Central Composite Design Model Research Articles

Optimisation of POME biodiesel with isobutanol additive to cater UN sustainable development goal on affordable and clean energy

Biodiesel is a suitable alternative to solve global pollution and declining non-renewable resources. This aligns with the sustainable climate goals and policies to cater to SDG 7. Sustainable technology and solutions have to be fostered. The higher blends of biodiesel will result in engine performance and emissions degradation. Therefore, this research aims to improve the engine performance and emissions of diesel engines operating with B20 POME biodiesel and isobutanol. The Central Composite Design (CCD) model was used to construct the RSM model to determine the optimised condition of the engine testing. Engine performance and emissions of diesel engines were tested on the Yanmar TF120M at 50 % load. Six fuel samples, diesel, B20 Palm Oil Methyl Ester (POME) biodiesel blended fuel, and another four B20 POME biodiesel blends added with 5–20 % in volume percentage of isobutanol, were tested. The optimised RSM obtained a desirability of 0.7. The optimal conditions for the engine testing were at 1868 rpm and a 10 % isobutanol additive percentage. The brake power, BSFC, and BTE of the B95IBU5 and B90IBU10 show an improvement compared to the B20. The exhaust emission shows the lowest CO emission for B90IBU10 at 50 % load. The lowest NOx emission was obtained by B95IBU5 and B90IBU10, with an improvement of 1.8 % and 2.4 %, respectively. Therefore, from this study, it can be concluded that a lower percentage of isobutanol additive of 5–10 % is a promising additive for biodiesel blended fuels.

Development and characterization of topical ethosomal gel for improved antifungal therapeutics

The present study aimed to develop Eberconazole nitrate (EBZ)-loaded ethosomes using Central Composite Design (CCD) by the cold method. The three-factor CCD model was implemented to investigate the effect of variables on ethosome characteristics and performance. For CCD modelling, dependent and independent variables were chosen. The percentage of soy phosphatidylcholine (PC) (X1; % w/v), ethanol (X2; % v/v), and Propylene glycol (PG) (X3; % v/v) were designated as independent variables. However, the dependent variables were particle size (Y1; nm), polydispersity index (PDI) (Y2), and encapsulation efficiency (EE) (Y3; %). The level of independent variables was changed to produce a total of 17 batches (F1-F17). Formulation code F2 with particle size; 227.9 nm, PDI; 0.160 and % EE; 90 ± 0.34 % was selected as the optimized batch. The optimized batch was further incorporated into the hydrogel matrix and investigated for several physicochemical parameters. When compared to the free drug-loaded hydrogel, the ethosomes-loaded hydrogel demonstrated controlled EBZ release with higher skin permeation and skin retention of EBZ. The assessment of antifungal efficacy of the developed formulation was carried out by in vivo animal model method. In vivo studies demonstrated that EBZ-loaded ethosomal gel may alleviate fungal infection caused by a resistant strain of Candida albicans. The study showed satisfactory results, which suggest the suitability of the developed formulation for the effective treatment of fungal infection.

Optimization of Combined Microwave and Ultrasound Extractions of Cannabinoids Compounds from Cannabis indica L. (Blueberry Cultivar) Using Response Surface Methodology

Microwave and ultrasound are novel technologies that are widely used for extracting bioactive compounds from plant materials. In this study, combined extraction techniques, microwave and ultrasound, were applied to extract cannabinoids, cannabidiol (CBD) and delta-9-tetrahydrocannabinol (THC), from Cannabis indica L. (Blueberry cultivar). Three independent variables including solid to liquid ratio (X1, A: 1:10-1:30 w/v), microwave extraction time (X2, B: 5-20 min), and ultrasound extraction time (X3, C: 10-30 min) were optimized using central composite design (CCD). The experimental data obtained was fitted to a second-order polynomial equation using multiple regression analysis and additionally analyzed using appropriate statistical methods (analysis of variance, ANOVA). The optimum conditions were determined according to the solid to liquid ratio (X1, A) of 1:22 (w/v), microwave extraction time (X2, B) of 5 min and ultrasound extraction time (X3, C) of 14 min. Under these conditions, the experimental CBD and THC content was 0.298±0.001 mg/g dry weight and 91.35±0.35 mg/g dry weight, respectively. The experimentally-achieved values were in accordance with those estimated by the CCD model, suggesting the applicability of the utilized model and the favorable result of CCD’s application in the optimization of the combined microwave and ultrasound extraction.

Enzymatic Hydrolysis of Protein Hydrolysate from Pangasius sp. by-Product using Bromelain

Fish protein hydrolysate (FPH) is a product resulting from the degradation of fish protein into simple peptides and amino acids through hydrolysis. This study aims to optimise the enzymatic hydrolysis conditions of Pangasius sp. by-products to produce high-quality fish protein hydrolysate. Bromelain enzyme was used as the catalyst for hydrolysis. The degree of hydrolysis (DH), pH and antioxidant activity of FPH were used as response parameters. The optimisation was done using response surface methodology (RSM) by applying two factors (enzyme concentration and incubation time) with a 3-level Central Composite Design (CCD) model. The result showed that the bromelain concentration and incubation time of catfish protein hydrolysate gave significantly different effects (p<0.05) on the response parameters of Pangasius protein hydrolysate. Hydrolysis of Pangasius protein with 0.04% bromelain enzyme and incubation time of 2.8 hours resulted in DH, pH and DPPH antioxidant activity of 35.88%, 7.07 and 29.86%, respectively. The response value of Pangasius protein hydrolysate was within the range of the predicted value of hydrolysate. Therefore, the optimum conditions suggested by RSM can be used in the future production of Pangasius FPH. In addition, amino acid profiles of Pangasius protein hydrolysate showed high concentrations of Glycine, L-glutamic acid and L-aspartic Acid.

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.

PHB Production by Bacillus megaterium LSRB 0103 Using Cornstarch and Urea.

Polyhydroxybutyrates (PHBs) are biopolymers that are good green alternative for synthetic carbon-based polymers, and are also one of the most researched members of the Polyhydroxyalkanoates (PHA) family. In this study, a gram-positive bacterial strain Bacillus megaterium LSRB 0103 was isolated from Pallikaranai Marshland, Chennai, India. Primary screening using Sudan Black dye revealed the presence of intracellular PHB granules. Minimal Davis Media (MDM) which was used or PHB production gave a yield of 0.7107g/L. Subsequently, using response surface methodology (RSM), a central composite design (CCD) model was designed for media optimization having cornstarch, urea, and pH as independent variables. The findings of the CCD model were fitted into a second-order polynomial equation. The RSM model predicted the maximum PHB yield of 0.93g/L, at these independent variable levels, cornstarch, 5g/L; urea, 2.1g/L; and pH 7.0; while the experimental PHB yield was 0.94g/L, with a percentage error of 1.1%. This study is the first-time report of production of PHB by Bacillus megaterium using cornstarch and urea as substrate.

Aspergillus niger as an eco-friendly agent for potassium release from K- bearing minerals: Isolation, screening and culture medium optimization using Plackett-Burman design and response surface methodology

The potential of Aspergillus niger, to enhance non-exchangeable potassium (K+) release from mineral structures were investigated as a cost-effective and environmentally friendly alternative to traditional chemical fertilizers. Optimizing the culture medium for maximum K+ release, alongside identifying potential mechanisms of action of the A. niger including the production of various organic acids and pH reduction in the minerals feldspar and phlogopite, were among the primary objectives of the present study. K+ dissolution from feldspar and phlogopite in the presence of Aspergillus niger were examined through a two-step experiment; impact of different carbon sources (glucose, sucrose, and fructose) on K+ release using the Plackett-Burman design (PBD) with 12 experimental runs and effect of other independent variables including pH (ranging from 5 to 10), carbon concentration (3–12.3 g l−1), and incubation time (5–18 days) on K+ release using the central composite design (CCD). Our results indicated that the PBD demonstrated a strong predictive capacity (RMSE = 0.012–0.018 g l−1 and R2 = 0.85–0.89) for K+ release. According to the CCD model, pH exerted a significant positive influence on increasing soluble K+ release (P < 0.001). The highest levels of K+ release (157.8 and 175.3 mg l−1 in feldspar and phlogopite, respectively) were observed at the central levels (0) of time and carbon source, and at the +α level (+1.68) of pH. Furthermore, based on the CCD model, the optimal conditions for achieving high K+ release from feldspar and phlogopite in a medium were pHs of 10.36 and 10.31, sucrose concentrations of 11.23 and 11.32 g l−1, and incubation times of 15 and 18 days, respectively. The determination coefficients of the CCD model indicated that 89.5% and 92.6% of the changes in soluble K+ for feldspar and phlogopite, could be explained by this model, respectively. In the current study, the production of organic acids and the resulting pH reduction, along with the reduction in mineral particle size in feldspar and phlogopite, were identified as potential mechanisms influencing the enhancement of potassium solubility. The predominant acids in both feldspar and phlogopite were lactic acid (70.9 and 69.15 mg l−1) and citric acid (40.48 and 22.93 mg l−1), although the production levels of organic acids differed in the two minerals. Overall, our findings highlight the potential of A. niger to proficiently release non-exchangeable potassium from mineral matrices, indicating its promising potential in agricultural applications.

Modeling the adsorption of ibuprofen on the Zn-decorated S,P,B co-doped C2N nanosheet: Machine learning and central composite design approaches

The ibuprofen (IB) residuals in water resources are classified as toxic and non-biodegradable contaminants. In the previous study, the Zn-decorated S,P,B co-doped C2N (Zn-SPB@C2N) nanosheet exhibited significant effectiveness in adsorbing IB from aqueous solutions. However, the operating conditions were not optimized for the adsorption process. The current study modeled the adsorption of IB onto Zn-SPB@C2N nanosheets employing central composite design (CCD) and machine learning (ML) methods under various operating conditions. The operating conditions included adsorbent mass, initial IB concentration, and pH. The CCD model revealed a mean squared error (MSE) value of 36.56. The ML investigations showed MSE values of 28.12 for the artificial neural network (ANN), 10.12 for the decision tree (DT), 8.68 for the linear regression (LR), and 3.70 for the random forest (RF) models. The RF model demonstrated high reliability in predicting IB removal across various conditions compared to other methods. Using the RF model, a maximum removal efficiency of 98 % was achieved under the optimized operating conditions, containing a pH of 7, an initial concentration of 59 mg/L, and an adsorbent mass of 0.020 g.

Discrete Element Model Building and Optimization of Tomato Stalks at Harvest

The mechanical properties of tomato stalk, relevant to the harvesting and crushing of tomato vines, significantly impact its harvesting quality and efficiency. Establishing a simulation model, which accurately mirrors these properties, is foundational for designing related mechanical components. The discrete element method models tomato stalk harvesting and is optimized through mechanical tests and simulations. A blend of Plackett–Burman, steepest ascent, and central composite design modeling identified three contact model parameters influencing the maximum stalk shear force. The optimal values of these three parameters were a normal stiffness of 1.04 × 1010 N m−3, tangential stiffness of 7.59 × 109 N m−3, and bond radius of 1.06 mm. The relative error in the simulated versus measured shear force was &lt;1%, affirming the model’s accuracy in characterizing cutting properties. These findings lay the theoretical groundwork for numerical simulations of tomato-stalk-related equipment.

Electrocoagulation for the decolorization of textile wastewater in single-channel reactor: Response surface methodology for optimization and a novel model exploitation

This work focuses on the multi-objective optimization of the electrocoagulation process. The goal is to decolorize synthetic Reactive Black 5 wastewater using a single-channel reactor operating in a closed-circuit. Response Surface Methodology (RSM) is employed for the optimization. The Central Composite Design (CCD) is used to study the empirical relationship between two responses (decolorization efficiency (η) and energy consumption (WC)) and four independent variables (recirculation flow rate (Qv), current intensity (I), electrolysis time (τe) and active electrode area (Sa)), while the desirability function (D) is applied to determine optimal conditions. In addition, a novel approach for the exploitation of the RSM model is developed to set correlations between the limit of η (ηLim) and corresponding independent variables’ limit values (below of which η < ηLim). The developed correlations showed a good agreement between the graphical data and the estimated values (R2 > 0.99). The CCD model fits well the experimental data with a R2 of 0.94 and 0.95, for η and WC, respectively. The analysis of variance allowed to build quadratic models for both responses. For a maximal overall desirability D of 0.88, optimal conditions are Qv = 70 L h−1, I = 100 mA, τe = 34.6 min and Sa = 621 cm2. Under these conditions, η = 70.9% and WC = 4.6 Wh.m−3 were achieved. The sludge characterization using X-ray diffraction, Scanning Electron Microscopy, and Fourier Transform InfraRed reveals the formation of the amorphous Al(OH)3 without other precipitated substances and authenticates the successful sorption of RB5 onto the Aluminum hydroxide flocs.

Insights into levofloxacin adsorption with machine learning models using nano-composite hydrochars

Hydrothermal carbonization was applied to taro peel wastes to produce hydrochars using a facile and environmentally friendly process. Four different entities were prepared: hydrochar (TPh), phosphoric-activated hydrochar (P-TPh), and silver@hydrochars (Ag@TPh, Ag@P-TPh). The elemental compositions of the single and composite hydrochars were confirmed by EDX. Among the produced hydrochars, the morphology of the Ag@hydrochar composites demonstrated more wrinkled structure, and Ag nanoparticles decorated the surface. The optimal experimental conditions for levofloxacin adsorption were determined to be a contact time of 45 min, hydrochar dose of 0.15 g L−1, and pH of 7. The best adsorption performances were assigned to Ag@hydrochars. Two machine learning models were applied to predict the levofloxacin adsorption efficiency of the Ag@hydrochars. A central composite design (CCD) and a 3-10-1 artificial neural network (ANN) model were developed to estimate the removal performance of levofloxacin using Levenberg-Marquardt backpropagation algorithm based on correlation and error analysis of the adopted training functions. Furthermore, the ANN sensitivity analysis revealed the order of the relative importance variable as initial concentration> hydrochar dose> pH. The predicted values of the CCD and ANN models fitted the experimental results with R2> 0.989. Therefore, the applied models were effective in predicting levofloxacin removal under different operating conditions. This work provides an open option for the sustainable management of food industry wastes and the possibility of waste valorization to effective hydrochar composites to be applied in water treatment processes.

Green synthesis of metal oxides (CaO-K2O) catalyst using golden apple snail shell and cultivated banana peel for production of biofuel from non-edible Jatropha Curcas oil (JCO) via a central composite design (CCD)

The use of biomass as a renewable, sustainable, and eco-friendly energy source is now widely recognized as a potential solution for a variety of environmental problems. To develop biodiesel production, cost-effective feedstocks such as agricultural waste, food waste, and non-edible/waste cooking oil were utilized. A heterogeneous solid base catalyst was synthesized by calcining a mixture of waste golden apple snail shell (Pomacea canaliculata) and cultivated (Musa sapientum) banana peel. In transesterification process, potassium oxide (K2O) derived from banana peel is used as a cocatalyst to improve the catalytic activity of calcium oxide (CaO) catalyst derived from waste shell. The innovative CaO-K2O catalyst was investigated by X-ray diffraction (XRD), X-ray fluorescence (XRF) and the Brunauer-Emmett-Teller (BET) technique. The morphology and elemental composition of calcium (Ca), potassium (K), and oxygen (O) in the catalyst were validated by field emission-scanning electron microscopy (FE-SEM) and energy dispersive X-ray spectroscopy (EDX). The CaO catalyst exhibited a BET surface area of 10.88 m2/g, which was enhanced to 14.62 m2/g upon combination with K2O. The Hammett indicator of CaO catalyst fell between 7.2 < H_< 9.8. However, the CaO-K2O catalyst exhibited a higher value of 15.0 < H_< 18.4, which could be attributed to the phase transition from CaO to CaO-K2O. To investigate the effects of catalyst concentration, ethanol/oil molar ratio, and transesterification time on the yield of fatty acid ethyl ester (FAEE). The optimal conditions for FAEE synthesis were determined using a central composite design (CCD) approach with response surface methodology (RSM). The regression equation obtained for the CCD model has a determination coefficient (R2) of 0.9921, indicating that this model is well-fitted. At 3.69 wt% catalyst concentration, 19.48:1 ethanol/oil molar ratio, and 1.80 h transesterification time, the highest FAEE yield from Jatropha Curcas oil (JCO) of 97.06 % was obtained. The novel catalyst has a strong yield and can be utilized for up to 6 cycles. It was found that the corresponding yield was 90 % when employing the same process parameters, demonstrating the high reusability of this catalyst. The biodiesel produced from non-edible JCO meets the criteria for standard biodiesel (ASTM D-6751 and EN 14214). The CaO-K2O catalyst is inexpensive, easy to make, biodegradable, recyclable, and environmentally friendly because it is derived from a biological residue. Because of these characteristics, it may be an appropriate candidate for the role of “green catalyst” in sustainable energy production.

Optimization of xylanase production by Bacillus sp. MCC2212 under solid-state fermentation using response surface methodology

Xylanases have become increasingly significant due to their versatile applications in the food, paper, and pharmaceutical industries. Nevertheless, the current production of these enzymes relies on costly substrates, with estimates indicating that over 30% of the production expenses are attributed to these substrates. The objective of this study is to optimize the physico-chemical parameters for obtaining the maximum production of xylanase enzyme from Bacillus sp. MCC2212. The production conditions were statistically optimized using Plackett-Burman design (PBD) and Central Composite design (CCD). The significant variables identified through PB design including temperature, substrate to moisture ratio, MgSO4, peptone, tween 20, inoculum size, inoculum age, incubation time, and extracted xylan, were further optimized using the CCD approach. This optimization process revealed the most influential factors affecting xylanase production, with optimal conditions observed at a temperature, 35 °C; a substrate to moisture ratio, 1:1.75 g:mL; MgSO4, 1.1%; peptone, 1.5%; tween 20, 1.25%; inoculum size, 10 % (v/w); inoculum age, 20 h; an incubation time, 50 h; and crude xylan extracted from wheat bran at a concentration of 6% v/w. The R2 value of the model was found to be 0.9901, indicating its effectiveness. The optimized CCD model displayed a 1.84-fold greater xylanase production than the PB design approach. These findings suggest that wheat bran, a readily available agro-waste, could be a feasible alternative to the conventional substrate, beechwood xylan, for the production of xylanase enzyme with the possibility of achieving higher production levels optimized by using statistical design approach.

Synthesis and characterization of chitosan-bentonite biocomposite: Parametric optimization and adsorption potential

The study investigates the fabrication of chitosan-bentonite biocomposites through an ultrasonic-assisted technique. The influence of the chitosan: bentonite molar ratio, ultrasonication temperature, and ultrasonication time on the yield of biocomposites was assessed and juxtaposed using the response surface methodology, employing both Box Behnken design (BBD), and central composite design (CCD) methodologies. The pristine compounds and synthesized biocomposites were characterized using various spectral techniques viz. Fourier transform infrared (FT-IR), X-ray diffraction (XRD), and thermogravimetric analysis (TGA). The optimal conditions for the synthesis of biocomposites were: uni-molar ratio of chitosan: bentonite, ultrasonication temperature of 60 °C, and ultrasonication time of 30 min. BBD demonstrated a slightly better fit (R² > 0.981) as compared to CCD (R² > 0.936). Additionally, the error values for BBD and CCD were determined to be 0.307 and 0.459, respectively, highlighting the higher accuracy of the BBD model compared to the CCD model. The structural analysis through FT-IR and XRD revealed the intercalation of chitosan into the bentonite interlayer. TGA indicated elevated thermal stability and increased resistance of the biocomposites toward degradation. Furthermore, the consistent bonding mechanism observed between chitosan and bentonite remained unchanged regardless of the molecular weight variations. The results revealed that synthesized biocomposites have the potential to be exploited as adsorbents for the removal of pesticides, dyes, drugs, and heavy metals from aqueous solution, demonstrating their application in wastewater remediation technology.

Exploring the impact of taurine on the biochemical properties of urate oxidase: response surface methodology and molecular dynamics simulation

This paper investigates the impact of taurine as an additive on the structural and functional stability of urate oxidase. First, the effect of the processing parameters for the stabilization of Urate Oxidase (UOX) using taurine was examined using the response surface methodology (RSM) and the central composite design (CCD) model. Also, the study examines thermodynamic and kinetic parameters as well as structural changes of urate oxidase with and without taurine. Fluorescence intensity changes indicated static quenching during taurine binding. The obtained result indicates that taurine has the ability to preserve the native structural conformation of UOX. Furthermore, molecular dynamics simulation is conducted in order to get insights into the alterations in the structure of urate oxidase in the absence and presence of taurine under optimal conditions. The molecular dynamics simulation section investigated the formation of hydrogen bonds (H-bonds) between different components as well as analysis of root mean square deviation (RMSD), root mean square fluctuations (RMSF) and secondary structure. Lower Cα-RMSD and RMSF values indicate greater stabilization of the taurine-treated UOX structure compared to the free enzyme. The results of molecular docking indicate that the binding of taurine to the UOX enzyme through hydrophobic interactions is associated with a negative value for the Gibbs free energy.

Modeling Cu removal from aqueous solution using sawdust based on response surface methodology.

Copper (Cu), as one of the heavy metals widely used in industrial and agricultural activities, has a fundamental role in the pollution of water resources. Therefore, removing Cu from the aqueous solutions is considered an important challenge in the purification of water resources. Thus, in this study, sawdust with a diameter of 260-600 μm was used to remove Cu from the aqueous solutions. At first, sawdust was washed using distilled water and dried at laboratory temperature. Cu absorption experiments in closed conditions were performed based on the central composite design (CCD) model and with a range of initial Cu concentrations equal to 1-25 mgl-1. The amount of changes for other variables, including pH, time, and amount of sawdust, was equal to 2-10, 5-185 (min), and 5-25 (gl-1), respectively. After the completion of each test, the remaining Cu concentration in the solution was measured using atomic absorption, and the percentage of Cu removed was determined from the difference between the initial and final concentrations. The results showed that the CCD model has a favorable ability to predict Cu removal from the aqueous solutions (R2=0.90 and RSME=3.34%). Based on the Pareto analysis, contact time, the amount of sawdust, pH, and the Cu concentration had the most significant effect on removing Cu from the solution. Contact time, amount of sawdust, and pH were directly related, and the amount of dissolved Cu was proportional to the removal of Cu from the solution. Therefore, sawdust is desirable as a natural adsorbent, and the removal efficiency of Cu from solutions with low Cu concentration is very high (94%). In this regard, it is advised to use sawdust in the process of targeting Cu and heavy metals due to its low cost and availability.

Cost-effective removal of Cr(VI) ions from aqueous media using L-cysteine functionalized gold nanoparticles embedded in melamine-based covalent organic framework (Cys-AuNPs@COF)

Due to the growing concern about the environmental effects of heavy metals, researchers are developing materials that possess high absorption capacity in addition to selectivity and high absorption speed. Recently, covalent organic frameworks (COFs) have been considered as emerging and promising adsorbents for the removal of many types of pollutants. In this work, a novel and selective adsorbent (Cys-AuNPs@COF) was prepared by embedding gold nanoparticles functionalized with L-cysteine in melamine-based COF for the removal of Cr(VI) ions from wastewater. The synthesized Cys-AuNPs@COF were characterizedby Fourier-transform infrared spectroscopy (FT-IR), X-ray diffraction analysis (XRD), Field emission scanning electron microscopy (FESEM), Energy-dispersive X-ray spectroscopy (EDX), Thermo-gravimetric analysis (TGA), and elemental mapping (EMA) analysis. The removal of Cr(VI) ions was performed using a batch mode process by taking advantage of response surface methodology (RSM) based on a central composite design (CCD) model. The maximum adsorption capacity of Cys-AuNPs@COF was 151.5 mg g−1. The experimental results followed the Langmuir model and showed pseudo-second-order kinetics. A portable, low-cost, and highly sensitive device with a smartphone colorimeter platform was developed for in situ measurement of trace amounts of chromium (VI) ions. Due to its simplicity and versatility, this method has the potential to serve as an alternative to conventional field analysis methods.

Improved production of lactiplantibacillus plantarum RO30 exopolysaccharide (REPS) by optimization of process parameters through statistical experimental designs

BackgroundIn investigating of (exopolysaccharide) EPS from unconventional sources, lactic acid bacteria have a vital role due to their generally recognized as safe (GRAS) status. EPSs have diverse applications such as drug delivery, antimicrobial activity, surgical implants, and many more in many sectors. Despite being important, the main hindrance to the commercial application of these significant biopolymers is low productivity. Therefore, this study primarily focuses on optimizing physio-chemical conditions to maximize the previously produced EPS from probiotic Lactiplantibacillus plantarum RO30 (L. plantarum RO30) using one factor at a time (OFAT) and method Response Surface Methodology (RSM).ResultsThe EPS obtained from L. plantarum RO30 named REPS. The medium formulation for REPS production using the OFAT method revealed that sucrose (20 g/L, beef extract (25 g/L), and ammonium sulfate at 4 g/L concentration were the optimum carbon, organic and inorganic nitrogen sources, and REPS yield was increased up to 9.11 ± 0.51 g/L. RSM experiments revealed that, a greatly significant quadratic polynomial attained from the Central Composite Design (CCD) model was fruitful for specifying the most favorable cultural conditions that have significant consequences on REPS yield. The maximal amount of REPS (10.32 g/L) was formed by: sucrose (40 g/L), beef extract (25 g/L), pH (5.5), incubation temperature (30 °C), and incubation period (72 h). A high closeness was obtained between the predicted and experimental values and it displayed the efficiency of the RSM.ConclusionThis study was conducted to reinforce REPS production in the probiotic LAB L. plantarum RO30 by utilizing various experimental parameters. The maximum REPS yield of 10.32 g/L was attained under the circumstances optimized in the study.

Modelling and optimization of biodiesel production from waste fish oil using nano immobilized rPichiapastoris whole cell biocatalyst with response surface methodology and hybrid artificial neural network based approach

In this study, zinc oxide (ZnO) nano particle immobilized recombinant whole cell biocatalyst (rWCB) was used for bioconversion of waste fish oil in to biodiesel in a lab scale packed bed reactor (PBR). Central composite design and hybrid artificial neural network (ANN) models were explored to optimize the production of biodiesel. Developed rWCB exhibited maximum lipase activity at 15 % (v/v) of glutaraldehyde concentration and 6 % (w/v) of ZnO nanoparticles at pH of 7. Maximum biodiesel yield reached about 91.54 ± 1.86 % after 43 h in PBR using hybrid ANN model predicted process conditions of 13.2 % (w/v) of nano immobilized rWCB concentration and 4.7:1 of methanol to oil ratio at 33 °C. Importantly, developed nano immobilized rWCB was adequately stable for commercialization. Thus, production of biodiesel from waste fish oil using ZnO nano immobilized rWCB could become potential candidate for commercialization.

Central composite design approach for concurrent desulfurization and denitrogenation of model liquid fuel over Mo‐AAC

AbstractThe primary objective of this work was to investigate the simultaneous removal of refractory sulfur (dibenzothiophene, DBT) and nitrogen compounds (indole, IND) from model gasoline. This research aimed to develop an efficient adsorption process to mitigate the emission of sulfur oxide and nitrogen oxides, which pose direct threats to human health and the environment. Additionally, the study focused on addressing technical challenges encountered in the refinery process, particularly the poisoning of catalysts due to the presence of these compounds. To achieve the study's goals, granular activated carbon (GAC) was subjected to acid treatment for modification. Molybdenum species were then incorporated into the acid‐treated GAC using the wet impregnation technique. The composition and distribution of elements on the surface of the modified GAC (Mo‐AAC) were analyzed via X‐ray photoelectron spectroscopy (XPS). Various characterization techniques were employed to confirm the uniform dispersion of molybdenum species on the GAC surface. The specific surface area of the adsorbent was increased from 257 m2/g to 316 m2/g following the acid treatment and molybdenum modification. The study explored the impact of several key parameters on the removal of DBT and IND using central composite design (CCD). A regression model was derived using CCD through hybrid central composite design, and its adequacy was verified with the means of tests such as analysis of variance (ANOVA), a lack of fit test, and residuals distribution consideration. Experimental results match well with the results obtained by CCD model, justifying the accuracy of models. The results showed that all four parameters have a significant impact on removal of DBT and IND. Increasing temperature and the adsorbent dose positively affected the removal of both DBT and IND, indicating that higher temperatures and greater adsorbent dosages improved removal efficiency. On the other hand, the concentration of DBT and IND had a negative impact, signifying that higher initial contaminant concentrations hindered removal efficiency. The study determined the optimum initial contaminant concentrations for DBT and IND, temperature, and adsorbent dose to be 186 mg/L, 50 mg/L, 25.5°C, and 32 g/L, respectively. These conditions yielded the highest removal efficiency. The research demonstrated that Mo‐AAC has the capability to effectively reduce the sulfur and nitrogen content of model gasoline, making it a promising solution for addressing environmental and refinery challenges associated with these compounds.