Response Surface Methodology Approach Research Articles

Evaluation of free convection cooling and entropy efficiency in a porous W-shaped cavity using RSM and CFD approaches

The cooling and entropy efficiencies of natural convection inside cavities within fully saturated metal foam play a vital role in thermal engineering applications systems. The current study employed response surface methodology (RSM) coupled with ANSYS FLUENT-CFD for a two-dimensional W-shaped cavity, which has not been examined previously. Several effective physical factors were considered, including the aspect ratio (W = h/H = 0, 0.2, 0.4, 0.6 and 0.8) and the applied temperature variation (ΔT = Th − Tc = 10, 20, 30 and 40 K). Additionally, the thermal performance (NumW/NumO and QW/QO ) and entropy generation (EGW/EGO ) improved under the optimum design of pore per inch (10 ≤ PPI ≤ 30), the number of wave amplitude (1 ≤ n ≤ 3) and aspect ratio (0.2 ≤ W ≤ 0.8) utilizing both RSM and CFD. The results showed that the optimal design of natural convection within a fully saturated metal foam W-shaped cavity at PPI = 10, n = 3 and W = 0.8 resulted in enhancements of approximately 2.875 times for both NumW/NumO and QW/QO while maintaining reasonable EGW/EGO at 1.193 times. Moreover, the novelty aspect of this research lies in the successful achievement of multi-objectives improving NumW/NumO , QW/QO and EGW/EGO by applying the RSM approach in combination with ANSYS FLUENT-CFD. Thus, the optimal working procedure of RSM and CFD is aligned with the desired goals, as the cooling and entropy efficiencies of the porous W-shaped cavity are considered acceptable and satisfactory.

Effect of Thermally Extracted Carrot Oil Using Biomass Solvent to Boost Consumer Goods: Box-Behnken Design Approach

Massive production of fast-moving consumer goods boosts the economy of any nation. Thus, the need to look into ways of accelerating and transforming carrot utilization into more commercialized usage in order to add value to the developmental strides of any nation where carrot is cultivated like Nigeria. This study concluded that the optimization of the carrot oil extracted from crushed carrot and coconut oil (biomass volume) via soxhlet equipment using Box-Behnken Design (BBD) of Response Surface Methodology (RSM) approach, generated an ideal optimal output of 18.58 wt% at SW of 60 g, BV of 200 ml and ET of 14mins. Although, three input elements which are sample weight (SW), biomass volume (BV), and extraction time (ET) were modelled to generate 17 setups by BBD, but standard order (Std) 8 from the 17 setups generated an ideal output of 18.58 wt%. Thus, the process optimization of the extraction process via BBD of RSM showed that both the coefficient of determination (R2) which is 0.9886, and the difference between adjusted R2 and predicted R2 which is less than 0.2 fall within an ideal range. Also, the elements considered for modeling are mutually significant with the quadratic model equation and the interactive effects of the independent parameters on the carrot oil output showed that an increase in the sample weight and time led to a corresponding increase in the carrot oil output, and this is corroborated by the perturbation plot. Additionally, the physiochemical valuables of the carrot oil generated showed that the free fatty acid and the acid value were very low signifying that the carrot oil is edible and can be used in the manufacturing process using the saponification method in making soap and moisturizing cream for the benefit of mankind.

Biodiesel production and exploring properties of Datura stramonium L. oil with its optimization using combined approaches-Taguchi, grey relational analysis, and response surface methodology.

Biodiesel production through the synthesis of Datura stramonium L. oil is studied to explore the most efficient approaches to suggest an alternate feedstock for biodiesel production. The main objective of this work is to optimize the process variables of biodiesel synthesis by using some statistical approach (Taguchi method, grey relational analysis (GRA), and response surface methodology (RSM) analyzing three parameters, i.e., alcohol-to-oil molar ratio, catalyst (NaOH) concentration, and process temperature for achieving maximum biodiesel derived from Datura stramonium L. oil. The transesterification process is applied by using an ultrasonic-assisted technique. Grey relational analysis (GRA) was successfully applied with the Taguchi method resulting in the optimum combination of A2B1C1. Based on the findings, the best operating conditions for transesterifying are attained with the RSM approach consisting of a 5.697:1 molar ratio (level 2), 0.3 (wt.%) NaOH concentration (level 1), and 70 °C process temperature (level 1). With a value of 87.02%, these ideal operating conditions produce the maximum yield as compared to grey relational analysis (GRA) yields 83.99%. The obtained results have been verified through the characterization of oil and biodiesel as well. Also, the fuel qualities of DSL biodiesel were identified and assessed. DSL oil was found 137.6 degrees of unsaturation during fatty acid profile analysis. DSL biodiesel was found the best kinematic viscosity (4.2 mm2/s) and acid value (0.49) when compared to Karanja and palm biodiesel. D. stramonium L. was recognized as a suitable species for biodiesel feedstock according to the findings.

Comprehensive assessment of the extraction of Phyllanthus emblica L. based on the G1-Entropy Method and Response Surface Methodology

In this study, the G1-Entropy method was employed together with a response surface methodology (RSM) approach to establish a multi-index-based system for evaluating and optimizing the extraction of bioactive compounds from Phyllanthus emblica L. Seven chemical classes' contents were evaluated, including TPC (total phenolic content), TC (tannin content), DEY (dry extract yield), GA (gallic acid), CO (corilagin), CA (chebulagic acid), and EA (ellagic acid). The resultant extracts were also assessed for their antioxidant activity, as measured based on their DPPH radical, ABTS+, and hydroxyl radical scavenging activity levels and their capacity to reduce iron ions. The inhibition of fluorescent AGEs generation was also assessed in BSA-Fru and MGO-BSA model assays to gauge the anti-glycation activity of these extracts. In total, a comprehensive score was derived based on 13 indices and corresponding weight values, with these scores then being used to determine the extraction conditions that yielded the best outcome. Through this strategy, the ideal extraction processing parameters were found to include an ethanol concentration of 56%, a liquid-solid ratio of 8 mL/g, and an extraction duration of 100 min. Using these optimized conditions yielded a predicted comprehensive score (Y) of 94.91, while the response value based on three independent variables from the validation experiments performed using these conditions was 92.03. These results highlight the promise of leveraging Phyllanthus emblica as a resource for use in the cosmetic, food, biochemical, and pharmaceutical industries.

Prediction of Compressive Strength of Silica Fume Blended High Strength Concrete Using Response Surface Methodology Approach

Objectives: In this study, a model was developed to predict the compressive strength of High Strength Concrete (HSC) mixed with silica fume using Response Surface Methodology (RSM). This study investigated the effects of cement, water, Silica Fume (SF), Coarse Aggregate (CA), and silica fume-cement ratio (SF/C) on the 28-day compressive strength of HSC. Silica fume is added with varying amounts of SF (5% to 25%) to cement content. Methods: Response surface methodology (RSM) was performed to investigate the influence of independent variables on the compressive strength of HSC. Findings: Analysis of the response surface plot reveals a remarkably low error percentage of less than 5%. This reveals a high degree of confidence (95%) in the model's accuracy. This study yielded a coefficient of determination (R2) of 0. 9968. It is observed negligible deviation between predicted and actual 28-day compressive strength values, indicating high model accuracy. Novelty: The predicted equation is reasonably predicting the compressive strength of high strength concrete. Keywords: High strength concrete, Response surface methodology, Silica fume, Compressive strength, Prediction model

Modeling the Properties of Sustainable Self-Compacting Concrete Containing Marble and Glass Powder Wastes Using Response Surface Methodology

This study aims to apply the response surface methodology (RSM) to develop a statistical model that predicts and models the performance of both the fresh and hardened properties of self-compacting concrete (SCC). RSM was used to model processes involving three variables: the water/binder ratio, and the percentages of waste marble, and glass powder. Tests, including slump flow diameter, sieve stability, and L-box, were carried out to evaluate the fresh properties of the self-compacting concrete; compressive strength was analyzed at 7, 28, and 90 days. Statistical significance was only observed in the water/binder ratio for both the slump flow and sieve stability tests. Furthermore, these results indicate that the models used in the compressive strength tests demonstrate a high statistical significance for all ages. The findings suggest that incorporating waste marble powder (MP) and glass powder (GP) in SCC necessitates a significant amount of superplasticizer to counteract the workability loss, and it improves the compressive strength of SCC. The coefficients analyzed using the RSM approach validate its effectiveness as a predictive tool for determining the hardened properties of self-compacting concrete.

Fabrication optimization of polyethersulfone adsorptive mixed matrix membrane for enhanced dynamic methylene blue removal: Response surface methodology approach

AbstractIn the present study, the polyethersulfone (PES)/graphene oxide (GO)/polyethylene glycol 200 (PEG‐200) adsorptive mixed matrix membranes (AMMMs) were fabricated using a non‐solvent induced phase inversion method. According to the design of experiments using response surface methodology (RSM), PES concentration (15, 17.5, and 20%), PEG‐200 concentration as a pore former additive (0.5, 2.5, and 4.5%), and GO nanosheet loading percent (0, 0.5, and 1%) were varied during fabrication. A green solvent, dimethyl sulfoxide, was used as an alternative to conventional solvents, while it exhibited better membrane performance. Methylene blue (MB) removal was optimized in PES AMMM synthesis using RSM. Attenuated total reflectance Fourier transform infrared spectroscope confirmed the presence of GO functional groups in the optimum membrane. Scanning electron microscopy analysis revealed the optimum membrane was thicker than the neat membrane and atomic force microscopy analysis demonstrated higher surface roughness. The membranes hydrophilicity increased, with a decrease in water contact angle from 61.1 ± 0.2° (neat PES) to 54.4 ± 0.9° (optimal membrane). Pure water flux of 524.8 L m−2 h−1 and MB rejection of 97.46% were obtained for the optimum membrane. Furthermore, the optimum PES AMMM demonstrated a greater dynamic adsorption capacity in comparison to the unmodified PES membrane.Highlights The optimization of the polyethersulfone (PES) adsorptive mixed matrix membranes (AMMM) for methylene blue removal was performed utilizing the response surface methodology. The calculation of the dynamic adsorption capacity (DAC) was performed through the breakthrough curves. The integration of graphene oxide into the AMMM resulted in an enhanced DAC of the AMMM. The PES AMMM performance is based on Donnan electrostatic exclusion.

Optimizing smart building energy management systems through industry 4.0: A response surface methodology approach

Effective control of inner climatic conditions in a Heating ventilation and air conditioning (HVAC) system based on efficiency of the system is imperative to ensure occupant comfort, energy efficiency, and preservation of indoor air quality. The existing problems with HVAC system includes inefficient adjustment to internal room parameters and limited optimization based on varying loads. The current study investigates the influence of Industry 4.0 by assessing internal room parameters in an indoor ventilated room conditions for system output. Smart systems like AI and IoT, integrated with sensors, dynamically adjust indoor conditions based on external climate, enhancing HVAC system efficiency. Using Artificial Neural Networks (ANN) and Response Surface Methodology (RSM), integrated with AI and IoT sensors, optimally adjust indoor conditions in correlation with external climate, maximizing HVAC system efficiency. The study utilizes input parameters including Dry Bulb Temperature (DBT), Relative Humidity (RH), and Solar Radiation across diverse days to analyze system efficiency. ANN and RSM iteratively optimize system efficiency by training on RH, DBT, and air quality data. They converge on recommending 42% RH, 28 °C DBT, and 25 PM air quality, achieving 0.851 desirability. This approach enhances heat load (75.89%) and ventilation load (69.12%) efficiency significantly. Integration of Industry 4.0 sensors in the HVAC system resulted in a 16% efficiency increase and 15% cost effectiveness in the controlled room compared to prior measurements.

Prediction of steel plate-based damper for improving the behavior of concentrically braced frames based on RSM and ML approaches for sustainable structures.

The stiffness (K) and slenderness factor (λ) of a steel plate-based damper has been studied on the basis of elastic-inelastic-plastic buckling (EIP) modes and flexural/shear/flexural-shear failure mechanisms (FSF-S), which has been designed for the improvement of the behavior of concentrically braced frames. Steel plate-based dampers offer significant benefits in terms of mode shapes and failure mechanisms, contributing to improved dynamic performance, enhanced structural resilience, and increased safety of civil engineering structures. Their effectiveness in mitigating dynamic loads makes them a valuable tool for engineers designing structures to withstand extreme environmental conditions and seismic events. This study was undertaken by using the learning abilities of the response surface methodology (RSM), artificial neural network (ANN) and the evolutionary polynomial regression (EPR). Steel plate dampers are special structural designs used to withstand the effect of special loading conditions especially seismic effects. Its design based on the prediction of its stiffness (K) and slenderness factor (λ) cannot be overlooked in the present-day artificial intelligence technology. In this research work, thirty-three entries based on the steel plate damper geometrical properties were recorded and deployed for the intelligent forecast of the fundamental properties (λ and K). Design ratios of the steel plate damper properties were considered and models behavior was recorded. From the outcome of the model, it can be observed that even though the EPR and ANN in that order outclassed the other techniques, the RSM produced model minimization and maximization features of the desirability levels, color factor scales and 3D surface observation, which shows the real model behaviors. Overall, the EPR with R2 of 0.999 and 1.000 for the λ and K, respectively showed to be the decisive model but the RSM has features that can be beneficial to the structural design of the studied steel plate damper for a more robust and sustainable construction. With these performances recorded in this exercise, the techniques have shown their potential to be applied in the prediction of steel damper stiffness with optimized characteristic features to withstand structural stresses.

Optimization of operational parameters using central composite design in the peroxi‐alternating current‐electrocoagulation process for the pollutant removal with determination of power consumption from industrial wastewater

AbstractThe utilization of electrochemical and advanced oxidation technologies for industrial wastewater (IW) treatment has grown in popularity during the last two decades. The effectiveness of several methods for treating IW, including hydrogen peroxide (H2O2), direct‐current (DC) and alternating‐current (AC)‐electrocoagulation (EC), and the combination of H2O2 with DC/AC‐EC (H2O2‐DC/AC‐EC) processes were all investigated. In comparison to the H2O2, DC/AC‐EC, and H2O2‐DC/AC‐EC technologies, the results showed that the H2O2‐AC‐EC process produced 100% total colour and 100% chemical oxygen demand (COD) removal efficiency with a low power consumption of 4.4 kWhm−3. The H2O2/AC‐EC technology was optimized for treating IW using a response surface methodology approach based on a central composite design using a five‐factor level. Utilizing statistical and mathematical techniques, the optimum parameters were determined to minimize consumption of power (1.02 kWhm−3) and maximum COD elimination (75%). The experimental parameters comprised the following: H2O2 of 600 mg/L, current of 0.65 Amp, pH of 7.6, COD of 1600 mg/L, and treatment time (TT) of 1.26 h. When using a Fe/Fe electrode combination with the wastewater pH of 7, the COD removal efficiency was shown to be enhanced by increasing the TT, current and H2O2, and decreasing the COD concentration. The synergistic impact, quantified as the combined efficiency of eliminating % COD utilizing the H2O2, AC‐EC, and H2O2/AC‐EC procedures, was found to be 15.75%. Therefore, employing a hybrid H2O2‐AC‐EC approach is considerably more effective in treating IW.

Optimizing Seaweed (Ascophyllum nodosum) Thermal Pyrolysis for Environmental Sustainability: A Response Surface Methodology Approach and Analysis of Bio-Oil Properties

This study focuses on optimizing the thermal pyrolysis process to maximize pyrolysis oil yield using marine biomass or seaweed. The process, conducted in a batch reactor, was optimized using response surface methodology and Box–Behnken design. Variables like temperature, residence time, and stirring speed were adjusted to maximize bio-oil yield. The optimal conditions yielded 42.94% bio-oil at 463.13 °C, with a residence time of 65.75 min and stirring speed of 9.74 rpm. The analysis showed that temperature is the most critical factor for maximizing yield. The bio-oil produced contains 11 functional groups, primarily phenol, aromatics, and alcohol. Its high viscosity and water content make it unsuitable for engines but suitable for other applications like boilers and chemical additives. It is recommended to explore the potential of refining the bio-oil to reduce its viscosity and water content, making it more suitable for broader applications, including in engine fuels. Further research could also investigate the environmental impact and economic feasibility of scaling up this process.

Sustainable and Circular Adsorption of Anionic Dye from Textile Wastewater Using Economical Adsorbent

ABSTRACT Dyes are significant contributors to the chemical oxygen demand of the wastewater produced by textile industries. In the current study, naturally occurring, inexpensive, non-hazardous clay was pillared and used to remove Acid Green 25 dye (AG) from aqueous solutions. The pseudo-second-order model provided a better fit to describe the adsorption kinetics data for AG, with a rate constant of 9.82 × 10−3 g/mg min. Langmuir isotherm provided a better fit for describing the adsorption isotherm of the AG dye. Thermodynamic analysis revealed that adsorption was exothermic with an enthalpy change of −48 kJ/mol. The effects of pH, adsorbent dosage, and initial concentration of AG dye on adsorption capacity were analyzed, and an empirical model was developed using the response surface methodology approach. Optimization studies revealed that a maximum adsorption capacity of 105.15 mg/g for the AG dye could be achieved at a pH of 2, an adsorbent dosage of 1.5 g/L and an initial dye concentration at its upper limit of 200 mg/L. The adsorbent could be regenerated and reused up to three adsorption cycles, and the desorbed dye and treated water may be reused in the dyeing process, thereby making the entire operation circular. Circularity reduces the cost of production and makes the process more sustainable. Mechanistic insights revealed that electrostatic interaction was the driving force for the adsorption of AG dye. In this work, it has been demonstrated that after pillaring, clay-based material may be used as an effective adsorbent for treating dye-containing wastewater.

Response Surface Methodology Approach to Optimize the Expression of Thioredoxin-MOG Fusion Protein

Background: The N-terminal domain of the myelin oligodendrocyte glycoprotein (MOG) has been shown to generate experimental autoimmune encephalomyelitis (EAE), an animal model of MS. A considerable amount of MOG must be accessible for EAE induction. Here, for the first time, Response Surface Methodology-Box-Behnken (RSM-BBD) was employed to identify the ideal culture conditions for causing Escherichia coli (E. coli) BL21 to overproduce the Thioredoxin-MOG (Trx-MOG) fusion protein. The RSM method is a powerful, efficient, and reliable alternative to the One-Factor-At-A-Time (OFAT) method in optimizing process variables, allowing for a smaller number of experimental runs, investigating variable interaction, and being cheaper and less time-consuming. Methods: Here, using the 29 experimental assays, the direct and indirect effects of factors including post-induction time, IPTGinducer concentration, pre-induction optical density, and post-induction temperature on the protein expression level content were evaluated. Results: The proposed quadratic model demonstrated a significant effect of the two variables A (time) and C (temperature) on protein synthesis. An inducer concentration of 0.491 mM, the pre-induction optical density (OD600) of 0.8, and a temperature of 23 °C for 23.878 hours were found to be the best growth conditions for high yield Trx-MOG synthesis. The optimum protein concentration was attained (163.96 µg/mL) and was within the range of (200.04 µg/mL), which was the value predicted. Conclusion: The study concluded that RSM optimization effectively increased the production of Trx-MOG in E. coli, which could have the potential for large-scale fermentation.

Multi-Objective Parametric Optimization of Micro-Electro Discharge Machining of Hastelloy C276 Super Alloy Using Response Surface Methodology and Particle Swarm Optimization

The need for the application of superalloys in aerospace industries in recent years has increased owing to its benefits such as extensive load-bearing capability under high temperatures. Hastelloy is one such superalloy that is extensively utilized in the aerospace sector because of its good corrosion and heat resistance among the other nickel-based superalloys. In this work, the investigation is conducted to understand the effects of input process parameters such as voltage, pulse off time (Toff), and pulse on time (Ton) on the response variables, namely Material removal rate (MRR), Tool wear rate (TWR), Overcut (OC), and Taper Ratio (TR) during micro-EDM of Hastelloy C276. For micro drilling the Hastelloy C276 material, a copper electrode with a diameter of 0.5 mm is utilized. To investigate the connections between the input and output characteristics, a technique known as the Response Surface Methodology (RSM), which is a collection of mathematical and statistical methodologies, is applied. The experimental runs are carried out with the help of the RSM-based Box-Behnken design (BBD). The experimental outcomes were computed, and ANOVA was used to identify the most influential variables. In addition, particle swarm optimization (PSO) was utilized to optimize the results, which were compared to the Response surface methodology approach. The outcomes of the PSO-optimized results revealed a strong correlation between expected and experimental outcomes over the RSM approach.

A response surface methodology approach for the removal of methylene blue dye from wastewater using sustainable and cost-effective adsorbent

Potable water availability is becoming increasingly challenging due to increasing level of global population and industrial revolution. The disproportionate use of methylene blue (MB), particularly in industrial applications, is a growing concern due to its high resistance to biodegradation and propensity to taint aquatic environments. In this study, we developed novel eco-friendly calcium oxide nanoparticles from eggshells and fishbones (CaONPs-ES and CaONPs-FB) and decorated them on graphene oxide (GO) surfaces. Both nanocomposites (CaONPs-ES@GO and CaONPs-FB@GO) were characterized using state-art-instruments and used for the removal of MB from aqueous solutions. transmission electron. Additionally, the adsorptive performance of CaONPs-ES@GO and CaONPs-FB@GO and their mechanisms of interaction with MB were investigated. BET, SEM/EDX, and XPS results revealed that the CaONPs-ES@GO and CaONPs-FB@GO were predominantly mesoporous, with surface areas of 112 m²/g and 108 m²/g, respectively. The temperature-dependent adsorption isotherms and kinetics of CaONPs-ES@GO and CaONPs-FB@GO towards MB were consistent with Redlich-Peterson and pseudo-second-order models, respectively. The Redlich-Peterson model demonstrated an adsorption similarity to the Freundlich model more than the Langmuir model, suggesting the dominance of a heterogeneous multilayer mechanism. The synthesized nanocomposites exhibited high reusability and stability for MB adsorption (>70%) even after 10 successive adsorption-desorption cycles. Thermodynamic evaluations revealed that the adsorption process was spontaneous, endothermic, and physically driven. The nanocomposites exhibited an outstanding selective adsorption behaviour towards MB from the mixture containing MB/RhB and MB/MO with separation efficiency of 99.10% and 77.34% for CaO-ES@GO, and 61.23% and 47.81% for CaO-FB@GO respectively. The particulate interaction mechanisms within the nanocomposites primarily involved π-π interaction, hydrogen bonding, pore-filling, and electrostatic attraction. The cost analysis revealed that the developed nanocomposites are more economical for treating MB in a large-scale application. Based on the statistical analysis using response surface methodology (RSM), the contributing effects of temperature and adsorbent dosage, as well as the single effect of pH, had the most significant impact on MB removal. The nanocomposites demonstrate a promising potential for sustainable MB treatment.

Optimization of the extracellular secretion of black goat rumen metagenome-derived KG42 xylanase by Bacillus subtilis

Xylanase (E.C. 3.2.1.8) is the enzyme that breaks down β-1,4 xylan by cleaving β-1,4 glycosidic linkages. Production of xylanases is important for various industrial applications. Here, we aimed to determine the optimal incubation conditions for expression and secretion of KG42 xylanase in Bacillus subtilis using response surface methodology based on Box-Behnken design in preparation for industrial applications. Among nine broth media tested in this study, Power Broth was chosen as a basal medium. In addition to the basal medium, the four other independent variables of extra carbon sources (glucose, lactose, mannose, fructose, and sucrose), extra nitrogen sources (beef extract, yeast extract, tryptone, urea, NaNO3, and (NH4)2SO4), isopropyl β-D-1-thiogalactopyranoside concentrations, and induction times were individually tested using one factor at a time in an optimization experiment. Next, a Box-Behnken design-based response surface methodology approach was used to identify and validate the optimized incubation conditions with the four variables in batch culture. The statistically optimized incubation conditions obtained from this study yielded a maximum of approximately 3- to 4-fold increases in the expression and secretion of KG42 xylanase by B. subtilis in comparison with unoptimized medium and incubation conditions.

Experimental Modeling and Multi-Response Optimization in Friction Stir Welding Process Parameters of AA2024-T3 Using Response Surface Methodology and Desirability Approach

Experimental Modeling and Multi-Response Optimization in Friction Stir Welding Process Parameters of AA2024-T3 Using Response Surface Methodology and Desirability Approach

Unlocking the anti-aging ingredients of Sacha inchi husk through ultrasound-assisted extraction: Response surface methodology and comprehensive analytical approach

Sacha inchi husk (SIH), brown covering material of the seed, is considered as a by-product from edible oil production, yet it might be a functional ingredient for anti-aging applications owing to its polyphenols. The aims of this study were to evaluate the anti-aging functional properties including antioxidant and anti-enzymatic capacities of the optimally extracted SIH which was achieved by optimizing the ultrasound-assisted extraction (UAE) using response surface methodology (RSM), with a focus on identifying the most effective conditions for maximal yield and polyphenol content. In addition, UHPLC-ESI-QTOF-MS/MS was employed for phytochemical profiling of the extract. Furthermore, a docking simulation was conducted to elucidate the binding affinities of identified phytochemicals towards enzymes related to the aging process. Besides, the safety evaluation was determined in terms of cytotoxicity and irritation profile through Hen's egg test chorioallantoic membrane as an alternative method used to evaluate the irritation potential of chemicals. From the results of extraction optimization, the empirical mathematic models from RSM implied that a solvent composition of 70 % v/v ethanol in DI water, a Solvent-to-Plant ratio of 12:1, and a sonication time of 40 min, yielded an extract with high polyphenol content and favorable yield. Additionally, UAE markedly enhanced extraction efficiency compared to conventional maceration. Besides, the optimal extract demonstrated significant antioxidant and anti-enzymatic effects against collagenase, elastase, and hyaluronidase. Phytochemical analysis revealed the presences of hydroxy acids, sphingoids, catechins and flavonoids of which kaempferol 3-(2″-p-coumarylglucoside), catechin, and epigallocatechin exhibited strong enzyme binding affinities with dock scores less than −6.5 kcal/mol. Furthermore, safety evaluation showed no irritation at a concentration of 1 mg/mL, and non-toxicity to human dermal fibroblasts at concentrations ranging from 0.001 to 0.1 mg/mL. In conclusion, our findings demonstrated that the SIH could be a promising functional ingredient for anti-aging applications showing significant antioxidant and anti-enzymatic effects.

A response surface methodology approach to crafting superior performance of potassium salt-based solid biopolymer electrolytes

In this study, we developed an eco-friendly solid electrolyte by blending pectin (PT) with methylcellulose (MC) and complexed with potassium carbonate (K2CO3) salt and ethylene carbonate (EC) plasticizer. The goal of the study was to understand the salt-plasticizer interaction and to optimize the electrochemical performance using response surface methodology (RSM) within the central composite design (CCD). RSM approach unveiled that K2CO3 and EC interact significantly, impacting the ionic conductivity and potential window of the solid biopolymer electrolytes (SBEs). After optimization, this work achieved ideal conditions with 35.05 wt% K2CO3 and 16.78 wt% EC, yielding an ionic conductivity of ∼ 1 × 10-3 Scm−1 and a 4.77 V potential window. Structural analysis confirmed coordination among constituents and increased amorphous content. This work, therefore, highlights PT/MC/K2CO3/EC's potential for electrochemical device applications. An electrochemical cell constructed using the optimized sample exhibited the highest specific capacitance of 50.74 Fg–1, as determined by cyclic voltammetry (CV) analysis.

Experimental Investigation to Optimize the Manufacturing Parameters of Ankle–Foot Orthoses Using Composite and Titanium Nanoparticles

The optimum structural characteristics of lamination materials used in the fabrication of prosthetic and orthotic parts were investigated in this work. Optimization was chosen based on high yields, ultimate stresses, and bending stress properties. The ideal materials were determined through the use of an RSM (response surface methodology) which considers three factors: Perlon reinforcement, a layer of glass fiber, and the percentage of titanium nanoparticles combined with the matrix laminating resin. The RSM approach suggests thirteen samples by manipulating two variables: the Ti nano percentage and the number of Perlon layers. Laminating materials, defined by RSM methods and treated with a vacuum system, were submitted to a series of tests. The ideal lamination material was compared with the laminations from the initial study through the use of tensile, flexural, and fatigue testing according to ASTM standards. Tests carried out using version 10.0.2 of Design Expert software showed that, compared with the 12 other laminations, the one with 10 Perlon layers and 0.75 percent Ti nano had the highest overall yield and ultimate and bending loads. Fatigue eventually showed that stamina tension constraints were applied for optimal lamination, compared to ten Perlon lamination layers. We additionally tested the fatigue life of the best material and compared it with the available materials used at prosthetics and orthotics centers.