Abstract The depletion of fossil energy resources and the emission of greenhouse gases are among the key factors driving attention toward renewable energies. Hydrogen storage, as an energy carrier, is considered one of the promising methods for sustainable utilization of these resources. In underground hydrogen storage (UHS), a portion of the gas remains unrecovered due to insufficient production pressure and is retained as cushion gas. To minimize hydrogen loss, it is proposed to replace hydrogen with more cost-effective gases, such as methane, nitrogen, or carbon dioxide, as the cushion gas. This study provides a comprehensive assessment of various cushion gases and the parameters influencing hydrogen storage in their presence, aiming to propose strategies for optimizing this process. In this study, over 300 scientific papers were reviewed, among which 80 articles were selected as the primary sources related to the underground storage process of hydrogen gas and other gases in the presence of cushion gas, while an additional 82 articles were reviewed as complementary references. These papers were categorized into three groups: the impact of cushion gas properties, reservoir properties, and operational parameters. The content was summarized and presented coherently, providing a comprehensive foundation for analyzing hydrogen storage in the presence of cushion gas. The results indicated that using gases such as methane, nitrogen, and carbon dioxide as cushion gas could significantly contribute to the economic aspects of UHS by reducing the amount of trapped hydrogen in the reservoir. The density and viscosity of the cushion gas play an important role in UHS. Nitrogen gas, due to its favorable physical properties, is a superior option for injection as it reduces the risk of phenomena such as gravitational segregation and fingering. In contrast, carbon dioxide, due to its solubility in formation water and high compressibility, requires the injection of larger volumes to achieve the desired reservoir pressure. Additionally, reduction in pressure and increase in temperature, porosity, and permeability lead to a decrease in hydrogen purity. Furthermore, simulations show that injecting the appropriate cushion gas and increasing the reservoir's recovery factor before starting hydrogen storage can help improve hydrogen purity and recovery.

Abstract Abstract
Research subject: Bioleaching of lithium has emerged as an innovative and sustainable approach for extracting this valuable metal from solid sources, including mineral ores, spent lithium-ion batteries, and other electronic waste. The increasing global demand for lithium-ion batteries, especially for consumer electronics and electric vehicles, has intensified the need for efficient lithium resource recovery. Given the environmental and economic challenges associated with traditional extraction methods, bioleaching has been proposed as an eco-friendly and cost-effective alternative.
Research approach: This study provides a comprehensive review of the scientific literature and research on lithium bioleaching. It begins with an overview of lithium resources and applications, followed by an evaluation of studies on the bioleaching of lithium-ion batteries and other solid sources. The mechanisms involved and commonly used microorganisms are analyzed. Additionally, key parameters influencing metal recovery efficiency—such as pH, temperature, medium composition, pulp density, and leaching time—are investigated. The study also explores innovative approaches, including the use of artificial intelligence, systems biology, and synthetic biology, to optimize bioleaching processes.
Main results: The findings demonstrate that bioleaching not only achieves efficient lithium recovery from solid sources but also significantly reduces environmental hazards. Spent lithium-ion batteries are identified as a rich and valuable source for lithium extraction. The use of microorganisms such as Aspergillus niger and Acidithiobacillus ferrooxidans has achieved lithium recovery rates of up to 100%. Studies indicate that optimizing microbial strains through synthetic and systems biology, along with refining cultivation conditions using modern AI-based techniques, can address industrial challenges in bioleaching. Furthermore, this research highlights the role of bioleaching in promoting a circular economy and presents a promising outlook for its industrial application in sustainable lithium resource recovery.

Abstract Today, different filament cross-sectional shapes of spinneret are used in the production of synthetic multifilament yarns to create various applications and properties. This research aimed to investigate the effect of round and rectangular cross-sectional shapes of polyethylene terephthalate (PET) multifilament yarn on the physical and crimp properties of melt-spun and draw-textured yarn with partially oriented yarns (POY) produced by an industrial melt spinning machine and then draw textured yarns (DTY) was produced by an industrial frictional false twist texturizing machine under the same conditions. All yarn samples with two round and rectangular cross-sectional shapes, linear density, thermal shrinkage, degree of crystallinity, and tensile properties (strength, elongation at break, and initial modulus) were analyzed. Also, for DTY yarns, in addition to the mentioned tests, the crimp properties (crimp shrinkage, crimp modulus, and crimp stability), the amount of resiliency, and the ratio of tension after twisting zone to tension before twisting zone (T2/T1) were also investigated. In addition, to know the microstructural condition of fibers, an X-ray diffraction test was performed for textured yarn samples. According to the results, it was found that the difference in tensile properties and thermal shrinkage of POY yarns of both cross-sections is small, despite the statistical significance. However, these variations are greater for two round and rectangular cross-sectional shapes in DTY yarns. In comparing the resiliency values of DTY yarns, the yarn with a rectangular cross-sectional shape showed a higher resiliency value than the round cross-sectional shape. In addition, the crimp properties of DTY yarn with a round cross-sectional shape were better than a rectangular cross-sectional surface, which can be attributed to its higher crystallinity and consequently, softness and texturability compared to a round cross-sectional shape.
 

Abstract Research subject: In this study, Fe₃O₄ nanoparticles were synthesized using the co-precipitation method. Subsequently, core–shell Fe₃O₄@SiO₂ nanoparticles were prepared via the Stöber method using tetraethoxysilane as the silica source. In the next step, the core–shell nanoparticles were functionalized with theophylline molecules. Finally, these nanoparticles were employed as an adsorbent for the removal of nickel ions from aqueous solutions using the solid-phase extraction method.
Research approach: The structural, crystalline, thermal, magnetic, morphological, and size-related properties of the nanoparticles were investigated using X-ray diffraction, Fourier transform infrared spectroscopy, thermogravimetric analysis, vibrating sample magnetometry, transmission electron microscopy, and scanning electron microscopy. The key parameters affecting solid-phase extraction were then optimized following confirmation of the successful synthesis of the proposed nanostructure. For this purpose, the effects of adsorbent dosage, contact time, pH, and initial nickel ion concentration on adsorption capacity were systematically studied.
Main results: The results showed that using 27 mg of adsorbent in 75 mL of nickel ion solution with an initial concentration of 0.45 mmol/L achieved a maximum adsorption capacity of 94% at pH 7 within 28 minutes at ambient temperature. Furthermore, the recyclability and reusability of the nanoadsorbent were examined through sequential adsorption–desorption cycles using an external magnet. The results demonstrated excellent performance in removing divalent nickel ions from aqueous solutions. Moreover, the synthetic nanoadsorbent could be recovered and reused across successive adsorption–desorption cycles without any loss of functional activity.

Abstract Research Subject:Drilling operations frequently encounter numerous challenges that can lead to significant financial, human, and environmental losses. Therefore, predicting potential problems before they occur and implementing necessary preventive measures is crucial to minimizing risks. In this context, this study investigates the impact of employing artificial intelligence (AI) algorithms to forecast drilling complications using real-time mud logging data collected from existing wells in an Iranian oilfield.
Research approach: A hybrid architecture combining Long Short-Term Memory (LSTM) and Fully Connected neural networks was developed for the identification and detection of anomalies such as kicks and stuck pipe. Given the scarcity of these anomalies in the dataset, which could adversely affect model accuracy and performance, the Synthetic Minority Oversampling Technique (SMOTE) was applied to balance class distribution and enhance the overall effectiveness of the network. Furthermore, the influence of varying hyperparameters on reducing network error was systematically analyzed.
Main Results: Various network architectures and structures were examined. The experimental results indicated that the optimal model achieved an accuracy of 94.45% on the testing dataset with the following hyperparameters: a lookback of 7, a learning rate of 0.001, a dropout rate of 0.2, a batch size of 32, and a four-layer network architecture with 512, 256, and 256 units in the first, second, and third hidden layers, respectively. This configuration yielded higher accuracy and fewer false alarms in anomaly detection compared to other tested models. Based on the obtained results, this approach demonstrates significant potential for real-time anomaly detection in drilling operations.