Emerging Trends in Engineering and Sustainability
AI-augmented networked control systems subject to network latency and packet loss
Networked Control Systems (NCSs) are increasingly deployed in critical infrastructure, yet their performance is often compromised by communication impairments such as time-varying delays and packet loss. These disturbances can undermine stability, reliability, and overall efficiency. To address these challenges, this study proposes an AI-augmented supervisory control framework that integrates communication metrics directly into the control strategy rather than relying solely on conventional error signals. The architecture employs machine learning classifiers—specifically K-Nearest Neighbors (KNN) and Random Forest (RF)—to assess real-time network conditions (normal, degraded, and emergency) and dynamically adjust the parameters of a core PID controller. Under degraded conditions, PID gains are attenuated to enhance robustness against latency, while in emergency scenarios, a conservative safe-mode configuration is activated to preserve plant stability under severe packet loss. Comparative analysis demonstrates that Random Forest, due to its ensemble-based design, achieves superior situational awareness, smoother mode transitions, and more reliable global stability than KNN. Moreover, RF reduces volatile parameter updates, thereby protecting physical actuators from excessive stress and extending component lifespans. The findings highlight the potential of ensemble learning approaches to enhance resilience and operational safety in delay-prone NCS environments.
Electrical, Electronics, Computer, and Control
Control Systems, Electronics, and Signal Processing
Communication and Networking Technologies
Copyright (c) 2026 Emerging Trends in Engineering and Sustainability
Impact of shading ratio on Silicon monocrystalline solar module yield and power output
Shadows dramatically reduce solar panel output because individual cells are wired in chains (series). Blocking one cell chokes the current for the entire chain. While bypass diodes bypass shaded sections, severe shadows can still cause "hot spots", which permanently damage panels. The proposed work presents an experimental measurements on the impact of shading on monocrystalline power losses. The solar module is exposed under the real environment conditions for direct solar radiation of 500 W/m2 and 80 W/m2 solar radiation at penumbra shading of shaded area with operating solar module temperature 25oC at ambient temperature 18oC. The Solar Module Analyzer simulator was used in this study, where 10 pattern of shading are tested. The photovoltaic solar module performance in terms of outlet power and fill factor with its corresponding efficiency are greatly affected by shadow. The maximum output power drops drastically, often much more than the shaded area percentage would suggest ; 25% shading can lead to 60% power loss and also 50% shading can lead to 60% power loss. It is noticeable that there is a sudden change in the behavior of the I-V and P-V characteristic curves at a certain range of open circuit voltage 9-9.5V.
Early Detection of Autism Spectrum Disorder in Children Using Different Machine Learning Algorithms
Autism spectrum disorder (ASD) is a neurological condition marked by impaired communication abilities, social detachment, and repetitive behaviors in individuals. Global health organization facing difficulties in establishing an effective ASD diagnostic system that facilitates precise analysis and early autism prediction. It is a scientific issue that necessitates resolution. This research presents an approach for the early prediction of children with ASD utilizing significant variables through machine learning (ML) methods. Three stages comprise the suggested technique. First, a 1250-case ASD dataset was identified and preprocessed. Five extremely effective traits with high Pearson correlation coefficient (PCC) are chosen from 10 chosen ASD feature dataset through its paces using five ML techniques: Naive Bayes (NB), K-Nearest Neighbor (kNN), Decision Tree(DT), Support Vector Machine (SVM), and AdaBoostM1 (ABM1). The proposed framework is assessed in the third phase utilizing five measurements such as accuracy, precision, predicting time ,recall, and F1-score, . The findings revealed that: NB and K-NN approaches exhibit superior accuracy rates of 99.2% and 97.2%, with minimal prediction times of approximately 0.3 and 0.45 seconds, correspondingly. Conversely, the DT and AdBM1 methods demonstrate a minor decline in accuracy, achieving 94.8% and 87.6%, respectively, along with increased prediction times. Nonetheless, the SVM approach exhibits the least performance, achieving an accuracy of 80.4% with prediction time of 0.84 seconds. This research aligns with several Sustainable Development Goals (SDGs), including ٍSDG3, which focuses on good health, SCG10, which addresses reducing inequalities, including those related to disability. and SCG9 by strengthening technological capabilities, particularly in scientific research
Autism Spectrum Disorder, Diagnostic Engineering, Machine learning, Sustainable Development Goals
Optimization and Control of Hybrid Renewable Energy Systems: A Short Critical Review
The worldwide evolution towards cleaner and more sustainable energy infrastructures has fostered intense research on Hybrid Renewable Energy Systems (HRES) that combine two or more energy sources that are complementary to each other to tackle the limitations of individual renewables. This paper reviews the optimization, control, and energy management strategies used for the HRES and provides a critical short review of the same. This paper covers system classification, component modeling, classical and metaheuristic optimization techniques, AI-based energy management, and techno-economic assessment. The review also highlights key current challenges and offers directions for future research, including AI-based smart systems, hydrogen integration, digital twins, and blockchain-based energy trading.
Development of new models for predicting the oil recovery of sandstone reservoirs
In this paper, a new correlation was developed (139 data sets) in this study to predict the oil recovery for sandstone solution gas drive reservoirs (SGDR) using NLMR with a coefficient of determination, R2 of 0.90 (compared to 0.87 for Gulstad correlation). An artificial neural network (ANN) model was developed for this reservoir giving an R2 of 0.92. A correlation was developed (111 data sets) in this study to predict the oil recovery for sandstone water drive reservoirs (SWDR) using NLMR with a coefficient of determination, R2 of 0.93 (compared to 0.91 for American Petroleum Institute, API correlation). The developed ANN model for this reservoir gives an R2 of 0.94.
Response Surface Methodology Applications in Drilling Engineering: Toward Multi-Objective Optimization of Bit Hydraulic Performance—A Critical Review
Response Surface Methodology (RSM) has been widely applied in drilling engineering to model and optimize multivariable operational systems. This review critically evaluates recent RSM applications in drilling engineering, identifies their methodological limitations, and examines the extent to which bit-level hydraulic performance parameters have been incorporated into drilling optimization frameworks. A structured review of studies published between 2021 and 2026 was conducted. The selected studies were classified into three categories: mechanical performance optimization, drilling-fluid optimization, and hybrid optimization approaches. Each study was evaluated according to its input variables, response variables, modeling techniques, optimization objectives, reported performance improvements, consideration of physical and operational constraints, and integration of data-driven methods such as Artificial Neural Networks (ANN). The review indicates that most RSM-based studies prioritize outcome-based indicators, particularly rate of penetration (ROP), drilling-fluid rheology, and operational parameter selection. In contrast, bit-level hydraulic parameters, specifically Bit Hydraulic Horsepower (BHHP) and Jet Impact Force (JIF), are rarely treated as primary optimization objectives despite their direct influence on hydraulic energy transfer, rock fragmentation, and cuttings removal at the bit–rock interface. The findings also show that most existing studies employ single-objective optimization and second-order polynomial models, which may limit their ability to represent nonlinear and coupled hydraulic behavior under field operating conditions. This review adds to the existing literature by systematically identifying the limited integration of physics-based hydraulic indicators into RSM-driven drilling optimization and by defining a research direction for hybrid frameworks that combine RSM-based design-space validation, ANN-based nonlinear prediction, and constrained multi-objective optimization of BHHP and JIF. Such frameworks could support more robust, scalable, and field-applicable drilling optimization strategies.
Response Surface Methodology, Bit Hydraulic Horsepower, Jet Impact Force Artificial Neural Networks
Simulation Approaches for Polyurethane Materials: A Multiscale Review
Polyurethane (PU) products enjoy remarkable versatility due to their tunable chemistry, segmented structure, and a wide range of mechanical properties, making them useful in flexible foam products, structural systems, and biomedical applications. However, the complex multiphase morphology and the strong interaction between reaction and processing processes make experimental characterization incomprehensible on its own. In turn, computational studies have become essential to study and design PU systems at a range of spatial and temporal scales. The current review provides an overview of simulation methodologies that are relevant to polyurethane, including atomistic molecular dynamics (MD), coarse-grained (CG), and mesoscale simulations, including dissipative particle dynamics (DPD), finite element method (FEM) modeling, and computational fluid dynamics (CFD) simulations. Atomistic models provide data on molecular interactions, hydrogen bonding, and thermomechanical behavior, and CG and mesoscale methods on phase separation and morphological evolution. At the bigger length scale, nonlinear mechanical response can be predicted using FEM, whereas foaming and mold-filling processes can be predicted using CFD that is coupled with reaction kinetics and population balance equations. Its focus is on multiscale modeling strategies, which combine these apparently different approaches, hence allowing the explanation of structure-property-process links. New trends and modern issues, including the integration of machine learning and tool models of digital twins, are also mentioned, highlighting new opportunities in predictive design, based on simulations, of polyurethane materials.
Effect of Solar Module Operating Temperature on Electric Parameters of Photovoltaic Monocrystalline Module
50W monocrystalline silicon solar module performance is tested with experimental measurements conducted at Baghdad city /Al-Jaderia (33.26 N, 44.21E). Solar irradiance striking is subjected to more losses which after the experiments conducted resulted approximately in 15% of the total energy which is converted into electric power energy. To study the effect of temperature variations on solar performance, solar irradiance must be kept constant and vice versa. Therefore, to have of the temperature range and for more accuracy, the measurements was done for tested module with three solar radiations levels; 500, 750 and 1000 W/m2. The maximum value of power (Pmax) at solar radiation intensity 1000W/m² was 46.34 W on January 2025 at cell temperature 24.1 oC, with the corresponding the maximum open voltage, and open circuit current 18.28 V, and 2.944 A respectively. The highest value of efficiency was 13.5 % January 2025 at solar radiation 500W/m². Consequently, The minimum value of power (Pmax) at solar radiation intensity 500W/m² was 27.54 W on October 2024 at cell temperature 40.5 oC, with the corresponding the maximum open voltage, and open circuit current 18.01 V, and 1.752 A respectively. The lowest value of efficiency was 6.9 % October 2024 at solar radiation 1000W/m². In general, the results showed slightly decrease in short circuit current with temperature increasing. With temperatures change great influence on the output voltage especially on open circuit voltage while very small decrease in the output current has been noticed.
Optimization of ESP Performance Using Pump Frequency and Wellhead Pressure Sensitivity Analysis in the Rmelan Oil Field (PIPESIM-Based Study)
The aim of this study is to optimize ESP performance by evaluating the current conditions and the performance optimization of the electrical submersible pump (ESP) for six oil wells in the Rmelan oil field. fluid and reservoir properties (API = 23, T = 78 C°, pressure of reservoir = 160 atm and the WC is 70%). This paper presents a sensitivity assay conducted by Nodal Analysis (Using PIPESIM Software) on the pump frequency and wellhead pressure. The outflow tubing performance and inflow performance relationship were generated and plotted for each well. The curves are investigated, indicating problems in some wells (W-12R, W-21KH, and W-21SH). The results of this study show that we can increase the flow rate by optimizing the ESP performance by decreasing the wellhead pressure to 71.58 psi and raising the frequency of ESP to a specific value of about 65 Hz based on the limites of production of each types pump capacity . Increasing the frequency from 55 to 65 Hz resulted in increasing the production from 634 to 1092 bb/day for W-12R, from 1928 to 2806 bbl/day for W-21KH, and from 1722 to 2279 bbl/day for W-21SH.
A Quantitative Analysis of Factors Affecting the Adoption of Micro-management in Engineering Projects
Micro-management is a type of management in which the manager accurately monitors and intervenes or regulates the work of his subordinates or his employees in detail, and it is the attempt of managers to interfere, influence and control anything in the team, situation or place, Everything in this world carries two different directions, one negative and the other positive, and we aim in this research to exploit the advantages of the method of micromanagement and prove that it can be a successful tool if used correctly and at specific times and within certain limits, in this research the factors affecting Applying this method in construction contracting companies in Iraq to take advantage of this method in identifying the most important problems leading to financial corruption in order to eliminate or reduce them in order to advance the current reality of projects, so the researcher reached the most important factors, including (administrative style, organizational culture) and other factors that have been clarified in a way. In detail in this research, the characteristics of the micromanager were also studied, which represent the cornerstone of the success and failure of this technique, as the effect of each characteristic and the possibility of its availability in the vicinity of engineering companies was addressed.
Comparative Analysis of Fatigue Performance in Asphalt Concrete Modified with Nano-Alumina and Nano-Silica
This comprehensive study undertakes a two-tiered comparative analysis to systematically evaluate the fatigue and cracking performance of a 40-50 penetration grade asphalt binder and its corresponding asphalt concrete mixtures, modified with varying dosages (2%, 4%, and 6% by binder weight) of Nano-Alumina (NA) and Nano-Silica (NS). The experimental methodology involved extensive binder-level testing, including the evaluation of physical properties (penetration, softening point, ductility), rheological behavior (Rotational Viscosity (RV)), and fatigue characteristics using the Superpave parameter G* sin δ and the advanced Linear Amplitude Sweep (LAS) test. Furthermore, compatibility was assessed via storage stability and Scanning Electron Microscopy (SEM). The research culminated in mixture-level performance evaluation using the Indirect Tensile Cracking Test (IDEAL-CT) to derive the Cracking Tolerance Index (CT-Index), Flexibility Index (FI), and Crack Resistance Index (CRI). The results confirmed that both nanomaterials significantly enhance binder stiffness and thermal stability. Nano-Alumina (NA) consistently induced the most profound stiffening effect, reflected by a major reduction in penetration. Rheological and LAS testing indicated that NA provides a stable and progressive, dose-proportional enhancement in fatigue life from 2% to 6%, attributed to the formation of a sustained nanoscale reinforcement network. Conversely, Nano-Silica (NS) exhibited a potent viscosity-building effect due to its high surface area, achieving superior initial cracking tolerance and fatigue life at low concentrations (2% to 4%). Crucially, the study identified a narrow optimal range for NS; concentrations at 6% led to an adverse reduction in fatigue resistance (G* sin δ increase) and diminished flexibility, suggesting a constraint imposed by excessive stiffening and potential particle agglomeration. Mixture-level IDEAL-CT results further validated these trends: NA offered a balanced overall contribution, maximizing the CT-Index at 6% and CRI at 4%, while NS yielded an exceptionally high CT- Index value at 2% but showed a decline in performance at higher contents. The overall findings recommend an optimal practical dosage of 2-4% for NS and 4-6% for NA, underscoring the necessity of material-specific optimization for achieving enhanced durability and fatigue life under repeated loading.
Multi-Functional Enhancement of Water-Based Drilling Fluid Using Copper Nanoparticles: A Study on Lubricity, Rheology, and Filtration Properties
Nanoparticle additives emerge as a modern solution to eliminate the performance gap between conventional water-based drilling fluids (WBDFs), and more superior but environmentally challenging oil-based drilling fluids (OBDFs). This study focuses on the enhancement of KCl polymer mud using nano-additives. While nano-additives like copper oxide (CuO NPs) were studied and showed promising results, another form of copper (elemental copper nanoparticles, Cu NPs) with a potential as a multifunction mud additive remains largely unexplored. This research systematically investigates the impact of Cu NPs (0.04–0.8 wt%) on the lubricity, rheology, and filtration properties of KCl polymer mud. All the measurements were done in the lab at room temperature, using lubricity tester, viscometer, and low-pressure filter press. Most additives tend to enhance one property of the mud, but the Cu NPs acted as a more superior properties enhancer, as it didn't enhance only one aspect of KCL polymer mud, but acted as multifunctional additive. For the lubricity, the effect of Cu NPs was significant on the coefficient of friction (CoF), with maximum reduction of 41.68% observed at 0.8% concentration, however at the 0.2% concentration, a relatively similar result of CoF reduction was observed with 39.78% making it the optimal concentration for the lubricity aspect. For the rheological properties, the addition of Cu NPs to the KCL polymer mud enhanced the overall rheological properties, increasing the plastic viscosity (PV), yield point (YP), apparent viscosity (AV), and gel strength, the highest values [PV (44.5 cP), YP (69.4 lb/100ft²), AV (77.35 cP)] were observed at 0.2% concentration. Unlike its beneficial effects on lubricity and rheology, the addition of Cu NPs to KCl polymer mud resulted in increased fluid loss and thicker filter cakes. The study concludes that a concentration of 0.2 %wt of Cu NPs is optimal for the simultaneous enhancement of lubricating and rheological properties in KCl polymer mud. This study highlights the potential of Cu NPs as a multifunctional additive that can be used in advanced water–based drilling fluids systems.
Modeling and Simulation of Hydraulic Fracturing in Tight Gas Reservoirs: A Review of Geomechanical and Flow Dynamics Approaches
This paper reviews the developments of modeling hydraulic fracturing in tight gas formations, progressing from elementary analytical models to more advanced and coupled geomechanical-flow simulators. We discuss the significant progress that has been made in understanding fluid flow behavior of ultra-low permeability formations, which has significantly improved methodology for analyzing this complex problem. Findings demonstrate the importance of using Discrete Fracture Network (DFN) and Embedded Discrete Fracture Model (EDFM) for representation of complex fracture geometries and connectivity. However, it remains a great challenge to model the stress-dependent changes in permeability and porosity and the dynamic changes of fracture properties during fracturing, as well as the multi-scale interactions between induced hydraulic fractures and natural ones. This paper provides a novel iterative modeling framework that integrates multi-scale interactions and proposes a roadmap for data-driven modeling coupled with fluid flow to enhance predictive accuracy in TGR stimulation.
Review Tight Gas Reservoir Hydraulic Fracturing Simulation
Copyright (c) 2025 Emerging Trends in Engineering and Sustainability
A Novel Approach to Improve Octane Number and Reduce Sulfur Emissions in Regular Gasoline by Selective Naphtha Blending at Al-Diwaniyah Refinery
ABSTRACT This paper proposes a low CAPEX selective blending strategy to upgrade regular gasoline quality in Diwaniyah Refinery. It tests the hypothesis that segregating heavy naphtha from the gasoline pool and blending light naphtha only with imported high octane gasoline can increase octane number (RON) and reduce sulfur content while decreasing import requirements. Four volumetric cases were evaluated: the refinery’s current practice (72 vol% imported gasoline + 28 vol% mixed naphtha) and three alternatives replacing mixed naphtha with light naphtha at 72/28, 67/33, and 62/38 vol%. Blends were prepared at ambient conditions and characterized using ASTM D2699 (RON) and ASTM D5453 (sulfur content). Replacing mixed naphtha with light naphtha at the same import ratio increased RON from 82.5 to 84.5 and reduced sulfur content from 157 to 70 ppm. Further reductions in imported high octane gasoline to 67 and 62 vol% maintained sulfur content below 100 ppm (77 and 87 ppm), with RON values of 83.5 and 80.5, respectively. These results were confirmed by Aspen Hysys simulation and ANOVA, indicating that heavy naphtha exerts the strongest negative effect on quality of regular gasoline. The proposed segregation requires only modifications to pipeline routes, enabling improved fuel quality and compliance with sulfur standards while reducing the need for imported gasoline in smaller refineries.
Blending,Heavy naphtha,Light naphtha,Sulfur content,Octane number
The Influence of the Cell Temperature on the Performance of Mono and Poly-Crystalline Silicon Solar modules
In this research paper, it has been studied the influence of the temperature of the cell on the performance and behavior of two types of modules, which are mono-crystalline silicon (mc-Si) and poly-crystalline silicon (pc-Si) solar modules. The experimental work has been achieved under the outdoor conditions, where the range of cell temperature is between 20 and 60 °C. It was applied three different values of solar radiation [500, 750, and 1000W/m2 (standard condition, where cell temperature of 25 °C, solar irradiance of 1000 W/m², and air mass AM 1.5)]. All tests are achieved under the Iraqi weather conditions in the city of Baghdad city. It was computed the temperature coefficients for each module and during any time during the experiment. It was found that the open circuit voltage decreased with -0.0912 V/ºC and -0.07 V/ºC when using the pc-Si module and mc-Si, respectively. While, the short circuit current increased slightly with 4.4 mA/ºC and 0.3 mA/ºC corresponding to the pc-Si and mc-Si, respectively. Finally, the lowest drop in output power was found when using the pc-Si module (-0.0915 W/ ºC), and the highest drop when using the mc-Si module (-0.1353 W/ ºC).
mono-crystalline silicon, poly-crystalline silicon, thermal effect, Photovoltaic performance.
A Comprehensive Review of Advanced Solar Drying Technologies: Concentrators, Optical Enhancements, and Thermal Energy Storage Systems
The conventional open sun drying is not efficient, it is slow and contaminated and there is a necessity to develop highly advanced technologies in solar drying. The review looks critically at solar dryers that are improved with concentrator, optical, thermal energy storage (TES) or phase-change materials (PCM). The incorporation of parabolic trough or compound parabolic concentrators leads to a high temperature of over 100-115 oC and a thermal efficiency of up to 88 %. Reflective walls are also made to enhance optical capturing by up to 37.6 %, and shorten drying time by 15-20 %. TES/PCM systems increase the operation of TES systems beyond the sunset, nano-enhanced PCMs reduce drying time by 40% and enhance thermal efficiency by more than 48%. These systems demonstrate short payback periods (0.43-5.14 years) with regard to economics. They minimise the emission of CO2 by 2-44 tons/ lifetime of systems. These combined technologies have addressed intermittency and low efficiency and enabled solar drying to be a reliable and cost-effective and sustainable solution, as the UN Sustainable Development Goals of clean energy and climate action suggest.
A Comparative Analysis of Federated Learning on Non-IID Data for an Intrusion Detection System
Federated learning (FL) offers a robust and privacy-preserving approach for developing collaborative intrusion detection systems (IDS). However, statistical variance severely hinders its practical application. Although privacy-preserving federated learning models have been used to develop intrusion detection systems for cyberattacks, problems arise when statistical variance is present. In practice, the performance of the FedAvg algorithm is significantly affected by the heterogeneous distribution of customer data in a real-world network. This distribution causes skewness among customer data, resulting in poor detection accuracy, delayed convergence, and model instability. In this paper, presents conduct a comprehensive comparison of the Scaffold algorithm with the FedAvg baseline using the CICIDS2017 datasets. Because the Scaffold algorithm addresses the client skew problem using control variables, it is considered a state-of-the-art federated optimization technique under the heterogeneous partitioning approach. This paper documents the importance of using the Scaffold algorithm as a reliable and essential tool for building high-performance detection systems in a variety of scientific settings. Therefore, our results demonstrate that Scaffold achieved more stable convergence and outperformed FedAvg, with a 15.1% increase in F1-score and a 13.6% higher overall accuracy under highly skewed data distributions. The present evaluation process operates through simulation testing, but physical testbed implementation remains essential for future work to evaluate real-world deployment challenges.
Al₂O₃ Nano and Micro Films Deposited using PLD on Stainless Steel Substrates for Medical Applications
In this study, we deposited the Al2O3 nanostructure on 304 stainless steel using PLD technique. The stainless-steel specimens were successfully coated with Al2O3 nanostructure, and surface morphology was examined using an optical microscope. The findings confirmed the nanostructured nature of the films. Tafel curve analysis was used to determine the polarization of the sample before being subjected to laser shock penning treatment. Additional tests were then carried out on the samples following pulse laser deposition.
Simulation the Performance of Blowing agents for Polyurethane polymerization reaction
The objective of the current study is to determine the accuracy of a computational model that has been developed to simulate polyurethane foaming reactions by comparing its results with experimental findings on the system using both physical and chemical blowing agents. There was high concordance between the model outputs and the laboratory results in regard to the temporal development of reaction temperature as well as the resulting foam density, both of which were highly faithful recreations. The discussion provided further information about the optimization of the performance of cyclohexane, particularly when used in synergy with chemically active blowing agents, which speed up foaming. Besides, the polymerization dynamics were contained in the simulation, thus providing rich information on the structural changes that occur during the foaming process. Taken together, the results present a strong basis for the process performance optimization, as well as the predictive modeling of the blowing agent behavior. In the future, it will involve expanding the simulation model to include a wider range of agents, reaction mechanisms, and kinetics.
Characterization and Influence of Sustainable Hybrid Silica Nanoparticles on Enhanced Oil Recovery and Carbon Geosequestration
The growing demand for energy, coupled with the continued dominance of fossil fuels as the primary energy source, necessitates eco-friendly technologies that simultaneously enhance oil recovery (EOR) and reduce the impact of their emissions. Only one task, which is the CO2-EOR project, can combine these two sustainable development goals. Further, employing green nanotechnology, including nanoparticles and nanofluids, ensures a sustainable approach to controlling and enhancing rock wettability, thereby enhancing hydrocarbon production and carbon storage. However, the performance of nanofluids in subsurface formations is limited by the stability of these nano-dispersions at the harsh conditions of reservoirs. This work thus synthesizes silica nanoparticles from waste bentonite as a green source and modifies the surface properties with a silane group to formulate a stable nanofluid for subsurface applications. The produced nanoparticles were characterized via Fourier Transform Infrared (FTIR) spectroscopy, X-ray diffraction (XRD), scanning electron microscopy (SEM), zetasizer, and dynamic light scattering (DLS). Moreover, the efficiency of nanoparticles as wettability-modifying agents was studied using contact angle and spontaneous imbibition tests. FTIR measurements confirmed the presence of silane on the surface of hybrid silica nanoparticles, as indicated within the Wavenumber 2950 cm-1. Moreover, XRD measurements revealed that hybrid nanoparticles showed lower noise than pure ones. Results also showed that silane-treated nanoparticles (hybrid) are more tolerant to high salinity (≥ 0.5wt% brine), and green-synthesized nanoparticles have a drastic ability to invert the wettability of oil-wet surfaces (θ≥123°) to water-wet (θ ≤ 28°) at ambient conditions and also reduce the contact angle from 175° to 68°) at CO2-EOR conditions. The study concludes that these green nanofluids are highly efficient for EOR and carbon geosequestration projects when properly formulated.