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This study elucidates the presence of a cathode electrolyte interphase (CEI) at graphite positive electrodes (PEs) and assesses its impact on the performance of dual‐ion batteries, being promising candidates for cost‐efficient and sustainable stationary energy storage. Indeed, electrolyte oxidation increases during charge (5 V vs Li|Li+) for decreased C rates, that is longer duration at high state‐of‐charges (SOC) , but effective protection and evidence for CEI formation is missing as no increase in Coulombic efficiencies is observed, even with literature‐known electrolyte additives like vinylene carbonate, fluoroethylene carbonate, or ethylene sulfite in a highly concentrated base electrolyte (4.0 m LiPF6 in dimethyl carbonate) as reference. Via studying charged and pristine PEs by X‐ray photoelectron spectroscopy, PF6−‐graphite intercalation compounds and cointercalated solvent molecules are identified, while indications for CEI are absent within 1000 charge/discharge cycles. Nevertheless, a high capacity retention of ≈94% (referring to 0.1C) is demonstrated. Affirmed by Raman spectroscopy and scanning electron microscopy, the active material remains structurally stable, suggesting capacity fading to be dominated by resistance rise at the PE, likely due to an electronic contact resistance from active material grain boundaries and/or from the interface between electrode particles and the current collector in course of high volume changes; as systematically derived by impedance spectroscopy. In dual‐ion batteries (5 V), electrolyte oxidation and formation of cathode electrolyte interphase (CEI) can be reasonably assumed. However, neither a protective “passivation” effect with varied electrolyte additives is observed within direct current applications, nor any hints for a surface layer via X‐ray photoelectron spectroscopy or electrochemical impedance spectroscopy, rendering presence of CEI unlikely.

Thermosets are known to be very reliable polymeric materials for high-performance and light-weight applications, due to their retained dimensional stability, chemical inertia and rigidity over a broad range of temperatures. However, once fully cured, they cannot be easily reshaped or reprocessed, thus leaving still unsolved the issues of recycling and the lack of technological flexibility. Vitrimers, introduced by Leibler et al. in 2011, are a valiant step in the direction of bridging the chasm between thermoplastics and thermosets. Owing to their dynamic covalent networks, they can retain mechanical stability and solvent resistance, but can also flow on demand upon heating. More generally, the family of Covalent Adaptable Networks (CANs) is gleaming with astounding potential, thanks to the huge variety of chemistries that may enable bond exchange. Arising from this signature feature, intriguing properties such as self-healing, recyclability and weldability may expand the horizons for thermosets in terms of improved life-span, sustainability and overall enhanced functionality and versatility. In this review, we present a comprehensive overview of the most promising studies featuring CANs and vitrimers specifically, with particular regard for their industrial applications. Investigations into composites and sustainable vitrimers from epoxy-based and elastomeric networks are covered in detail.

The use of advanced materials in the field of aerospace and automotive applications has led to use of metal matrix composites (MMC’s) due to their excellent mechanical properties. Aluminium metal matrix composite is one of the materials which can be strengthened by reinforcing it with hard ceramic particles. In the current work Al6082 matrix hybrid composites reinforced with silicon carbide (SiC) and aluminium oxide (Al2O3) was developed by using stir casting technique. The weight percentage of SiC was varied from 0 wt.% to 8 wt.% and keeping 3 wt.% Al2O3 constants. The tensile, hardness, density and impact tests were conducted, and the results obtained revealed that the addition of silicon carbide and Al2O3 particles in Al6082 enhances the mechanical properties of the prepared hybrid composites. The artificial neural network (ANN) model, which was trained using a dataset consisting of experimental results, has effectively captured the correlation between the weight percentage (wt.%) of silicon carbide (SiC) and the mechanical properties of the composite material. Through the examination of this model, valuable insights can be obtained regarding the distinct contributions of SiC to the mechanical properties of Al6082.

This study presents a hybrid modeling approach that integrates Kragelsky’s friction law and Archard’s wear law with an artificial neural network (ANN) to predict the coefficient of friction (COF) and specific wear rate (SWR) in epoxy‐based self‐lubricating composites reinforced with graphite and MoS₂. Given the complex, nonlinear interactions among tribological parameters such as contact pressure, sliding speed, hardness, and filler composition, traditional empirical models often fail to capture wear behavior accurately. The proposed ANN architecture comprises an input layer, three hidden layers employing sigmoid, ReLU, and power activation functions, and an output layer predicting COF and SWR. The network is trained using a feed‐forward method with backpropagation to minimize prediction error. SEM analysis reveals that graphite imparts superior wear resistance compared to MoS₂. The ANN achieved significantly higher prediction accuracy for graphite‐reinforced composites. For COF, graphite yielded an MSE of 0.00073 and R² of 0.9047, while MoS₂ showed an MSE of 0.00318 and R² of 0.5567. For SWR, graphite attained an MSE of 1.3351 and R² of 0.9809, compared to MoS₂ with an MSE of 1.6993 and R² of 0.8271. The reduced performance in MoS₂ predictions is attributed to its oxidative degradation forming MoO₃. The model also offers 3D surface simulations, aiding in composite design optimization and reducing experimental costs. This study presents a hybrid modeling approach that integrates an artificial neural network (ANN) with Kragelsky’s and Archard’s laws to predict the coefficient of friction (COF) and wear rate in epoxy composites. It emphasizes the superior predictive accuracy for graphite over MoS₂, highlighting the need to consider environmental sensitivity when modeling tribological behavior in self‐lubricating composites.

This paper presents an enhanced artificial neural network (ANN) to predict the displacement in composite pipes impacted by a drop weight having different velocities. The impact response of fiber-reinforced polymer composite pipes depends on several factors including thickness, stacking sequence, and the number of layers. These factors were investigated in an earlier study using sensitivity analysis, and it was found that they had the most prominent effect on the impact resistance of the composite pipes. In this present study, composite pipes with a diameter of 54 mm are considered to explore the damages induced by low-velocity impact and the influence of these damages on their strength. To evaluate the effect of low-velocity, the pipes were exposed to impacts at different velocities of 1.5, 2, 2.5, and 3 m/s, and preliminary damage was initiated. Next, we used Jaya and E-Jaya algorithms to enhance the ANN algorithm for good training and prediction. The Jaya algorithm has a basic structure and needs only two requirements, namely, population size and terminal condition. Recently, Jaya algorithm has been widely utilized to solve various problems. Due to its single learning technique and limited population information, Jaya algorithm may quickly be trapped in local optima while addressing complicated optimization problems. For better prediction, an enhanced Jaya (E-Jaya) algorithm has been presented to enhance global searchability. In this study, ANN is enhanced based on the influential parameters using E-Jaya to test its effectiveness. The results showed the effectiveness of the E-Jaya algorithm for best training and prediction compared with the original algorithm.

Aluminum-based metal matrix composites reinforced with B4C are advanced materials recognized for their exceptional combination of lightweight properties, high hardness, and superior wear resistance. These characteristics make them perfectly suited for applications demanding exceptional performance in extreme mechanical and tribological environments. This study investigates the wear behavior, microstructural characteristics, and predictive modeling of Al10Cu-B4C composites fabricated via powder metallurgy with varying B4C contents (0, 2.5, 5, and 7.5 wt.%). The addition of B4C microparticles to Al10Cu composites significantly influenced their tribological properties with 2.5 wt.% B4C achieving a 21.74% reduction in the coefficient of friction (COF) and 7.5 wt.% B4C providing a remarkable 65.00% improvement in wear resistance. Microstructural analysis using SEM and EDS was conducted on the unreinforced materials and the reinforced composites both before and after the wear tests. To further analyze and predict the tribological performance, a Gated Recurrent Unit neural network was developed to predict COF values. The need for this model arises from its potential to cost-effectively facilitate the prediction of COF, diminishing the need for extensive experimental testing while being noted for its simplicity and ease of implementation in practical applications. The model achieved excellent accuracy with an R2 of 0.9965 for the test set and 0.9917 for the validation set. Additionally, feature importance analysis using Random Forest models identified reinforcement-related features as the dominant predictors for both COF and mass wear. These findings demonstrate the potential of Al10Cu-B4C composites for emerging industrial applications, where enhanced wear resistance and controlled friction are critical for improving efficiency and durability under rigorous operating conditions. Furthermore, this study highlights the efficacy of neural network models in accurately predicting COF, providing a powerful tool for optimizing the performance of advanced composite materials.

This study investigates the machinability of a novel LM25 aluminum alloy reinforced with vanadium carbide composite material (LM25Al/VC) using computer numerical control (CNC) lathe operation. By optimizing CNC lathe process parameters such as depth of cut, feed rate, and cutting speed, the aim is to maximize material removal rate, minimize surface roughness, reduce power consumption, and optimize costs. The study employs analytical modeling, deep neural networks (DNN), and grey relational grade (GRA) coupled with response surface methodology (RSM) for performance evaluation. The effectiveness of these methods was compared using four objective verification mechanisms. In this case, the DNN technique delivered superior results among the methods considered. Additionally, new analytical models and DNN programming were developed in this work to predict machining costs, power consumption, material removal rate, and surface finish quality. These findings contribute to creating energy-efficient, cost-effective machining techniques and promote sustainable practices in the manufacturing industry.

Carbon fiber reinforced polymer (CFRP) composites are increasingly utilized for their lightweight and superior mechanical properties. This study uses machine learning models to predict the mechanical properties of CFRP composites based on the volume fraction of carbon nanotubes (CNTs), interlayer volume fraction, glass transition temperature, and manufacturing pressure. Sixty-two samples covering nine different types of CFRPs were designed, manufactured, and experimentally tested. Three machine learning models, namely ridge regression, random forest, and support vector regression, were trained on the data and compared. The results demonstrated a high prediction accuracy for the flexural strength (R2 =  0.966), flexural modulus (R2 =  0.871), and the mode-II energy release rate (R2 =  0.903). The study highlights the effectiveness of data-driven models in predicting key mechanical properties of CFRP composites, potentially reducing the need for extensive experimental testing and facilitating more efficient material design.

Waterborne polyurethane (WPU) is a waterborne coating with excellent physicochemical properties. Its deficiencies of water resistance, chemical resistance, staining, and hardness have limited the wide application of polyurethane in the wood lacquer market. In this study, polycarbonate diols (PCDL) were used as soft segments and WPCU was modified by cross-linking using Trimethylolpropane (TMP) to prepare polycarbonate type WPU (WPCU) with cross-linked network structure. The new wood lacquer was prepared by adding various additives and tested by applying it on wood board. The successful synthesis of WPCU was determined by FTIR testing, and the cross-linking degree of WPCU was probed by low-field NMR. The viscosity of the cross-linked WPCU emulsion showed a decreasing trend compared to the uncross-linked WPCU emulsion, and WPCU-2 had the smallest particle size. Compared with the uncrosslinked WPCU film, the crosslinked WPCU film had lower water absorption (2.2%), higher water contact angle (72.7°), excellent tensile strength (44.02 MPa), higher thermomechanical, and better water and alcohol resistance. The effect of crosslinker content on the microphase separation of WPCU chain segments on the surface roughness of the film was investigated by SEM. The wood paint prepared by WPCU emulsion has good dry heat resistance, chemical resistance, and adhesion, and the hardness of the wood paint when the TMP content is 3% reaches H. It also has good resistance to sticky stains, which can be used to develop new wood lacquer.

This study pioneers an integrated approach combining experimental analysis and machine learning (ML) predictions to assess the low-velocity impact (LVI) response of synthetic/natural bio-hybrid fiber-reinforced polymer (HFRP) composite materials. Five different stacking sequences of carbon/flax bio-HFRP were tested for LVI with impact energies from 15 to 90 J, and data such as peak impact force, damage area, and damage extension were recorded. Symmetric configuration with consistent dispersal of natural flax fibers across laminate demonstrated improved impact resistance. Furthermore, six ML algorithms were used: decision tree (DT), random forest, deep neural network with Adam optimizer (DNN-Adam), DNN with stochastic gradient (SGD) optimizer (DNN-SGD), recurrent neural network (RNN) with Adam optimizer (RNN-Adam), and RNN with SGD optimizer (RNN-SGD). Model performance was evaluated using coefficient of determination ( R 2 ), mean square error (MSE), and mean absolute error (MAE). The DT ML model achieved best performance in predicting peak impact force having maximum depth count of 8 and leaf nodes count of 28. For damage area, again, DT model with maximum depth count of 6 and leaf nodes count of 23 exhibited better performance. On the other hand, for damage extension, the RNN-SGD model, having four hidden layers and 70 neurons, outperformed other ML models. Among the investigated parameters, the highest correlation ( R 2  = 0.9987 for training and 0.9922 for test datasets) and lowest errors (MSE = 0.0294 and MAE = 0.1344) were achieved for predicting damage extension. This study is the first to apply advanced ML techniques to predict mechanical responses such as peak impact force, damage area, and damage extension in carbon/flax bio-HFRP composites under LVI conditions, enhancing accuracy and reducing the testing, thereby optimizing resources and significantly minimizing time.