Central Composite Design Approach Research Articles

Nanotechnological prospective for enhancing the antibacterial activity of mupirocin and cinnamon essential oil: a combination therapy

BackgroundsThe aim of the current study was to develop a distinctive nanolipid formulation, namely, nanostructured lipid carrier (NLC), which would deliver an antibacterial medication such as mupirocin (MP). Additionally, cinnamon essential oil (CEO), which is reported to exhibit antibacterial activity, was utilized in the development process in an attempt to improve the influence of MP.MethodsAs a consequence, different MP–NLC formulations were developed using the central composite design (CCD) approach. One optimized formula was selected and incorporated within the pre-formulated gel matrix, providing the MP–NLC-gel formula for efficient topical application. MP–NLC-gel was assessed for its physical characteristics to check its suitability for topical application and evaluated for its in vitro drug release over 6 h. Furthermore, it studied the formulation for its stability at different conditions; 25°C ± 2°C and at 4°C ± 3°C for 6 months. Finally, the formulation was examined for its antibacterial performance against gram-positive and -negative bacteria.ResultsThe developed topical NLC-gel formulation demonstrated pH 5.8, viscosity 14,510 cP, and spreadability 58.1 mm, which were seemed to be satisfactory properties for successful topical application. The drug was released successfully for over 6 h with 52.9%. Additionally, it was stable in both storage conditions for 6 months since it displayed non-significant variations in its evaluated characteristics compared to those of fresh preparation. Ultimately, the developed gel formulation could inhibit the growth of different bacterial strains, especially gram-negative strains.ConclusionTo sum up, these findings would demonstrate the efficiency of NLC prepared with CEO and incorporating MP to be a promising antibacterial lipid nanocarrier.

Formulation and optimization of xanthohumol loaded solid dispersion for effective treatment of Parkinson's disease in rats: In vitro and in vivo assessment

Xanthohumol (XH) is a prenylated flavonoid that possesses neuroprotective effects by increasing the levels of dopamine (DA), reducing oxidative stress, and neuroinflammation against Parkinson's disease (PD). However, the neuroprotective efficacy of XH in managing PD is limited by its low solubility, poor bioavailability, and less blood-brain barrier (BBB) permeability leading to decreased therapeutic activity. To overcome these limitations, XH-loaded solid dispersion (SD) has been formulated using the Central Composite Design (CCD) approach. The optimized formulation was evaluated by determining the angle of repose, drug loading, percentage drug release, PXRD, DSC, FTIR, and SEM. The results of PXRD, DSC, and SEM demonstrated the formation of SD. The XH-SD showed a 6.45-fold enhancement in dissolution rate, a 12.7-fold increase in Cmax, and 26.11-fold increase in XH's area under curve (AUC) as compared to the naïve XH. In the pharmacokinetic studies, XH-SD showed a 42.20 folds increase in relative bioavailability(0-∞) compared to naïve XH. To assess pharmacodynamic effects, PD was induced using rotenone. The motor and non-motor functions were evaluated by assessing various behavioral parameters such as catalepsy, spontaneous locomotor activity and muscle coordination test. The formulation exhibited dose-dependent effects, with both low and high doses of XH-SD resulting in significant enhancements (P < 0.001) in motor functions in rats induced with PD. Furthermore, biochemical estimations showed that XH-SD at both doses increased the levels of dopamine (DA), and reduced oxidative stress and neuroinflammation.

Eco-friendly synthesis of silver nanoparticles: Enhancing optimization reaction, characterization, and antimicrobial properties with Lantana camara from geothermal area

Green synthesis methods for producing silver nanoparticles (AgNPs) have garnered significant attention for their potential in medical applications. Despite the known potential of Lantana camara in AgNP synthesis, there is a lack of studies investigating its application when grown in extreme geothermal environments, which may influence the properties and efficacy of the synthesized nanoparticles. This research aimed to fabricate AgNPs utilizing aqueous extract from L. camara, a plant growing in an extreme geothermal manifestation area. Another aim of this study is to evaluate their antimicrobial activity. Qualitative and quantitative phytochemical analyses of the plant's leaves were also conducted. Reaction optimization was performed using response surface methodology (RSM), employing a central composite design (CCD) approach. The characterization of AgNPs involved UV–vis spectroscopy, FTIR, SEM-EDX, and PSA. The antimicrobial testing was conducted against Gram-positive bacterium (S. aureus), Gram-negative bacterium (E. coli), and the fungus (C. albicans). The phytochemicals analysis revealed that the L. camara leaf extract contains flavonoids, phenolics, saponins, tannins, and steroids, lacking alkaloids and terpenoids, with total phenolic and flavonoid contents of 11.94 mg (GAE/g) and 6.70 mg (QE/g), respectively. The AgNPs exhibited a spherical shape with a surface plasmon resonance (SPR) peak at a wavelength of 417 nm, and the smallest particle size measured was 44 nm. Based on FTIR analysis, AgNPs have functional groups such as OH, NH, CC, and CH that were identified as groups involved in the reduction of Ag+ to Ag0 during the green synthesis of AgNPs. The AgNPs demonstrated the lowest antifungal activity against C. albicans. In summary, the aqueous leaf extract of L. camara from geothermal manifestation areas can serve as a bioreductant for AgNPs, exhibiting higher antibacterial activity against Gram-positive bacteria compared to Gram-negative ones.

Simultaneous removal of Pb(II) and Cr(VI) from a steel company wastewater using various green adsorbents: Material characterization and numerical optimization

ABSTRACT This study focuses on the simultaneous uptake of Pb(II) and Cr(VI) from industrial wastewater by walnut shell (WS), almond shell (AS), peanut shell (PS), and coconut shell (CS) adsorbents. Among the used adsorbents, the CS adsorbent exhibited the greatest BET surface area of 18.97 m2/g and porosity of 63.17% and the WS adsorbent also had the highest pore volume of 0.3536 m3/g. Lead and chromium removal were optimized using response surface methodology via a central composite design (CCD) approach. The efficiency of lead and chromium uptake from the wastewater was enhanced by increasing the concentration of WS, AS, PS, and CS adsorbents (Cads.) and decreasing the flow rate (Q) of the wastewater. Under the optimal conditions (Cads. = 0.85 g/L and Q = 2.5 mL/min), the maximum lead and chromium uptake from steel company wastewater was achieved using CS (92%) and WS (97.2%) adsorbents, respectively. The actual lead and chromium removal values were well-fitted based on a high Rpred2, confirming the validity of the CCD model. The acceptable performance of these green adsorbents in the simultaneous removal of chromium and lead from the wastewater introduces the WS, AS, PS, and CS adsorbents as inexpensive and available candidates for industrial wastewater treatment containing heavy metals.

Self-microemulsifying drug delivery system-based gastroretentive in situ raft of pazopanib with enhanced solubility and bioavailability.

Pazopanib hydrochloride (PZH) is a Biopharmaceutics Classification System class II drug that faces challenges at the formulation forefront including low aqueous solubility (0.043 mg/mL) and poor oral bioavailability (14-39%). The present investigation aimed to develop a self-microemulsifying drug delivery system (SMEDDS) of PZH using a blend of Capryol® 90, Labrasol®, and propylene glycol to improve its solubility. Furthermore, a sustained-release SMEDDS-based gastroretentive floating system was developed and optimized using the Central Composite Design approach of DoE. The optimized SMEDDS-based in situ gelling raft, R-SM-PZH, exhibited minimal floating lag time (3.09 ± 0.8 s), optimal viscosity (1229.4 ± 20.9 cP) and density (0.327 ± 0.15 g/mL) as compared to other formulations under study. Additionally, R-SM-PZH was evaluated for its in vitro dissolution in FaSSGF and FeSSGF, pharmacokinetic profile, and MTT assay (against NCI-H460 lung cancer cells) compared to pure PZH. A 12 h sustained release, three-fold augmentation in dissolution rate and bioavailability, and 15-fold enhancement in cytotoxicity were observed in comparison to pure PZH. Thus, the SMEDDS-based in situ gelling raft presents a promising approach to advancing the developability potential of PZH.

The Optimisation of Ultrasound-Assisted Extraction for the Polyphenols Content and Antioxidant Activity on Sanguisorba officinalis L. Aerial Parts Using Response Surface Methodology

The aim of this study was to optimise ultrasound-assisted extraction (UAE) of the herb Sanguisorba officinalis L. in terms of the antioxidant activity (DPPH and FRAP method) and total polyphenol content (TPC). Optimisation was performed using the response surface methodology (RSM) with a third-degree (33) Central Composite Design (CCD) approach. The RSM was applied to obtain the optimal combination of (1) raw material content (2.25–7.5 g raw material/100 mL of solvent), (2) ethanol concentration (20–60% v/v), and (3) extraction time (1–15 min). The optimal conditions for the extraction of polyphenols and antioxidant potential were a raw material content of 7.5 g/100 mL of solvent (solid/solvent ratio 13.3 mL/g), an ethanol concentration of 47% v/v, and an extraction time of 10 min. At these optimal extraction parameters, the maximum extraction of polyphenols and antioxidant activity obtained experimentally was found to be very close to its predicted value and was 12.9 mmol Trolox/L (DPPH method), 19.4 mmol FeSO4/L (FRAP method), and 2.1 g GA/L (TPC). The mathematical model developed was found to fit with the experimental data on the antioxidant potential and polyphenol extraction. The n-octanol/water partition coefficient of the optimised extract was used to determine their lipophilicity. Our studies have shown that the optimised extract is highly hydrophilic (log P &lt; 0). Optimal parameters can be used for the industrial extraction of the S. officinalis herb for the needs of, among others, the pharmaceutical or cosmetic industry.

Achieving performance and functionality in PLA-based packaging: Insights from hybrid composites with false banana (Enset) fiber and ZnO nanoparticles

The use of poly(lactic acid) (PLA)-based composites reinforced with natural fibers is becoming increasingly popular in sustainable packaging. However, these materials often lack adequate barriers and desirable functional properties. Concurrently, zinc oxide nanoparticles (ZnO NPs) have been incorporated into PLA-based composites to develop functional packaging films. Despite this progress, there is a gap in the literature regarding packaging materials that require both functional and performance properties, specifically those that combine natural fibers and ZnO NPs within the PLA matrix. This study aims to develop and optimize the effects of ZnO nanofillers and false banana, also called Enset fibers (EFs) as hybrid reinforcements in PLA matrices. A central composite design (CCD) approach was used to evaluate the effects of EFs at three levels (5 %, 15 %, and 25 % w/w) and ZnO NPs at three levels (0 %, 5 %, and 10 % w/w) on the performance (tensile and flexural strength) and functional properties (water and antibacterial activity) of the resulting nanocomposite materials. The findings indicate that the inclusion of ZnO NPs significantly enhanced both the performance and functional properties of the nanocomposites, whereas the inclusion of EFs improved the performance properties but reduced the functional properties of the nanocomposites. Ultimately, this study identified an optimal formulation for producing durable and functional composites for various packaging applications.

Investigation the yarn to yarn static and kinetic friction based on the response surface method

One of the most significant physical characteristics of textiles is friction, which has a direct bearing on how they behave mechanically. This characteristic has been researched in a variety of methods thus far, demonstrating its significance. The static and kinetic coefficients of friction in yarn to yarn interaction have been examined in this paper by considering a number of variables, including yarn count (33, 50, 57, and 65 Tex), yarn movement speed (30, 45, and 60 mm/min), angle of yarn placement (0, 45, and 90 degrees), and surface reaction force (5, 10 and 15 g). To this aim, 32 different experiments were designed using the central composite design approach in order find the static and kinetic coefficients of friction. The outcomes were then statistically analyzed. The relationship between the friction coefficients and the aforementioned parameters was investigated as well. Next, a model with 93% and 85% accuracy for the static and kinetic coefficients of friction, respectively, was obtained using the response surface method. Additionally, the relationship between the friction coefficients and each of the aforementioned parameters was assessed.

Optimizing parameters for additive manufacturing: a study on the vibrational performance of 3D printed cantilever beams using material extrusion

Purpose This study aims to investigate the impact of different printing parameters on the free vibration characteristics of 3D printed cantilever beams. Through a comprehensive analysis of material extrusion (ME) variables such as extrusion rate, printing pattern and layer thickness, the study seeks to enhance the understanding of how these parameters influence the vibrational properties, particularly the natural frequency, of printed components. Design/methodology/approach The experimental design involves conducting a series of experiments using a central composite design approach to gather data on the vibrational response of ABS cantilever beams under diverse ME parameters. These parameters are systematically varied across different levels, facilitating a thorough exploration of their effects on the vibrational behavior of the printed specimens. The collected data are then used to develop a predictive model leveraging a hybrid artificial neural network (ANN)/ particle swarm optimization (PSO) approach, which combines the strengths of ANN in modeling complex relationships and PSO in optimizing model parameters. Findings The developed ANN/PSO hybrid model demonstrates high accuracy in predicting the natural frequency of 3D printed cantilever beams, with a correlation ratio (R) of 0.9846 when tested against experimental data. Through iterative fine-tuning with PSO, the model achieves a low mean square error (MSE) of 1.1353e-5, underscoring its precision in estimating the vibrational characteristics of printed specimens. Furthermore, the model’s transformation into a regression model enables the derivation of surface response characteristics governing the vibration properties of 3D printed objects in response to input parameters, facilitating the identification of optimal parameter configurations for maximizing vibration characteristics in 3D printed products. Originality/value This study introduces a novel predictive model that combines ANNs with PSO to analyze the vibrational behavior of 3D printed ABS cantilever beams produced under various ME parameters. By integrating these advanced methodologies, the research offers a pioneering approach to precisely estimating the natural frequency of 3D printed objects, contributing to the advancement of predictive modeling in additive manufacturing.

Optimization of torrefaction conditions on antioxidant activity of prickly pear seeds extract using response surface methodology and chemometric analysis

The prickly pear (Opuntia ficus-indica L., Cactaceae) seeds can be used as an alternative food product by the torrefaction process. Torrefaction is a pre-treatment process of biomass into solid fuel within a temperature range of 60-200°C. This study aimed to investigate the effect of torrefaction parameters on the antioxidant potential of prickly pear seeds, which is considered a food by-product, by using the Central Composite Design approach (CCD).The best torrefaction parameters found to have the optimal phytochemical contents were: 200°C and 50 min with 104.86±1.94 GAE/g extract for Total Phenolic Content (TPC), 200°C and 50 min with 81.23±0.90mg QE/g extract for Total Flavonoid Content (TFC), 200°C and 50 min with IC50=90.66±2.09 μg/ml for free radical scavenging activity (DPPH assay), 200°C and 50 min with IC50=124.9±4 μg/ml for radical cation total antioxidant capacity (ABTS assay). Our results demonstrated an increase of antioxidant activities with the increase of torrefaction parameters. Therefore, torrefaction has proven to be a value-added process to exploit prickly pear seed biomass and use it as a source of antioxidant food additives.

A quality by design assisted RP-HPLC method utilizing central composite design for the assay of eliglustat and its organic impurities in drug

This study aimed to develop a stability-indicating RP-HPLC assay method for eliglustat and its organic impurities using a central composite design approach. The chromatographic separation was achieved with a 10 mM phosphate buffer at pH 4.0 on a Thermo Accucore C18 column (150 x 4.6 mm, 2.6 μm) at a wavelength of 210 nm. Gradient elution was performed using a mobile phase-A consisting of buffer and acetonitrile in a 90:10 v/v ratio and a mobile phase-B composed of water and acetonitrile in a 35:65 v/v ratio, at a flow rate of 1.1 mL/min at 45°C for a total run time of 55 minutes. Based on preliminary experiments, central composite design was utilized to evaluate the effects of independent variables such as flow rate, acetonitrile ratio and column oven temperature on the response factors. Eliglustat and its impurities (EGS-5A, EGS-Diastereomers, and EGS-N-Oxide) had retention time of 3.5 minutes, 21.5 minutes, 23.2 minutes and 25.9 minutes, respectively. The limits of detection and quantitation for eliglustat and its impurities were established in relation to the test concentration, achieving a desirability of 1. The developed method was validated as per regulatory guidelines and successfully applied in bulk drugs and formulation.

Assessing disintegration effectiveness: A thorough evaluation using the SeDeM-ODT expert system for doxylamine succinate orodispersible formulation.

The SeDeM-ODT expert system is designed to assess the suitability of the pharmaceutical ingredients for their conversion into an orodispersible formulation by direct compression. The tool can be utilized to select the most appropriate excipients that improve the compressibility and buccodispersibility of the formulation. This study aimed to utilize the SeDeM-ODT expert system to evaluate the performance of superdisintegrants and select an appropriate superdisntegrant for Doxylamine Succinate orodispersible formulation. The SeDeM-ODT expert system scrutinized the excipients to develop an orodispersible Doxylamine Succinate formulation. Among the 15 parameters of the tool, some of them were determined through experimental work, while the remaining were calculated through the experimental values of other parameters. The central composite design approach was used for formulation development. The prepared powder blends were compressed using the direct compression method and evaluated for different parameters (hardness, thickness, diameter, friability, weight variation, water absorption ratio, wetting time, and disintegration time). The results of the SeDeM-ODT expert system were correlated with the values obtained by the post-compression tests. The Crospovidone formulation (F7) was found to be an optimized formulation as it disintegrated quickly compared with the other formulations containing other superdisintegtrants. The results perfectly endorsed the SeDeM-ODT expert system evaluation, as Crospovidone showed the highest IGCB value of 6.396. The study observed the effectiveness of the expert system in accurately examining the performance of disintegrating agents. The study observed the effectiveness of the expert system in accurately examining the performance of disintegrating agents. The assessment proved Crospovidone to produce quicker disintegration in Doxylamine Succinate orodispersible formulation.

Omnidirectional soft pneumatic actuators: a design and optimization framework.

Soft pneumatic actuators (SPAs) play a pivotal role in soft robotics due to their unique characteristics of compliance, flexibility, and adaptability. There are plenty of approaches that examine the modeling parameters of SPAs, aiming to optimize their design and, thus, achieve the most advantageous responses. Current optimization methods applied to SPAs are usually performed individually for each design parameter without considering the simultaneous effect all parameters can have on the output performance. This modeling shortcoming is essential to be addressed since customized SPAs are used in a variety of applications, each with different output requirements. This study provides a generalized design optimization framework for modeling the SPA performance for their motion profiles, the produced strain energy while being deformed, and their stiffness characteristics. Utilizing experimentally validated finite element methods, all geometrical and material parameters of the models are investigated in response surface methodology optimization using the central composite design approach. The results showcase the entire design space of omnidirectional SPAs, along with their output performance, providing guidelines to the end user for design optimization. The offering of this modeling process for SPAs can be adapted to the demands of any potential application and ensure the best performance with respect to the required output responses.

Parameter optimisation of marine four-stroke engine EGR systems using RSM and NSGA-III

ABSTRACT To reduce NOx emission of marine engines, an engine parameter optimisation method combining response surface method (RSM), Bayesian neural network and non-dominated sequence genetic algorithm (NSGA-III) is proposed. First, a simulation model of a marine four-stroke engine with an external exhaust gas recirculation (EGR) system was established and validated in AVL-BOOST software. Then, the five parameters of EGR valve opening, ignition advance angle, compression ratio (CR), intake valve opening (IVO) and exhaust valve closing (EVC) are selected as the decision parameters, and the three parameters of engine power, brake specific fuel consumption (BSFC) and NOx emission are taken as the target parameters to be optimised. In Design-Expert software, the response surface model is established by using the experimental design approach of central composite design (CCD), and the impact of decision parameters on target parameters are discussed by using response surface analysis. NSGA-III was used for the multi-objective optimisation of parameters, and the accuracy of NSGA-III was improved by the Bayesian neural network. The optimal solution is selected and verified by the technique for order of preference by similarity to the ideal solution (TOPSIS) approach. The results show that when the EGR valve opening is 47.5%, ignition advance angle is −18.99°CA, CR is 14.995, IVO is 15.27°CA and EVC is 7.68°CA, compared with the standard setting, the optimised power is increased by 4.17%, BSFC is reduced by 4.27%, and NOx emissions are reduced by 12.37%. Therefore, the combination of the Bayesian neural network and NSGA-III multi-objective optimisation can improve engine performance while reducing fuel consumption and NOx emissions.

Development and optimization of sustainable and functional food packaging using false banana (Enset) fiber and zinc-oxide (ZnO) nanoparticle-reinforced polylactic acid (PLA) biocomposites: A case of Injera preservation

In a global context marked by food insecurity, it is essential for food science and packaging technology researchers and stakeholders to ensure the availability of safe and adaptable foods with minimal environmental impact. Achieving sustainability in food packaging requires multiple approaches, including the use of natural and biodegradable materials including cellulosic fibers. The current study aimed to develop and characterize and optimized an effective biocomposite food packaging/storing materials, specifically for a popular Ethiopian flatbread called injera, made from a grain called ‘Teff’ (Eragrostis tef). The proposed biocomposite food storage and packaging was designed by incorporating fiber-reinforcing materials, namely false banana, also called Enset fibers (EFs), and ZnO nanoparticles (ZnO NPs) into a polylactic acid (PLA) matrix. A central composite design (CCD) approach was used to evaluate the impact of the reinforcing Enset fibers (EFs) at 5 %, 15 %, and 25 % and ZnO NPs at 0 %, 5 %, and 10 % levels. The developed functional biocomposite packaging materials were tested and characterized for various properties, including mechanical strength, water activity, antifungal activity, and migration properties. The results showed that the inclusion of ZnO NPs improved the tensile strength, migration, and barrier properties, while the reinforcing fiber enhanced mechanical and migration properties but reduced barrier properties. The combined effect of the reinforcement fibers (EFs) and ZnO NPs led to further improvements in the mechanical strength and migration properties, though no interaction effect was observed on barrier properties. The optimal solution, consisting of 6.7 % ZnO nanoparticles and 6 % Enset fibers, resulted in a highly effective packaging and storage prototype that extended the freshness of the food for over eight days.

Biogas production from Udara seeds inoculated with food waste digestate and its optimal output for energy utilities: Central composite design and machine learning approach

AbstractAnaerobic digestion of abundant feedstock from biomaterials is a good innovative fossil fuel alternative approach for the synthesis of green fuel (biogas). Rotatable central composite design (CCD) and machine learning (ML) via Python coding were successfully used to design, optimize, and predict the rate of biogas production from stew‐rice and eggs digestate with Udara seeds in an anaerobic unit. Two‐input parameters, such as inoculation ratio (S/I) and hydraulic reaction time (HRT) were considered, resulting in 13 experimental setups under mesophilic surroundings of 25–34°C. Mixture ratios of substrate/inoculum (S/I) of 0.98:1, 1.5:1, 2.75:1, 2.75:1, 4:1, 1.5: 1, and 4.52:1 were used against 30, 20, 44.14, 15.86, 40, 40, and 30 days HRT as modeled by CCD rotatable to optimize biogas production from crushed Udara seeds with spoilt stew‐rice and eggs digestate. From the results, it was observed that the coefficient of determination (R2) of 0.9573 was generated via CCD rotatable whereas, the R2 of 1 was generated from the multivariate regression of ML approach. Also, the data and graphs derived via ML were superior to the ones derived from CCD rotatable. However, the maximum output of 4.84 L at 4 mixing ratio and 40 days HRT from CCD rotatable is close to the ML value of 4.89 L under the same input factors, yet ML yielded more. Thus, it is clear that the Python‐based ML algorithm approach has the potential to predict biogas output better than CCD rotatable. However, the Gas Chromatography Mass Spectrometry analysis of the highest output produced generated 63.29% biomethane and 26.71% CO2 by volume and produced a flashpoint of −167°C which is flammable. Thus, the generated biogas via an anaerobic unit can be transmitted into large‐scale commercial applications for the betterment of mankind.

Design of biphasic Fe and Zn doped hydroxyapatite: Novel strategy for combating osteomyelitis infections

Hydroxyapatite (HA) bioceramics are widely used materials for orthopaedic applications, offering enhanced cellular adhesion and differentiation. However, the lack of antibacterial features makes the HA biomaterials prone to bacterial infections in in-vivo conditions. The osteomyelitis-like conditions are often treated with prolonged doses of antibiotics and may require surgical removal. Such problems can be tackled by inducing the antibacterial characteristics in the biomaterials which prevent such infections. Various research studies have been conducted to improve the mechanical and antibacterial characteristics of biomaterials like HA through doping. The present study focuses on the synthesis, characterization, and antibacterial study of Zn and Fe doped HA (ZFHAp) for application in the treatment of osteomyelitis. Response Surface Methodology (RSM) based Central Composite Design (CCD) approach was used to optimize the process parameters using the Design Expert software. The analyses indicated the presence of Calcium Iron Hydrogen Phosphate (CIHP) and Parascholzite (PS) as major phases. The optimized experimental conditions were estimated as 0.09 M Zn and 0.04 M Fe at 65 °C, corresponding to 71.29 % and 28.71 % of the CIHP and PS phases, respectively. The synthesized nanopowder showed mixed morphology comprising spherical and rod-shaped particles due to biphasic composition. The ZFHAp sample showed a broad-spectrum antibacterial activity, with IC50 values of 4.02 mg/ml and 4.79 mg/ml against E. coli and S. aureus, respectively. The optimized sample showed excellent biocompatibility with the osteoblast-like cells (MG-63) with improved biomineralization properties. The antibacterial and osteoinductive characteristics of the synthesized biphasic Fe and Zn doped HA suggest its potential application against osteomyelitis-related conditions.

The optimization of mechanical properties of polypropylene/styrene butadiene rubber/silicon carbide nanocomposites using response surface methodology

AbstractPolypropylene is a useful and widely consumed thermoplastic with very good properties that can be mixed with a combination of rubber and nanoparticles in order to eliminate its shortcomings. In this paper, the simultaneous combination of both the toughening effect of styrene–butadiene rubber (SBR) and the reinforcing effect of silicon carbide (SiC) nanoparticles were investigated in polypropylene matrix. Mechanical properties (tensile and impact strength [TS and IS]) of compounds were analyzed using the central composite design approach in Design‐Expert software. With two factors for input variables of SBR and SiC weight percentages, and their five levels (5, 10, 15, 20, and 25) and (0, 1.5, 3, 4.5, and 6), respectively, the central composite design approach employs 11 experiments for creation of models and the analysis of variance investigations with two output responses of TS and IS. Statistical results revealed that SBR and SiC contents had a significant effect on TS, IS, and the microstructure of compounds, as confirmed by scanning electron microscopy and energy‐dispersive spectroscopy images. To maximize all mechanical properties simultaneously with desirability functions, TS and IS of the optimum sample were computed to be 21.98 MPa and 8.66 kJ/m2 in amounts of 13.45 and 2.80 weight percentage (wt%) of SBR and SiC, respectively.Highlights By increasing silicon carbide (SiC) in polypropylene/styrene–butadiene rubber (SBR), tensile strength decreases after a peak due to agglomeration effect. By increasing SiC in polypropylene/SBR, impact strength decreases after a peak due to agglomeration effect. By increasing SiC, the rubber particles' dimensions decrease according to scanning electron microscopy analysis. Simultaneous maximum of tensile strength and impact strength are 21.98 MPa and 8.66 KJ/m2, respectively. Optimum value of SBR and SiC in above maximums are 13.45 and 2.8 wt%, respectively.

Synthesis of ZnV2O6 nanosheet photocatalysts for efficient photodegradation of Rhodamine B: Experimental and RSM modeling

A ZnV2O6 photocatalyst with nano-sheet morphology was synthesized using a facile solvothermal approach. To optimize the photo-decomposition of Rhodamine B (RhB), a new surface response methodology (RSM) based on the Central Composite Design (CCD) approach was employed. Several structural/microstructural and spectroscopic characterization techniques were utilized to describe the physical and chemical characteristics of ZnV2O6 (ZVO) nanosheet such as XRD, Raman, SEM-TEM, DRS, PL/TRF. The optimal conditions for RhB photo-decomposition were mathematically discussed as a function of four variables such as pollutant dose, pH values, photocatalyst-weight, and irradiation time, which were modeled by CCD-RSM employing a quadratic statistical model and an optimization approach (ANOVA analysis). RhB degradation efficiency of 97 % was achieved for the optimal conditions of pH = 8, photocatalyst mass = 95 mg, RhB concentration = 9 ppm, and irradiation time = 120. The registered reaction constant was 0.029 min−1. The removal efficiencies of TOC and COD when using the ZVO photo-catalyst for the RhB degradation were 83 % and 88 % respectively. The re-usability of ZnV2O6 samples has been assessed in 4 consecutive series revealing a high stability of the material after extended periods of photo-catalytic reaction. Furthermore, the active radical species involved in the breakdown of RhB were further confirmed through quenching tests in which hydroxyls and holes were found to be the major species contributing to the decomposition of RhB.

Investigation the effects of operational parameters and multi-factor optimization of butene metathesis process via statistical approach

In this study, the production of propylene from 2-butene and 1-butene via the metathesis process was investigated and simulated. The effects of operational parameters, including reactor temperature, residence time, and 1-butene/2-butene molar ratio, on reactor performance were examined, and statistical models were developed. The design of experiment (DoE) was performed using the response surface methodology (RSM)-central composite design (CCD) approach for efficient experimentation and model development as well as optimization. The study's findings indicate a high conversion rate for 2-butene. By statistically analyzing the obtained data and performing analysis of variance (ANOVA), the influential parameters and their degree of importance were determined. Sensitivity analysis was conducted based on the developed models. Finally, using these models and desirability function as a powerful optimization algorithm, optimal conditions for the process were obtained. The results of the optimization showed that the maximum propylene molar fraction (32.5 %) was obtained at a temperature of 508.4 °C, a residence time of 20.7 s, and a 1-butene/2-butene molar ratio of 0.1.