 Subjects -> SCIENCES: COMPREHENSIVE WORKS (Total: 374 journals)
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- Enhancing Carbonate Reservoir Permeability Using a Novel Porosity Model
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Abstract: Abstract Carbonate reservoirs hold a significant portion of the world’s hydrocarbon resources. However, they present a significant challenge in determining reservoir permeability utilizing standard evaluation techniques. Previous studies estimated the permeability of carbonate reservoirs based on well logs using artificially intelligent techniques. Nevertheless, to estimate the permeability of a carbonate reservoir with intergranular and vuggy porosity, an evaluation method that includes pore size distribution information is required. In this study, the permeability of a carbonate reservoir with intergranular and vuggy porosity was enhanced by integrating standard resolution log data with pore size distribution-sensitive log data and augmented with carbonate pore size distribution information from borehole resistivity images. Porosity partitioning and permeability analysis results were validated by comparing the calculated permeability with permeability data from a modular formation dynamics tester (MDT) permeability. The results showed a good correlation between carbonate permeability and MDT permeability. The integration of the NMR-based porosity partitioning method into standard petrophysical workflow has improved the permeability estimation in Well B. Neutron and density cross plot over the water zone identifies calcite and dolomite as the two main minerals. Pickett plot over the water zone provided the value for formation water resistivity as 0.097 at 102 °C and salinity of 22,000 ppm. Hence, the enhanced workflow provided a better permeability estimation for Astaka carbonate compared to the classical approach, which assumes porosity proportional to permeability. Therefore, it can be concluded that the improved carbonate evaluation workflow can provide a more reliable and accurate permeability estimate without using core data.
PubDate: 2024-08-09
DOI: 10.1007/s13369-024-09418-3
- Synthesis and Characterization of Al-Doped SnO2 Semiconducting Thin Films
on Glass Substrate by Sol–Gel Technique for Gas Sensors in Aerospace
Applications-
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Abstract: Abstract This paper outlines the synthesis and characterization of semiconducting films composed of 0–2.0% Al-doped SnO2, tailored for gas sensor applications in aerospace contexts. The films were fabricated via the sol–gel method on glass substrates. Transparent solutions were prepared from Al and Sn salt precursors, methanol, and glacial acetic acid. Solution characteristics, such as pH and rheological properties, were evaluated prior to the coating process. Gel coatings were dried at 300 °C for 10 min and annealed at 600 °C for 1 h in air. Structural, microstructural, and optical properties were analyzed using Fourier transform infrared spectrophotometer, X-ray diffraction, scanning electron microscopy, refractometer, and UV/VIS spectrophotometer. The study revealed that solution properties influenced film structure and microstructure, with acidic conditions affecting hydrolysis, condensation, and gelation, and higher viscosities resulting in thicker films. SnO2 formation occurred between 410 and 500 °C, with a preferential (110) texture observed after annealing. Incorporating Al altered film morphology and microstructure, reducing microcrack formation and introducing nano-sized particles, thereby enhancing film quality and structural integrity. Refractive index, film thickness, and energy range of the Al–SnO2 films met requirements for gas sensor production. Gas sensitivity tests showed approximately 53% sensitivity to CO2 at room temperature. The findings suggest that Al-doped SnO2 films exhibit promising characteristics for aerospace gas sensing applications.
PubDate: 2024-08-09
DOI: 10.1007/s13369-024-09416-5
- Comprehensive Overview on the Present State and Evolution of Global
Warming, Climate Change, Greenhouse Gasses and Renewable Energy-
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Abstract: Abstract The impact of the climate and environmental problems experienced in the world with the Industrial Revolution has prominently begun to be felt today, and the consequences of climate change on the environment and public health have now become visible. The increase in greenhouse gas emissions resulting from human activities, which is the main cause of global climate change, caused the global surface temperature to be 1.1 °C higher between 2011 and 2020 compared to 1850–1900. In parallel with this global problem, the transition to clean energy has increased significantly with Russia's invasion of Ukraine, more aggressive energy and climate policies, technological developments, and increasing concerns about energy security. In this study, global climate change indicators, including land and sea surface air temperatures, sea level rise, sea ice extent, ocean heat content, surface humidity, and total column water vapor, are reviewed and updated in parallel with a comprehensive analysis of the progress in renewable energy. The results showed that if no measures are taken to reduce human-induced greenhouse gas emissions, the global average temperature will increase further in the coming years and the negative effects of other climate parameters will be felt even more. It has been emphasized that limiting human-induced global warming requires renewable and sustainable energy sources and net zero CO2 emissions and that the simultaneous adoption of emission reduction and adaptation strategies will be the most effective economic and technical solution to the global warming problem.
PubDate: 2024-08-09
DOI: 10.1007/s13369-024-09390-y
- Accelerated Primal–Dual Deep Reinforcement Learning for Efficient Energy
Management of Hybrid Electric Vehicles-
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Abstract: Abstract This research aims to devise an effective energy management strategy (EMS) to enhance the fuel efficiency of power-split type hybrid electric vehicles. Utilizing advancements in reinforcement learning (RL), the study introduces a novel EMS strategy that fuses deep deterministic policy gradient (DDPG) and accelerated primal–dual optimization (APDO), resulting in accelerated primal–dual deep deterministic policy gradient (APD3). Addressing issues like overestimated values and slow convergence in traditional deep RL (DRL) methods, a cutting-edge APD3 algorithm enhances the learning performance of EMS. APD3 employs a dual-critic structure to simultaneously update primal and dual variables. Moreover, it integrates an off-policy trained dual variable ( \(\lambda\) ) update process to enhance sampling efficiency and expedite the dual variable search process, thereby aiding in optimal action selection for the DRL agent. The overall performance under the New European Driving Cycle (NEDC), encompassing fuel economy, convergence speed, and robustness, is investigated. Simulation results illustrate that APD3 control reduces total fuel consumption by 6.78% and 7.75% compared to DDPG and TD3, respectively. Additionally, the fuel economy of the EMS system based on DDPG and TD3 reaches 81.8% and 87.7%, while APD3 achieves 95.1% of DP. The adaptability of the APD3-based EMS is further assessed with combined unknown test drive cycle under realistic city and highway conditions. Moreover, the robustness is evaluated with varying initial values of state of charge, fostering the advancement of a sustainable transportation system.
PubDate: 2024-08-09
DOI: 10.1007/s13369-024-09353-3
- Undrained Uplift Capacity of Square and Circular Plate Anchors Embedded
Near Clayey Slope-
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Abstract: Abstract The decrease in undrained uplift capacity of horizontally placed square and circular plate anchors due to presence of a nearby saturated undrained clayey slope has been analysed using 3-D elastoplastic finite element method for non-homogeneous as well as homogeneous clay. The uplift capacity has been calculated considering the anchor plate being embedded at different distances (s) from the crest of the slope starting from zero to a finite value where there is no effect of the slope. The different parameters like embedment ratio (Ha/Ba), slope inclination angle (β), normalized cohesion (cu0/γBa) and the normalized value of increment rate of cohesion with depth (rc) have been examined for their influence on the undrained uplift capacity. The study confirmed that an increase in slope inclination angle leads to a continuous decrease in the plate anchor’s uplift capacity. The effect of the slope, however, reduces significantly when the anchor fails by local yielding. A reduction factor (η) has been used to describe the decrease in the undrained uplift capacity. Increasing the distance of the plate anchor from the slope crest up to a certain optimal value (s/Ba)opt results in a higher reduction factor (η) after which the value of η becomes constant and beyond that distance there is no effect of the slope.
PubDate: 2024-08-09
DOI: 10.1007/s13369-024-09391-x
- Sensitivity Analysis of Hydraulic Fracturing Parameters for Optimum
Horizontal Well Spacing in Tight Oil Reservoirs-
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Abstract: Abstract Three-dimensional reservoir simulation models were developed to perform different simulation runs which aim to get an estimate for the optimal well productivity in such unconventional very low permeability reservoirs. It will take into consideration the limitations, uncertainties and capabilities of each parameter like matrix permeability, hydraulic fracturing spacing, half-length, height and conductivity to get an enhanced estimate of the optimal production ultimate recovery by using the uncertainty analysis. Hydraulic fracturing parameters with reservoir data were used to estimate the optimum well spacing distance between the horizontal wells in tight oil reservoirs. The estimated recovery factor and production forecasting using the reservoir simulation with its capabilities to get computational cases with different combinations will get the optimal well count with optimum economic evaluation. Different reservoir modeling scenarios were built with various arrangements of affecting factors. Characterizing hydraulic fracture parameters, such as fracture spacing, fracture height, fracture half-length and fracture conductivity, are studied by setting different ranges per parameter and results were investigated by comparing the oil production rate trends and total cumulative oil production after a ten-year lifetime. Sensitivity analysis was performed on the different hydraulic fracturing parameters by setting different ranges and setting the objective function to be the cumulative oil production. A complete statistical analysis for the results from the simulation runs using response surface analysis including Morris’s method and Sobol method which is a variance-based analysis was carried out. This study sensitivity analyses shed light on the long-term production behavior of tight oil reservoirs. The findings indicate that the most important parameters that influence the performance of such tight reservoirs are those related to fracture half-length and fracture height, fracture conductivity and matrix permeability. A general production trend analysis and comparisons are run for various well spacings with different numbers of wells per 100 acres (40.4687 ha). Because of their quick rates of decline, unconventional reservoirs typically favor high initial rates. Furthermore, drilling and fracturing procedures are expensive. As a result, it is crucial to quantify these fracture parameters using different data to optimize the fracture design for both single and many wells using economic analysis. The impact of the oil price and the other operational costs required for various instances will be considered in an economic evaluation based on a new well spacing optimization process. The net present value was calculated for several cases by changing the number of wells (2, 3, 4, 5, 6, 7 and 8 wells), and showed that the optimum well spacing is 300 feet (91.44 m) per 100 acres (40.4687 ha) for a tight oil reservoir.
PubDate: 2024-08-09
DOI: 10.1007/s13369-024-09311-z
- Honokiol from Magnolia Tree Exhibits Antibacterial and Anti-virulence
Potential against Pseudomonas aeruginosa-
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Abstract: Abstract Pseudomonas aeruginosa is one of the most significant opportunistic infections in clinical setting and acquires resistance to diverse antibiotics. Honokiol (HON) is a natural bioactive compound isolated from the bark and seed cones of the Magnolia tree and acquires several benefits to health. The purpose of this research is to assess the antibacterial activity and anti-virulence properties of HON against P. aeruginosa. The effect of HON on the bacterial cell membrane as well as on bacterial virulence was estimated in vitro and in vivo. A docking investigation was carried out to determine the HON’s affinity to quorum sensing (QS) receptors and the influence of HON on QS expression’s genes. The outcome of the combination of HON and different antibiotics was performed. The resistance induction test was done to evaluate the HON's probability of inducing resistance. The effect of HON on mailman RBCs (red blood cells) was done to estimate the HON's safety. The results showed that HON damaged the bacterial cell membrane and reduced PMF and ATP synthesis. HON exhibited significant in vitro anti-virulence and anti-biofilm effects at sub-MIC and protected mice in vivo. These actions were attributed to its downregulation of QS-encoding genes and interfering with QS receptors. HON showed minimal impact on RBCs, indicating safety, and low likelihood of resistance induction. When mixed with antibiotics, HON revealed synergistic effectiveness against P. aeruginosa. In summary, HON shows potential as an effective adjuvant in treating severe P. aeruginosa infections.
PubDate: 2024-08-08
DOI: 10.1007/s13369-024-09303-z
- Fiber Elastomer Modified Asphalt for the Development of Resilient Porous
Asphalt Mixtures-
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Abstract: Abstract Heavy rain is one of the extreme weather events which pose a variety of serious risks on transportation infrastructures. Porous asphalt pavement can be used as a sustainable solution to mitigate the effects of such heavy rains. The objective of this study was to study the potential of using fiber elastomer modifier (FEM) to produce porous asphalt mixtures of high quality and enhanced performance. This was done through an experimental program composed of three different phases. The first phase was the development and the rheological, chemical, and microstructural characterization of the FEM modified asphalt. The second phase focused on using FEM to produce porous asphalt mixtures using different techniques. The third phase was the characterization of the porous asphalt mixtures to study their anticipated performance. The FEM asphalt performance grade, PG (76-22), proved enhanced rheological properties in terms of better rutting resistance indicated by higher G*/sin δ over a wide range of temperatures and lower Jnr3.2 value of about 19% compared to the virgin asphalt and an enhanced fatigue cracking resistance manifested by the significant reduction in the fatigue cracking indicator G* sin δ with about 94%. Finally, porous asphalt mixtures were produced of an enhanced performance based on the dynamic modulus. Higher E* values at higher temperatures/lower frequencies and lower E* values at lower temperatures/higher frequencies were reported for the FEM porous asphalt mixture in reference to the, control dense-graded HMA mixture, promising an enhanced both rutting and fatigue resistances of the produced porous asphalt mixtures.
PubDate: 2024-08-08
DOI: 10.1007/s13369-024-09318-6
- High-Frequency Dual-Branch Network for Steel Small Defect Detection
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Abstract: Abstract Strip surface defect detection is pivotal in the steel industry for improving strip production quality. However, there is still a big gap between the existing working and the detection of small defects in strip steel in practical applications. In this paper, we propose the SSD-YOLO model, which is designed specifically for detecting small defects on strip steel surfaces. Given the challenge of feature extraction due to the small defect size, it utilizes a dual-branch feature extraction and channel-level feature fusion to enhance the expression capability of small defects. Moreover, it integrates a multiscale high-resolution detection module to achieve precise segmentation, thereby improving the overall detection accuracy of the model. The experimental results illustrate that the SSD-YOLO model, as proposed, attains a 98.0% mean average precision (mAP) and operates at 66 frames per second (FPS) when evaluated on the SSDD (Steel Small Defect Dataset). In comparison with YoloV8s, the SSD-YOLO achieves a significant improvement in accuracy, with an increase of 19.9%. The inference time and performance of our SSD-YOLO is well balanced, making it suitable for real-world deployment.
PubDate: 2024-08-07
DOI: 10.1007/s13369-024-09352-4
- Biopolymer and Gypsum Added Na Bentonite for a More Effective Clay Liner
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Abstract: Abstract Bentonite soil is frequently utilized as a compacted clay liner, which is a critical component of municipal waste landfill systems. This study aims to investigate the feasibility of treating sodium bentonite (NAB) with natural biopolymers to obtain an effective clay liner. The NAB was treated with three biopolymers: sodium alginate (SA), agar gum (A), and xanthan gum (X), at different replacement percentages (2%, 4%, 6%, and 8%). Additionally, an investigation was conducted to determine the extent to which replacing 50% of these additives with gypsum (G) would improve the biopolymer treatments. Fourier-transform infrared spectroscopy (FTIR), pH, one-dimensional swelling, and unconfined compressive strength (UCS) were carried out in this study. The FTIR results indicated the presence of intermolecular hydrogen bonding when NAB was treated with biopolymers and gypsum, which is crucial for enhancing the UCS. Furthermore, the thermal treatment of biopolymers significantly contributes to improving the UCS. Among the various biopolymers tested, agar gum demonstrated the most significant improvement, specifically, replacing 8% of the NAB with agar gum resulted in a 55% increase in UCS. Volume change behavior was most influenced by replacement of NAB with gypsum by 8%, which reduced the vertical swelling to 21% as opposed to 79% for the untreated NAB. The use of SA conversely resulted in an increased vertical swelling of 91%.
PubDate: 2024-08-07
DOI: 10.1007/s13369-024-09386-8
- Enhanced Feature Representation for Multimodal Fake News Detection Using
Localized Fine-Tuning of Improved BERT and VGG-19 Models-
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Abstract: Abstract The spread of fake news poses significant challenges across various sectors, including health, the economy, politics, and national stability. Social media and modern technology have facilitated the rapid dissemination of fake news, predominantly in multimedia formats. Despite advancements, multimodal fake news detection models struggle to achieve optimal accuracy, primarily due to the quality of feature representation. This study aims to enhance feature representation to improve fake news identification. Pre-trained models for feature extraction, typically designed for general public domains, may not suit the specific characteristics of our task using the Fakeddit dataset. We propose a localized fine-tuning strategy, refining pre-trained BERT and VGG-19 models for accurate multimodal feature representation in fake news detection. BERT was fine-tuned by retraining all layers, while only the last block of VGG-19 was fine-tuned. To further enhance the representations, we made structural modifications to VGG-19, including the use of a global average pooling layer and a redesigned classifier. This approach significantly improved our multimodal fake news detection model’s performance, achieving a high accuracy of 92%. Compared to state-of-the-art studies that use generic pre-trained models, our model demonstrates superior performance. Our research underscores the importance of feature representation in multimodal contexts and opens avenues for exploring the synergy between textual and visual modalities in fake news detection.
PubDate: 2024-08-07
DOI: 10.1007/s13369-024-09354-2
- Investigation of the Dewatering Process with Geotextile Tubes by
Sedimentation, One- and Two-Dimensional Filtration Test Methods-
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Abstract: Abstract Dewatering applications are carried out with geotextile tubes for the disposal or reuse of industrial wastes with high water content. Class F Seyitomer thermal power plant fly ash, an industrial waste, was selected in this study. Turbidity, sedimentation and filtration experiments were carried out using anionic and cationic polymers and polypropylene synthetic fiber to investigate the effect of polymers and fibers on the dewatering of fly ash. The use of polymers was determined to significantly accelerate filtration and soil sedimentation speed while leading to a slight increase in the volume of the filter cake. When effective polymer and dosage are used, slurry filtration time can be reduced up to one-eighth of the time and dewatering can be achieved much faster. The addition of synthetic fiber accelerated the sedimentation of the slurry and increased the filtration in the vertical direction, while it did not show a significant effect on the total filtration in two-dimensional filtration. In geotextile tube applications, although one-dimensional filtration experiments might give misleading results in terms of estimating the effectiveness of the polymers used in solid–liquid separation and dewatering times, the jar test, sedimentation and two-dimensional filtration experiments were determined to give compatible and more realistic results. In two-dimensional filtration experiments, approximately 75% of the filtration occurred in the radial direction and the dewatering time was approximately 21–55% of the time estimated by one-dimensional filtration experiment. Geotextile tube dewatering design can be made more predictable and cost-effective in the field by performing small-scale laboratory experiments with the two-dimensional filtration test system designed for this study and various dewatering applications.
PubDate: 2024-08-06
DOI: 10.1007/s13369-024-09323-9
- Large-Eddy Simulation of Turbulent Airflow and Pollutant Dispersion from a
Ground-level Point-Source in a Model Urban Area-
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Abstract: Abstract This study aims to comprehensively investigate pollutant dispersion within a scaled urban model and assess associated risks from emissions. Specifically, we focus on a ground-level point-source in the first row of buildings, continuously releasing a tracer gas for passive scalar transport analysis. The research seeks to understand flow patterns and pollutant dispersion considering the diverse heights and rooftop configurations typical of urban environments. Turbulence significantly influences pollutant dispersion and airflow around structures, prompting large-Eddy simulation (LES) to quantify these effects within the urban model’s regularly spaced buildings. We utilize the dynamic Smagorinsky subgrid-scale (SGS) model to resolve the instantaneous flow field and passive scalar transport. Artificial turbulent structures are generated at the inlet using the synthetic inflow generator method. The validation shows that, the average deviations from the wind tunnel measurements for Wall A at positions \(x_2/H=0\) and \(x_2/H=3.79\) are approximately 12.09% and 16.52%, respectively. We found that, as free-stream flow encounters the first high-rise buildings in the urban canyon, high streamwise velocity is experienced, followed by the formation of a wake region around obstacles, causing flow separation due to boundary layer detachment from building rooftops. Pollutants released from the ground-level point-source are transported from primary recirculations to secondary ones through turbulent diffusion and advection until evacuated from the urban area. Velocity and concentration contours reveal that in-canyon vortex dynamics and pollutant distribution are highly sensitive to rooftop configurations. The height and shape of buildings not only influence in-canyon vortex structure, but also determine vortex strength. Furthermore, pollutant dispersion characteristics and pollution levels vary across buildings, with distinct regions near high- and low-rise structures showing differing patterns.
PubDate: 2024-08-06
DOI: 10.1007/s13369-024-09371-1
- Multi-objective Optimal Antiwindup Compensation of Discrete-Time Nonlinear
Systems Under Input Saturation-
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Abstract: Abstract This paper deals with the discrete-time antiwindup compensator (AWC) synthesis for nonlinear discrete-time systems under input saturation. The proposed method considers the objective of an optimal AWC design for fast convergence and for improved performance against the saturation nonlinearity. A discrete-time full-order AWC architecture is presented for nonlinear discrete-time systems to achieve an improved performance against the saturation nonlinearity. Additionally, an equivalent decoupled AWC architecture for nonlinear discrete-time system is derived through algebraic analysis and transformation of saturation to dead-zone function. To achieve fast convergence, a more generic Lyapunov function has been applied for the AWC design by incorporating an exponential term in the Lyapunov function. Then, new conditions for the AWC synthesis are revealed by application of the resultant decoupled discrete-time architecture, nonlinearity condition, a modified quadratic-exponential Lyapunov function, optimally exponential \(L_{2}\) approach, and input saturation properties. The design conditions are provided for both global and local design scenarios, which can be applied to both stable and unstable plants. Compared with the conventional methods, the proposed approach deals with nonlinear systems, can be more practical due to discrete-time scenario, provides an optimal design for both fast convergence and performance, and applicable to both stable and unstable plants. A simulation example has been provided to demonstrate the efficacy of the proposed nonlinear AWC design.
PubDate: 2024-08-06
DOI: 10.1007/s13369-024-09385-9
- Functional Connectivity and Graph Embedding-Based Domain Adaptation for
Autism Classification from Multi-site Data-
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Abstract: Abstract Many machine learning-based classification models for autism spectrum disorder (ASD) using neuroimaging data have been proposed. In recent developments, research has transitioned its focus to using extensive multi-site brain imaging datasets to increase the clinical applicability and statistical robustness of findings. However, the classification performance is hampered by the inherent heterogeneity of these combined datasets. This paper introduces a novel correlation-based functional connectivity method designed to extract improved Region of Interest (ROI) coupling features from the Autism Brain Imaging Data Exchange (ABIDE) dataset. We assess graph embedding domain adaptation (GEDA) to mitigate dataset heterogeneity, mapping data points from source and target domains into a common low-dimensional space while preserving their similarity relationships. We employ a novel dataset-splitting approach called the ’rectified environment’ to enhance classification accuracy. To validate our proposed model, we compared it with related works. Our result shows that the proposed model with support vector machine (SVM) has an accuracy of 78.1% and AUROC 83.9% in identifying ASD patients. Our model demonstrates a substantial improvement, increasing accuracy by 6.1% and AUROC by 5.3% compared to the maximum independence domain adaptation (MIDA) model. These findings reveal an anticorrelation in brain function and disruptions in brain connectivity between anterior and posterior brain regions in ASD.
PubDate: 2024-08-06
DOI: 10.1007/s13369-024-09362-2
- A Novel Approach for Forecasting and Scheduling Building Load through
Real-Time Occupant Count Data-
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Abstract: Abstract The smart buildings’ load forecasting is necessary for efficient energy management, and it is easily possible because of the data availability based on widespread use of Internet of Things (IoT) devices and automation systems. The information of buildings’ occupancy is directly associated with energy consumption. Therefore, we present a hybrid model consisting of a Long Short-Term Memory (LSTM) network, Extreme Gradient Boosting (XgBoost), Random Forest (RF) and Linear Regression (LR) for commercial and academic buildings’ load forecasting. The correlation between occupants’ count and total load of the building is calculated using Pearson Correlation Coefficient (PCC). The comparative analysis of the proposed approach with LSTM, XgBoost, RF and Gated Recurrent Unit (GRU) is also performed. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE) and Normalized Root Mean Square Error (NRMSE) are used as performance indicators for evaluating performance. Findings indicate that the proposed hybrid approach outperforms other models. The RMSE and MAE of 2.99 and 2.18, respectively, are recorded by the proposed model for commercial building dataset while for academic building the RMSE and MAE are 4.48 and 2.85, respectively. Occupancy and load consumption have a positive correlation as evident from PCC analysis. Therefore, we have scheduled the forecasted load based on occupancy patterns for two different cases. Cost is reduced by 17.42% and 33.40% in case 1 and case 2, respectively. Moreover, the performance of the proposed hybrid approach is compared with different techniques presented in literature for buildings load forecasting.
PubDate: 2024-08-06
DOI: 10.1007/s13369-024-09296-9
- Fabrication of New Hyper-cross-linked Polymer for Efficient Heavy Metal
Adsorption from Industrial Wastewater-
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Abstract: Abstract Water pollution is the emerging issue in modern world that may cause water scarcity for our future generations. Therefore, it is ultimate need to develop highly efficient and cost-effective methods to solve this issue. Due to this intense demand, a new hyper-cross-linked polymer (HCP) of resorcinol (1, 3-dihydroxybenzol) “R-HCP” is synthesized using Friedel–Craft reaction for the removal of cadmium metal ions from industrial wastewater. Real industrial wastewater samples are used to evaluate the adsorption capability of R-HCP. Fabricated R-HCP is characterized through Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction analysis (XRD), EDX (energy-dispersive X-ray spectroscopy) and BET (Brunauer–Emmett–Teller). The BET surface area of R-HCP is 221.5848 m2g−1. Through salt addition method, the point of zero charge (PZC) was also determined, and its value is 2.0 pH. The maximum adsorption capacity of R-HCP is 10 mg/g for cadmium metal ions. The greatest adsorption value of 93% was obtained at pH 10 with 0.5 g of adsorbent and 9-min contact time and favors exothermic reaction. Langmuir, Freundlich and Temkin isotherms were studied, and results shows that Freundlich model is the best fit with R2 value of 0.9917. Adsorption kinetic investigation shows that it follows pseudo-first-order kinetic model R2 value 0.9874. The study of the effect of interfering ions including calcium, magnesium, sodium and potassium demonstrates the decrease in the adsorption capacity to a little extent. R-HCP can be recycled and have regeneration capacity, which is novel and distinguished feature of this adsorbent.
PubDate: 2024-08-06
DOI: 10.1007/s13369-024-09259-0
- Thin-Film Nanocomposite Forward Osmosis Membranes Incorporated with
Hydrophilic TiO2/Fe3O4 Nanoparticles: Toward Alleviated ICP-
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Abstract: Abstract In this study, highly hydrophilic TiO2/Fe3O4 nanoparticles with a high level of photocatalytic activity were used to improve the performance of thin-film nanocomposite (TFN) membranes in forward osmosis (FO) process. The influence of TiO2/Fe3O4 nanoparticles binary metal oxides incorporation on the properties of the TFC FO membrane in terms of hydrophilicity, porosity, pore size, and cross-sectional morphology was thoroughly studied. Results demonstrate that with the addition of TiO2/Fe3O4 nanoparticles, the structure of the membrane top layer has changed due to nanoparticles’ reaction with the amino and organic monomers in the surface polymerization process. Furthermore, the thickness of the membrane cross section has changed with the addition of TiO2/Fe3O4 nanoparticles due to changes in the rate of the amine monomer penetration into the sublayer. The TiO2/Fe3O4 loading caused changes in the overall porosity and improved membrane hydrophilicity. The effect of UV light on the synthesized membranes was also tested. It was found that in the presence of UV light, the high photocatalytic activity of TiO2/Fe3O4 nanoparticles is the primary cause of their excellent performance in the membrane structure. As the membrane was exposed to UV light, the increase in hydrophilicity increases the membrane flux and decreases its structural parameter. These changes resulted in a 43% improvement in membrane water permeability and reduced the structural parameter up to 410 μm. Water flux of improved membrane also increased by 74% in the forward osmosis process, which was achieved without significantly decreasing membrane selectivity.
PubDate: 2024-08-05
DOI: 10.1007/s13369-024-09387-7
- Evaluation of Some Heavyweight Minerals as Sustainable Neutron and
Gamma-Ray Attenuating Materials: Comprehensive Theoretical and Simulation
Investigations-
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Abstract: Abstract This study comprehensively evaluates the radiation attenuation efficiencies of hematite and barite, commonly used materials in radiation shielding, using theoretical and simulation investigations. The MCNP-5 code was used to obtain the linear attenuation coefficient (LAC) within the energy range of 0.015–15 MeV, with validation by the XCOM program. Based on these LAC values, various gamma-ray shielding parameters were determined: mass attenuation coefficient, half-value layer, radiation protection capacity, mean free path, transmission factor, and equivalent thickness to lead (ETPb). Additionally, effective atomic number (Zeff) and electron density (Neff) were calculated, including both single-energy and energy-dependent forms for photon absorption and interaction. Furthermore, MCNP-5 simulations and NGCal program calculations were used to assess thermal neutron attenuation, while the NXcom program determined fast neutron behavior. This analysis revealed superior γ-ray shielding for barite compared to hematite. Similarly, the NXcom program indicated better fast neutron shielding for barite. However, interestingly, simulations validated a 210% higher effectiveness in thermal neutron attenuation for hematite. Finally, comparing the studied materials with other shielding materials demonstrated promising potential as environmentally friendly alternatives for effective shielding against various radiation types.
PubDate: 2024-08-05
DOI: 10.1007/s13369-024-09300-2
- Influencing Laws of Obliquities/Incident Angles on Ricochet and
Trans-ricochet of Projectile-Target Impact for Armor Steel Plate
Structural Design-
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Abstract: Abstract The effectiveness of protective armor supports the projectile ricochet phenomenon as it clearly restrains projectile from penetration and can potentially form basis for design optimization of protective systems. The present numerical study has been carried out to find the most appropriate obliquity/incident angle which can effectively be used for design of protective armor. Although it is not possible to practically control the incident projectile angles, but numerical investigation can potentially provide solution for design and performance optimization of overall structure and surface geometry of target plate, or adaptive adjustment in terms of target obliquity angle. The projectile has been impacted with velocity of 700 ± 20 m/s at incident angles ranging from \(15^\circ \le \theta \le 75^\circ\) . The experimental results at 0° has been taken as a reference to validate material model and simulation results. The verification parameters such as eroded mass and steady residual velocity have also been investigated. After all validations and calculations, the window of obliquities/incident angle, safe thickness limit as well as steady residual velocity was obtained. Based on the attained optimum angle 45°, the minimum target plate thickness calculated is 6.4 mm against the thickness of reference plate, 4 mm (UHSLA-XF1700) armor steel. The result was partial penetration at 0° incident angle. The numerical simulation for 6.4-mm-thick target plate under similar circumstances revealed that it has ability to defeat the incoming threat more effectively. For the respective cases of different incident angles, a modified analytical model has also been developed and results coincided with the findings of numerical simulations.
PubDate: 2024-08-05
DOI: 10.1007/s13369-024-09397-5
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