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Abstract The treatment of methylene blue (MB) dye wastewater through the adsorption process has been a subject of extensive research. However, a comprehensive understanding of the thermodynamic aspects of dye solution adsorption is lacking. Previous studies have primarily focused on enhancing the adsorption capacity of methylene blue dye. This study aimed to develop an environmentally friendly and cost-effective method for treating methylene blue dye wastewater and to gain insights into the thermodynamics and kinetics of the adsorption process for optimization. An adsorbent with selective methylene blue dye adsorption capabilities was synthesized using rice straw as the precursor. Experimental studies were conducted to investigate the adsorption isotherms and models under various process conditions, aiming to bridge gaps in previous research and enhance the understanding of adsorption mechanisms. Several adsorption isotherm models, including Langmuir, Temkin, Freundlich, and Langmuir–Freundlich, were applied to theoretically describe the adsorption mechanism. Equilibrium thermodynamic results demonstrated that the calculated equilibrium adsorption capacity (q e ) aligned well with the experimentally obtained data. These findings of the study provide valuable insights into the thermodynamics and kinetics of methylene blue dye adsorption, with potential applications beyond this specific dye type. The utilization of rice straw as an adsorbent material presents a novel and cost-effective approach for MB dye removal from wastewater.

The human body is a remarkably aggressive medium and applied materials must be extremely resistant to corrosion and destruction, so copper, which is a biological material and is widely used in medicine, is being considered. Copper corrosion may be associated with inflammation in body because it releases ions that are toxic to humans. In aggressive solutions, biomolecules called amino acids act as corrosion inhibitors. In the present research, the purpose is to study the inhibitory action of l-alanine (L-Ala) and l-Leucine (L-Leu) amino acids on the corrosion of Cu. By density functional theory (DFT), inhibition behavior of l-Ala-and l-Leu-and their conformers against Cu corrosion have been investigated. According to values of back-donation of electrons (Eback-d), fraction of electron transferred (ΔN), electrophilicity (ω), electronegativity (χ), softness (σ), hardness (η), energy gap (Eg), ELUMO, and EHOMO computed reactivity factors between copper surface and inhibitors are studied. l-Leu's theoretical indicators confirm its tendency towards adsorption. l-Leu-activity against corrosion has been investigated and favorable results have been obtained.

Here, we investigate the chemical as well as the structural attributes of Apigenin and Kaempferol as viable gas sensors with high sensitiveness via density functional theory (DFT). The adhesion behavior of typical gas molecules was examined on Apigenin and Kaempferol by performing DFT calculations. These compounds can sense the physisorption of SO2, NO, and NO2 because of the considerable charge transport. The adhesion of NO2 in particular led to a considerable increase in the transport performance because of the considerable charge transport, which makes Apigenin and Kaempferol) promising candidates to selectively detect NO2 with high sensitiveness. The present study can provide insights into the potential use of natural products in order to sense gasses with high toxicity.

Carbon Capture and Storage (CCS) field is growing rapidly as a means to mitigate the accumulation of greenhouse gas emissions. However, the geomechanical stability of CCS systems, particularly related to bearing capacity, remains a critical challenge that requires accurate prediction models. In this research paper, we investigate the efficacy of employing an Autoregressive Deep Neural Network (ARDNN) algorithm to predict the geomechanical bearing capacity in CCS systems through shear wave velocity prediction as an index for bearing capacity evaluation of deep rock formations. The model utilizes a dataset consisting of 23,000 data points to train and test the ARDNN algorithm. Its scalability, use of deep learning techniques, automatic feature extraction, adaptability to changes in data, and versatility in various prediction tasks make it an attractive option for accurate predictions. The results demonstrate exceptional performance, as evidenced by an R-squared value of 0.9906 and a mean squared error of 0.0438 for the test data compared to the measured data. This research has significant practical implications for effectively predicting geomechanical stability in CCS systems, thus mitigating potential risks associated with their operation.