Geochemical characterization and societal use of a fault-controlled low-enthalpy geothermal system: Evidence from the Munger-Saharsa Ridge, eastern India

Field development plan of geothermal hotspot in eastern India: A case study from Bhimband, Bihar

Friction stir welding (FSW) has emerged as a highly promising solid-state joining technique for polymeric materials, offering an effective alternative to conventional fusion welding, adhesive bonding, and mechanical fastening. This review critically analyzes recent progress in the friction stir welding of polymers, with emphasis on quantitative relationships between process parameters, tool design, thermal behavior, and joint performance. Reported studies indicate that optimized tool pin geometries, particularly threaded cylindrical and frustum profiles, can achieve joint efficiencies exceeding 85%?95% of the base material strength in polymers such as HDPE, PP, and PMMA. The weld-to-velocity (w/v) ratio is identified as a key governing parameter, with optimal ranges varying across polymers (e.g. ?25?60?rev/mm for PP and ?100?130?rev/mm for PE), directly influencing heat input, material flow, and defect formation. Due to the inherently low thermal conductivity of polymers, heat accumulation and dissipation play a decisive role in microstructural evolution, including crystallinity changes and zone formation within the weld region. Recent advances in tool tilt control, plunge depth optimization, and sensor-based monitoring have significantly improved process reliability and repeatability. By synthesizing the experimental, numerical, and monitoring-based studies published recently, this review provides a comprehensive understanding of polymer FSW mechanisms, performance limits, and industrial applicability, particularly for lightweight and hybrid structures in automotive and aerospace sectors.

Friction stir welding of polymer: A comprehensive review of techniques, materials, applications and challenges

Micromachining (EDM) Using 3D Printed Tool

This study investigates the effectiveness of surface texturing for enhancing steady-state and dynamic performance of three-lobe journal bearings using electrorheological (ER) lubricants. ER fluids consist of dielectric particles suspended in non-conductive oil. The fluid flow in bearing clearance is modeled by generalized Reynolds equation. The non-linear characteristics of lubricant are captured using continuous Bingham fluid model. Surface textures are designed with arrays of spherical and conical dimples arranged in various circumferential configurations. The Reynolds equation is solved using finite element approach and Newton-Raphson method, incorporating a mass-conserving algorithm to address gaseous cavitation in film. Different surface texture configurations are analysed to maximize bearing's direct stiffness parameters. The findings indicate that partial texturing in first half of the bearing maximizes these stiffness parameters. The study further examines impact of dimple shape, surface texture configuration, and applied electric field on the performance of circular and three-lobe journal bearings. The equation of motion for journal is solved using fourth-order Runge-Kutta method to predict journal's center trajectories. The application of ER lubricant and partial surface texturing in three-lobe journal bearing enhances direct stiffness parameter by 339.3% and increases damping parameter by 43%. The threshold speed, a crucial design factor for journal bearings is significantly improved (72.8%) through surface texturing and ER effect in three-lobe bearings. Moreover, three-lobe journal bearings utilizing ER lubricants demonstrate smaller and more stable linear motion trajectories of journal. Under simulated conditions, designers should consider textured surface three-lobe journal bearings with ER lubricants for enhanced stability and performance.

Dynamic analysis of textured surface three-lobe hydrodynamic journal bearing operating with electro-rheological lubricant

THERM-OPEX: A Multi-Dimensional Risk Quantification Model for Thermal Power Plant Outsourcing Using Stochastic Severity Assessment and Dynamic Cost-Performance Indexing


                    Purpose
                    This study aims to investigate effectiveness of surface texturing in improving steady-state and dynamic performance characteristics of two-lobe journal bearings using electrorheological (ER) lubricants.
                  
                  
                    Design/methodology/approach
                    The Reynolds equation is solved using finite element approach and Newton?Raphson method, with gaseous cavitation in lubricant addressed via a mass-conserving algorithm. The ER fluids constitute dielectric particles suspended in non-conductive oil. A continuous Bingham fluid model describes lubricant?s non-linear behavior. The study explored how dimple shape, surface texture configuration and applied electric field affect performance of two-lobe journal bearings. To assess the journal?s dynamic stability, equation of motion is solved using fourth-order Runge?Kutta method, predicting the journal?s center trajectory.
                  
                  
                    Findings
                    The findings reveal that applying ER lubricants and partial surface texturing in two-lobe journal bearings boosts direct stiffness by 1037.1% and damping by 46%. In addition, the threshold speed improves by 100.1%, and the bearings exhibit smaller, more stable linear motion trajectories and limit cycles. These results suggest that designers should consider using first-half textured surface two-lobe journal bearings operating with ER lubricants to enhance stability and performance under simulated conditions.
                  
                  
                    Originality/value
                    This study presents rotor-dynamic performance of textured surface two-lobe journal bearing operating with ER lubricant. Texture shape, attributes and configuration are determined to maximize direct stiffness coefficients and minimize the journal center motion trajectories.
                  
                  
                    Peer review
                    The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-12-2024-0458/
                  

Rotor-dynamic analysis of electro-rheological lubricant operated textured surface two-lobe hydrodynamic journal bearing

Federated Learning with LSTM Based Active User Detection for CF-mMIMO Network for 6G Communication System

Effective user detection and resource allocation techniques are required in cell-free massive multiple input multiple output (CF-mMIMO), a sixth generation (6G) communication system to improve network performance by providing grant-free access to the active user. Accurate active user detection (AUD) while reducing needless resource allocation to idle users is one of the main issues in such systems. This work proposes a Federated Learning (FL) with long-short-term memory (LSTM) deep learning-based decentralized AUD and user clustering framework for CF-mMIMO systems. We employ an LSTM model for active user classification and K-Means clustering to identify the user?s activity within the cluster based on signal strength and available bandwidth. The Rician fading technique is incorporated into the simulation to ensure a realistic wireless environment. Experimental results demonstrate that the proposed method significantly reduces communication overhead and provides high detection accuracy.

Enhanced metal ion adsorption using ZnO-MXene nanocomposites with machine learning-based performance prediction

The aim of the present research work is to conduct productivity assessment studies, to improve operational efficiency, and provide recommendations for productivity improvement for a solar module manufacturing plant. An effort is made in this work to construct a current state value stream map for capturing the existing data for subsequent analysis of a solar panel manufacturing plant. After a thorough analysis, the future state value stream map is developed. The recommendations derived from the future value stream help the organization implement corrective solutions to existing bottlenecks in order to enhance customer satisfaction. Also, the requirement for incorporating the lean tools in organizations leads to better productivity as well as provides insights on potential constraints.

Productivity Assessment Studies for a Solar Module Manufacturing Plant

An Electro-rheological (ER) fluid comprises dielectric particles blended in a non-conducting oil. The ER lubricants are commonly known as smart lubricants. This study utilizes an ER lubricant to examine the static and dynamic response of multi-lobe journal bearing. The lubricant flow within the journal-bearing clearance space is described by Reynold?s equation, while the non-linear behavior of the lubricant is characterized using the Bingham model. The solution of the Reynolds equation is obtained employing the Newton-Raphson method, with numerical addressing of gaseous cavitation in the fluid film through a mass-conserving algorithm. The effects of lobe geometry and the applied electric field are scrutinized on bearing performance metrics. The equation of motion for the journal is solved utilizing the fourth-order Runge-Kutta method to predict journal center locus/trajectories. The utilization of ER lubricant in conjunction with a two-lobe journal bearing notably enhances the minimum film thickness by 63.5%, the direct stiffness parameter by 182.4%, and the damping parameter by 210.14%. The multi-lobe configuration adversely affects the frictional power loss of the bearing system. The threshold speed, a critical parameter in the design and operation of journal bearings, is improved by the ER effect and is higher for multi-lobe bearings. In multi-lobe configurations of journal bearings operating with ER lubricant, linear motion journal trajectories are noted to be smaller and more stable. Under given operating conditions, designers should prioritize two-lobe journal bearings operating with ER lubricants, as they exhibit greater stability and superior performance metrics in both steady-state and dynamic conditions.

Rotor Dynamic Behaviour of Electro-Rheological Lubricant Operated Multi-Lobe Journal Bearings

Sustainable Practices Through Carbon Offset Coins: Incentivizing Green Energy and Efficiency

An electrorheological (ER) fluid consists of dielectric particles blended in a nonconducting oil. ER lubricants are often considered smart lubricants. This paper aims to examine the steady state and dynamic response of multilobe journal bearings using an ER lubricant.
Design/methodology/approach
Reynold?s equation has been used to describe the lubricant flow in the journal-bearing clearance space. The Bingham model is used to characterize the nonlinear behavior of the lubricant. The solution of the Reynolds equation is obtained using the Newton?Raphson method, with gaseous cavitation in the fluid film numerically addressed by applying a mass-conserving algorithm. The effects of lobe geometry and the applied electric field are investigated on film pressure profile, fluid film thickness, direct stiffness and damping parameters. The equation of motion for journal center coordinates is solved using the fourth-order Runge?Kutta method, to predict journal center motion trajectories.
Findings
Using ER lubricant combined with two-lobe journal bearing significantly improved the minimum film thickness by 49.75%, the direct stiffness parameter by 132.18% and the damping parameter by 206.3%. However, the multilobe configuration was found to negatively impact the frictional powerloss of the bearing system. In the case of multilobe configurations of journal bearings using ER lubricant, linear motion journal trajectories are observed to be reduced and exhibit increased stability.

Stability analysis of electrorheological (ER) lubricant operated multilobe hydrodynamic journal bearing

Composites, polymers, ceramics, and high-strength metal alloys are some materials amenable to being micro-milled. Because of its elasticity, complex shapes may be achieved with it. Traditional milling is comparable to CNC milling in terms of spindle speed, feed rate, depth of cut, and tool type. One of the latest developments in the industrial 4.0 movement is micro-milling. This trend encourages adopting eco-friendly practices that produce less trash than conventional ways. Micro-milling's key characteristics are compactness, high accuracy, complicated geometries, trim waste, and material compatibility. For the sake of this study, the micro-milling parameters affecting the process's efficiency have been broken down into six main categories. Materials, cutting forces, surface topography, burr formation, process monitoring, and cutting fluid are some of these components. This paper offers a comprehensive review of these criteria. A comprehensive critical analysis of these parts is paired with the literature review to conclude.

Advances in Micro-milling: A Critical Review

Background: The recent development of new and advanced materials has led to industry experiments, innovation, and patents. As a result, various alloys were developed and implanted for multiple applications in the space, automobile, chemical, steel, and manufacturing industries. Cobalt- and nickel-based alloys have been designed to cater to high-temperature and oxidationresistance alloys. Objective: Here, various literature reviews are investigated, and the machining of Haynes-25 is done using an electrical discharge machine with an optimization technique of the L27 orthogonal selection of Taguchi. The controllable input procedure constraints are Pulse On time (Ton), Duty factor (Df), Current (I), Gap voltage (Vg), and Flushing pressure (Fp). Methods: The performance characteristics output parameters considered are material removal rate, tool wear rate, and surface roughness values (Ra). Moreover, confirmation tests are conducted to determine the percentage error of the predicted model. Results: The confirmation tests and results showed that the duty factor and current greatly influence the M.R.R, E.W.R, and Ra machining performance. The optimized condition is obtained at the implementation of the confirmation test and using the level of significance, i.e., A3B2C3D3E3, which can be used for the patent with the value of M.R.R..R as 0.05702, E.W.R. of 0.0091, and Ra of 2.34. Furthermore, regression models are developed to predict the material removal rate, tool wear rate, and surface roughness. Conclusion: It is suggested that industries use these optimum conditions to lessen unused material and raise the productivity of Haynes-25 Electrical Discharge Machining, which can be patented.

Multi-Response Optimization of Haynes-25 Using Taguchi Grey Relational Analysis with Die-Sinking Electric Discharge Machine

E.D.M has played an essential role in the machining industry, but as technology has advanced, advanced alternative methods of machining have emerged. In terms of performance characteristics such as higher material removal rate (M.R.R.) and improved surface characteristics of the machined surface with high tolerance quality products, this technology has been proven to be more efficient than traditional E.D.M. Electric discharge machining is a sophisticated method of machining rigid conductive materials with complex geometries that are difficult to machine using standard methods. In this study, Die Sink E.D.M, Wire E.D.M, Green E.D.M, Dry E.D.M, and Hybrid E.D.M were analyzed, and experiments were conducted on Die sink E.D.M to achieve better material removal and lower tool wear rates.

Recent advances in electrical discharge machine techniques and machining: A review

advance in micro milling: a critical review

Advances in Micro-Milling: A Critical Review.


A critical review on thermal spray based manufacturing technologies

Advances in Micro-Milling: A Critical Review.

This paper investigates serially concatenated (SC) and interleaved transmission schemes for a new class of continuous phase modulation (CPM), which has the property being a single sideband (SSB). This new CPM is commonly called Single Side-Band Frequency Shift Keying (SSB-FSK). Due to the ample space of SSB-FSK parameters, finding good serially concatenated schemes using SSB-FSK with desired spectral and energy efficiencies as well as reasonable complexity, is challenging. We present a systematic, simulation-based extrinsic information transfer (EXIT) chart approach to evaluate this multi-objective problem (improve spectral and energy efficiencies and reduce complexity) and subsequently design a competitive serially concatenated SSB-FSK based scheme. We show that iterative decoding of SC schemes using SSB-FSK (SC-SSB-FSK) provides close to optimal performance. Furthermore, the bit error rate (BER) of SC-SSB-FSK is compared to that using raised cosine (RC) based CPM signals. It has been shown that the proposed technique outperforms the state-of-the-art serially concatenated systems.

Serially Concatenated Schemes for Single Sideband Continuous Phase Modulation

This article deals with the steady and dynamic performance analysis of rough surface circular and multi-lobe journal bearings operating with non-Newtonian lubricant in turbulent regimes. The numerical solution of Reynold's equation was obtained using the finite element method. To account for surface roughness, the Patir and Cheng model was used, and fluid turbulent behavior was modeled using the turbulent theory as proposed by Ng and Pan. The non-Newtonian behavior of the lubricant is presented by the Rabinowitsch fluid model, and JFO boundary conditions are used to solve Reynold's equation, considering gaseous cavitation. The bearing with micro-roughness has been considered in numerical analysis and transverse roughness has been reported to improve bearing performance indices. Among the different bearing design configurations, the two-lobe bearing configuration provides significantly ?

	 Performance of rough surface hydrodynamic circular and multi-lobe journal bearings in turbulent regimes

This article presents numerical simulations of rough surface circular and lemon bore journal bearing operating with non-Newtonian shear thinning lubricant, in turbulent regime. Numerical simulations of bearing have been performed by finite element solution of modified Reynolds equation for non-Newtonian lubricant. Rabinowitsch fluid model is used to define the shear-thinning nature of lubricant. Surface roughness has been modelled using the average film thickness model proposed by Patir and Chang. The effects of orientation of surface roughness, turbulent flow (Reynolds number), and fluid flow coefficient are investigated on film pressure, minimum film thickness, frictional torque, and stiffness coefficients of bearing. It has been found that lemon bore bearing outperforms circular bearing in terms of the above-mentioned performance indices. Turbulent flow condition is a notice to provide a higher value of

Performance analysis of circular and lemon bore hydrodynamic journal bearing considering surface roughness and shear thinning effect

This paper presents a computational procedure for the determination of the dynamic behaviour of graphene with different types of defects. The lattice of graphene is modelled using the molecular structural mechanics (MSM) approach, where the C?C covalent bonds are replaced by equivalent beam elements. Cantilever and bridged boundary conditions are applied for the analysis. Four types of Stone?Wales (SW) defects such as S-W (555-8), S-W (555-888-3), S-W (555-77-8) and S-W (888-3), and two types of pinhole defects with 6 and 24 elements eliminated are examined on armchair, zigzag and chiral type of graphene sheet. The effect of the structural length of the sheet, chirality and defect type on the vibrational properties of graphene sheets is investigated.

Influence on dynamic behaviour of single layer graphene by Stone wales and pinhole defects

Plate-fin heat exchangers, are also called compact heat exchangers. Due to their small size, compared to other heat exchangers they are user friendly which allow greater flexibility in plant architecture. The heat exchanger is made by stacking up fin surfaces which are separated by a sheet. With the increase in the area of the offset fin structure, the heat transfer also increases. But on the other end total annual cost and entropy generation is also affected by geometric variables. In the present work, seven geometric variables of the heat exchanger are investigated for the optimization of effectiveness, total annual cost and entropy generation minimization. Genetic algorithm is implemented for the optimization. Finally, parametric analysis is also performed to investigate the effect of design variables on the optimized results.

Efficiency and cost optimization of offset Plate-Fin heat exchanger

Performance of rough surface hydrodynamic circular and multi-lobe journal bearings in turbulent regimes

ECDM is important micro-machining technique to machine mostly non-conductive material like glasses, ceramics, composites, and alloys. The process has tremendous potential to replace earlier existing expensive processes. In this paper, an attempt has been made to machine important N-BK7 glass material with ECDM drilling process, to investigate the process capabilities. The TM (Taguchi Method) has been used for modeling purpose, and multi optimization process is also done by TM (Taguchi method) incorporated along with weighted principal component (WPC). In this study the signal to noise ratio (S/N) has been used to find the relative contribution of main input parameter such as supplied voltage, electrolyte concentration, inter electrode gap and work piece thickness in controlling the machining performance, such as material removal rate (MRR) and tool wear rate (TWR) of ECDM drilling process, by

Experimental investigation of electrochemical discharge drilling (ECDM-D) performance characteristics for N-BK7 glass material

Nowadays, fine surface finish is in great demand for many industrial applications. An MFAF process is discussed in this conference paper to achieve a highly finished internal surface of non-ferromagnetic Alumina ceramic tube. Conventional grinding and polishing techniques cannot be used to finish this material because they cannot achieve high surface precision and produce surface defects like micro cracks. Therefore, MFAF process is an alternative method as it minimizes the surface damage because of flexible finishing conditions. MFAF Process, has flexible abrasive brush which can easily finish complex geometry of the workpiece. The workpiece material is alumina ceramic which is in higher demand as a substrate in electronic device applications. The MFAF process was done on alumina ceramic tube and 3 parameters, each having 2 different levels was selected. The parameters considered in the study are chuck speed (rpm), quantity of Diamond abrasive and iron power, machining time. 4 experiments with different levels of parameters were conducted. After finishing experiment using MINITAB 19 software, optimum parameters were determined. From the results of experiment, it was concluded that quantity of diamond abrasive and iron powder did not have much influence on finishing time compare to the chuck speed (r.p.m).

Magnetic field assisted finishing (MFAF) process for internal finishing of alumina ceramic tube


With evolution of various manufacturing technologies, thermal spray based approaches have captured a great importance in several large scale industries, for example, power plants, boilers, chemical industries and aviation etc. Thermal spray technologies are extensively employed in heavy duty as well as high temperature applications for developing corrosion and creep resistant coatings. They have become one of the critical industrial technologies of importance and they are yet to penetrate deeply into the markets of many developing nations like India. In this paper, a critical review on thermal spray technologies along with latest innovations and market potentials in the Indian market will be presented.

Face gear is an important mechanical tool that is utilized in various aerospace and automobile applications where high-power transmission is necessary. CAD modeling has made the designing part easier but the fabrication process is a challenge as there is a lot to stress generation on the face gear due to the conventional grinding process which causes unacceptable surface roughness values that can lead to cracking and failure of the machine. This report tends to follow the work of a research based on modeling and optimization of face gear grinding and improve the surface finish. Response surface methodology was used to perform the analysis and find the optimal points using Minitab, and the data is compared with the previous work done. The confidence level of the RSM model was 91.44%

Optimization of Process Parameters Using Response Surface Methodology to Improve Surface Finish in Face Gear Grinding

Use of heat transfer search algorithm while optimizing the process parameters of ECDM to machine quartz material

Cloud-Based Smart Manufacturing: Implementation in Food Industry

Ranking and Evaluation of Suppliers using AHP and TOPSIS in Calibration Laboratory

Improvement of the Machining Performance of the TW-ECDM Process Using Magnetohydrodynamics (MHD) on Quartz Material

Improving quartz micro-machining performance by magnetohydrodynamic and zinc-coated assisted traveling wire-electrochemical discharge machining process

Micro-Machining Characteristics of Quartz Using Travelling Wire-Electrochemical Discharge Machining (TW-ECDM) Process

Improving Quartz Micro-machining Performance By Magnetohydrodynamic And Zinc-coated Assisted Traveling wire-Electrochemical Discharge Machining Process

Design and Fabrication of Outboard Braking System for All Terrain Vehicle

Experimental Investigations of Electrochemical Deburring Process Parameters

Experimental Study on Conductivity versus Concentration of Electrolytes for Electrochemical Deburring Process

Experimental study of combined electrolytes for electrochemical deburring process

Electrochemical Deburring of Al6082 Using NaCl Electrolyte: An Exploratory Study

Traveling Wire Electrochemical Discharge Machining (TW-ECDM) of Quartz Using Zinc Coated Brass Wire: Investigations on Material Removal Rate and Kerf Width Characteristics

Investigation of Nickel Coated Tool for Electrochemical Deburring of Al6082

4th International Conference on Electrochemistry

Experimental Study of Compound Electrolytes for Electrochemical Deburring Process

Strategical parametric investigation on manufacturing of Al-Mg-Zn-Cu alloy surface composites using FSP

Effect of polygonal pin profiles on friction stir processed superplasticity of AA7075 alloy

Influence of Pin Profile on the Tool Plunge Stage in Friction Stir Processing of Al?Zn?Mg?Cu Alloy

Friction Stir Processing as a Novel Technique to Achieve Superplasticity in Aluminum Alloys: Process Variables, Variants, and Applications

CAVITATION IN FRICTION STIR PROCESSING OF AL-ZN-MG-CU ALLOY

EFFECT OF VELOCITY INDEX ON GRAIN SIZE OF S=FRICTION STIR PROCESS AL-ZN-MG-CU ALLOY

INFLUENCE OF FRICTION STIR PROCESS PARAMETERS ON SUPERPLASTICITY OF AL-ZN-MG-CU ALLOY

BURR FORMATION, CONTROL AND MEASUREMENT

FRICTION STIR PROCESSING AS A GRAIN REFIREMENT TECHNIQUE FOR ALUMINIUM 7075 ALLOY

Fabrication of Al7075 / B4C surface composite by Friction Stir Processing (FSP) and investigation on Hardness

FRICTION STIR PROCESSING AS A GRAIN REFINEMENT TECHNIQUE FOR ALUMINIUM 7075 ALLOY

Quality Modeling and Analysis of Electroplating System Using Graph Theory Matrix Approach

Optimum Selection and Ranking of Electroplating System Process Parameters: Taguchi-MADM Approach

Concurrent Design of Electroplating System for X- Abilities: A Graph Theoretic Approach

Attribute Based Design Specification, Comparison and Selection of Electroplating Waste Treatment System Using MADM Approach

Structural modeling and analysis of an effluent treatment process for electroplating ? A graph theoretic approach

Attribute based specification, comparison and selection of electroplating system using MADM approach

Structural modelling and analysis of electroplating system: a graph theoretic system approac

Application of carbon dioxide injection to enhance oil recovery is one of the best strategies to achieve a balance between energy security and reduced atmospheric emissions. Several injection schemes can be adopted to implement carbon dioxide-enhanced oil recovery. Injected CO2 mobilizes trapped oil by developing partial or full miscibility through diffusion, oil swelling, and viscosity reduction, but mechanisms such as viscous fingering, gravity override, and early gas breakthrough limit recovery efficiency. This study presents an experimental investigation of four different CO2 injection schemes, including continuous gas injection (CGI), cyclic injection (huff-n-puff), water-alternating-gas (WAG), and polymer-assisted WAG (PWAG) for enhancing oil recovery. Core was characterized for mineralogy, porosity, permeability, and wettability, while density, API gravity, rheology, and safety?critical properties of crude oil were evaluated. Core-flooding experiments were conducted at 70 ?C and 800 psi overburden pressure, beginning with secondary waterflooding followed by tertiary CO2-EOR. Waterflooding recovered 47.47 % of the original oil in place (OOIP), leaving 52.53 % OOIP as the target for EOR method. CGI, cyclic, WAG, and PWAG injection schemes using CO2 resulted in significant incremental recovery. The results highlighted the critical role of mobility control in improving sweep efficiency, incremental oil recovery, and effective CO2 utilization by establishing a clear performance hierarchy: PWAG > WAG > CGI > cyclic injection.

Experimental investigation of different CO2 injection schemes to enhance oil recovery in sandstone reservoir

Conventional drilling fluids are known to potentially damage the well's production zone. To mitigate the damage due to fluid invasion into the formation, cleaner drilling fluids with lower solids content have been developed using almond husk-derived cellulose nanoparticles (AH-CNPs) to use when drilling through production zones. These fluids not only offer effective fluid loss control but also maintain the rheological properties necessary to fulfil the traditional roles of conventional drilling fluids. In this work, cellulose nanoparticles were derived from almond husks by the solvothermal method. The synthesized nanoparticles were characterized with the help of various analytical methods such as Fourier transform infrared (FTIR) spectroscopy, particle size analyzer by dynamic light scattering (DLS) and scanning electron microscopy (SEM). The thermal degradation behavior of the nanoparticles was examined using thermogravimetric analysis (TGA). The drilling fluids were prepared using AH-CNPs, achieving a density of 8.7 ppg. Rheological properties were estimated using an Ofite 8-speed viscometer, and stable rheology was observed at 600 rpm of 60 cP with a 10% increase. The addition of AH-CNPs to 8.7 ppg water-based drilling fluid raises the yield point (YP) and gel strength from 30 to 33 lbs./100 ft2, indicating improved suspension and better rheological stability. Filtration tests were performed using the FANN filtration apparatus. A fluid loss of 18 ml was observed in reference drilling fluids, and a 10 ml fluid loss was observed within 30 min with AH-CNPs. These results demonstrate that AH-CNPs effectively mitigate fluid loss while enhancing rheological stability. The incorporation of AH-CNPs improves filtration control and maintains consistent flow behavior, thereby enabling the development of an environmentally sustainable drilling fluid system suitable for diverse operational conditions.

Synthesis, characterization and performance analysis of almond husk-derived cellulose nanoparticles as a novel additive for sustainable water-based drilling fluids

Hydrogen production from flare gas: a dual opportunity to turn emissions into energy - a prospective review

Smart Crosslinked Polymer Gels for Water Shutoff in Oil Reservoir: A Comprehensive Review on Materials, Mechanisms, and Field Applications

Synthesis and characterization of oxidized carbon black nanoparticles and its application in enhanced oil recovery as surfactant-assisted polymeric nanofluids

? A detailed overview of sector-wise initiatives and developments across refining and petrochemicals, fertilizers, steel, mobility, power & utilities, and emerging applications.
? Geographic distribution of projects and their progress through the phases from announced to active.
? Eight expert opinions covering the entire hydrogen ecosystem, including market transparency, inclusive market development, value chain optimization, strategic sectoral integration, innovation and R&D commercialization, investment mobilization, policy enablement, and implementation.

Green Hydrogen: Insights on Realizing its Inclusion and Impact in India

This study evaluates the oil recovery potential of sandstone reservoirs through sequential water-alternating CO? gas injection. Experimental work focuses on optimizing injection patterns, including alternating CO? and water cycles, to assess their impact on enhanced oil recovery (EOR). Analytical methods performed before detailed analyses include Fourier Transform Infrared (FTIR) Spectroscopy and X-ray diffraction (XRD) on the crude oil and core samples from the same field of Cambay Basin. FTIR analysis revealed a dominance of long-chain aliphatic hydrocarbons, suggesting favorable interactions with CO? for viscosity reduction and oil swelling. XRD revealed quartz-dominant cores with subordinate feldspar that control fluid permeability and chemical interactiveness. Sequential Water-Alternating-Gas (WAG) injection improved sweep efficiency by minimizing gas channeling and maximizing CO?-oil contact. Soak periods conducted during injection enhance some miscibility effects with addition of extra oil mobility. High permeability cores experienced early gas breakthroughs, while low permeability delayed gas movement, achieving better recovery. Reservoir heterogeneities and water-shielding effects were critical challenges, underscoring the role of petrophysical properties in recovery performance. The results demonstrate that CO? WAG injection in sandstone reservoirs significantly enhances crude oil recovery by improving oil displacement efficiency. The presented work emphasizes the parameters porosity, permeability, and oil saturation as prerequisites for the process. Collected sandstone core samples show that variations in these properties influence the effectiveness of CO? WAG injection showing the resultant oil recovery increase by 20?25% after secondary brine injection. The integration of advanced characterization and strategic injection patterns establishes a foundation for optimizing CO2 EOR in mature fields.

Experimental analysis of sequential water alternating CO? gas injection for enhancing oil recovery in X-field sandstone reservoir of Cambay basin

One of the major problems encountered during drilling deeper wells is the loss of critical properties such as rheology and fluid loss control. Effective water-based muds with potential additives can improve the effectiveness of drilling operations all over the world and are very successful at resolving such issues without damaging and penetrating the formation. Many materials have been studied to prove their economic aspects for the purpose of controlling filtration loss in drilling operation. The concentration of additive is a crucial variable in assessing the efficiency and effectiveness of drilling fluid. The use of coconut husk as an additive is a novel approach and can be economically favorable for sustainable drilling. It also aids in waste management. This paper focuses on the development of drilling fluid using coconut husk as a potential additive as well as determining its rheological properties along with filtration loss using specialized laboratory equipment such Fann VG viscometer and API filtration press respectively. Mud balance was used for determination of drilling fluid density. Moreover, different particle sizes (600-425 ?m, 425-300 ?m, 300-150 ?m and <150 ?m) and concentrations (1 g, 3 g, and 5 g) of coconut husk are taken into consideration and their properties are then compared with that of conventional drilling fluid to optimize the size and concentration of the husk. The experimental results illustrated the success of coconut husk biofiber in improving the rheological and fluid loss properties of the drilling fluids. Results show that coconut husk biofiber powder can be used as effective candidate for improvement of rheological properties and controlling filtrate loss of water-based drilling muds.

Investigation on the effect of coconut husk biofiber powder on the rheological and filtration properties of water-based drilling fluid

Numerical Investigations on Water-Alternating CO2 Injection for Enhanced Oil Recovery

AbstractIn hospitals, a medical computed tomography (CT) scan is used to detect damage to infected areas of the human body. Using this technology, scientists and engineers have found a way to detect the internal pore connections and characterize rock samples of oil and gas reservoirs in the petroleum industry. Nowadays, the micro?CT scan technique is gaining considerable interest in reservoir rock characterization and in situ monitoring of fluid flow through porous media during different flooding experiments. Along with this digital rock physics (DRP) idea, images have been used to accurately describe and model for simulations of rock samples. In this review, the application of micro?CT and medical?CT scanning in the oil and gas industry has been thoroughly discussed. Recent improvements in DRP and modern imaging techniques in the oil and gas industry have been modeled using both experimental and simulation work. The combination of a DRP study and a CT scan has also been discussed as a unique idea for the current scenario of research work in this field. The available literature shows that the modern imaging technique and the DRP concept can enable an understanding of the pore network model. It has also been observed that the visualization of fluid flow behavior through porous media is now possible during fluid movement through the core samples. This review contributes to the new research area and aids those in this field in quickly gaining an understanding of applied image techniques in the oil and gas industry.

A perspective review of applications of the computed tomography (CT) scan imaging technique for microscopic reservoir rock characterization

This study explores SMO chemiresistors for detecting C1?C4 alcohols, distinguishing C3 isomers and quantifying their mixtures. 1-Butanol shows the highest response at 300 ?C, while 1-propanol outperforms 2-propanol.

Unveiling the potential of a discrete titania chemiresistor: broad-spectrum sensing of C1?C4 alcohols and precise C3 isomer discrimination in binary mixtures

A key challenge in the oil industry that leads to a sharp decline in production is the generation of unwanted excess water. Several polymer gels obstruct the water?s route to mitigate this issue and lower the amount of water produced. In the current study, graphene oxide nanoparticles were synthesized and characterized using in preparation of polymer gels to improve the effectiveness. Several gel systems were prepared using partially hydrolyzed polyacrylamide (PHPA) in presence of organic crosslinkers such as hydroquinone (HQ) and hexamethylenetetramine (HMTA). Particle size distribution, X-ray diffraction (XRD), Raman spectroscopy and scanning electron microscope (SEM) analysis were performed to characterize the synthesized graphene oxide. Particle size distribution of graphene oxide reveled that nano-sized (219 nm) graphene oxide has been formed during synthesis. XRD, Raman spectroscopy and SEM analysis reveals the formation of crystalline structure of graphene oxide. The application of nanoparticles improves the gel framework?s resilience in environments with high salinity and high temperature reservoir. The polymer gel system was designed taking into account the variables like pH, chemical concentration, thermal stability, and brine composition. To determine the gelation time and type, the prepared polymer solution was placed in a stoppered test tube and heated to 90 ?C using a hot air oven. For the creation of various gel systems, PHPA and nanographene oxide concentrations were adjusted from 0.8 to 1.4 wt% and 0.01 to 0.15 wt% respectively. Gelation time was reduced from 98 h to 52 h while polymer concentration was increased from 0.8 wt% to 1.4 wt%. Similarly, gelation time was only reduced from 98 h to 90 h for change of nanographene concentration (0.01 to 0.15 wt%). To examine the impact on gelation time, the organic crosslinker concentration was varied from 6000 to 12000 ppm. It has been found that the gelation time was reduced from 69 h to 42 h for changing crosslinker concentration. A range of 1.4 to 8 wt% brine was selected to assess the effects of high salinity. With increase in salinity gelation time was reduced from 100 h to 60 h. Low gelation time was found at lower pH i.e., acidic region. It is clear that all these factors like concentrations of polymer, crosslinkers, graphene oxide nanoparticles, NaCl salt and pH influence the gelation time. Therefore, this work attempts to improve the gel performance by considering nanographene oxide for better efficiency in the water shutoff operation.

Gelation studies of nanographene oxide-augmented nanocomposite polymer gel systems for water shutoff technique in oil reservoirs

This study aims to investigate the potential of utilising natural polymer like cornstarch acting as fluid loss control agents in water-based drilling mud. The filtration and rheological properties of the water-based mud were analysed at 30?C to 80?C temperature range with 4,000 to 20,000 ppm of cornstarch concentration. Experimental results showed that the higher concentration of cornstarch offers better fluid loss control behaviour. Therefore, cornstarch has a high potential to be used as a fluid loss control agent in drilling mud. Filtration loss testing uses low-pressure low-temperature and high-pressure high-temperature equipment. Rheological tests such as viscosity was measured by Anton Paar viscometer devices while plastic viscosity and gel strength used the Fann VG Meter tool. The filtration loss, mud cake thickness and viscosity of corn starch-based mud were obtained as 10 ml/30 min, 0.8 mm and of 70 cP with higher effectiveness of the mud.

Study on the development of effective drilling fluid based on cornstarch

In this study, a TiO2/sodium dodecyl sulfate (SDS)/xanthan gum (XG) nanofluid is prepared to investigate its effectiveness in additional oil recovery. TiO2 nanoparticles were synthesized via the sol?gel method and subsequently calcined at 350 ?C. The crystalline nature and microstructural features of the synthesized TiO2 were characterized using X-ray diffraction, Fourier transform infrared, and scanning electron microscopy. The TiO2/SDS/XG nanofluid was prepared using a probe sonicator until a clear dispersion of the mixture was achieved. The particle size of the nanofluid was determined through a dynamic light scattering analyzer, revealing a size of approximately 12?18 nm. The prepared nanofluid altered the wettability toward water-wet and reduced the interfacial tension between crude oil and the nanofluid to 0.00771 mN/m, along with surface tension values dropping to 33.2 mN/m. Such changes led to a reduction in capillary force, which in turn facilitated enhanced oil recovery (EOR). An imbibition study was also conducted using collected sandstone cores to measure excess oil recovery in the presence of the prepared nanofluid at a temperature of 50 ?C and atmospheric pressure. The imbibition study showed that the nanofluid comprising 0.1% XG, 0.1% SDS, and 0.01% TiO2 recovered 28% of the original oil in place, which is 33.33% higher than the formulation with 0.1% XG and 0.1% SDS and 100% higher than the 0.1% XG alone. Hence, the proposed formulation shows promise as a potential candidate for chemical EOR methods.

TiO2/Sodium Dodecyl Sulfate/Xanthan Gum Nanofluid: A Promising Candidate for Enhanced Oil Recovery

Kalol has nine pay zones and is a multi-layered field in Gujarat, India. Oil and gas are found in pay zones II, III, and IV, while oil is found in pay zones V, VI + VII, VIII, IX + X, XI, and XII. Water injection is being done through 104 wells at 2119 m3/d in pay zones K-III, V, VI+VII, IX+X, and XII. Horizontal, multi-lateral, and high-angle wells, microbial enhanced oil recovery technologies, PDB technology, hydrofracturing, stimulations of tight sandstone layer, and increasing the quantity and quality of water injection for effective pressure maintenance and improving the recovery factor are some of the technologies that have been tried to maintain the oil production rate from this Brownfield. This research has examined novel ways for developing the field to maintain and improve production levels in order to maximize the recovery from the field with minimal capital and operating expenditure.

A Case Study on Reservoir Management and Performance Prediction of an Indian Oilfield for Enhanced Oil Recovery

TiO2/Sodium Dodecyl Sulfate/Xanthan Gum Nanofluid: A Promising Candidate for Enhanced Oil RecoveryClick to copy article link

The motivation behind the advancement of novel technologies for enhanced oil recovery (EOR) originates from the increasing energy demand and imperative to explore new oil reserves. Novel approaches, including the injection of carbon dioxide (CO2) and sequestration of CO2 in oil reservoirs, have emerged as potential remedies. In response to the rising apprehensions regarding global warming attributed to anthropogenic activities and the consequent rise in CO2 emissions, scholars are directing their attention toward the utilization of CO2 sequestration methods for EOR. This methodology presents an achievable strategy to reduce anthropogenic CO2 emissions and diminish their influence on the atmosphere. The implementation of CO2 sequestration in EOR operations has the potential to yield the dual benefits of cost reduction and additional oil recovery. The paper presents a study that investigates distinct scenarios of CO2 injection and sequestration in various formations. The study incorporates conventional methodologies, such as gas injection, for the purpose of EOR. It explores diverse fields of CO2 injection and sequestration, deliberates on the decision-making process concerning injection strategies, and employs dynamic as well as static models to generate long-term production profiles, evaluate production profiles, and manage the process. The paper explores the challenges encountered by various industries in the process of capturing, transporting, and storing of CO2 in geological formations. Additionally, the study assesses the geological potential and practicality of implementing this methodology in India. In brief, this study presents a thorough examination of Indian scenarios for CO2 injection and sequestration utilized in EOR techniques.

Perspectives of CO2 Injection Strategies for Enhanced Oil Recovery and Storage in Indian Oilfields

Fluctuating oil prices have profound implications on the economies
of oil-exporting nations and the global economy. A historic oil price crash from
$18 to ?$38/barrel occurred in April 2020 during the global COVID-19
pandemic and negative oil prices were observed. This study explores the
drivers of oil price fluctuations including geopolitical factors, environmental
concerns, clean energy initiatives, and surplus oil supply. It also delves into the
transformative influence of the COVID-19 crisis on oil prices and the global
economy. The study emphasises the future of oil prices and industry efforts to
recover the oil market while offering insights into potential long-term solutions
for stable petroleum prices. This research sheds light on the significant impact
of COVID-19 on the energy market and various aspects of daily life, paving the
way for discussions on remedial actions to stabilise oil prices and the world
economy in the context of the COVID-19 pandemic.

Effect of COVID-19 crisis on crude oil price and the world economy ? a state-of-art review

Enhanced oil recovery by CO2 injection technology (CO2-EOR) plays a crucial role in enhancing oil production and the permanent sequestration of anthropogenic CO2 in depleted oil reservoirs. However, the availability of CO2 in oil field locations and its mobility in contrast with reservoir fluids are prime challenges in CO2-EOR. The cost of CO2 and its availability at the oil fields has prompted investigations on efficient injection of CO2 at the fields to achieve the best sweep efficiency possible. Injection strategies such as water-alternating-gas (WAG), simultaneous vertical and horizontal WAG, simultaneous water injection into the aquifer and vertical WAG, water and gas injection simultaneously but separately (SS-WAG), and water and gas injection simultaneously but not separately (SNS-WAG) play a significant role, as well as the purity of CO2. In this work, the significance of the above criteria was investigated individually and in combination. The coupled sequence of injection rate, soaking time, WAG ratio, and purity of injected CO2 for enhancement of oil production were delineated. A realistic reservoir simulation model conceptualizing the CO2-EOR system with five spot injection patterns was developed by the company CMG. The history-matched model that was developed was used to investigate the sensitivity of the coupled effects to the criteria listed above on oil recovery. Numerical investigations quantitatively emphasized that purity and soaking time of CO2 have an inverse effect in the oil production rate and that SNS-WAG resulted in a better oil production rate than SS-WAG.

Comparative studies on numerical sensitivity of different scenarios of enhanced oil recovery by water-alternating-gas (CO2) injection

The world is combating climate change by adopting a low-carbon energy mix and pursuing a net-zero target. Being the third-largest CO2 emitter, net-zero target for India is crucial for global decarbonization. Hydrogen is a promising fuel for the future to aid in this transition, but its storage is a significant challenge. Surface-based storage options are inadequate and expensive, making underground hydrogen storage (UHS) in geological formations a more practical solution for its ample storage capacity and economic feasibility. Herein, this review critically investigates (i) the possibility of surplus renewable energy storage in India, (ii) UHS?s applicability to store this surplus energy, and (iii) scientific aspects, feasibility, and challenges with suitable geological storage locations for storage of hydrogen in India. This review starts with briefing ?net-zero? at both the global and national (India) levels. The urgency for green energy is highlighted by the disparity between the Human Development Index and ecological footprint. The Indian hydrogen market and the production of green hydrogen from renewable resources are also discussed. The review discusses UHS storage types, reservoir and fluid parameters, and flow behavior, including physiochemical, geochemical, and microbial interactions. Based on the geological basins and formation, availability and capacity of renewable energy sources, the potential of UHS in India is analysed. This review also provides vital information including technical data, regulations, initiatives, and policies needed to explore the opportunity of UHS and its development. Finally, the article presents challenges and opportunities for UHS development and emphasizes the need for continued research and development.

Underground hydrogen storage and its roadmap and feasibility in India toward Net-Zero target for global decarbonization

India is currently producing crude oil from matured fields because of insufficient discoveries of new fields. Therefore, in order to control the energy crisis in India, enhanced oil recovery (EOR) techniques are required to reduce the import of crude from the OPEC (Organization of the Petroleum Exporting Countries). This review mentions chemical EOR techniques (polymers, surfactants, alkali, nanoparticles, and combined alkali-surfactant-polymer flooding) and operations in India. Chemical EOR methods are one of the most efficient methods for oil displacement. The efficiency is enhanced by interfacial tension (IFT) reduction using surfactants and alkali, and mobility control of injected water is done by adding a polymer to increase the volumetric sweep efficiency. This paper also reviews the current trend of chemical EOR, prospects of chemical EOR in Indian oilfields, the development of chemical EOR in India with their challenges raising with economics, and screening criteria for chemical EOR implementation on the field scale. Furthermore, the review gives a brief idea about chemical EOR implementation in Indian oilfields in future prospects to increase the additional oil recovery from existing depleted fields to reduce the import of crude oil. The outcome of this review depicts all chemical EOR operations and recovery rates both at the laboratory scale and field scale around the country. The additional recovery rates are compared from various chemical EOR methods like conventional chemical flooding methods and conventional chemicals combined with nanoparticles on a laboratory scale. The development of chemical EOR in the past few decades and the EOR policy given by the government of India has been mentioned in this review. The analysis provides an idea about enhanced recovery screening and implementation of chemical EOR methods in existing fields will significantly reduce the energy crisis in India. ? 2023 The Authors

An overview of chemical enhanced oil recovery and its status in India

Comparative studies on numerical sensitivity of different scenarios of enhanced oil recovery by water-alternating-gas (CO2) Injection

Global demand for energy is increasing day by day rapidly as a result of the population, industrial, and economic growth of developing countries, especially in emerging market economies. Hence, to meet this growing and continuing global crude oil demand, innovative enhanced oil recovery (EOR) techniques are required to emerge, and currently, implemented techniques need to be revisited and optimized to recover more residual oil trapped in the reservoirs. Among different chemicals, foam has good mobility control in the oil recovery method. Nowadays, the study on the stability of foam is an utmost interest for petroleum engineers to find a way to obtain stable foam for application in EOR. In this review, the basics of foam and its preparations, properties, application, and some special applications mainly in the oil industry have been depicted to convey an idea of the degree of foam research in EOR. Basically, this review will provide a detailed discussion on the applications of foam in oil recovery and some short ideas that can be applied in the near future for nanoparticle (NP)-assisted stable CO2 foam flooding in EOR. Increasing the NP concentration initially improved foam stability because a higher number of NPs participated in strengthening the gas bubble and initially increased oil recovery as a result of the formation of more stable foam. The polymer-coated, nanoclay-surfactant-stabilized foam and surface-modified silica nanoparticles have exceptional foaming ability and foam stability at high temperatures, making them suitable for the production from heavy oil reservoirs. Some other nanoparticles, like Al2O3, ZrO2, TiO2, Fe2O3, and NiO, have also been used for enhancing foam stability by preventing liquid drainage from lamella. Also, NP-stabilized CO2 foam flooding can enhance oil recovery by 10-15% after secondary recovery by creating a more homogeneous gas front for better mobility control. ? 2023 American Chemical Society.

Perspectives of Foam Generation Techniques and Future Directions of Nanoparticle-Stabilized CO2 Foam for Enhanced Oil Recovery

The globally increasing demand for crude oil has prompted the oil industry to enhance oil production from primary, secondary, and tertiary recovery methods to fulfill the global requirement. Therefore, enhanced oil recovery (EOR) is expected to increase its importance in the coming years. Because of limited efficiency, applicability, and higher gas and thermal injection costs, chemical EOR processes have been considered as an alternative in some special cases. Chemical EOR processes are accepted widely because of their higher efficiency, economic and technical feasibility, and optimum capital cost. Therefore, research of novel chemical compounds is immense in the oil industry. Research of nanotechnology in the petroleum industry has attracted attention among all novel chemical compounds in the past few years. This work focuses on the review of previous research regarding the current scenario of polymer flooding, challenges of polymer flooding, polymeric nanofluid in EOR, recent developments in polymer flooding, characteristics of polymeric nanofluids, prospects, and economic feasibility for polymeric nanofluids? applications in EOR. This paper also reviews the characterization of polymeric nanofluids such as rheology, stability, wettability alteration, interfacial tension reduction, and absorption control. It has been found from a literature review that polymeric nanofluids are more effective in oil recovery considering viscosity modification, stability enhancement, and chemical adsorption control. Oil recovery can be additionally increased by up to 10-15% original-oil-in-place applying polymeric nanofluids. ? 2023 American Chemical Society.

A Perspective Review on the Current Status and Development of Polymer Flooding in Enhanced Oil Recovery Using Polymeric Nanofluids

Paradigm shift towards the sustainability in upstream oil industry for enhanced recovery - A state-of-art review

A review on advanced nanoparticle-induced polymer flooding for enhanced oil recovery

Effects of nanoparticles, polymer and accelerator concentrations, and salinity on gelation behavior of polymer gel systems for water shut-off jobs in oil reservoirs

Multiphysics gas transport in nanoporous unconventional reservoirs: Challenges of mathematical modelling

A critical review on selection of microemulsions or nanoemulsions for enhanced oil recovery

Potential applications of nanomaterials in oil and gas well cementing: Current status, challenges and prospects

Investigations on the relationship among the porosity, permeability and pore throat size of transition zone samples in carbonate reservoirs using multiple regression analysis, artificial neural network and adaptive neuro-fuzzy interface system

Recent progresses of microemulsions-based nanofluids as a potential tool for enhanced oil recovery

A review on modern imaging techniques for characterization of nanoporous unconventional reservoirs: Challenges and prospects

Impact of newly implemented enhanced oil and gas recovery screening policy on current oil production and future energy supply in India

Wettability Alteration of the Oil-Wet Carbonate by Viscosity-Augmented Guar Galactomannan for Enhanced Oil Recovery

A review on pore-scale modeling and CT scan technique to characterize the trapped carbon dioxide in impermeable reservoir rocks during sequestration

New insight of correlation between digital rock analysis and petrographic study for rock type classification within carbonate reservoir transition zone

Mechanistic study on silica nanoparticles-assisted guar gum polymer flooding for enhanced oil recovery in sandstone reservoirs

Experimental and simulation studies for optimization ofwaterealternating-gas (CO2)flooding for enhanced oil recovery

Investigations on Geological Aspects of Capillary Transition Zones of Carbonate Reservoirs based on Applied Imaging Techniques

Recent Advances in Ionic Liquids as Alternative to Surfactants/Chemicals for Application in Upstream Oil Industry

A review of CO2 storage in geological formations emphasizing modeling, monitoring and capacity estimation approaches

Effect of Native and Injected Nano-Particles on the Efficiency of Heavy Oil Recovery by Radio Frequency Electromagnetic Heating

Relative Permeability of Foamy Oil for Different Types of Dissolved Gases

Status of Electromagnetic Heating for Enhanced Heavy Oil /Bitumen Recovery and Future Prospects: A Review

Screening of Microemulsion Properties for Application in Enhanced Oil Recovery

Interfacial Tension and Phase Behavior of Surfactant-Brine-Oil System

This study investigated the efficacy of various deep learning models in performing sentiment analysis on code-mixed Hinglish text, a hybrid language widely used in digital communication. Hinglish presents unique challenges due to its informal nature, frequent code-switching, and complex linguistic structure. This research leverages datasets from the SemEval-2020 Task 9 competition and employs models such as RNN (LSTM), BERT-LSTM, CNN, and a proposed Hybrid LSTM-GRU with a 1D CNN model. Combining the strengths of LSTM and GRU units along with 1D CNN, demonstrated superior performance with an accuracy of 93.21%, precision of 93.57%, and recall of 93.02%, along with Sensitivity and Specificity of 93.62 and 93.24% respectively. It also achieved an F1 Score of 93.44%. We also evaluated the model on some other parameters, such as PPV, PNV, RPV, and RNV. This model outperformed existing approaches, including the HF-CSA model from the SemEval-2020 dataset, which achieved an accuracy of 76.18%.

Hinglish Sentiment Analysis Using LSTM-GRU with 1D CNN

Potato is one of the most extensively cultivated crops in India as well as worldwide andserves as a staple food in many regions. Due to its agricultural and economic importance, effectivedisease management is essential to ensure healthy crop yields. Traditional methods of disease detectionrely on manual visual inspection by farmers or agricultural experts, which often lack precision and areprone to misdiagnosis, leading to substantial crop losses. This study proposes a deep learning-basedapproach for automated disease detection in potato plants using a Convolutional Neural Network(CNN). The novelty of this work lies in developing a two-stage CNN framework that first classifiesthe potato leaf images as either healthy or unhealthy. Next, the unhealthy leaf images are furtherclassified as either early blight or late blight diseases. The proposed CNN models are optimized usingthe Adam optimizer with a learning rate of 0.0001. Extensive experimentation on a dataset of 1,500images demonstrates high classification accuracies of 98.3% and 99% for the two stages, respectively.The results demonstrate that our proposed CNN models are highly effective for automated diseasedetection and improve decision-making in potato crops.

ENHANCING POTATO CROP HEALTH MONITORING USING TWO STAGE CNN BASED DISEASE CLASSIFICATION MODELS

Phishing attacks are among the most sophisticated threats that have endangered legitimate web resource owners and internet users. In this paper, we propose a URL phishing detection system that employs multiple classification algorithms based on the lexical features of URLs. The proposed model achieved an accuracy rate of 98.8%, outperforming standard algorithms and other techniques reported in the literature on the same dataset. Furthermore, the proposed stacking classifier achieved the highest performance among all compared models, with an accuracy of 97.19%, an f-score of 0.972, an area under the receiver operating characteristic curve (AUC) of 0.95, a TPR of 0.92, an FPR of 0.031, and a MCC-value (Matthews Correlation Coefficient) of 0.843.

Efficient and Robust Phishing URL Detection based on Lexical Feature Extraction and Ensemble Learning

Identifying and recognizing visually similar vegetables in a cluttered environment, such as a market, is a challenging task. We propose an efficient vegetable detection and recognition system that can effectively detect vegetables, specifically Gourd species and cucumber, by drawing bounding boxes around them and successfully recognizing the vegetable species. This system uses YOLO (You Only Look Once) for both vegetable detection and identification. The system is trained on a curated dataset containing images of these vegetables, with annotated bounding boxes for effective detection and classification even in a cluttered environment where multiple vegetables are present. We include an interactive user interface using Streamlit, through which users can upload images to detect and recognize the vegetables instantly.

Design and Development of Vegetable Detection and Recognition Model Using Deep Learning in Market Environment

Lung cancer remains a leading cause of cancer-related mortality globally, primarily
due to late-stage detection. To significantly enhance diagnostic precision and patient
outcomes, this paper presents a novel Convolutional Neural Network (CNN) model
for the early classification of lung cancer from CT images. The novelty of this work
is involved in meticulously crafting the CNN architecture by integrating state-of-theart
deep learning techniques to differentiate between benign, malignant, and normal
lung tissues with high precision. The publicly available dataset from Iraq-Oncology
Teaching Hospital is used for the experiments. The Synthetic Minority Over-sampling
Technique (SMOTE) is used to address the issue of class imbalance. Despite
challenges like class imbalance and computational constraints, our model demonstrates
robust generalizability and clinical potential. Extensive results show that
the proposed model outperforms the traditional diagnostic methods to achieve an
accuracy of 99.27%, precision of 99.44%, recall of 98.56%, and F1 score of 99.00%.
This work establishes a new benchmark for lung cancer screening and emphasizes
the need to utilize cutting-edge machine-learning algorithms in medical imaging to
enable earlier interventions that lead to better patient outcomes.

Enhancing Clinical Outcomes Using Deep Learning Solution for Accurate Lung Cancer Classification

Monkeypox, a zoonotic viral infection, has emerged as a global health concern. Early and accurate diagnosis is crucial for effective containment. This research investigates the application of Convolutional Neural Networks (CNNs) for the classification of monkeypox skin lesions from other pox types and normal skin. Leveraging a comprehensive dataset of skin images, we explored various CNN architectures, including pre-trained models and custom designs. Data augmentation techniques were employed to enhance model robustness. The performance of the models was evaluated using metrics such as accuracy, precision, recall, and F1-score. Our findings demonstrate the potential of CNNs as a valuable tool for the rapid and accurate detection of monkeypox, aiding in public health efforts to control the spread of this disease.

A Hyper-tuned Metrics based Augmented CNN Model for Accurate Prediction of Monkeypox Risk

The Flying ad-hoc network uses wireless communication for establishing the infrastructure-less, temporary network among the Unmanned Aerial Vehicles (UAV). The dynamic formation of the FANET communication network necessitates an efficient routing strategy to manage the routing requirements in real-time. The data transmission between two UAVs may involve one or more intermediate relay devices to efficiently disseminate the data from the sender to the receiver. The reliability of the communication channel between two UAVs mainly depends on the trustworthiness of the selected intermediate relay device. This work presented an ANOVA-based analysis approach for improved routing in FANET. The efficacy of the various routing schemes is demonstrated by simulation experiments utilizing the network simulator (NS-2) under low-density and high-density traffic scenarios. The simulation results exhibit the comparative performance of various routing protocols in different network scenarios. Moreover, experimental outcomes demonstrate that the proposed ANOVA-based analysis method helps to provide effective routing schemes for the efficient communication process.

An ANOVA Based Analysis of Routing Techniques for Improving QoS in Flying Ad-Hoc Networks (FANETs)

Pneumonia is a common respiratory illness that requires prompt and precise diagnosis to provide appropriate treatment. By concentrating on the interpretability of AI-driven medical diagnoses, this research aims to solve this important problem. The primary aim is to disentangle the decision-making mechanisms of intricate artificial intelligence systems that detect pneumonia. Explainable AI models will be strategically implemented to do this, focusing on Local Interpretable Model-Agnostic Explanations (LIME). LIME is selected because of its capacity to approximate complex model?s behavior in a way that is comprehensible locally, thereby offering clear insights into their predictions. The study entails a thorough assessment and comparison of several AI models, focusing on their interpretability as much as their diagnostic accuracy. By doing this, the project hopes to close the gap between the necessity for healthcare practitioners to understand and have faith in the decision-making processes of AI models and the models? potent predictive powers. The anticipated results include improving pneumonia detection techniques, guaranteeing that AI models perform accurately and deliver comprehensible and reliable insights.

Enhancing Pneumonia Detection Transparency: Exploring Explainable AI Model

A smart shopping trolley incorporates technology to improve the shopping experience by providing features such as automatic checkout, item scanning, tailored suggestions, and budget tracking, boosting efficiency and convenience while optimizing purchases. This paper presents a prototype for an intelligent trolley system that follows a human being and does automatic billing. Human followers leverage HC05 Bluetooth, HMC5883L Compass, and GPS Neo 6M. Automatic billing leverages RFID tags for the unique identification of the product. The absence of GPS sensors led to the adoption of a sonar sensor for human followers. The article unfolds innovative solutions and insights gained from navigating IoT complexities, costs, and environmental factors. The paper envisions a future where machine learning and AI optimize the shopping journey, contributing to the continuous evolution of the retail narrative.

Intelligent Trolley System: Revolutionizing Retail Shopping

IoT-assisted mushroom farming in an agile environment is a modern approach to mushroom cultivation that utilizes IoT technology to optimize the growing conditions in real time. This approach involves IoT sensors to monitor environmental parameters such as temperature and humidity inside a cultivation chamber. The data collected from these sensors is then analyzed using data analytics software, which provides real-time feedback to the farmer and enables them to make adjustments as needed. Additionally, an automated humidification system and an air exchange system can be controlled by IoT sensors to optimize the growing conditions for the mushrooms. IoT technology in mushroom cultivation offers several benefits, including increased efficiency, reduced labor costs, and higher yields. However, this approach also comes with some challenges, such as the need for technical expertise and a significant initial investment in technology and equipment. Overall, IoT-assisted mushroom farming in an agile environment is a promising approach that can help meet the growing demand for sustainable and efficient farming practices.

IoT-Assisted Mushroom Cultivation in Agile Environment

The expressions on the face of humans are crucial in identifying a person's current state of mind. It helps in identifying and recognizing the emotion that a person is feeling based on characteristics of the body like the eyes, cheeks, forehead, or even the curve of the smile. These factors are analyzed and used to detect the facial expression of any person which is mapped to the related and suitable emotion. The focus of this project system is to develop novel techniques that can detect human emotions. Based on emotion an appropriate playlist of music is recommended. Therefore, creating a recommendation system, helps users to select music that relates to their current mood. The system uses the Keras library which is a deep learning API that helps to design deep learning models. Facial expressions are captured through a webcam and such expressions are fed into a deep learning algorithm that gives 91.25% accuracy of an image captured from a webcam in real-time and helps to predict the most probable emotion. Once the emotion is detected by a neural network then the emotion is processed and an appropriate playlist of songs will be recommended redirecting the user to YouTube saving a bunch of time for the user.

Emotion-based Music Recommendation System using a Novel Neural Network

The medical domain has seen an increased use of image steganography and encryption for securing medical pictures and data. Image steganography is the procedure of hiding a text (message) within a picture, while encryption of a message would be the state of transforming a readable message into an unreadable format. These two methods can be used together to provide a higher level of security for medical images and data. In this paper, The proper system for securing medical images and data using image steganography and encryption has been proposed. The proposed approach uses the Advanced Encryption Standard (AES) for encryption and decryption. Subsequently, the encrypted data is then embedded into a cover image using a steganographic technique, Linked Pixel pixel steganography. The methodology is tested on large data-sets that include various medical images and are evaluated on various performance parameters. The observations demonstrate that the suggested approach allows secure information transmission even though it is prone to threats.

Enhancing Medical Domain Data Security using Inbuilt Data Encryption and Steganography

The medical domain has seen an increased use of image steganography and encryption for securing medical pictures and data. Image steganography is the procedure of hiding a text (message) within a picture, while encryption of a message would be a state for migrating (transforming) a readable message into an unreadable format. These two methods can be used together to provide a higher level of security for medical images and data. This paper proposes a system for securing medical images and data using image steganography and 
encryption. The proposed approach uses Advanced Encryption Standard (AES) for encryption and decryption. Subsequently, the encrypted data is then embedded into a cover image using a steganographic technique, Linked Pixel Steganography. The methodology is tested on large datasets which include various medical images and evaluated on various performance parameters. The observations demonstrate that the suggested approach allows secure information transmission even though it is prone to threats.

Enhancing Medical Domain Data Security using Inbuilt Data Encryption Steganography

Cube Sampled K-Prototype Clustering for Featured Data

Probabilistically sampled and spectrally clustered plant species using phenotypic characteristics

Vector Quantized Spectral Clustering Applied to Whole Genome Sequences of Plants

Scaled and Projected Spectral Clustering with Vector Quantization for Handling Big Data

Density-wise two stage mammogram classification using texture exploiting descriptors

Efficient and effective security model for database specially designed to avoid internal threats

Two novel triazine-based scaffolds, 9a and 9b, were synthesized through sequential substitution on cyanuric chloride. The resulting compounds were characterized using 1H NMR, 13C NMR, IR, UV-Vis, and Mass spectrometry. Subsequently, target prediction was performed to identify their potential biological targets using the SwissTargetPrediction tool. To explore the mechanism of action of these synthesized compounds and identify the putative drug target, molecular docking calculations were performed on the seven shortlisted druggable targets. Based on docking studies, mammalian target of rapamycin (mTOR) was selected as the target for the synthesized molecules 9a and 9b with docking scores of ?7.367?kcal/mol and ?9.022?kcal/mol, respectively. Extensive molecular dynamics simulations were conducted on the docked complexes to understand the dynamic behaviour. Moreover, molecules, 9a and 9b were also subjected to density functional theory (DFT) calculations to analyse the Molecular electrostatic potential and study the electronic density distribution over the scaffolds of these molecules. Subsequent biological evaluation in MCF-7 (cancerous), HeLa (cervical cancer cells), HepG2 (liver cancer cells) and WS1 (non-cancerous) cell lines were performed. Collectively across all studies, it was concluded that 9b is a promising antiproliferative agent, with an IC50 of 90??M against all three cancer cell lines. Furthermore, compound 9b exhibited little to no cytotoxicity towards the WS1 cell line, indicating an acceptable safety profile and selective cytotoxicity against cancer cells. Hence, these findings highlight the potential of 9b as a promising anticancer lead candidate for further structural optimisation and therapeutic development.

Integrated synthesis, molecular mechanics, quantum mechanics and in vitro studies of triazine derivatives as anticancer compounds

While pharmaceutically active flavonoid-based sulfonamide compounds have been reported, their crystal structures and supramolecular features remain unexplored. Here, we synthesized two N-(4-oxo-2-phenyl-4H-chromen-7-yl)benzenesulfonamide derivatives and subsequently determined their crystal structures using single crystal X-ray diffraction. Although both compounds crystallize in the monoclinic system, M1 exists as a monomer in space group P21/n, whereas M2 forms a homodimer in space group P21/c. The molecular packing of both compounds is stabilized by intermolecular Nsingle bondH?O hydrogen-bonding interactions between the sulfonamide NH donor and the carbonyl oxygen acceptor. Hirshfeld surface analysis and two-dimensional fingerprint plots indicate that the crystal packing of all molecules is predominantly governed by H?H, O?H, and C?H contacts. The supramolecular association of M1, M2A and M2B is further reinforced by ?-? stacking interactions, as evidenced by the corresponding shape index and curvedness analyses. Finally, we carried out molecular docking and molecular dynamics simulation studies against Keap1 protein to rationalize the binding affinity of title scaffold.

Synthesis, structural analysis and in silico Keap1-Nrf2 protein-protein interaction inhibition studies of N-(4-oxo-2-phenyl-4H-chromen-7-yl)benzenesulfonamide derivatives

Mycobacterium tuberculosis (Mtb) remains a major global health threat, intensified by multidrug-resistant (MDR) and extensively drug-resistant (XDR) strains. We synthesized a series of N-benzoylarylthiourea derivatives (IITKDA1?20) as hybrids of isoniazid/ pyrazinamide and ethionamide to explore their antimycobacterial potential. Our evaluation of synthesized library members for antimycobacterial activity has identified IITKDA10 (N-benzoyl-arylthiourea possessing p-(N-Boc)-thionamide) as the maximally effective inhibitor of Mtb (1 ?g/mL MIC). Further, the physicochemical properties indicated a trend of high topological polar surface area (tPSA) and partition coefficient (ClogP) in the range of 3?4 was optimal for the compounds to be active against Mtb. Molecular docking of IITKDA10 into the InhA (enoyl-[acyl-carrier-protein] reductase) active site revealed strong binding (?9.63 kcal/mol), stabilized by hydrogen bonds and ?alkyl interactions. Further, crystal packing analysis indicated that hydrogen bonding networks guided supramolecular architecture, and structural planarity (e.g., IITKDA4, IITKDA8) correlated with higher activity. In contrast, twisted or L-shaped conformations (IITKDA2, IITKDA5) showed reduced potency. This study presents a structurally and functionally diverse set of N-benzoylarylthioureas with promising anti-TB activity, supported by structure?activity relationships, docking, and crystallographic insights. 

N-(Arylcarbamothioyl)benzamide Derivatives as Selective Antimycobacterial Agents and Their Supramolecular Structural Features

Leveraging the versatile redox behavior of transition metal complexes with heterocyclic ligands offers significant potential for discovering new anticancer therapeutics. This study presents a systematic investigation of a pyridinecarboxaldimine ligand (PyIm) with late 3d-transition metals inhibiting cancer cell proliferation and the mechanism of action. Synthesis and thorough characterization of authentic metal complexes of redox-active late 3d-transition metals enabled the validation of antiproliferative activity in liver cancer cells. Notably, (PyIm)2Mn(II) (1) and (PyIm)2Cu(II) (5) complexes exhibited a good inhibitory profile against liver cancer cells (EC50: 4.0 ?M for 1 and 1.7 ?M for 5) with excellent selectivity over normal kidney cells (Selectivity index, SI = 17 for 5). Subsequently, evaluation of these complexes in cancers cell lines from four different sites of origin (liver, breast, blood, and bone) demonstrated a predominant selectivity to liver and a moderate selectivity to breast cancer and leukemia cells over the normal kidney cells. The mechanism of action studies highlighted no expected DNA damage in cells, rather, the enhancement of extracellular and intracellular reactive oxygen species (ROS) resulting in mitochondrial damage leading to oxidative cell death in cancer cells. Notably, these complexes potentiated the antiproliferative effect of commercially used cancer therapeutics (cisplatin, oxaliplatin, doxorubicin, and dasatinib) in liver cancer cells. These findings position redox-active metal complexes for further evaluation as promising candidates for developing anticancer therapeutics and combination therapies.

Copper and Manganese Complexes of Pyridinecarboxaldimine Induce Oxidative Cell Death in Cancer Cells

In this work, we have studied the ability of urease immobilized on glutaraldehyde crosslinked chitosan coated magnetic iron oxide nanoparticles (Urease/GA/CS/MIONPs), for the hitherto unreported comparative hydrolysis of urea in synthetic (SUr) and real human urine (HUr). The prepared Urease/GA/CS/MIONPs were characterized by a combination of Fourier transform infrared spectroscopy (FTIR), field emission-scanning-electron-microscopy (FESEM), energy dispersive X-ray spectroscopy (EDX) and dynamic light scattering (DLS). The nanoconstructs display the highest ammonium ion liberation post-urea hydrolysis in 1/20 or 1/24-fold dilutions of SUr and HUr, respectively. The optimum activity of immobilized urease is observed at pH 7, and the nanoconstructs facilitate efficient urea-hydrolysis till at least 45 ?C. Kinetic analysis of the immobilized urease shows km and vmax of 14.81 mM, 12.36 mM, and 18.55 ?M min?1 and 10.10 ?M min?1, towards SUr and HUr, respectively. The magnetization of the immobilized urease is suitable for reuse across multiple cycles of urea hydrolysis in SUr and HUr. The robust performance of Urease/GA/CS/MIONPs in SUr and HUr is promising for generating ammonium as a useable source of nitrogen from human urine, and underscores the suitability of SUr as a urine mimic for such interventions.

Ammonium release in synthetic and human urine by a urease immobilized nanoconstruct

Inhibitors of carbonic anhydrase (CAIs) hold promise for addressing various diseases, including cancer, diabetes, and other metabolic syndromes. CAV is the only isoform present in the mitochondria and is considered a potential drug target for obesity. In this work, we have developed C2, and C4 substituted oxazole-5(4H)-one derivatives as a new scaffold for the selective inhibition of human carbonic anhydrase VA (hCAVA). Synthesized compounds were characterized by 1H NMR, 13C NMR, and LC-MS mass spectrometry and subsequently evaluated for in vitro hCAVA inhibition. Two compounds, 4 and 5 showed a considerably higher binding affinity for hCAVA in comparison to the hCAII. Further, cell-based studies showed that these compounds decrease the expression of CAVA and GLUT4 in adipocytes and non-toxic to HEK293 cells. The present work opens a platform for the use of oxazole-5(4H)-ones and holds promise for further refinement of potent and selective hCAVA inhibitors.

Design and development of 5-(4H)-oxazolones as potential inhibitors of human carbonic anhydrase VA: towards therapeutic management of diabetes and obesity

Phase-separated membraneless organelles or biomolecular condensates play diverse functions in cells, however recapturing their characteristics using small organic molecules has been a challenge. In the present study, cell-lysate-based screening of 843 self-assembling small molecules led to the discovery of a simple organic molecule, named huezole, that forms liquid droplets to selectively sequester tubulin. Remarkably, this small molecule enters cultured human cells and prevents cell mitosis by forming tubulin-concentrating condensates in cells. The present study demonstrates the feasibility of producing a synthetic condensate out of non-peptidic small molecules for exogenous control of cellular processes. The modular structure of huezole provides a framework for designing a class of organelle-emulating small molecules.

Discovery of a phase-separating small molecule that selectively sequesters tubulin in cells

Hybrid antimicrobials that combine the effect of two or more agents represent a promising antibacterial therapeutic strategy. In this work, we have synthesized N-(4-(4-(methylsulfonyl)phenyl)-5-phenylthiazol-2-yl)benzenesulfonamide derivatives that combine thiazole and sulfonamide, groups with known antibacterial activity. These molecules are investigated for their antibacterial activity, in isolation and in complex with the cell-penetrating peptide octaarginine. Several of the synthesized compounds display potent antibacterial activity against both Gram-negative and Gram-positive bacteria. Compounds with 4-tert-butyl and 4-isopropyl substitutions exhibit attractive antibacterial activity against multiple strains. The isopropyl substituted derivative displays low MIC of 3.9 ?g mL?1 against S. aureus and A. xylosoxidans. The comparative antibacterial behaviour of drug?peptide complex, drug alone and peptide alone indicates a distinctive mode of action of the drug?peptide complex, that is not the simple sum total of its constituent components. Specificity of the drug?peptide complex is evident from comparison of antibacterial behaviour with a synthetic intermediate?peptide complex. The octaarginine?drug complex displays faster killing-kinetics towards bacterial cells, creates pores in the bacterial cell membranes and shows negligible haemolytic activity towards human RBCs. Our results demonstrate that mere attachment of a hydrophobic moiety to a cell penetrating peptide does not impart antibacterial activity to the resultant complex. Conversely, the work suggests distinctive modes of antibiotic activity of small molecules when used in conjunction with a cell penetrating peptide.

Emergent antibacterial activity of N-(thiazol-2-yl) benzenesulfonamides in conjunction with cell-penetrating octaarginine

Sulfonylureas provide a useful motif for carrying donor and acceptor sites capable of hydrogen bonding. These are a prominent class of therapeutic agents in the pharmaceutical industry. However, the use of aryl sulfonyl oligomers in drug discovery, supramolecular chemistry and crystal engineering are unexplored. This motivated us to design, synthesize and understand the structural features of aryl sulfonylurea oligomers (n = 1?3). Here, we report the synthesis and spectroscopic characterization details such as 1H NMR, 13C NMR, mass spectrometry and single-crystal X-ray diffraction analysis for three sulfonylurea oligomer derivatives. Further, the molecular packing analysis of three derivatives reveals the significance of Nsingle bondH?O and Csingle bondH?O intra and intermolecular hydrogen bonding. These hydrogen bonding contacts enable the sulfonylurea derivatives to form 2D framework/architecture. We quantify various intermolecular interactions in these derivatives by Hirshfeld analysis and 2D fingerprint plots. We have performed in-silico docking studies against Plasmodium falciparum (Pf) prolyl-tRNA synthetase (ProRS) to rationalize the binding affinity of title compounds.

Supramolecular architecture in sulfonylurea, sulfonyldiurea and sulfonyltriurea drugs: Synthesis, X-ray structure and Hirshfeld surface analysis

The enzyme soluble epoxide hydrolase (sEH) plays a central role in metabolism of bioactive lipid signaling molecules. The substrate-specific hydrolase activity of sEH converts epoxyeicosatrienoic acids (EETs) to less bioactive dihydroxyeicosatrienoic acids. EETs exhibit anti-inflammatory, analgesic, antihypertensive, cardio-protective and organ-protective properties. Accordingly, sEH inhibition is a promising therapeutic strategy for addressing a variety of diseases. In this review, we describe small molecule architectures that have been commonly deployed as sEH inhibitors with respect to angiogenesis, inflammation and cancer. We juxtapose commonly used synthetic scaffolds and natural products within the paradigm of a multitarget approach for addressing inflammation and inflammation induced carcinogenesis. Structural insights from the inhibitor complexes and novel strategies for development of sEH-based multitarget inhibitors are also presented. While sEH inhibition is likely to suppress inflammation-induced carcinogenesis, it can also lead to enhanced angiogenesis via increased EET concentrations. In this regard, sEH inhibitors in combination chemotherapy are described. Urea and amide-based architectures feature prominently across multitarget inhibition and combination chemotherapy applications of sEH inhibitors.

Small molecule soluble epoxide hydrolase inhibitors in multitarget and combination therapies for inflammation and cancer

Sphingosine kinase 1 (SphK1) is one of the well-studied drug targets for cancer and inflammatory diseases. Recently discovered small-molecule inhibitors of SphK1 have been recommended in cancer therapeutics; however, selectivity and potency of first-generation inhibitors are great challenge. In search of effective SphK1 inhibitors, a set of small molecules have been designed and synthesized bearing urea, sulfonylurea, sulfonamide, and sulfonyltriurea groups. The binding affinity of these inhibitors was measured by fluorescence-binding assay and isothermal titration calorimetry. Compounds 1, 5, 6, and 7 showed an admirable binding affinity to the SphK1 in the sub-micromolar range and significantly inhibited SphK1 activity with admirable IC50 values. Molecular docking studies revealed that these compounds fit well into the sphingosine binding pocket of SphK1 and formed significant number of hydrogen bonds and van der Waals interactions. These molecules may be exploited as potent and selective inhibitors of SphK1 that could be implicated in cancer therapeutics after the required in vivo validation.

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