-fabricated pixelated scintillator array with an enhanced light directional confinement for high-resolution X-ray imaging is demonstrated. The corresponding film achieves a spatial resolution of approximately 20 lp mm−1 and a detection limit of 152 nGy·s−1. Moreover, the Cs5Cu3Cl6I2@AAO film performs excellent stability under long-term X-ray irradiation, making it a reliable option for cost-effective pixelated indirect X-ray imaging.

Impact of imperfect annotations on CNN training and performance for instance segmentation and classification in digital pathology

Segmentation and classification of large numbers of instances, such as cell nuclei, are crucial tasks in digital pathology for accurate diagnosis. However, the availability of high-quality datasets for deep learning methods is often limited due to the complexity of the annotation process. In this work, we investigate the impact of noisy annotations on the training and performance of a state-of-the-art CNN model for the combined task of detecting, segmenting and classifying nuclei in histopathology images. In this context, we investigate the conditions for determining an appropriate number of training epochs to prevent overfitting to annotation noise during training. Our results indicate that the utilisation of a small, correctly annotated validation set is instrumental in avoiding overfitting and maintaining model performance to a large extent. Additionally, our findings underscore the beneficial role of pre-training.

Numerical and analytical investigation of thermal hydraulic behavior of large pools in passive heat exchangers

Passive residual heat removal safety systems are a modern design concept employed in Nuclear Power Plants (NPPs), and operate based on natural circulation. Several passive safety systems such as the emergency condensers (EC) use heat exchanger tube bundles immersed in large pools of coolant acting as a heat-sink (secondary side). The primary side of the heat exchanger is connected to the reactor core and, when activated, removes the decay heat in case of an accident. Recent research revealed the limitation of the state-of-the-art heat transfer models to capture the heat transfer rates achieved in these systems. The problems can be identified in both – primary and the secondary sides. The main focus of this study is to investigate the thermal hydraulic behavior of the secondary side of an EC. The data from the NOKO test facility has been considered as the validation base of this study. It has been observed that strong temperature stratification establishes in the pool due to the heat transfer process happening in a confined volume zone. Under this condition, the saturation temperature is reached at higher elevations of the tank while lower the liquid remains subcooled. This leads to different thermal behavior of the liquid at different elevations in the tank which is not properly captured by simple models. In addition, due to the complexity of the heat transfer process the required computational power is another challenge to study the thermal hydraulic behavior of the system. Therefore, the main aim of this study was to correctly predict the temperature stratification in the pool and identify boiling onset at different locations specially on the heated surfaces, while maintaining minimum numerical complexity. To achieve these targets, 2D multi-phase CFD simulations of secondary side using two different frameworks was conducted using ANSYS Fluent. Then, the results were validated against experimental data to assess their accuracy to achieve the best simulation approach.

Finite element modeling simulation of oxygen evolution during charging in lithium-oxygen batteries

The quest for advanced energy storage solutions has intensified the focus on developing next-generation secondary batteries, with lithium-oxygen batteries (LOB) standing out for their superior theoretical gravimetric energy density. This study introduces a novel model-based approach to battery development, enabling the detailed analysis of charge–discharge cycles and oxygen evolution efficiency within a virtual environment. Our model distinctively simulates the oxidative decomposition of lithium peroxide (Li2O2) and differentiates between its formation through solution and surface pathways, addressing the complexities of the charging process and its multiple elementary steps. The developed model further categorizes the oxidative decomposition species into four distinct types, facilitating a comprehensive understanding of their interactions, voltage profile changes, and O2 evolution within the battery's porous cathode. This approach not only enhances the understanding of battery behavior but also aids in refining the design of component materials, thereby propelling forward the development of LOBs with improved energy density and cycle performance.