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Optimization and sensitivity analysis of heat transport of hybrid nanoliquid in an annulus with quadratic Boussinesq approximation and quadratic thermal radiation
The quadratic convective flow of hybrid nanoliquid in an annulus subjected to quadratic thermal radiation is studied for the first time. The impact of suction/injection and the uniform movement of the rings are considered. Nonlinear equations are handled numerically by adopting the shooting technique. An optimization procedure is performed by using response surface methodology. The maximum heat transport is observed for chosen values of effective parameters (thermal radiation parameter (5 ? Rt? 15) , temperature ratio parameter (1.1 ? ?w? 5.1) and nanoparticle volume fraction of copper (1 % ? ?Cu? 3 %)) at three different levels (low(? 1), middle(0) and high(+ 1)). In addition, a slope of the data point is evaluated for the friction coefficient and the Nusselt number. The results showed that the impact of quadratic thermal radiation on velocity and temperature distributions is more significant than linear thermal radiation. Further, an increase in quadratic convection and quadratic thermal radiation leads to an improvement in the friction coefficient of the skin on the inner surface of the outer annulus. Furthermore, the sensitivity of the friction coefficient is positive for the appearance of quadratic thermal radiation. 2020, SocietItaliana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature. -
Optimization Based Rice Leaf Disease Classification in Federated Learning
Numerous farmers worldwide are impacted by diseases connected to rice leaves that frequently endanger the sustainability of the rice industry. Diseases that affect the leaves of rice plants severely limit their ability to produce rice, and they are typically brought on by bacteria, viruses, or fungi. This paper proposes an innovative classification scheme for rice leaf diseases based on Federated Learning (FL). Here, FL framework comprises two entities, namely nodes and servers. Every node does initial local training using local data. Moreover, produced local model is then updated on server. Model aggregation is done at the server since several nodes update their local models and send them to it. The nodes download the global model that server has generated as a result. The nodes update their training using transferred global model and local model. The following series of actions are taken in the training model. The input image is mainly obtained from a database and pre-processed with a Kalman filter to eliminate noise. Then, numerous operations for data augmentation are applied. In addition, feature extraction is done and generated features are used in LeNet for rice leaf diseases classification. LeNet is trained using the Spotted Hyena Archimedes Optimizer (SHAO). The developed method shows better precision of 91.3%, recall of 92.2%, f-measure of 91.7%, loss function of 3.3%, Mean Square Error (MSE) of 7.3%, and Root MSE of 27.1%. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024. -
Optimization of Abrasive Wear Parameters of Halloysite Nanotubes Reinforced Silk/Basalt Hybrid Epoxy Composites using Taguchi Approach
The demand for environmentally friendly and sustainable materials for nonstructural and structural applications grows by the day. Polymeric composites reinforced with fillers and fibres are considered to have increased strength and desirable wear resistance. Abrasive wear of industrial and agricultural based components are currently one of the most serious issue. Therefore, the current research reports on the influence of Halloysite-Nanotubes (HNTs) loading on the three body abrasive behavior of bi-directional silk fibre (SF) and basalt fibre (BF) reinforced epoxy (Ep) composites. Rubber wheel with dry sand abrasion testing in accordance with ASTM G65-16e1 was performed with four control parameters such as filler content, load, abrading distance and silica sand size. The tests were planned as per orthogonal array of Taguchi (L27). Significant impact of control factors were identified using ANOVA (Analysis of variance). The results demonstrated that adding HNTs to SF-BF/Ep nanocomposites significantly improved the wear resistance and the combination of A2, B1, C3 and D1 control factors yields the lower specific wear rate (SWR). Findings exhibit that the load and abrading distance were the most significant parameters affecting the abrasive wear of SF-BF/Ep nanocomposites followed by filler content and silica sand size. Microstructural features were observed via scanning-electron-microscopy (SEM). 2022 Published by Faculty of Engineering. -
Optimization of anti-corrosion performance of novel magnetic polyaniline-Chitosan nanocomposite decorated with silver nanoparticles on Al in simulated acidizing environment using RSM
The suitability of newly synthesized magnetic polyaniline-Chitosan nanocomposite decorated with silver nanoparticles (Ag@PANI-CS-Fe3O4) as a robust corrosion inhibitor for Aluminum (Al) in a 5 M HCl environment has been investigated via Weight Loss (WL), Alternating Current (AC)-Impedance Spectroscopy (IS), Potentiontiodynamic polarization (Tafel plots), and Scanning Electron Microscopy (SEM) techniques. The protection efficiency (PE) was mathematically modeled using the Response Surface Methodology (RSM) to fit an empirical relation in terms of temperature, nanocomposite concentration, and time using the face-centered central composite design. The model was accurate with a coefficient of determination (R2 = 99.27%). The negative Gibb's free energy of adsorption (?Gads) values confirmed the spontaneity of Freundlich adsorption isotherm process on Al in 5 M HCl solution. The optimization simulation yielded maximum protection efficiency (of 97.88%) at 5 mg/L nanocomposite concentration, 1 h time, and an intermediate temperature of 304.8 K. Furthermore, the sensitivity of PE was evaluated to find that the low temperature 303 K is favorable for PE, whereas higher temperature will act adversely on PE. The results obtained by the RSM model are in agreement with the experimental observations. 2021 Elsevier B.V. -
Optimization of Biodiesel Production from Waste Cooking Oil by Box Behnken Design Using Response Surface Methodology
Interest in Biodiesel production has grown over the years due to concerns related to the environment, and the solutions include deriving energy from waste as the replacement for diesel, a petroleum-derived fuel. Biodiesel has been accepted as a "green fuel" as it is a renewable, non-toxic, safe and biodegradable energy material. The utilisation of waste cooking oil (WCO) by converting it into biodiesel is one of the promising alternatives to diesel. An attempt to optimise the biodiesel production from WCO (a waste material) has been made via this study. The process adopted was Trans-esterification of pretreated WCO, and the optimization of biodiesel production was carried out by Box-Behnken method using a response surface methodology. The variations between the analytical and experimental results were within acceptable limits. The response surface methodology resulted in an optimum yield of 96.88% (analytical), which was validated through an experiment within an acceptable error of 0.58%. 2021,International Journal Of Renewable Energy Research.All rights reserved. -
Optimization of Flexible Manufacturing Production Line System Based on Digital Twin
This research presents a revolutionary Digital Twin (DT)driven method aimed at quick customization of computerized industrial processes. The DT includes dual components, the semi-physical replication that transfers system information and gives data input to the subsequent clause, which is enhanced. The outcomes of the optimum section are returned directly to the semi-physical replication used for validation. The term Open-Architecture Machine Tool (OAMT) led to a fundamental class of machine tools that consists of a basic unified platform and many individually designed modules that may be quickly added or replaced away. Designers can dynamically modify the production system for responding to process planning by inserting personalized components into its OAMTs. Major enabling approaches, along with how to identical virtual and substantial systems and how to instantly bi-level program the invention size and efficiency of developed structures to accommodate sudden variations of goods, are explained. A real execution is done to demonstrate the efficacy of the method to achieve increased enactment of the system by minimizing the overhead cost of the recompose method by systematizing and quickly enhancing it. 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. -
Optimization of graded catalyst layer to enhance uniformity of current density and performance of high temperature-polymer electrolyte membrane fuel cell
The optimal use of catalyst materials is essential to improve the performance, durability and reduce the overall cost of the fuel cell. The present study is related to spatial distributions of current and overpotential for various graded catalyst structures in a high temperature-polymer electrolyte membrane fuel cell (HT-PEMFC). The effect of catalyst gradient across the catalytic layer (CL) thickness and along the channel and their combination on cell performance and catalyst utilization is investigated. The graded catalytic structure comprises two, three, or multiple layers of catalyst distribution. For a total cathode catalyst loading of 0.35 mg/cm2, higher loading near the membrane presents improved cell performance and catalyst utilization due to reduced limitations caused by oxygen and ion diffusions. However, non-uniformity in the current distribution is significantly increased. The increase in the catalyst loading along the reactant flow provides a substantially uniform current density but lower cell performance. The synergy of varying catalytic profiles across the CL thickness and along the cathode flow direction is investigated. The results emphasize the importance of a rational design of cathode structure and mathematical functions as a strategic tool for functional grading of a CL towards improved uniform current distribution and catalyst utilization. 2021 Hydrogen Energy Publications LLC -
Optimization of heat transfer in the thermal Marangoni convective flow of a hybrid nanomaterial with sensitivity analysis
The heat transfer rate of the thermal Marangoni convective flow of a hybrid nanomaterial is optimized by using the response surface methodology (RSM). The thermal phenomenon is modeled in the presence of a variable inclined magnetic field, thermal radiation, and an exponential heat source. Experimentally estimated values of the thermal conductivity and viscosity of the hybrid nanomaterial are utilized in the calculation. The governing intricate nonlinear problem is treated numerically, and a parametric analysis is carried out by using graphical visualizations. A finite difference-based numerical scheme is utilized in conjunction with the 4-stage Lobatto IIIa formula to solve the nonlinear governing problem. The interactive effects of the pertinent parameters on the heat transfer rate are presented by plotting the response surfaces and the contours obtained from the RSM. The mono and hybrid nanomaterial flow fields are compared. The hybrid nanomaterial possesses enhanced thermal fields for nanoparticle volume fractions less than 2%. The irregular heat source and the thermal radiation enhance the temperature profiles. The high level of the thermal radiation and the low levels of the exponential heat source and the angle of inclination (of the magnetic field) lead to the optimized heat transfer rate (Nux = 7.462 75). 2021, Shanghai University. -
Optimization of multiple responses using overlaid contour plot and steepest methods analysis on hydroxyapatite coated magnesium via cold spray deposition
In this work, sequential optimization strategy based statistical design was employed to enhance the mechanical properties of hydroxyapatite coatings onto a pure magnesium substrate using a cold spray technique. A fractional factorial design (24-1) was applied to elucidate the process parameters that significantly affected the mechanical properties of the coating samples. Standoff distance, surface roughness, and substrate heating temperature were identified as important process parameters affecting thickness, nanohardness, and the elastic modulus of the coating sample. The overlaid method analysis was employed to determine tradeoff optimal values from multiple regressive equations. Then, finally, steepest method analysis was used to reconfirm and relocate the optimal domain from which the factor levels for maximum mechanical properties of the coating were determined at 49.77mm standoff distance, 926.4grit surface roughness, and 456C substrate heating temperature, which can accommodate the optimum requirements for the cold spray process with a coating of 49.77?m thickness, 462.61MPa nanohardness, and 45.69GPa elastic modulus. Scanning electron microscopy revealed that a short standoff distance, high surface roughness, and high substrate temperatures improved the bond between the coated layers and substrates. 2015 Elsevier B.V. -
Optimized deep maxout for breast cancer detection: consideration of pre-treatment and in-treatment aspect
Breast cancer is one of the deadliest diseases, accounting for the second-highest rate of cancer mortality among females. Breast tissue begins to develop cancerous, malignant lumps as the disease progresses. Self-examinations and routine clinical checks aid in early diagnosis, which considerably increases the likelihood of survival. Because of this, we have created a revolutionary method for finding breast cancer that has the following four steps. Fuzzy filters are used in the initial pre-processing stage to reduce noise and improve outcomes from the incoming data. In the second stage, we have presented an Improved Hierarchical DBSCAN (Density-based clustering algorithm) for the segmentation of anomalous areas. Feature extraction will be carried out following segmentation. We have also developed a better kurtosis-based feature to complement traditional statistical and shape-based features and deliver better results. The Optimized Deep Maxout Neural Network is used for classification in the final step, with the suggested Shark Smell Indulged Shuffled Shepherd Optimization used to optimize the weight parameter (SSISSO). At 90% the learning percentage of the proposed model SSISSO model has achieved 0.984391 accuracy, which is superior to 22.54%, 28.46%, 17.44%, 17%, 15.04%, 13.28%, 29.45%, 28.59%, 21.58%, and 30.72% as compared to other methods like SVM-BS1, CNN-BS7, LSTM, NN, Bi-GRU, RNN, ARCHO, AOA, HGS, CMBO, SSOA, and SSO. Finally, the results of the proposed breast cancer detection technique are compared with conventional techniques. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. -
Optimized green synthesis of ZnO nanoparticles: evaluation of structural, morphological, vibrational and optical properties
In this study, leaf extracts of Aloe vera (AV), Azadirachta indica (AI), and Amaranthus dubius (AD) were used to synthesize zinc oxide nanoparticles utilizing a simple green synthesis process. The structural, optical, band energy, size, surface area, and shape of as-prepared nanoparticles were studied using analytical techniques. The hexagonal phase was revealed by XRD studies for all three samples: AV-ZnO, AI-ZnO, and AD-ZnO, with crystallite sizes of 35.8nm, 30.83nm, and 33.1nm, respectively. The UVVisible spectra of AV-ZnO, AI-ZnO, and AD-ZnO exhibit the characteristic absorption in the range of 200 to 450nm, and the band gap energy was found to be 3.10eV, 3.12eV, and 3.07eV, respectively. FESEM and TEM studies revealed that the ZnO NPs are rod-shaped with a roughly spherical appearance. EDAX analysis confirmed the presence of zinc and oxygen and indicates that the formed product is a pure phase of ZnO NPs. Increased antibacterial activity was noted for AV-ZnO, AI-ZnO, and AD-ZnO against gram-negative (Klebsiella pneumonia, Shigella dysenteriae), gram positive (Staphylococcus aureus, and Bacillus) bacterial strain. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
Optimized handwritten character recognition using artificial neural network
Handwritten character recognition (HCR) plays important role in the modern world and is one of the focused area of research in the field of image processing and pattern recognition. Handwritten character recognition refers to the process of conversion of hand-written character into printed/word file character which can immensely improve the interface between man and machine in numerous application. It is difficult to process with great variations in writing styles, different size and orientation angle of the character that are existing. Also segmentation of cursive handwritten text is difficult as the edges cant be detected easily. There are numerous approaches to recognize handwritten data. The images are acquired using a digital camera or scanner and stored in standard format like JPG, PNG etc. The second stages include pre-processing techniques like Binarization, Skeletonization, thinning, resizing the image and segmentation. In our work we mainly concentrated on extracting statistical features of alphabets like mean, variance, standard deviation, Skewness and kurtosis, which differentiates a character from another. We used feed forward algorithm to train Artificial Neural Network (ANN). The features of input character after pre-processing are fed into ANN. A database of 650 samples is created to test input samples for recognition of character by neural net-work. The Experimental results that we have achieved show 88.46 % accuracy rate with minimum time taken for training. IJSTR 2020. -
Optimized heat transport in Marangoni boundary layer flow of a magneto nanomaterial driven by an exponential interfacial temperature distribution
In a small boundary layer of the fluid interface, the temperature distribution deviates from being linear with the spatial coordinate and exhibits an exponential form. Hence, the Marangoni convective flow of a nanoliquid driven by an exponential interfacial temperature distribution is modeled in this study. Due to practical applicability, the working fluid is chosen to be ethylene glycol-based magnesium oxide nanoliquid, which is modeled using experimentally estimated properties. In the system, the external effects of an inclined magnetism, thermal radiation, and an internal heat source are considered. Heat transport is rigorously analyzed using an empirical model, which is estimated using the robust response surface methodology (RSM) to find the optimal working conditions and to estimate the sensitivity. The modeled problem is simulated numerically using the finite difference-based scheme and a parametric analysis is conducted to study the effect of magnetic field, inclination of magnetic field, radiation, and internal heat source parameters. The internal heat generation (increase of 0.94%) factor dominates the augmentation in the thermal field but at some distance, the thermal radiation factor has a predominant impact (58.99%). The inclination angle of the magnetic field has a prominent decremental impact on the velocity profile. Also, the radiative heat flux enhances the temperature profile. Optimal working conditions are estimated to be with a magnetic inclination of 10 and using a liquid with 0.25% volume fraction of 100 nm. This study finds applicability in crystal growth, drying silicon wafers, and heat exchangers. 2022 Wiley-VCH GmbH. -
Optimized Load Balancing Technique for Software Defined Network
Software-defined networking is one of the progressive and prominent innovations in Information and Communications Technology. It mitigates the issues that our conventional network was experiencing. However, traffic data generated by various applications is increasing day by day. In addition, as an organization's digital transformation is accelerated, the amount of information to be processed inside the organization has increased explosively. It might be possible that a Software-Defined Network becomes a bottleneck and unavailable. Various models have been proposed in the literature to balance the load. However, most of the works consider only limited parameters and do not consider controller and transmission media loads. These loads also contribute to decreasing the performance of Software- Defined Networks. This work illustrates how a software-defined network can tackle the load at its software layer and give excellent results to distribute the load. We proposed a deep learning-dependent convolutional neural networkbased load balancing technique to handle a software-defined network load. The simulation results show that the proposed model requires fewer resources as compared to existing machine learning-based load balancing techniques. 2022 Tech Science Press. All rights reserved. -
Optimized Multi-Scale Attention Convolutional Neural Network for Micro-Grid Energy Management System Employing in Internet of Things
The combination of micro-grid energy management systems (EMSs) with the Internet of Things (IoT) offers a promising way to improve energy use and distribution. However, challenges such as device compatibility and the difficulty of managing energy efficiently make it hard to implement these systems effectively. This study offers a significant advancement in energy management by using IoT for microgrid systems. An Optimized Multi-scale Attention Convolutional Neural Network for microgrid EMS employing IoT (OMACNN-MGEMS-IoT) is proposed in this study, which enables efficient monitoring and control of energy resources. The proposed model's input data are gathered from the MQTT dataset. This research employs a Regularized Bias-aware Ensemble Kalman Filter (RBAEKF) for pre-processing input data, ensuring the removal of outliers and updating missing values. The MACNN is then used for effective fault detection within the microgrid. To enhance its performance, the Sheep Flock Optimization Algorithm (SFOA) is introduced to optimize the MACNN parameters, ensuring accurate fault detection. Implemented on the MATLAB platform, the performance of the OMACNN-MGEMS-IoT method is assessed through various performance metrics, demonstrating significant improvements. Notably, the proposed method achieves higher cost reductions of 25%, 22%, and 26% compared to existing approaches such as the IoT platform for energy management in multi-micro grid systems (IoT-PEM-MMS), a micro-grid system infrastructure implementing IoT for efficient energy management in buildings (MSII-IoT-EEM) and a hybrid deep learning-based online energy management scheme for industrial microgrids (HDL-OEM-IM). The findings highlight the impact of the proposed OMACNN-MGEMS-IoT method in enhancing energy efficiency and cost-effectiveness in microgrid systems. 2025 John Wiley & Sons Ltd. -
Optimized production of keratinolytic proteases from Bacillus tropicus LS27 and its application as a sustainable alternative for dehairing, destaining and metal recovery
The present study describes the isolation and characterization of Bacillus tropicus LS27 capable of keratinolytic protease production from Russell Market, Shivajinagar, Bangalore, Karnataka, with its diverse application. The ability of this strain to hydrolyze chicken feathers and skim milk was used to assess its keratinolytic and proteolytic properties. The strain identification was done using biochemical and molecular characterization using the 16S rRNA sequencing method. Further a sequential and systematic optimization of the factors affecting the keratinase production was done by initially sorting out the most influential factors (NaCl concentration, pH, inoculum level and incubation period in this study) through one factor at a time approach followed by central composite design based response surface methodology to enhance the keratinase production. Under optimized levels of NaCl (0.55 g/L), pH (7.35), inoculum level (5%) and incubation period (84 h), the keratinase production was enhanced from 41.62 U/mL to 401.67 9.23 U/mL (9.65 fold increase) that corresponds to a feather degradation of 32.67 1.36% was achieved. With regard to the cost effectiveness of application studies, the crude enzyme extracted from the optimized medium was tested for its potential dehairing, destaining and metal recovery properties. Complete dehairing was achieved within 48 h of treatment with crude enzyme without any visible damage to the collagen layer of goat skin. In destaining studies, combination of crude enzyme and detergent solution [1 mL detergent solution (5 mg/mL) and 1 mL crude enzyme] was found to be most effective in removing blood stains from cotton cloth. Silver recovery from used X-ray films was achieved within 6 min of treatment with crude enzyme maintained at 40 C. 2023 Elsevier Inc. -
Optimized task group aggregation-based overflow handling on fog computing environment using neural computing
It is a non-deterministic challenge on a fog computing network to schedule resources or jobs in a manner that increases device efficacy and throughput, diminishes reply period, and maintains the system well-adjusted. Using Machine Learning as a component of neural computing, we developed an improved Task Group Aggregation (TGA) overflow handling system for fog computing environments. As a result of TGA usage in conjunction with an Artificial Neural Network (ANN), we may assess the models QoS characteristics to detect an overloaded server and then move the models data to virtual machines (VMs). Overloaded and underloaded virtual machines will be balanced according to parameters, such as CPU, memory, and bandwidth to control fog computing overflow concerns with the help of ANN and the machine learning concept. Additionally, the Artificial Bee Colony (ABC) algorithm, which is a neural computing system, is employed as an optimization technique to separate the services and users depending on their individual qualities. The response time and success rate were both enhanced using the newly proposed optimized ANN-based TGA algorithm. Compared to the present works minimal reaction time, the total improvement in average success rate is about 3.6189 percent, and Resource Scheduling Efficiency has improved by 3.9832 percent. In terms of virtual machine efficiency for resource scheduling, average success rate, average task completion success rate, and virtual machine response time are improved. The proposed TGA-based overflow handling on a fog computing domain enhances response time compared to the current approaches. Fog computing, for example, demonstrates how artificial intelligence-based systems can be made more efficient. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
Optimized Tree Strategy with Principal Component Analysis Using Feature Selection-Based Classification for Newborn Infant's Jaundice Symptoms
One of the most important and difficult research fields is newborn jaundice grading. The mitotic count is an important component in determining the severity of newborn jaundice. The use of principal component analysis (PCA) feature selection and an optimal tree strategy classifier to produce automatic mitotic detection in histopathology images and grading is given. This study makes use of real-time and benchmark datasets, as well as specific approaches for detecting jaundice in newborn newborns. According to research, the quality of the feature may have a negative impact on categorization performance. Additionally, compressing the classification method for exclusive main properties can result in a classification performance bottleneck. As a result, identifying appropriate characteristics for training the classifier is required. By combining a feature selection method with a classification model, this is possible. The major outcomes of this study revealed that image processing techniques are critical for predicting neonatal hyperbilirubinemia. Image processing is a method of translating analogue images to digital formats and manipulating them. The primary goal of medical image processing is to collect information useful for disease detection, diagnosis, monitoring, and therapy. Image datasets can be used to validate the performance of newborn jaundice detection. When compared to conventional approaches, it offers results that are accurate, quick, and time efficient. Accuracy, sensitivity, and specificity, which are common performance indicators, were also predictive. 2021 Debabrata Samanta et al. -
Optimized uplink scheduling model through novel feedback architecture for wimax network
Broadband Wireless Access has drawn the fine attention due to the wide range of data requirement and user mobility all the time. Moreover, WiMAX provides the best QoE (Quality of Experience) which is based on the IEEE 802.16 standards; this includes several services such as data, video and audio. However, in order to provide the effective and smooth experience i.e. QoS scheduling plays one of the critical part. In past several mechanism has been proposed for effective scheduling however, through the research it is observed that it can be furthermore improvised hence in this we propose a mechanism named as OUS (Optimized Uplink Scheduling) which helps in improvising the QoS. In here, we have proposed a novel feedback architecture and proposed optimized scheduling which helps in computing the bandwidth request this in terms helps in reducing the delay as well as jitter. Moreover, the performance evaluation is performed through extensive simulation by varying the different SS and frequency and the results analysis confirms that our mechanism performs way better than the existing algorithm. BEIESP. -
Optimizing malachite green adsorption with Co-PTC metal organic framework: Insights into mechanisms and performance
The removal of organic pollutants from aqueous environments has garnered significant attention in environmental science and engineering. Metal-organic frameworks (MOFs) have emerged as promising materials for this purpose due to their intriguing structures, high surface area, and perpetual porosity. In this study, we investigate the adsorption performance of Co-based MOF for the removal of malachite green (MG), a common organic dye pollutant. The MOF, abbreviated as Co-PTC is synthesized via a one-pot green approach using perylene-3,4,9,10-tetracarboxylic dianhydride (PTC) as the ligand at room temperature. Basic to advanced characterization techniques are employed to elucidate the structure and interactions within the MOF. Through a comprehensive analysis, the underlying mechanisms governing the adsorption process are explored, and optimization studies have been carried out. Co-PTC in minute amounts exhibits an adsorption capacity of 79.3 % selectively for MG in 50 min. The kinetics and isotherm models governing the adsorption process are well investigated. 2024 Elsevier B.V.