Response Surface Plots Research Articles

Separation and Detection of Catechins and Epicatechins in Shanxi Aged Vinegar Using Solid-Phase Extraction and Hydrophobic Deep Eutectic Solvents Combined with HPLC.

This research presents a new, eco-friendly, and swift method combining solid-phase extraction and hydrophobic deep eutectic solvents (DES) with high-performance liquid chromatography (SPE-DES-HPLC) for extracting and quantifying catechin and epicatechin in Shanxi aged vinegar (SAV). The parameters, such as the elution solvent type, the XAD-2 macroporous resin dosage, the DES ratio, the DES volume, the adsorption time, and the desorption time, were optimized via a one-way experiment. A central composite design using the Box-Behnken methodology was employed to investigate the effects of various factors, including 17 experimental runs and the construction of three-dimensional response surface plots to identify the optimal conditions. The results show that the optimal conditions were an HDES (tetraethylammonium chloride and octanoic acid) ratio of 1:3, an XAD-2 macroporous resin dosage of 188 mg, and an adsorption time of 11 min. Under these optimal conditions, the coefficients of determination of the method were greater than or equal to 0.9917, the precision was less than 5%, and the recoveries ranged from 98.8% to 118.8%. The environmentally friendly nature of the analytical process and sample preparation was assessed via the Analytical Eco-Scale and AGREE, demonstrating that this method is a practical and eco-friendly alternative to conventional determination techniques. In summary, this innovative approach offers a solid foundation for the assessment of flavanol compounds present in SAV samples.

Optimization of wear parameters for ECAP-processed ZK30 alloy using response surface and machine learning approaches: a comparative study

The present research applies different statistical analysis and machine learning (ML) approaches to predict and optimize the processing parameters on the wear behavior of ZK30 alloy processed through equal channel angular pressing (ECAP) technique. Firstly, The ECAPed ZK30 billets have been examined at as-annealed (AA), 1-pass, and 4-passes of route Bc (4Bc). Then, the wear output responses in terms of volume loss (VL) and coefficient of friction (COF) have been experimentally investigated by varying load pressure (P) and speed (V) using design of experiments (DOE). In the second step, statistical analysis of variance (ANOVA), 3D response surface plots, and ML have been employed to predict the output responses. Subsequently, genetic algorithm (GA), hybrid DOE–GA, and multi-objective genetic algorithm techniques have been used to optimize the input variables. The experimental results of ECAP process reveal a significant reduction in the average grain size by 92.7% as it processed through 4Bc compared to AA counterpart. Furthermore, 4Bc exhibited a significant improvement in the VL by 99.8% compared to AA counterpart. Both regression and ML prediction models establish a significant correlation between the projected and the actual data, indicating that the experimental and predicted values agreed exceptionally well. The minimal VL at different ECAP passes was obtained at the highest condition of the wear test. Also, the minimal COF for all ECAP passes was obtained at maximum wear load. However, the optimal speed in the wear process decreased with the number of billets passes for minimum COF. The validation of predicted ML models and VL regression under different wear conditions have an accuracy range of 70–99.7%, respectively.

Fluoride removal using tartaric acid-modified rice husk biochar: Comprehensive batch and column studies

Tartaric Acid-modified Rice Husk biochar (TARH) was evaluated as an efficient and cost-effective adsorbent to eliminate Fluoride (F¯) ions from aqueous solutions. F¯ is a major contaminant in groundwater, and current conventional treatment methods have certain drawbacks in treating higher concentrations of fluoride. The adsorption efficiency of TARH was improved by pre-treating rice husk biochar using tartaric acid (organic acid), which was confirmed by FT-IR measurement, indicating the presence of carboxylic acids, hydroxyl groups, and amine surface functional groups. The study optimized fluoride batch adsorption experiments by considering the parameters affecting adsorption, including pH, contact time, initial concentration, and adsorbent dosage using the Central Composite Design (CCD) from Response Surface Methodology (RSM). Maximum fluoride adsorption of 74.73% was attained by TARH under ideal circumstances (an initial fluoride concentration of 32 mg/L, a pH of 7, 0.25 g/100 mL of adsorbent dosage, and 180 minutes contact duration). The CCD models showed an exceptional R2 value of 0.988 for fluoride adsorption, illustrating their efficacy. Three-dimensional response surface plots were visualized to analyse the effects of control parameters on %adsorption, and statistical analysis supported the validity of the CCD model. Isotherm models and adsorption kinetics were investigated. The adsorption exhibited monolayer adsorption according to the Langmuir isotherm model and a pseudo-second-order rate-limiting phase due to chemisorption. The column adsorption studies were performed for various experimental factors such as influent fluoride concentration (4–16 ppm), influent flow rate (4–8 mL/min), and fixed-bed depth (4–8 cm). The experimental data were examined using the Yoon-Nelson, Thomas, and BDST models, which revealed a substantial correlation between the experimental findings and model predictions. The effectiveness of TARH was examined by regeneration study and case study was performed to evaluate the fluoride removal from actual water samples.

Comparative study of different machine learning approaches for predicting the compressive strength of palm fuel ash concrete

Nowadays, the challenge of industrialising concrete is to produce economical, ecological and efficient concrete. As a result, cementitious additions are increasingly used in concrete binders with proportions conferring the desired properties, particularly compressive strength. Palm oil fuel ash (POFA) from palm trees is one of these additions and introduced by substitution of cement in concrete. Although laboratory tests applied to concrete samples are expensive and time-consuming, considerable research is directed towards modelling approaches and machine learning (ML) applications. The purpose of this study is to explore different ML techniques, including artificial neural network (ANN), ANN with combined inputs (ANNX), particle swarm optimisation (PSO) and genetic algorithm (GA). These techniques are used to predict the compressive strength of POFA concrete derived from a dataset consisting of 249 samples with regard to 6 input parameters, namely, cement and POFA contents, aggregate ratio, water-to-binder ratio, superplasticiser dosage and curing duration. The performance of these techniques is evaluated using six performance indices: correlation coefficient, Willmott index of agreement, Nash–Sutcliffe efficiency, root mean squared error, mean absolute error and mean squared relative error. The comparison shows that ANN and ANNX have better predictive accuracy than PSO and GA, and ANNX performs relatively better than ANN. Results are presented using a 3D surface response plot which visually depicts the relationship between predicted outcomes and influential parameters. The comparison made between ANNX and other models in the literature demonstrated that the present model is reliable to predict CS of POFA concrete with high precision in a wide range of data.

Application of group method of data handling and gene expression programming to modeling molecular diffusivity of CO2 in heavy crudes

Successful enhancement of heavy oil and bitumen production using CO2-based enhanced oil recovery (EOR) techniques requires extensive knowledge about the diffusivity of CO2 in heavy crudes. In this work, two advanced correlative approaches, namely group method of data handling (GMDH) and gene expression programming (GEP), were utilized to establish simple-to-use mathematical correlations by applying a comprehensive dataset including 260 laboratory data of CO2 diffusion coefficient (DC) in heavy crudes and taking into account all the parameters affecting the accuracy of the models. Moreover, the response surface and contour plots were analyzed to obtain the desired response values and operating conditions. The GMDH correlation represented more reliable results with better statistical criteria including average absolute percentage error (AAPRE) values of 7.47%, 7.83%, and 7.54% for training, testing, and the total sets, respectively. Also, the results showed that the increase in pressure and/or temperature caused an increment in the amount of diffusivity of CO2 in heavy crudes; and the maximum amount of CO2 DC was obtained at the upper limit of these operational parameters. At a given temperature and pressure, it appears that the highest CO2 DC in heavy crudes was obtained when the CO2 mass fraction was less than 0.02. Furthermore, the Pearson and Spearman correlation coefficients were used to investigate linear and non-linear relationships between input variables and the responses of GMDH and GEP correlations. The mass fraction of CO2, temperature, pressure, and temperature_PVT had a direct relationship, and on the other hand, the viscosity and density of crudes had a reverse relationship with CO2 DC. Finally, the reliability of the data bank of CO2 DC in heavy crudes along with the high validity of GMDH and GEP correlations was verified by the leverage technique. Notably, no out-of-leverage data were identified, with only seven and five data points considered as suspected for GMDH and GEP models, respectively.

Experimental and statistical study on the effect of process parameters on the quality of continuous fiber composites made via additive manufacturing

Ongoing research in additive manufacturing towards structural and industrial application has led to the use of commingled roving as a manufacturing feedstock for printing high fiber volume fraction composites. The prospects of using this technology for high performance applications necessitates the need for a comprehensive experimental investigation into the effects of processing parameters on the quality of an additively manufactured composite printed from commingled roving feedstock. In this work, transverse flexure and void fraction matrix pyrolysis testing are both performed to evaluate composite quality. The transverse flexure test is a testing approach that evaluates the quality of the interfacial fiber-matrix bond while the void fraction test estimates the void content in the printed composite. A full observational study consisting of 27 different test combinations is done to investigate the effects of three different process parameters namely, temperature, pressure, and print speed across three different levels. Composite samples were made from commingled roving of E-glass and amorphous PET using an in-house built continuous fiber composite digital manufacturing system. Least squares regression analysis is performed to study the main, interaction and quadratic effects of process parameters. A statistical regression model having an R2 adjusted value of 80.1% is generated from the transverse flexure study, which is used to explain main and interaction effects and also predict performance. Response surface plots are also generated and are used to optimize process parameters which can subsequently be of help in scaling up composite manufacturing. Results show that all three process parameters are highly statistically significant at the 0.01 level of significance. Pressure * Temperature and Pressure * Printspeed are significant interaction terms. Pressure plays a weightier role when print speed is increased or temperature is decreased as it closes more voids that would ordinarily have been introduced because of drop in polymer melt viscosity. Micrographic analysis is also performed.

Investigation of dissipation kinetics and half-lives of fipronil and thiamethoxam in soil under various conditions using experimental modeling design by Minitab software

To determine the extent of pesticide buildup and their environmental contamination, the environmental half-lives of pesticides are examined. The influence of the factors affecting the half-lives of fipronil and thiamethoxam including soil type, sterilization, temperature, and time and their interactions was studied using experimental modeling design by Minitab software. Based on the dissipation kinetics data, fipronil concentrations reduced gradually over 60 days while thiamethoxam concentrations decreased strongly. Also, fipronil and thiamethoxam dissipated more rapidly in calcareous soil than in alluvial soil. Thiamethoxam, however, disappeared more rapidly than fipronil in all treatments. Incubation at 50 °C leads to rapid the pesticide degradation. For prediction of the dissipation rate, model 5 was found to be the best fit, Residue of insecticide (%) = 15.466 − 11.793 Pesticide − 1.579 Soil type + 0.566 Sterilization − 3.120 Temperature, R2 = 0.94 and s = 3.80. Also, the predicted DT50 values were calculated by a model, DT50 (day) = 20.20 − 0.30 Pesticide − 7.97 Soil Type + 0.07 Sterilization − 2.04 Temperature. The shortest experimental and predicted DT50 values were obtained from treatment of thiamethoxam at 50 °C in calcareous soil either sterilized (7.36 and 9.96 days) or non-sterilized (5.92 and 9.82 days), respectively. The experimental DT50 values of fipronil and thiamethoxam ranged from 5.92 to 59.95 days while, the modeled values ranged from 9.82 to 30.58 days. According to the contour plot and response surface plot, temperature and sterilization were the main factors affecting the half-lives of fipronil and thiamethoxam. The DT50 values of fipronil and thiamethoxam increased in alluvial soil and soil with low temperature. In general, there is a high agreement between the experimental results and the modeled results.

Optimizing media and substrate for efficient anaerobic biological ammonia synthesis: A response surface methodology approach

Ammonia ranks among the most extensively produced natural and anthropogenic chemicals due to its immense applications, especially in agriculture as fertilizer. The global demand for ammonia has thus been projected to increase. Haber process, the conventional method of ammonia production is costly, energy intensive and responsible for ∼2% of CO2 emission. The rumen Hyper Ammonia Producing Bacteria (HAB) has the potential to convert protein to ammonia. To develop an industrial feasible ammonia production alternative, this study aims to optimize soy-based media composition for biological ammonia using response surface methodology (RSM). A one-factor-at-a-time (OFAT) statistical design was used to determine the most important media components affecting biological ammonia production. Eight media components were screened, and results showed that K2HPO4, KH2PO4, Na2SO4 and soy meal protein isolate (SMPI) had the most significant impact on biological ammonia production. The four identified media components were further optimized using a central composite design (CCD). The RSM model equation for independent and response variables was computed and used to create the response surface and contour plots. Results showed a maximum biological ammonia experimental yield of ∼7,500 mg/L at 0.0492 g/100 mL K2HPO4, 0.0492 g/100 mL KH2PO4, 0.64 g/100 mL Na2SO4, and 10 % SMPI. The observed yield was ∼80 % higher than the biological ammonia production from previous studies.

Loss of efficiency in flat belt quarter-turn drive system

In the flat belt quarter-turn drive systems, the drive efficiency loss is a noteworthy problem. However, many factors can lead to a loss of efficiency, such as large load, insufficient tension, and belt misalignment, but in the flat belt quarter-turn drive system, because of its special design principles, the belt misalignment becomes a very important factor. To address this issue, a set of flat belt quarter-turn drive devices and test systems are designed and built. To further study the causes of efficiency loss, a series of tests are carried out with the slip rate as the index and the belt misalignment, braking torque, motor speed, and pre-tension as the experimental factors. In the process of research, the OFAT experimental method and quaternary quadratic rotation orthogonal test are adopted. Through response surface analysis and variance analysis, the significance of these four factors on slip rate and the complex association is proved. Using the response surface plots, it is possible to estimate the extent of the speed loss of the flat belt quarter-turn drive under the range of operating conditions considered in this study.

An investigation into cut surface quality of Al6061/Al2O3/B4C composite using hydro-abrasive machining process

The modern era has realized the tremendous potential of metal matrix composites (MMCs) in slowly substituting the traditional materials used in automotive and aerospace industries. Traditional machining of second generation MMCs is quite challenging due to heat generation, tool wear, and low cut surface quality. This work presents an analysis on hydro-abrasive machining (HAM) of the Al6061/Al2O3/B4C composite by using an amalgamated method of gray incidence based response surfaces (GIRS) to produce a good surface quality. The influence of HAM variables [abrasive flow rate (FA), waterjet pressure (PWJ), jet traverse speed (SJT), and stand-off distance (DS)] on various cut surface characteristics [surface roughness (Ra), striation-free zone (SFZ), striation angle (ΘS), and kerf angle (ΘK)] is examined using L27 OA and response surface plots. A hybrid approach of the GIRS method is presented for necessary polynomial modeling and simultaneous optimization. The reduction of response values is primarily influenced by the waterjet pressure and jet traverse speed. An optimal cutting condition resulted in a significant reduction in surface roughness by 19.22% and striation angle by 10.82%. The improvements in striation-free zone and kerf angle were also notable with reductions of 7.89% and 4.74%, respectively. FESEM images of machined surfaces, profile graphs of surface roughness, and images observed using atomic force microscopy were also studied.

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

Box-Behnken optimization of pin-on-disc wear test process parameters

The Full factorial and Box-Behnken Designs for determination of optimal values of wear rate using pin –on-disc wear test parameters have been successfully carried out. The parameters used are track diameter of the disc, mass difference of specimen before and after the test and speed of the disc. The mathematical model developed for the “smaller-the-better” wear rate response by the Full factorial and Box-Behnken Design methods were adjudged adequate with a pvalue of 0.025 and 0.046 respectively. Also, the adjusted R2 values of the Factorial and Box-Behnken Designs were determined to be 0.89 and 0.69 respectively. The statistical ANOVA analysis carried out on the Box-Behnken Design showed that the track diameter and the mass difference were significant with a p-value of 0.041 and 0.007 respectively. Contour plots and response surface plot developed for the factorial method and the Box-Behnken Design displayed appreciable level of similarity. It was noticed that a reduction of wear was occasioned by an increase in track diameter and speed of the disc.

Comparison of Artificial Neural Fuzzy Inference System (ANFIS) and Response Surface Methodology (RSM) Model in Predicting the Outlet Flow Rate of Passive Treatment System Column

Acid mine drainage (AMD) is one of the major environmental problems the mining and mineral processing industries face. Treatment of AMD involves active and passive treatment. In the long term, passive treatment is the most effective way to treat acid mine drainage, but it can be expensive. if handled properly. Therefore, the study of flow rate in a passive treatment system is one of the important ways to identify optimum hydraulic retention time to ensure the maximum percentage of heavy metal removal can be achieved while keeping the cost to a minimum level. This study focused on developing and comparing the Response Surface Methodology (RSM) model and Artificial Neural Fuzzy Inference System (ANFIS) model to predict the outlet flow rate of the passive treatment system column based on three parameters inlet flow time, thickness of peat soil bed, and inlet flow rate. The RSM model was created by Design-Expert software whereas MATLAB created the ANFIS model with 80% of data used for the model training and 20% of the data for model testing. The models' performances were compared in terms of statistical errors (AAPE, RMSE, R2, STD, minimum error, and maximum error). It was found the ANFIS model has performed better in predicting the outlet flowrate with R2 value of 0.99 RSM model with the value of 0.97. The inlet flow rate was an insignificant parameter affecting the outlet flow rate of the passive treatment column. From the 3-D surface response plot, the highest outlet flow rate is predicted to be 524 mL/min.

DESIGN AND OPTIMIZATION OF NANO ENCAPSULATED BIO COMPOUNDS OF ASPARAGUS RACEMOSUS: BOX BEHNKEN APPROACH

Objective: The current study’s objective is to develop and optimize nanoencapsulated bio compounds of Asparagus racemosus (BCAR) utilizing the ionic gelation process to target the kidney for antiurolithiatic activity. Methods: Nanoencapsulated BCAR was prepared employing the ionic gelation method. Box Behnken Design (BBD) 3-factor, 3-level is used to examine the effects of formulation parameters and to enhance the desired responses. Characterization studies include Fourier transform infrared (FTIR), X-ray diffraction (XRD), particle size, zeta potential, scanning electron microscopy (SEM), and transmission electron microscopy (TEM) performed to study the quality of optimized nanoparticles. Results: Mathematical equations and response surface plots were used to relate the dependent and independent variables. Diagnostic charts were used to show the varied factor-level permutations. The percentages of entrapment efficiency (% EE) and drug release (% DR) used in evaluation studies of optimized bio compounds of BCAR nanoparticles (OBCARNPs) were determined to be 80.67% and 77.4%, respectively. The Fourier transform infrared (FTIR) results showed that chitosan, sodium tripolyphosphate (NaTPP), and BCAR were compatible. Due to chitosan and NaTPP gelation in the case of OBCBANPs, X-ray diffraction (XRD) analyses have acknowledged the crystallinity. The particle size and zeta potential of the optimized formulation, found to be 48.8 nm and 14.1 mV, respectively, indicate the nanoparticles are in the nanorange and possess extreme stability by preventing particle convergence. Scanning Electron Microscopy (SEM) and Transmission Electron Microscopy (TEM) studies reveal that the optimized formulation nanoparticles are spherical in shape, homogeneous, and have little aggregation. The accelerated stability studies showed that the optimized formulation was stable at different temperatures and relative humidity. Conclusion: The stable, optimized formulation was prepared, evaluated, and characterized. BBD is employed to optimize the formulation by minimizing the number of experimental runs and enhancing the desired responses. The optimized formulation further needs to investigate the in vivo studies for antiurolithiatic activity by targeting the kidney.

OPTIMIZATION AND CHARACTERIZATION OF MICROSPHERES OF BERBERINE HYDROCHLORIDE USING BOX-BEHNKEN DESIGN

Objective: The current work sought to optimize Berberine hydrochloride (BBH)-loaded microspheres by examining the link between design parameters and experimental results. Methods: BBH-loaded microspheres were prepared by using the water-in-oil emulsion cross-linking process and optimized with a three-factor, three-level Box-Behnken design (BBD). Grafted gum polyvinyl alcohol (PVA) ratio (w/w) (A), Revolutions per minute (RPM) (B), and Span 20 (%) (C) were independent variables. The dependent variables were Percent Entrapment Efficiency (% EE) (R1), Percent Drug Loading (% DL) (R2), and Particle Size (µm) (R3). The generated polynomial equations and response surface plots were used to relate the dependent and independent variables. Microscopic examination, %EE, and % DL were determined to evaluate the optimized formulation. Fourier transforms infrared (FT-IR) spectroscopy studies and stability studies of optimized formulation were also carried out. Results: The optimized formulation (FMS6) had a polymer content of 2% w/v [Grafted gum (36.96): PVA (63.04)], a span 20 (0.78 %), and a prepared at the speed of 1225.92 rpm. The observed responses were close to the improved formulation's predicted values. The particle size, % EE, and % DL were found to be 1.10 µm, 82.79% and 16.48%, respectively. FT-IR spectroscopy study indicated that the drug was entrapped in microspheres. Conclusion: BBD provides a systematic approach to optimize the BBH microsphere preparation process. Additionally, the stability study results confirmed that FMS6 is not only the ideal formulation but also stable, ensuring its suitability for practical applications.

Anaerobic co-digestion of human excreta, food leftovers and kitchen residue: 1 ternary mixture design, synergistic effects and RSM approach

Anaerobic digestion of multiple substrates can generate more biogas while remaining stable, if positive synergistic effects are achieved. The type of co-digested substrates and the mixing ratio used, are the most important variables as each substrate has unique set of characteristics. Optimizing the volume ratios by testing various substrate mixing ratios is a popular method for determining the best-performing ratio of substrate mixture. The ternary mixture design has reportedly been found to quicken the process of testing different mixing ratios with high accuracy without running several experiments. Therefore, a ternary mixture design and a response surface approach are used in this work to ascertain the relationship between substrate mix and responses (biogas yield, methane yield, and synergy). The findings of the experiment revealed that R9 comprising 78.8 % human excreta, 11.8 % food leftovers and 9.4 % kitchen residue, had the highest methane production of 764.79 mLCH4/gVS and a synergistic index of 3.26. Additionally, the 3D response surface plots from the response surface model showed important and shared interactions between Human Excreta, (HE), Food Leftovers (FLO), and Kitchen Residue (KR). HE and KR had a similar positive synergistic effect on biogas yield, methane yield, and synergy, which was not the case for FLO. The response surface plots showed that the predicted responses (methane yield, biogas yield and synergy) increased with increasing HE and KR fractions and decreased with increasing FLO fractions in the substrate mixtures.

Optimizing the synthesis conditions of aerogels based on cellulose fiber extracted from rambutan peel using response surface methodology

<p>A cellulose-based aerogel has been synthesized from rambutan peel to mitigate environmental pollution caused by agricultural waste, rendering it an eco-friendly material with potential applications in oil spill remediation as well as enhancing the value of this fruit. The objective of this study was to extract cellulose from rambutan peel using chlorination and alkalization processes, followed by optimizing the synthesis conditions of cellulose-based aerogels from rambutan peel through experimental designs to improve oil removal efficiency. In this research, cellulose-based aerogel material was synthesized using the sol-gel method, utilizing waste from rambutan peel as the substrate and polyvinyl alcohol as the cross-linking agent, followed by freeze-drying. A central composite design with 30 different experimental setups was employed to investigate the influence of cellulose content (1.0–2.0%), cross-linking agent (polyvinyl alcohol) content (0.1–0.3%), ultrasonic time (5–15 min), and ultrasonic power (100–300W) on the oil adsorption capacity (g/g) of cellulose-based aerogels from rambutan peel. The research findings demonstrated successful extraction of cellulose from rambutan peel through chlorination, followed by softening with 17.5% (w/v) sodium hydroxide. Response surface plots indicated that maximizing the cellulose component could lead to a maximum diesel oil adsorption capacity of up to 52.301 g/g. Cellulose-based aerogel exhibits ultra-lightweight properties (0.027±0.002 g/cm<sup>3</sup>), high porosity (97.88±0.19), hydrophobicity (water contact angle of 152.7°), and superior oil selective adsorption compared to several commercially available materials in the market, demonstrating promising potential for application in treating oil-contaminated water in real-world scenarios.</p>

DoE-assisted HPLC method development and validation of enzalutamide and repaglinide in rat plasma.

Aim: A simple and rapid HPLC technique was developed and validated to simultaneously estimate enzalutamide (ENZ) and repaglinide (REP) in rat plasma.Methods: In silico predictions using DDinter and DDI-Pred indicated possible drug-drug interactions between ENZ and REP. A central composite design was used to identify factors influencing the separation of the drugs. Interactions between chromatographic parameters were studied through 51 experiments, followed by illustration with three-dimensional response surface plots. The four factors optimized for the separation of the two drugs are column temperature (A), % organic strength (B), pH (C) and column type (D).Results: Plate count(R1), tailing factor (R2) and resolution (R3) responses in the experimental design were analyzed with the favorable chromatographic conditions predicted to be 0.1% formic acid and acetonitrile as mobile phases on a Phenomenex C18 LC column (250×4.6mm, 5μm). The method was applied to estimate the drugs in rat plasma using a simple protein-precipitation step and found to be linear, accurate and precise within the ranges of 0.5-16 and 5-50μg/ml for ENZ and REP, respectively.Conclusion: The optimized method can be used in future bioanalytical workflow for drug quantification and drug-drug compatible studies.

Response Surface Method for the Optimization of Glyphosate Degrada-tion by Bacillus sp. Isolated from Soils Near Lake Maninjau

The study utilized a Box-Behnken experimental design to assess the impact of three parameters on the percentage of glyphosate biodegradation by a bacterium. The variables examined were incuba-tion period (measured in days), glyphosate concentration (measured in grams per liter), and pH, each assessed at three different levels. The quadratic model, which has squared terms, interaction products, linear terms, and an intercept, most accurately characterizes the connection between the variables and the response. The findings demonstrated that glyphosate concentration exerted the most pronounced impact on glyphosate degradation, with pH ranking second, as supported by substantial F-values and low p-values. The incubation period had no discernible effect. The ANOVA analysis validated the dependability of the model, as evidenced by an R² value of 0.9602 and an adjusted R² value of 0.9091. These values indicate that the model accounts for 90% of the variability observed in the response data. The contour and response surface plots demon-strated substantial interactions among the variables, specifically between pH and glyphosate con-centration and between the incubation period and glyphosate concentration. The model's anticipat-ed optimal conditions were experimentally tested, demonstrating no significant deviation from the projected values. The predicted maximum biodegradation of 90.097% closely matched the exper-imentally observed value of 92.505% (p>0.05). The predicted combination to give the desired maximum response was at pH 6.81, glyphosate concentration of 0.692 g/L and an incubation period of 3.092 days. On the other hand, the predicted combination to give the desired maximum response is based on the requirement for the conditions where biodegradation is at the highest possible glyphosate concentration of 0.844 g/L and an incubation period of 3.112 days. A higher response of about 5.779% degradation was achieved through RSM. This study showcases the efficacy of the Box-Behnken design in optimizing biodegradation processes, offering a solid sta-tistical basis for further investigations on glyphosate degradation.

CoCrMo Alaşımının Toz Katkılı Elektrik Deşarj ile İşlenmesinde Malzeme Kaldırma ve Takım Aşınma Hızının Cevap Yüzeyi Modellemesi

Electric discharge machining (EDM) is commonly used in implant manufacturing due to the challenge of machining materials that are widely employed in these applications. The study applies response surface methodology to model the impact of powder concentration and machining parameters in powder mixed EDM of CoCrMo alloys, a prevalent material for implantation. AISI 316L stainless steel was selected as the electrode material, while Ti6V4Al was chosen as the additive powder based on their biocompatibility properties. An experimental design was created using a Taguchi L16 array, which involved selecting 4 levels for each parameter of additive ratio, discharge current, pulse on time, and pulse off time. Regression models were developed for material removal rate (MRR) add tool wear rate (TWR) with satisfactory coefficients of determinations (0.87). Effect of the process parameters on MRR and TWR were analysed by means of 3D response surface plots. As a result of the modeling, it was revealed that discharge current, puls on time positively affected MRR, powder concentrations and puls off time negatively affected it. On the other hand, all of the considered process parameters have increasing effect on TWR.