The modification of hydrophobic rock surfaces to the water-wet state via nanofluid treatment has shown promise in enhancing their geological storage capabilities and the efficiency of carbon dioxide (CO2) and hydrogen (H2) containment. Despite this, the specific influence of silica (SiO2) nanoparticles on the interactions between H2, brine, and rock within basaltic formations remains underexplored. The present study focuses on the effect of SiO2 nanoparticles on the wettability of Saudi Arabian basalt (SAB) under downhole conditions (323 K and pressures ranging from 1 to 20 MPa) by using the tilted plate technique to measure the contact angles between H2/brine and the rock surfaces. The findings reveal that the SAB's hydrophobicity intensifies in the presence of organic acids, with significant increases in both advancing (θa) and receding (θr) contact angles upon exposure to organic acid at 323 K and 20 MPa. Contrastingly, the application of SiO2 nanoparticles under these conditions results in a marked shift towards hydrophilicity, with θa and θr decreasing substantially, thus indicating an optimal nanoparticle concentration (0.1 wt% SiO2) for effecting the transition from H2-wet to water-wet states. This change in wettability aligns with the known pressure-dependent behavior of contact angles. Moreover, the treatment of organically-aged basalt with 0.1 wt% SiO2 nanofluids at 20 MPa and 323 K enhances the H2 column height significantly, from −424 m to 4340 m, suggesting a reduced risk of H2 migration across the caprock and thereby enhancing both the structural/residual trapping and containment security of H2 within the basaltic formations of Saudi Arabia. This article highlights the crucial role of SiO2 nanofluids in improving the efficacy of H2 storage in basalt, offering a new insight towards the optimization of geological storage solutions for hydrogen, a critical component in the transition to a sustainable energy future.
Abstract
The current research aims to identify the psychological security of students in the kindergarten department and identify if there is a significant difference between stage one and stage four students. To do this, the researcher adopted the psychological research scale of (Al-mohamdawi, 2007) that consisted of (30) items. It was administered to (120) female students chosen randomly from the kindergarten department in the college of education for women for the academic year (2029-2021). The results revealed that students in the kindergarten department have psychological security. There is a significant difference between stage one and four students in favor of stage four students.
Identification by biological features gets tremendous importance with the increasing of security systems in society. Various types of biometrics like face, finger, iris, retina, voice, palm print, ear and hand geometry, in all these characteristics, iris recognition gaining attention because iris of every person is unique, it never changes during human lifetime and highly protected against damage. This unique feature shows that iris can be good security measure. Iris recognition system listed as a high confidence biometric identification system; mostly it is divide into four steps: Acquisition, localization, segmentation and normalization. This work will review various Iris Recognition systems used by different researchers for each recognit
... Show MoreWireless sensor networks (WSNs) represent one of the key technologies in internet of things (IoTs) networks. Since WSNs have finite energy sources, there is ongoing research work to develop new strategies for minimizing power consumption or enhancing traditional techniques. In this paper, a novel Gaussian mixture models (GMMs) algorithm is proposed for mobile wireless sensor networks (MWSNs) for energy saving. Performance evaluation of the clustering process with the GMM algorithm shows a remarkable energy saving in the network of up to 92%. In addition, a comparison with another clustering strategy that uses the K-means algorithm has been made, and the developed method has outperformed K-means with superior performance, saving ener
... Show MoreThis article co;nsiders a shrunken estimator ·Of Al-Hermyari· and
AI Gobuii (.1) to estimate the mean (8) of a normal clistributicm N (8 cr4) with known variance (cr+), when <:I guess value (So) av11il ble about the mean (B) as· an initial estrmate. This estimator is shown to be
more efficient tl1an the class-ical estimators especially when 8 is close to 8•. General expressions .for bias and MSE -of considered estitnator are gi 'en, witeh some examples. Nut.nerical cresdlts, comparisons and
conclusions ate reported.
Big data analysis is essential for modern applications in areas such as healthcare, assistive technology, intelligent transportation, environment and climate monitoring. Traditional algorithms in data mining and machine learning do not scale well with data size. Mining and learning from big data need time and memory efficient techniques, albeit the cost of possible loss in accuracy. We have developed a data aggregation structure to summarize data with large number of instances and data generated from multiple data sources. Data are aggregated at multiple resolutions and resolution provides a trade-off between efficiency and accuracy. The structure is built once, updated incrementally, and serves as a common data input for multiple mining an
... Show MoreThis paper deals with the thirteenth order differential equations linear and nonlinear in boundary value problems by using the Modified Adomian Decomposition Method (MADM), the analytical results of the equations have been obtained in terms of convergent series with easily computable components. Two numerical examples results show that this method is a promising and powerful tool for solving this problems.
Sansevieriatrifasciata was studied as a potential biosorbent for chromium, copper and nickel removal in batch process from electroplating and tannery effluents. Different parameters influencing the biosorption process such as pH, contact time, and amount of biosorbent were optimized while using the 80 mm sized particles of the biosorbent. As high as 91.3 % Ni and 92.7 % Cu were removed at pH of 6 and 4.5 respectively, while optimum Cr removal of 91.34 % from electroplating and 94.6 % from tannery effluents was found at pH 6.0 and 4.0 respectively. Pseudo second order model was found to best fit the kinetic data for all the metals as evidenced by their greater R2 values. FTIR characterization of biosorbent revealed the presence of carboxyl a
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