Journals
2025 EN
Ergun Yavuz · Caliskan Hakan · Karali Halil Ibrahim
Abstract Porous geopolymer materials can be used in various fields such as thermal insulation, filtration, catalyst, and building materials. In this study, open porous geopolymer‐based cordierite materials are produced due to the porous structure, temperature resistance, and easy and low‐cost applications of geopolymer structures, which are oxide ceramic materials that can act as natural catalysts for emission treatment of diesel engines. For the composition of cordierite, waste boron clay, metakaolin, fly ash, and magnesium carbonate are used, while keeping the geopolymerization temperature constant, sodium silicate, sodium hydroxide, polypropylene, and glass fiber, hydrogen peroxide are used to create an alkaline environment. These materials are tested in a 4‐stroke 4‐cylinder diesel engine's exhaust system at 50, 75, and 100 Nm engine torques and 1500, 1700, and 1900 rpm engine speeds. The use of open‐cell geopolymer materials reduces CO emissions by 66%, NOx emissions by 25% and HC emissions by 68%. The open‐cell geopolymer materials are found to be effective in treating over 95% of particulate matter. The chemical and microstructures of the obtained open‐cell geopolymer structures are investigated. It is concluded that the developed products are useful tools for the emission treatments of diesel engines considering the oxidizing and filtering effects of the materials.
Journals
2025 EN
Barlak Melahat Semin · Cengiz Ibrahim · Degermenci Nejdet
+1 more
Abstract In this study, phenol removal by ozonation under strong alkaline conditions in a continuously operated jet loop reactor (JLR) is investigated. The effects of inlet ozone gas concentration, hydraulic retention time (HRT), and influent phenol concentration on phenol, chemical oxygen demand (COD), and total organic carbon (TOC) removal in the JLR effluent are evaluated. When the inlet ozone gas concentration is 17.5 gO 3 m −3 , the steady‐state phenol, COD, and TOC removal efficiencies are determined as 97.8%, 61.1%, and 32.2%, respectively. When the inlet ozone concentration increases from 17.5 to 56.5 gO 3 m −3 , phenol is not detected in the JLR effluent. The system operates at different HRTs, and the highest removal efficiency at steady‐state is obtained at 8 h HRT. While phenol is completely removed at this HRT, COD and TOC removals are 76.8% and 48.2%, respectively. An increase in phenol concentration in the JLR influent leads to a reduction in the phenol, COD, and TOC removal efficiencies in the steady‐state effluent.
Journals
2025 EN
Phan Peter T. · Ibrahim Hamed D.
ABSTRACT Integrated Global Radiosonde Archive Toolkit (IGRAT) is a software that allows users to process data from the Integrated Global Radiosonde Archive. The archive provides global radiosonde observations in a text‐based format that requires additional manipulation to make it suitable for analysis. IGRAT provides an easy‐to‐use set of tools to streamline this preprocessing step, allowing users to readily visualise temporal and spatial patterns, plot atmospheric profiles, and export processed data sets in the more standard formats. IGRAT is accessible through a Python library and web interface, and users can adopt it to their preferred workflow. IGRAT significantly reduces preprocessing time before analysis, making it suitable for applications in climate research, meteorology and atmospheric sciences. IGRAT is fully open‐source, allowing the community to make contributions as well as modify IGRAT for personal use.
Journals
2025 EN
Rosha Pali · Sajjadi Mohammad · Ibrahim Hussameldin
ABSTRACT This study outlines a comprehensive process design utilising glycerol‐steam reforming for an H 2 ‐enriched gas stream, coupled with carbon dioxide removal via a chemical absorption system, followed by a techno‐economic analysis. The Aspen Plus economic analyser assesses the developed model, incorporating simulation results and literature data. Initially, the CO 2 capture unit was planned with a standalone absorber and stripper, later integrated for solvent makeup calculation. Findings reveal that as catalyst loading increased from 5 to 50 kg, glycerol conversion and product molar fraction improved. For a targeted H 2 production of 10 t/day, optimal reactor dimensions are 3.2 m diameter and 30 m length, corresponding to a reactant flow of 105 t/day and a 2.52 MW heat duty at stoichiometry conditions. To capture 95% CO 2 from the reformed product stream, absorber and stripper packing heights of 12 and 7 m, respectively, with column diameters of 1.25 and 2.71 m are necessary. The production cost of H 2 is determined to be $3.8 per kg, as revealed by the techno‐economic analysis. Calculated values for net present value, discounted payback period, and internal rate of return stand at $30 million, 5 years, and 25.0%, respectively. © 2024 Society of Chemical Industry and John Wiley & Sons, Ltd.
Journals
2025 EN
Resitoglu Ibrahim Aslan · Sugozu Banu · Omar Muhammed Arslan
ABSTRACT Pollutant emissions such as carbon monoxide (CO), hydrocarbons (HCs), nitrogen oxides (NO x ), and particulate matter (PM) from diesel engines have serious adverse effects on both human health and the environment. Advanced post‐engine emission control systems, such as the diesel oxidation catalyst (DOC) and selective catalytic reduction (SCR), have proven effective in substantially reducing or minimizing emissions of CO, HC, and NO x . Additionally, the use of metal‐based fuel additives in diesel fuel has been widely studied and applied in practice to improve engine performance and optimize emission outcomes. The interaction between metal‐based fuel additives and the performance of DOC and SCR systems has become a key area of research focus. This study investigates the impact of metal‐based fuel additives—including cerium (IV) oxide, copper (II) oxide, magnesium oxide, nickel (II) oxide, and titanium (IV) oxide—on the performance of DOC and SCR catalysts under various engine load conditions. In the experiments, conventional DOC and SCR catalysts were used, specifically Pt/Al 2 O 3 for the DOC and V 2 O 5 ‐WO 3 /TiO 2 versus Ag/Al 2 O 3 for the SCR. The variations in CO, NO, and NO x levels in the exhaust gas were monitored, and the efficiency of the catalysts in converting these emissions was calculated and analyzed. The results indicate that the combination of metal‐based fuel additives with post‐engine emission control technologies can effectively reduce pollutant emissions from diesel engines. Among the metal‐based additives tested, cerium (IV) oxide and nickel (II) oxide were found to be particularly effective in enhancing the conversion efficiencies of DOC and SCR systems.
Journals
2025 EN
AlAmmari Wahib A. · Sleiti Ahmad K. · Hamilton Matthew
+6 more
ABSTRACT Leak detection (LD) in gas pipelines (GPs) is critical for ensuring operational safety and environmental protection. This study presents a novel digital/visual twin for detecting single‐ and multiple leaks in GPs under both single‐ and multiphase flow conditions. The framework of the digital twin leverages experimental data from a multiphase flow‐testing loop and synthetic data generated using OLGA software to validate and optimize machine learning (ML) models for leak detection and localization. Several ML models, including random forest (RF), support vector machine (SVM), k ‐nearest neighbors ( k ‐NNs), decision tree regression (DTR), and eXtreme gradient boosting (XGBoost), were tested individually for their ability to classify leak conditions and localize leaks. Initial results showed moderate performance for individual models, with accuracies ranging from 42% to 57%. However, a significant improvement was observed through the use of advanced techniques such as stacking models, feature engineering, and data averaging. The final stacking regressor model, which combined the strengths of RF, k ‐NN, and SVM, outperformed the individual models, achieving R 2 values exceeding 0.96 with an accuracy of 90% in complex multiple leak scenarios. The digital twin system integrates this ML framework with real‐time data visualization, allowing operators to visualize offshore pipeline conditions, detect leaks, and localize leak positions using a virtual twin representation of the physical pipeline. The virtual twin provides an interactive, high‐fidelity interface that enables users to monitor and analyze leak events as they occur, enhancing situational awareness and decision‐making capabilities. The combination of advanced ML techniques and digital twin technology provides a robust and accurate solution for real‐time LD in offshore pipelines. It significantly improves detection performance in multiphase flow conditions. This innovative approach sets a new benchmark for offshore pipeline monitoring systems, offering superior LD capabilities under a range of operational conditions. The system is readily adaptable for integration with SCADA platforms and pipeline monitoring infrastructures, supporting deployment in offshore oil and gas operations, industrial gas distribution networks, and critical energy corridors where early LD is essential.
Journals
2025 EN
Yousuf Ibrahim · Ahmad Talat · Rao D. V. Subba
+2 more
ABSTRACT The Central Indian Tectonic Zone (CITZ) runs across peninsular India and includes Proterozoic bimodal volcanics (basalt‐rhyolite), quartzite, mafic‐ultramafic rocks, volcanic sediments and Banded Iron Formation (BIF). The bimodal volcanic rocks of Betul–Chhindwara belt have been subjected to upper greenschist to lower amphibolite‐grade metamorphism and have well‐preserved remnants of pillow structures. Total alkali vs. silica diagram clearly discriminates all the samples into subalkaline basalts and rhyolites which corresponds to their bimodal nature. Mafic volcanic sequence of Betul–Chhindwara belt is represented by high Ti and low Ti Groups. I. High Ti basalt has undergone low degree of partial melting (~5%), whereas low Ti basalt has undergone a high degree of partial melting (~20%) of the same source rock. Fe and Ca decrease with decreasing Ti indicating clinopyroxene and iron‐titanium oxide fractionation in both the groups of basalt. These basalts are generally enriched in incompatible trace elements such as Rb and Ba and depleted in Nb, P and Ti, which collectively are good indicators of continental crust/lithosphere involvement in their genesis. The basalts show no Eu anomaly, which indicates little role of plagioclase during fractionation process. Positive anomalies of U–Th–Zr for the basalts indicate crustal involvement. Whole‐rock Sm–Nd isochrons for the mafic volcanic rocks indicate an age of crystallisation for these volcanic rocks at about 1232 ± 37 Ma (initial 143 Nd/ 144 Nd = 0.510752 ± 0.000035, mean square weighted deviate [MSWD] = 1.20) which is much younger than the basement rocks ca. 1500 Ma. The ε Nd t ( t = 1232 Ma) vary from −5.93 to −3.1 for the mafic volcanic rocks and between −5.81 and +0.14 for felsic volcanic rocks. Depleted mantle model ages of basalts vary from 2204 to 3040 Ma, and for rhyolites, these vary from 2174 to 2863 Ma, respectively. The ε Nd value for all the basaltic samples includes both the groups of basalts plot away from the CHUR line indicating their derivation from a depleted source and evolves to lower values, indicating longer crustal residence or more crustal contribution. Mafic magma might have been produced at the subduction zone interacted with the lower continental crust while ascending to the surface. This lowered the melting point of the continental crust which led to the production of felsic melt. Episodic mafic magma could have led to the production of rhyolite, produced at different levels of the continental crust.
Journals
2025 EN
Chu Cong · Santini Tales · Liou JrJiun
+6 more
ABSTRACT Magnetic resonance imaging (MRI) at 7T has a superior signal‐to‐noise ratio to 3T but also presents higher signal inhomogeneities and geometric distortions. A key knowledge gap is to robustly investigate the sensitivity and accuracy of 3T and 7T MRI in assessing brain morphometrics. This study aims to (a) aggregate a large number of paired 3T and 7T scans to evaluate their differences in quantitative brain morphological assessment using a widely available brain segmentation tool, FreeSurfer, as well as to (b) examine the impact of normalization methods for subject variability and smaller sample sizes on data analysis. A total of 401 healthy participants aged 29–68 were imaged at both 3T and 7T. Structural T1‐weighted magnetization‐prepared rapid gradient‐echo (MPRAGE) images were processed and segmented using FreeSurfer. To account for head size variability, the brain volumes underwent intracranial volume (ICV) correction using the Residual (regression model) and Proportional (simple division to ICV) methods. The resulting volumes and thicknesses were correlated with age using Pearson's correlation and false discovery rate correction. The correlations were also calculated in increasing sample size from three to the whole sample to estimate the sample size required to detect aging‐related brain variation. Three hundred and fifty subjects (208 females) passed the image quality control, with 51 subjects excluded due to excessive motion artifacts on 3T, 7T, or both. 7T MRI showed an overall stronger correlation between morphometrics and age and a larger number of significantly correlated brain volumes and cortical thicknesses. While the ICV is consistent between both field strengths, the Residual normalization method shows markedly higher correlation with age for 3T when compared with the Proportional normalization method. The 7T results are consistent regardless of the normalization method used. In a large cohort of healthy participants with paired 3T and 7T scans, we compared the statistical performance in assessing age‐related brain morphological changes. Our study reaffirmed the inverse correlation between brain volumes and cortical thicknesses and age and highlighted varying correlations in different brain regions and normalization methods at 3T and 7T. 7T imaging significantly improves statistical power and thus reduces the required sample size.
Journals
2025 EN
Diniz Eduardo · Santini Tales · Karim Helmet
+2 more
ABSTRACT The rapid advancements in magnetic resonance imaging (MRI) technology have precipitated a new paradigm wherein cross‐modality data translation across diverse imaging platforms, field strengths, and different sites is increasingly challenging. This issue is particularly accentuated when transitioning from 3 Tesla (3T) to 7 Tesla (7T) MRI systems. This study proposes a novel solution to these challenges using generative adversarial networks (GANs)—specifically, the CycleGAN architecture—to create synthetic 7T images from 3T data. Employing a dataset of 1112 and 490 unpaired 3T and 7T MR images, respectively, we trained a 2‐dimensional (2D) CycleGAN model, evaluating its performance on a paired dataset of 22 participants scanned at 3T and 7T. Independent testing on 22 distinct participants affirmed the model's proficiency in accurately predicting various tissue types, encompassing cerebral spinal fluid, gray matter, and white matter. Our approach provides a reliable and efficient methodology for synthesizing 7T images, achieving a median Dice coefficient of 83.62% for cerebral spinal fluid (CSF), 81.42% for gray matter (GM), and 89.75% for White Matter (WM), while the corresponding median Percentual Area Differences (PAD) were 6.82%, 7.63%, and 4.85% for CSF, GM, and WM, respectively, in the testing dataset, thereby aiding in harmonizing heterogeneous datasets. Furthermore, it delineates the potential of GANs in amplifying the contrast‐to‐noise ratio (CNR) from 3T, potentially enhancing the diagnostic capability of the images. While acknowledging the risk of model overfitting, our research underscores a promising progression toward harnessing the benefits of 7T MR systems in research investigations while preserving compatibility with existing 3T MR data. This work was previously presented at the ISMRM 2021 conference.
Journals
2025 EN
Mossazghi Nahom · Karim Helmet T. · Farhat Nadim
+4 more
ABSTRACT Sickle cell disease (SCD) is an inherited blood disorder caused by a mutation in the beta‐globin gene, resulting in chronic complications, including cognitive decline—particularly in executive functions. Neuroimaging studies have identified structural and functional abnormalities associated with SCD; however, the directionality of information flow between brain networks and how disruptions in these interactions contribute to cognitive deficits remains poorly understood. This study employed Granger causality (GC) analysis to investigate effective connectivity and information flow between brain regions and resting‐state networks using ultra‐high‐field 7T MRI in adult patients with SCD ( n = 51) and age‐, sex‐, and race‐matched controls ( n = 44). We first performed a whole‐brain network analysis, followed by an examination of specific brain regions within the default mode network (DMN), executive control network (ECN), dorsal attention network (DAN), and ventral attention network (VAN). For each analysis, we computed both the magnitude and directionality of information flow to capture the strength and directional influence of connectivity between brain regions. While patients with SCD exhibited a higher magnitude of information flow compared to controls, this difference was only statistically significant when computed at the brain region level, not at the resting‐state network level. In terms of directionality, afferent flow from DAN and VAN to ECN was significantly greater in patients with SCD than in controls. Subtype analysis revealed that patients with severe SCD demonstrated significantly higher magnitude of information flow than those with mild SCD and controls. We also observed subtype‐specific differences in afferent flow to ECN: mild SCD patients showed significant flow from VAN, while severe SCD patients showed significant flow from DAN. Additionally, multiple regression analyzes assessing correlations between information flow and cognitive performance showed that controls had higher R 2 values than patients with SCD, suggesting reduced network efficiency in SCD. This study is the first to apply GC‐based effective connectivity analysis in SCD, revealing unique pathways of information exchange in patients with SCD, potentially as compensatory mechanisms for disease‐related structural and functional disruptions. These findings provide novel insights into how SCD impacts brain network organization and cognitive function, emphasizing the importance of investigating network‐level dynamics in this population.