Optimal Parameters Research Articles

The Study of Sensing Elements Parameters Optimization for Developed Biosensor of SARS-CoV-2 Detection

New advancements in developing sensitive and selective biosensors have demonstrated outstanding potential for Deoxyribonucleic Acid (DNA biosensors). The detection mode of DNA biosensors primary depends on a particular DNA hybridization that precisely occurs on the surface of the physical transducer that can only be detected using high-performance assays due to slight current changes. The analytical performance (sensitivity) of the DNA biosensor is conclusively rely on the confluence constructing of the sensing surface, which must be optimized. Thus, in this study, the sensing elements of the developed biosensors were optimized for detecting RNA of SARS-CoV-2. This optimization included concentration of nanomaterials (carbon quantum dots), probe density (concentration of DNA probe) and concentration of linker (APTES). It was observed that 0.15 % V/V of concentration CQD, 0.1μM of DNA probe and 36% V/V of APTES were the optimum parameters which provided their maximum response during electrical measurements and increased the sensitivity of the developed biosensor for SARS-CoV-2 detection.

A novel pedicle screw design to maximize screw-bone interface strength using finite element analysis and design of experiment techniques.

Basic study. This study aimed to utilize finite element (FE) analysis and design of experiment (DoE) techniques to propose and optimize a novel pedicle screw design and compare its pull-out force with that of a control device. Pedicle screw-based fixation is the gold-standard treatment for spine diseases, particularly in fusion procedures. However, pedicle screw loosening and breakage still occur in osteoporotic and non-osteoporotic patients. This research investigates screw design modifications to enhance screw-bone interface strength and reduce the likelihood of loosening. We conceptualized a novel pedicle screw considering vertebral bone morphology and strength differences. A validated FE model was developed and used in conjunction with DoE to determine the screw՚s optimum geometrical parameters. The FE model was validated through simulation and laboratory experiments using the control device. The optimized thread profiles for cortical bone and cancellous bone were determined, with pull-out force as the primary factor for screw design evaluation. FE analysis results for the control device closely matched experimental results, with less than 5% difference. The chosen unique pitch/depth ratio showed maximum pull-out force for cortical bone, while DoE enabled the optimization of design parameters for cancellous bone. The optimized pedicle screw exhibited a 15% increase in pull-out force compared to the control device. The study proposes a novel pedicle screw design with better pull-out strength than the control device. Combining FE analysis with DoE is an effective approach for screw design optimization, reducing the need for extensive prototyping tests. A two-variable analysis suffices for optimizing cortical bone design parameters, while a multi-variable analysis is more effective for optimizing cancellous bone design parameters.

Enhancing surface integrity of additively manufactured Inconel 718 through numerical modeling of hydrostatic ball burnishing parameters using TOPSIS and ANOVA

Abstract Surface enhancement of laser powder bed fusion components is pivotal for their suitability in end-use applications. Hydrostatic ball burnishing, a mechanical surface hardening technique, induces compressive residual stress on the surface by the application of hydraulic force resulting in improved fatigue life of the material. This present study focuses on determining the optimum burnishing parameter combinations for better surface integrity (roughness, hardness, and residual stress) utilizing ANOVA and TOPSIS analysis. The cumulative performance score of the TOPSIS analysis states that the best parameter combination for Inconel 718 is found to be 300 bar pressure, 0.2mm burnishing width, and feed 800mm/min. The adjusted R² values of the ANOVA model for all three responses are 0.96, 0.93, and 0.94, confirming their close agreement with experimental results. With optimum parameters, the roughness decreased from 2.217μm to 0.305μm, Microhardness increased from 318.9HV to 475.9HV and residual stress was converted from tensile 216MPa to Compressive -54MPa demonstrating the significance of this process on surface enhancement.

High robust eddy current tuned tandem mass dampers-inerters for structures under the ground acceleration

In this work, a novel eddy current tuned tandem mass dampers-inerters, referred to as EC-TTMDI is proposed, a theoretical analysis model of the vibration control mechanism is established, and parameterized analysis and design are conducted using the intelligent optimization algorithm. This model is the single-degree-of-freedom (SDOF) structure with EC-TTMDI subjected to Gaussian white noise base excitation. Specifically, the expression for the displacement variance of the SDOF structure-EC-TTMDI system was firstly obtained with resorting to both the equivalent linearization of eddy current damper (ECD) and residue theorem. The optimization objective function was then defined as minimization of the displacement variance, which is product of the white noise intensity and the square of H2 norm of the model transfer function. Employing the pattern-search algorithm, the variation trends of the objective function and the optimum design parameters were subsequently scrutinized. The vibration damping mechanism of EC-TTMDI is then revealed in terms of control effectiveness, robustness, structural frequency response, suppression bandwidth, and stroke. Finally, the time history response analysis of the SDOF structure-EC-TTMDI system subjected to the near-field impulse, near-field non-impulse, and far-field earthquakes was conducted to further confirm the performance superiority of EC-TTMDI. Results demonstrates that EC-TTMDI offers higher robustness and better control effectiveness compared to TTMDI and TMDI. Furthermore, EC-TTMDI features the damping force limiting characteristic, which enhances the integrity and reliability of TTMDI.

Bioinspired Synthesis of Novel Different Nanoparticles and its Utility in Biodiesel and Animals Applications

Because of its ability to speed up the reaction, the catalyst is critical to its success. Most catalysts are either homogeneous or heterogeneous. It has been shown that utilizing a heterogeneous catalyst, which is easier to remove from the product after the reaction has been finished. Because of the large surface area of the Nano-catalyst results in high catalytic efficiency. To enhance the performance of catalysts a range of various types of support materials have been used. SO42--ZnO and So42-/TiO active acid catalyst was prepared and characterized. ZnO nanoparticles catalyst synthesized by precipitation of zinc nitrate for comparison with supported catalyst. Sulphated zinc oxide (SO42--ZnO) and sulphated titania (SO42-/TiO) catalysts were synthesized using impregnation methods, to test their efficacy in biodiesel production. Various waste oils from different wastes such as mutton or beef tallow, chicken fat, and methanol are preferred to use during the esterification of waste animal fat oils using solid acid catalysts to produce biodiesel. Biodiesel synthesis generates a substantial amount of glycerol as a byproduct. Effect of optimum parameters such as temperature 60 degree centigrade (°C) shown 90% yield, time 1 hour resulted in 85% yield, catalyst dose 2wt% resulted in 80% yield, stirring speed 250rpm resulted in 80% yield, methanol to oil ratio12:1 resulted as 85.5% yield for transesterification of waste fat oil. It is valuable that the supported acid catalysts showed more yield than simply synthesized ZnO nanocatalyst similarly sulphated zinc oxide showed more FAME yield than sulphated titania.

Finite element analysis and optimization studies on tibia implant of SS 316L steel and Ti6Al4V alloy

Tibial fractures account for approximately 15% of all fractures, typically resulting from high-energy trauma. A critical surgical approach to treat these fractures involves the fixation of the tibia using a plate with minimally invasive osteosynthesis. The selection and fixation of the implant plate are vital for stabilizing the fracture. This selection is highly dependent on the plate's stability, which is influenced by factors like the stresses generated in the plate due to the load on the bone, as well as the plate's length, thickness, and number of screw holes. Minimizing these stresses is essential to reduce the risk of implant failure, ensuring optimal stress distribution and promoting faster, more effective bone healing. In the present work, the finite element and statistical approach was used to optimize the geometrical parameters of the implant plate made of SS 316L steel and Ti6Al4V alloy. A 3D finite element model was developed for analyzing the stresses and deformation, and implant plates were manufactured to validate the results with the help of an experiment conducted on the universal testing machine. A strong correlation was observed between the experimental and predicted results, with an average error of 8.6% and 8.55% for SS316L and Ti6Al4V alloy, respectively. Further, using the signal-to-noise ratio for the minimum stress condition was applied to identify the optimum parameters of the plate. Finally, regression models were developed to predict the stresses generated in SS316L and Ti6Al4V alloy plates with different input conditions. The statistical model helps us to develop the relation between different geometrical parameters of the Tibia implant plate. As determined by the present work, the parameter most influencing is implant plate length. This outcome will be used to select the implant for a specific patient, resulting in a reduction in implant failure post-surgery.

A Crowded Object Counting System with Self-Attention Mechanism

Traditional object counting systems use object detection methods to count objects. However, when objects are small, crowded, and dense, object detection may fail, leading to inaccuracies in counting. To address this issue, we propose a crowded object counting system based on density map estimation. While most density map estimation models employ encoder–decoder or multi-branch approaches to generate feature maps at different scales for obtaining an accurate density map, improving the accuracy of crowded object counting remains a challenge. In this paper, we propose a novel model that can generate more accurate density maps, utilizing the context-aware network as the primary structure and integrating the self-attention mechanism. There are three main contributions in this paper. Firstly, the self-attention mechanism is employed to improve the accuracy of density map estimation. Secondly, the missing vehicle labels in the TRANCOS database are relabeled, ensuring that the ground truth data are more complete than the original TRANCOS database, thus enabling the proposed novel model to have higher crowded object counting accuracy. Thirdly, the parameters of the self-attention mechanism are analyzed to obtain the optimum parameter combination. The experimental results demonstrate that the accuracy of crowded object counting can reach 85.9%, 90.0%, 83.4%, and 92.6% for the TRANCOS, relabeled TRANCOS, ShanghaiTech Part A, and Part B datasets, respectively. Furthermore, the ablation study for the context-aware network with self-attention mechanism analyzes the optimum parameter combination.

Insight into efficient photocatalytic degradation of norfloxacin over a simple, economical biochar-based magnetic photocatalyst under solar illumination: a statistical and experimental approach.

The transformation of residual agricultural solid waste into an efficient value-added carbon product has been an ongoing strategy for solid waste management and a sustainable economy. Meanwhile, the upsurgence of antibiotic contamination in water bodies due to their inadvertent use poses a serious threat to human health and leads to antimicrobial resistance. Hence, to neutralize two evils in one stroke simultaneously, a simple, easy, and cost-effective pea shell-based magnetic photocatalyst (PSMC) has been synthesized and characterized by XRD (X-ray diffraction), VSM (vibrating sample magnetometer), XPS (X-ray photoelectron spectroscopy), TGA (thermogravimetric analysis), and FTIR (Fourier-transform infrared) spectroscopy techniques. Batch experimental studies revealed that PSMC efficiently eliminates norfloxacin (91.3%) within 180min from wastewater and mineralizes it into innocuous products at optimum parameters of norfloxacin concentration of 20mg/L, catalyst dosage of 15mg, and pH 3.5. Additionally, statistical parameters for the photodegradation of NX obtained from ANOVA by applying the Box-Behnken design are in close agreement with batch experiment parameters. PSMC has surplus advantages of facile recovery for recycling up to seven consecutive cycles by an external magnet and efficacy in natural solar light, making it cost-effective and economical. Radical scavenging studies revealed that O2•- and OH• were the potent reactive species in the photocatalytic degradation process.

Synthesis of Montmorillonite-Silica Nanocomposite Using Millet Husk and Montmorillonite Clay and Optimization of Adsorption Parameters for Congo Red Dye Adsorption

Dyes are substances that, when applied to a substrate provides color by a process that alters, at least temporarily, any crystal structure of the colored substances The discharge of these effluents into the receiving environments results in hazardous health problems as most of these dyes have carcinogenic effects on the living organisms. Generally, dyes are classified into three categories: a) anionic: direct, acid, and reactive dyes; b) cationic: basic dyes; and c) non-ionic: disperse dyes. The chromophores in anionic and non-ionic dyes mostly consist of azo groups or anthraquinone types. However, Congo red, are difficult to biodegrade due to their complex aromatic structures, which provide them with physico-chemical, thermal, and optical stability. This study is aimed at synthesizing Montmorillonite-silica nanocomposite, a cost effective adsorbent from millet husk and Montmorillonite-clay with the determination of its optimum adsorption parameters for Congo Red Dye adsorption. Here, synthesis of Montmorillonite-silica nanocomposite and optimization parameters were carried out using standard procedures. The unbiased result demonstrated that the percentage yield was found to be 94.51 %. The optimum adsorption parameters studied were adsorbent dose, adsorbate concentration, pH and Kinetics studies. The optimum adsorption parameters were found to be 8.0 g/L, 2.0 mg/L, 8 and 30 minutes, respectively. Hence, the synthesized Montmorillonite-silica nanocomposite obtained from montmorillonite clay and millet husk ash can serve as a cost-effective adsorbent for the removal of Congo Red Dye from waste waters.

Recovery of samarium and cobalt/iron oxide from SmCo magnets through acid baking and water leaching

AbstractRare earth elements (REEs) and cobalt (Co) are listed as critical raw materials because of their importance in global industrial production growth, high supply risk, and economic significance. The recovery of Co and REEs from secondary resources is therefore proposed as a key countermeasure to address this concern. In this study, a straightforward process that integrates acid baking and water leaching is proposed for the recovery of samarium (Sm) and Co from scrap SmCo magnets. Firstly, the chemical composition of SmCo magnets is revealed by ICP-OES and XRF. The Taguchi experimental design technique is employed to optimize nitric acid baking and water leaching. Based on the thermal decomposition behavior of Co, Fe, and Sm, the acid baking temperature is studied for the conversion of metal nitrates, excluding REEs nitrates, into metal oxides. The optimal conditions for acid baking and water leaching are identified, and a reactor for the pilot-scale acid baking process is proposed. The optimum parameters are tested with the proposed reactor.

Catalytic performance of FeCo2O4 spinel cobaltite for degradation of ethylparaben in a peroxymonosulfate activation process: Response surface optimization, reaction kinetics and cost estimation

The presence of cosmetics and personal care products in water resources has become an increasing concern for environment due to their toxicity and persistence in aquatic medium. Peroxymonosulfate mediated advanced oxidation methods have a great potential for the efficient removal of complex organic compounds due to the generation of highly reactive radicals with high oxidation ability. Iron cobaltite, FeCo2O4 catalyst was used as a peroxymonosulfate (PMS) activator for the degradation of ethylparaben which was selected as a model personal care product ingredient. Paraben degradation was modelled and the optimum operating parameters were determined by using Box-Behnken design. The ethylparaben degradation efficiency was calculated as 94.6 % under the optimized conditions which were determined as 0.9 g/L FeCo2O4 loading, pH 6.15, and 0.26 g/L PMS dosage. The ethylparaben degradation reaction followed second-order reaction kinetics. The activation energy was evaluated as 40.1 kJ/mol. Quenching radical tests showed that the dominant reactive species were hydroxyl radicals. Toxicity of the treated samples in terms of L. sativum growth inhibition was evaluated as 1.5 %. The total cost of the treatment including the operating and the amortization costs was calculated as 0.91 €/L.

Effect of physical field-assisted freezing on the quality attributes of frozen baked sweet potato and its optimization

Freezing plays a crucial role in ensuring the safety of foods over long storage periods. However, ice crystals within the cells can cause irreversible damages to tissue structures during the freezing process, resulting a significant problem on the quality. The present study aimed to investigate the effects of physical field-assisted freezing on the quality attributes of frozen baked sweet potato and identify the optimum operational parameters of quick-frozen process. Combination of magnetic field and ultrasound suggested the best results, with a 58.33% reduction in freezing time, 59.63% reduction in thawing loss, compared to IF. The hardness and the signal values of Sweetness and W2W of CF were 58.90%, 20.03%, and 27.07% higher than those of IF, which showed that CF have better maintenance of sample quality. Low field-nuclear magnetic resonance results showed that CF caused a decrease in the proportion of free water and an increase in the proportion of immobile water, and the water status are more similar to FB. Microscopic observations confirmed that the CF had the smallest and the most evenly distribution of ice crystals. Optimization of frozen baked sweet potato via response surface methodology, which revealed that the optimal conditions included a magnetic field intensity of 30 mT, an ultrasonic intensity of 320 W, and an ultrasonic time of 10 s. Therefore, treatment of combination with magnetic field and ultrasound is a promising novel method to improve the quality of frozen foods.

Harnessing Moringa oleifera root powder (MORP) for the sustainable remediation of heavy metal contaminated water.

Heavy metal environmental pollution is rapidly increasing due to the increase in industrialization and urbanization. Industrial processes, such as paint production, mining, and raw materials producing industries release effluents rich in heavy metals, like Pb2+, Cd2+, Cu2+, and Cr3+. These heavy metals are dangerous because they persist in nature, are non-biodegradable and they have high tendency to accumulate in the environment and in living organisms who are exposed to them. This work studied the removal of heavy metals (Cu, Pb, Cr, and Cd) from aqueous solution using Moringa oleifera root powder (MORP) as the adsorbent. The MORP was characterized by SEM, FTIR, BET, and XRD. Batch adsorption experiments carried out investigated the effects of adsorbate concentration, adsorbent dosage, agitation time, pH and temperature on adsorption. The optimum parameters are: contact time (90 min); pH (9); adsorbent dose (0.6); metal ion concentration (30 mg L-1) for Cr and 40 mg L-1 for the rest; and temperature (50 °C) for Cu and Pb, and 70 °C for Cr and Cd. These experimental data were analyzed with 5 isotherm models (Temkin, Flory-Huggins, Langmuir, D-R and Freundlich). The result obtained fitted best to Temkin isotherm in comparison to others. Kinetic studies revealed that the pseudo-second order kinetic model best described the adsorption (with high R2 values ranging from 0.9810-0.9976) compared to pseudo-first order and intra-particle diffusion kinetics model. Results of the thermodynamic study showed that the sorption process was endothermic for Cu and Pb, but exothermic for Cd and Cr. The adsorbent showed good adsorptive tendencies toward the ions studied, and could be applied on an industrial scale for the remediation of metal contaminated water.

Optimization through combined compromise solution and weight determination through SHANNON-ENTROPY for multi criteria decision making in tribological testing of ultrahigh molecular weight polyethylene nano-composite in SBF environment and compression testing with it’s invitro analysis

Aluminium oxide and graphene reinforced with ultrahigh molecular weight polyethylene (UHMWPE) composites offer significant potential for prosthetic and implant applications due to their unique combination of properties. In this article, a series of aluminium oxide and graphene reinforced with ultrahigh molecular weight polyethylene (UHMWPE) composites were successfully fabricated through compression moulding. The average hardness of all the composites was observed to be higher than the UHMWPE (6.1 Hv). Composite U-15A-5 G was found to be the hardest with hardness of 10.5 Hv owing to strong bond between Al2O3 and UHMWPE. The compression strength of the U-15A-5 G material was about 81.936 MPa, which is 40.9% better than the pure UHMWPE. The compression modulus was about 330 MPa which is 22.2% higher than neat UHMWPE. Composite samples were tribo tested under simulated body fluid (SBF) lubricating environment. Using combined entropy-COCOSO method it was found that 50 mm/s speed, 200N load and 5% graphene are the optimum tribological parameters. with Cof of 0.04 and wear rate of 4.8745*10−4 mm3/Nm. Microstructure analysis indicated that long-range structured lamellar structures and microfibers were created between the crystals with the assistance of graphene layers. Apatite layer was also found on the sample. The apatite layer included phosphate and carbonate ions, which is good for in vitro bioactivity on polymeric films. This composite is a good candidate for prosthetics and implant components.

High‐Resolution Cortical Dipole Imaging Using EEG Data Measured with a Small Number of Electrodes Based on the International 10–20 System

Cortical dipole imaging is a common technique utilized to visualize the electrical activity in the brain. However, this technique requires ~100 electrodes from a spatial resolution perspective, resulting in a long preparation time and a heavy burden on the subject. According to the international 10–20 system for recording electroencephalogram signals, 19 electrodes are placed on the scalp of the subject. In this study, high‐resolution cortical dipole imaging using the international 10–20 system was investigated for daily applications. The potential at any position on the scalp was interpolated using multiple regression analysis, and optimum parameter estimation was conducted using the proposed method. The simulation and experimental results suggest that the proposed interpolation method is effective for high‐resolution brain activity imaging when using a limited number of electrodes. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Development an effective adsorptive treatment strategy for the removal of cadmium from textile wastewater by CuBi2O4@Fe3O4 nanocomposites

In this study, copper-bismuth oxide/iron oxide (CuBi2O4@Fe3O4) nanocomposites were prepared by microwave-assisted synthesis and used as adsorbents for the adsorptive removal of cadmium from textile wastewater. The pH/volume of buffer solution, mixing type/period and adsorbent dosage were optimized univariately to enhance the removal efficiency of the adsorbent and determined as 1.5 mL of pH 8.0 buffer solution, vortexing for 60s, and 30 mg of CuBi2O4@Fe3O4 nanocomposite material. Following the determination of the optimum parameters, equilibrium adsorption studies were performed at five different initial concentrations of cadmium within the range of 0.50 − 10 mg L−1 in textile wastewater. A matrix-matching calibration strategy was utilized for the accurate and precise quantification of cadmium in the wastewater matrix with a R 2 value of 0.9961. The percent removal efficiencies were calculated within the range of 77.2 − 81.5% for the adsorptive removal of cadmium ions from textile wastewater in the equilibrium adsorption experiments. Furthermore, the Langmuir, Freundlich, and Sips adsorption isotherm models were employed for modeling the equilibrium data, and the results showed that all the models fitted well with the experimental data with R2 values higher than 0.99. The simple and efficient batch adsorption process developed was successfully utilized to remove cadmium ions from textile wastewater.

Experimental and numerical analysis to enhance the thermodynamic properties and biofuel stability: Sapindus mukorossi

ABSTRACT Microscale additives face challenges like sedimentation, conglomeration, and uneven size distribution, which can adversely impact biodiesel stability and thermochemical properties. This study investigates the stability and thermochemical properties of magnesium-doped calcium oxide (MdC) in Sapindus mukkarosi (SMBD25) biodiesel emulsified with antioxidants SPAN80 (SP80) and TWEEN80 (TW80) at different concentrations. 9 sets of experiments were carried out with the L9 OA as per the design matrix. Minitab 16 software was used to simplify the analysis of the results using the Taguchi method. Also, this research encompassed a comprehensive investigation by employing GCFID, SEM and XRD techniques. From the results, the heating value and cetane number for SMBD25 + 30 mg/L MdC + 30 mg/L TW80 + 30 mg/L SP80, surfactant and dispersant (1:1 ratio) biodiesel blend is improved. The enhanced absorbance and reduced transmittance for SMBD25 + 30 mg/L MdC + 30 mg/L TN80 + 30 mg/L SP80 were estimated to be 4.63 and 0.53%, respectively, while the heating value and cetane number were calculated to be 42.13 MJ/kg and 56, respectively. The Taguchi method revealed that SMBD27.8, 34ppm nanocatalyst, and 24ppm dispersant were optimum process parameters. The biodiesel blend is highly effective (71.18%) in controlling the optimal biodiesel thermochemical properties, followed by the dispersant (19.55%).

Optimization of Polyvinylidene Fluoride/Polyvinyl Alcohol Electrospun using Taguchi Method for Tissue Engineering Application

Composite nanofibers for tissue engineering applications were prepared with polyvinylidene fluoride and polyvinyl alcohol via a horizontal electrospinning process. The effect of the processing parameters (polymer concentration, voltage, feed rate and tip-to-collector distance) on the fibre diameter was studied using L9 orthogonal arrays of the Taguchi Method, S/N ratio, and ANOVA to produce uniform and finest fibre. The optimum parameters were at 90 wt% PVDF ratios for polymer concentration, applied voltage at 10 kV, feed rate at 0.5 mL/h and tip-to-collector distance at 15 cm. The PVDF/PVA electrospun compromises 171.60 nm nanofiber diameter with homogenous morphology and no bead formed. MTT and live/dead kit assay analysis toward human (HSF) cells confirmed the potential of the PVDF/PVA electrospun to be used as an alternative tissue template to allow new cell or tissue formation in the future.

Bio-sourced multifunctional adsorbent of chitosan and adipic acid activated-dragon fruit peels for organic dye removal from water: Eco-friendly management and valorization of biomass

Employing sustainable biomaterials obtained from waste biomass and biopolymers provides a very promising method for eliminating organic dyes from wastewater. This study presents a novel adsorbent of modified dragon fruit peels and chitosan, addressing wastewater treatment and environmental waste management. Precisely, a bio-sourced multifunctional (high level of functional groups) adsorbent (hereinafter, CHS/DFP-ADP) was developed from chitosan and adipic acid activated-dragon fruit (Hylocereus polyrhizus) peels. This biomaterial was applied to effectively adsorb organic pollutants (safranin O dye, SAF-O) from water. The adsorption variables, namely A: CHS/DFP-ADP dosage (0.02–0.08 g), B: pH (4–10), and C: duration (10–40 min), were modeled and optimized using the Box-Behnken Design (BBD). The results of the BBD model showed the optimum adsorption parameters for achieving the highest level of SAF-O removal (94.83 %) were as follows: CHS/DFP-ADP does = 0.049 g, the pH ∼ 10, and the contact duration = 40 min. The experimental results on the dye adsorption by CHS/DFP-ADP demonstrated conformity with the pseudo-first-order and Freundlich models. The biomaterial demonstrated a significant capability to adsorb SAF-O dye, with an adsorption capacity of 607.2 mg/g. The adsorption process of the cationic dye on the CHS/DFP-ADP involves several interactions, such as Yoshida H-bonding, electrostatic forces, n-π, and H-bonding. This work aligns with the principles of green chemistry and sustainable development, offering an innovative approach to tackle environmental concerns and promote the circular economy. The present effort meets several Sustainable Development Goals (SDGs), such as SDG 6 (Clean Water and Sanitation), SDG 12 (Responsible Consumption and Production), SDG 13 (Climate Action), and SDG 14 (Life Below Water).

Hybrid CNC–MXene Nanolubricant for Tribological Application: Characterization, Prediction, and Optimization of Thermophysical Properties Evaluation

In the present work, hybrid Cellulose Nanocrystal–MXene (CNC–MXene) nanolubricants were prepared via a two-step method and investigated as potential heat-transfer hybrid nanofluids for the first time. CNC–MXene nanolubricants were synthesized via a two-step method by varying the weight percentage of CNC–MXene nanoparticles (ranging from 0.01 to 0.05 wt%) and characterized using Fourier-Transform Infrared Spectroscopy and TGA (Thermogravimetric Analysis). Response surface methodology (RSM) was used in conjunction with the miscellaneous design model to identify prediction models for the thermophysical properties of the hybrid CNC–MXene nanolubricant. Minitab 18 statistical analysis software and Response Surface Methodology (RSM) based on Central Composite Design (CCD) were utilized to generate an empirical mathematical model investigating the effect of concentration and temperature. The analysis of variance (ANOVA) results indicated significant contributions from the type of nanolubricant (p < 0.001) and the quadratic effect of temperature (p < 0.001), highlighting non-linear interactions that affect viscosity and thermal conductivity. The findings showed that the predicted values closely matched the experimental results, with a percentage of absolute error below 9%, confirming the reliability of the optimization models. Additionally, the models could predict more than 85% of the nanolubricant output variations, indicating high model accuracy. The optimization analysis identified optimal conditions for maximizing both dynamic viscosity and thermal conductivity. The predicted optimal values (17.0685 for dynamic viscosity and 0.3317 for thermal conductivity) were achieved at 30 °C and a 0.01% concentration, with a composite desirability of 1. The findings of the percentage of absolute error (POAE) reveal that the model can precisely predict the optimum experimental parameters. This study contributes to the growing field of advanced nanolubricants by providing insights into the synergistic effects of CNC and MXene in enhancing thermophysical properties. The developed models and optimization techniques offer valuable tools for tailoring nanolubricant formulations to specific tribological applications, potentially leading to improved efficiency and durability in various industrial settings.