This paper aims to present an overview of the current state of hydrogen storage methods, and materials, assess the potential benefits and challenges of various storage techniques, and outline future research directions towards achieving effective, economical, safe, and scalable storage solutions. Hydrogen is recognized as a clean, secure, and cost-effective green energy carrier with zero emissions at the point of use, offering significant contributions to reaching carbon neutrality goals by 2050. Hydrogen, as an energy vector, bridges the gap between fossil fuels, which produce greenhouse gas emissions, global climate change and negatively impact health, and renewable energy sources, which are often intermittent and lack sustainability. However, widespread acceptance of hydrogen as a fuel source is hindered by storage challenges. Crucially, the development of compact, lightweight, safe, and cost-effective storage solutions is vital for realizing a hydrogen economy. Various storage methods, including compressed gas, liquefied hydrogen, cryo-compressed storage, underground storage, and solid-state storage (material-based), each present unique advantages and challenges. Literature suggests that compressed hydrogen storage holds promise for mobile applications. However, further optimization is desired to resolve concerns such as low volumetric density, safety worries, and cost. Cryo-compressed hydrogen storage also is seen as optimal for storing hydrogen onboard and offers notable benefits for storage due to its combination of benefits from compressed gas and liquefied hydrogen storage, by tackling issues related to slow refueling, boil-off, and high energy consumption. Material-based storage methods offer advantages in terms of energy densities, safety, and weight reduction, but challenges remain in achieving optimal stability and capacities. Both physical and material-based storage approaches are being researched in parallel to meet diverse hydrogen application needs. Currently, no single storage method is universally efficient, robust, and economical for every sector especially for transportation to use hydrogen as a fuel, with each method having its own advantages and limitations. Moreover, future research should focus on developing novel materials and engineering approaches in order to overcome existing limitations, provide higher energy density than compressed hydrogen and cryo-compressed hydrogen storage at 70 MPa, enhance cost-effectiveness, and accelerate the deployment of hydrogen as a clean energy vector.

The article discusses the influence of the post-process on the mechanical properties of elements produced with the use of the mask stereolithography (mSLA) method. Printed samples were subjected to the following post-process steps: Washing and post-curing, at various times. Then, static tensile and static bending tests were carried out, as well as Shore D hardness measurements for the inner and surface part of the sample, as well as profilographometric analysis of the surface. The post-curing time has been found to strongly affect the tensile and bending strength of printouts, and to improve their surface quality. Washing has an ambiguous effect on the strength of the printouts, but, in the end, it was found that extended washing slightly reduces the strength. Washing significantly affects the quality of the printout surface. A washing time that is too short results in a surface that strongly resembles the printing process, with high roughness. Increasing the washing time to 10 min lowers the roughness by one order of magnitude. Post-curing has also been shown to be beneficial for the cured sample with the application of shielding water. This approach results in an improvement in the flexural strength of the printouts. In general, the obtained research results indicate that, for printouts with cross-sectional dimensions of several mm, the optimal washing time is no more than 10 min and the post-curing time is at least 30 min.

This article focusses on predicting the compressive properties of polyurethane-derived carbon foam using an artificial neural network (ANN) approach. To train the model, strain, pore density (20, 40, and 60 ppi), and solvents (acetone, ethanol, and methanol) were used as inputs, while compressive stress was used as output. Categorical variables like acetone, ethanol, and methanol were converted to binary form before training the ANN model by using one-hot encoding mechanism. Both inputs and outputs were normalized to prevent features with larger numerical ranges from dominating the training process. A feed-forward ANN with four hidden layers, each containing 100 neurons, was constructed. The performance of the ANN model was tested using three metrics: mean square error (MSE), mean absolute error (MAE), and coefficient of determination (R²). The Adam optimizer was used to optimize the weights and biases of the ANN. The model was trained for 10,000 epochs with a batch size of 50. Rectified Linear Unit (ReLU) and linear functions were used as activation functions for the hidden layers and the output layer, respectively. From the results, overall average MSE, MAE, RMSE, and R² values of 36.34, 4.42, 6.00, and 0.9797, respectively, were obtained.

This article sought to determine the response of a carbon foam material derived from polyurethane foam when subjected to a quasi-static compression load. The effects of the foam pore densities and additives (solvents) on the compression strength, compressive modulus, and surface morphology of the carbon foam were investigated. In this study, three different carbon foam pore densities (20, 40, and 60 ppi) and three solvents for the phenol–formaldehyde resins that coated the polymer foam (acetone, ethanol, and methanol) were used. Carbon foams were derived from polyurethane foams by carbonization. Quasi-static compression testing was carried out using a universal testing machine. The compressive strength, compressive modulus, and relative density of these different carbon foams were computed and compared. Two-way ANOVA analyses were performed to compare the significance of solvents and pore density. These results showed that pore density and solvents significantly affected the compressive strength, compressive modulus, and surface morphology of the fabricated polyurethane-derived carbon foam. Finally, the maximum compressive strength and maximum compressive modulus were observed in carbon foam (60 ppi) with 40% methanol as the solvent. Conversely, a minimum compressive strength was observed for a 20 ppi carbon foam with a 20% acetone solvent, and a minimum compressive modulus was observed for a 20 ppi foam with 40% methanol. Lastly, the chemical composition of the polyurethane foams was investigated, and these results indicated that the polyurethane-derived carbon foam had 96% carbon atoms after carbonization.

In boilers operating in modern power plants, thick-walled elements of complex shapes, such as valves, superheater headers, T-pipes, Y-pipes, four-way pipes, and elbows, are especially prone to fatigue processes. Higher operation parameters and more frequent startups may speed up fatigue damage in these elements. Such damage is a local phenomenon and is caused by thermomechanical fatigue (TMF). This paper presents a method designed for predicting the behavior of components subjected to variable temperature and mechanical loading conditions. This method combines the results of measurements of operating parameters of devices under industrial conditions with those obtained using finite element modeling (FEM). Particular attention was given to the influence of the time-dependent heat transfer coefficient on the local thermomechanical stress–strain behavior of the material. It was stated that heat transfer conditions have a significant impact on local transient stresses and depend on the operation parameters of boilers. Consistency of the temperature changes as a function of time, determined in industrial conditions and calculated on the basis of the model approach, was obtained. This developed and described in the work approach enables defining the conditions of heat transfer on the surface of models of considered components.