Biosynthesis of gold nanoparticles using marine bacteria and Box–Behnken design optimization

Gold nanoparticles are exciting materials because of their potential applications in optics, electronics, biomedical, and pharmaceutical fields. In recent years, environmentally friendly, low-cost biosynthesis methods with bio-applicable features have continued to be developed for the synthesis of gold nanoparticles. In the present study, an actinobacterial strain was isolated from the Petrosia ficiformis (Poiret 1798) sponge, which was collected from a marine environment, and the gold nanoparticle synthesis was performed for the first time from the bacteria type belonging to the Citricoccus genus. The synthesis conditions were optimized using the Box–Behnken experimental design, with a statistical method that included three independent variables (temperature, time, and mixture ratio) to affect the synthesis at three levels (+1, 0, and ?1). Accordingly, the conditions proposed for the biosynthesis of gold nanoparticles at the maximum optical density values that are specific for the Citricoccus sp. K1D109 strain were estimated as 35°C temperature, 24?h, and 1/5 mixture ratio (cell-free extract/HAuCl₄?·?3H₂O). When recommended conditions were applied, it was determined that the maximum absorbance of the synthesized gold nanoparticles is 1.258 at 545?nm, and their sizes are in the range of 25–65?nm, according to transmission electron microscopy (TEM) data.     

Parametric optimization studies on drilling of sandwich composites using the Box–Behnken design

This study was carried out to investigate the parametric influence on the performance of drilling newly made sandwich composites. Sandwich composite was prepared by using steel and jute as reinforcements and polyester as the matrix material. Drilling experiments were carried out by considering input factors such as spindle speed, feed rate of the spindle, point angle of the drill and tool diameter. Three output factors, namely thrust force developed during drilling, surface roughness of the drilled hole and damage at the entrance surface, were studied. All output factors were optimized by using the Box–Behnken approach, and the best machining conditions were taken on the basis of the desirability approach. Confirmatory experiments were conducted and compared against the Box–Behnken model. The comparison showed only a minor error, and hence the optimization is satisfactory.     

Application of response surface methodology with a Box–Behnken design for struvite precipitation

Struvite crystals were precipitated by the reaction of magnesium chloride hexahydrate and ammonium dihydrogen phosphate using different concentrations of citric acid as the additive (100, 300, and 500 ppm). The structure, morphology, functional groups and particle size of the crystals were evaluated experimentally by scanning electron microscopy, X-ray diffraction, Fourier transform infrared spectroscopy (FTIR) and particle size analysis. The experimental results demonstrated that citric acid exerted a significant influence on the struvite precipitation and the crystal morphology changed from rod-like to tubular shaped with a larger size and hollow bodies. The average particle size changed from 17.60 to 33.60 μm with increasing citric acid concentration. The results of FTIR suggested that the citric acid adsorbed on the crystal surface. Following the characterization of the crystals prepared using different concentrations of citric acid, the response surface methodology coupled with Box-Behnken design were applied as a statistical tool to determine the effects of the key parameters affecting the precipitation process (temperature, pH and additive concentration) on the responses (namely, particle size and specific cake resistance of struvite). Second-order polynomial equations for both responses were improved to correlate the parameters. Analysis of variance (ANOVA) showed a significant quadratic regression model with high coefficients of the determination values. The optimum conditions for particle size were found to be 60 °C, pH 8 and 500 ppm additive concentration.     

Prediction and optimization of nanoclusters-based thermal conductivity of nanofluids: Application of Box–Behnken design (BBD)

The existing models to predict the thermal conductivity of nanofluids are based on single particle diameter, whereas, in actual solutions, nanoparticles mostly exist in a cluster form. Experiments are carried out to observe the effects of various surfactants on stability, nanocluster formation, and thermal conductivity of Al₂O₃–H₂O nanofluid, which is found to be improved considerably with SDS surfactant. The prolonged sonication was not adequate to break the clusters of Al₂O₃ nanoparticles, into an average size of less than 163 nm, indicating the tendency of Al₂O₃ nanoparticles to remain in the form of clusters instead of individual nanoparticles of primary size of 20 nm. Response surface methodology has been employed to design and optimize the experimental strategy by taking volumetric concentration, temperature, and surfactant amount as the contributing factors. The developed model has been validated against the experimental data and the existing models with an accuracy level of ± 8% in the former case. Analysis reveals about the formation of nanoclusters and enhancement in thermal conductivity. The results confirmed that the model can predict thermal conductivity enhancement with an accuracy level of R square value of the order of 0.9766.     

Lipid nanoparticles of zaleplon for improved oral delivery by Box–Behnken design: optimization,in vitro and in vivo evaluation

Purpose: Zaleplon (ZL) is a hypnotic drug prescribed for the management of insomnia and convulsions. The oral bioavailability of ZL was low (～30%) owing to poor water solubility and hepatic first-pass metabolism. The cornerstone of this investigation is to develop and optimize solid lipid nanoparticles (SLNs) of ZL with the aid of Box–Behnken design (BBD) to improve the oral bioavailability.

Methods: A design space with three formulation variables at three levels were evaluated in BBD. Amount of lipid (A₁), amount of surfactant (A₂) and concentration of co-surfactant (%) (A₃) were selected as independent variables, whereas, particle size (B₁), entrapment efficiency (B₂) and zeta potential (ZP, B₃) as responses. ZL-SLNs were prepared by hot homogenization with ultrasonication method and evaluated for responses to obtain optimized formulation. Morphology of nanoparticles was observed under SEM. DSC and XRD studies were examined to understand the native crystalline behavior of drug in SLN formulations. Further, in vivo studies were performed in Wistar rats.

Results: The optimized formulation with 132.89?mg of lipid, 106.7?mg of surfactant and 0.2% w/v of co-surfactant ensued in the nanoparticles with 219.9?±?3.7?nm of size, ?25.66?±?2.83?mV surface charge and 86.83?±?2.65% of entrapment efficiency. SEM studies confirmed the spherical shape of SLN formulations. The DSC and XRD studies revealed the transformation of crystalline drug to amorphous form in SLN formulation. In conclusion, in vivo studies in male Wistar rats demonstrated an improvement in the oral bioavailability of ZL from SLN over control ZL suspension.

Conclusions: The enhancement in the oral bioavailability of ZL from SLNs, developed with the aid of BBD, explicated the potential of lipid-based nanoparticles as a potential carrier in improving the oral delivery of this poorly soluble drug.     

Formulation and optimization of a novel oral fast dissolving film containing drug nanoparticles by Box–Behnken design–response surface methodology

Objective: The purpose of this study was to design and optimize a novel drug nanoparticles-loaded oral fast dissolving film (NP-OFDF) using Box–Behnken design–response surface methodology.

Methods: Drug nanosuspensions produced from high pressure homogenization were transformed into oral fast dissolving film containing drug nanoparticles by casting methods. Herpetrione (HPE), a novel and potent antiviral agent with poor water solubility that was extracted from Herpetospermum caudigerum, was studied as the model drug. The formulations of oral fast dissolving film containing HPE nanoparticles (HPE-NP-OFDF) were optimized by employing Box-Behnken design–response surface methodology and then systematically characterized.

Results: The optimized HPE-NP-OFDF was disintegrated in water within 20?s with reconstituted nanosuspensions particle size of 299.31?nm. Scanning electron microscopy (SEM) images showed that well-dispersed HPE nanoparticles with slight adhesion to each other were exposed on the surface of film or embedded in film. The X-ray diffractogram (XRD) analysis suggested that HPE in the HPE-NP-OFDF was in the amorphous state. In-vitro release study, approximate 77.23% of HPE was released from the HPE-NP-OFDF within 5?min, which was more than eight times compared with that of HPE raw materials (9.57%).

Conclusion: The optimized HPE-NP-OFDF exhibits much faster drug release rates compared to HPE raw material, which indicated that this novel NP-OFDF may provide a potential opportunity for oral delivery of drugs with poor water solubility.     

Development,optimization, and in vitro characterization of dasatinib-loaded PEG functionalized chitosan capped gold nanoparticles using Box–Behnken experimental design

Objective: The purpose of this research study was to develop, optimize, and characterize dasatinib loaded polyethylene glycol (PEG) stabilized chitosan capped gold nanoparticles (DSB-PEG-Ch-GNPs).

Methods: Gold (III) chloride hydrate was reduced with chitosan and the resulting nanoparticles were coated with thiol-terminated PEG and loaded with dasatinib (DSB). Plackett–Burman design (PBD) followed by Box–Behnken experimental design (BBD) were employed to optimize the process parameters. Polynomial equations, contour, and 3D response surface plots were generated to relate the factors and responses. The optimized DSB-PEG-Ch-GNPs were characterized by FTIR, XRD, HR-SEM, EDX, TEM, SAED, AFM, DLS, and ZP.

Results: The results of the optimized DSB-PEG-Ch-GNPs showed particle size (PS) of 24.39?±?1.82?nm, apparent drug content (ADC) of 72.06?±?0.86%, and zeta potential (ZP) of ?13.91?±?1.21?mV. The responses observed and the predicted values of the optimized process were found to be close. The shape and surface morphology studies showed that the resulting DSB-PEG-Ch-GNPs were spherical and smooth. The stability and in vitro drug release studies confirmed that the optimized formulation was stable at different conditions of storage and exhibited a sustained drug release of the drug of up to 76% in 48?h and followed Korsmeyer–Peppas release kinetic model.

Conclusions: A process for preparing gold nanoparticles using chitosan, anchoring PEG to the particle surface, and entrapping dasatinib in the chitosan-PEG surface corona was optimized.     

Development of olmesartan medoxomil optimized nanosuspension using the Box–Behnken design to improve oral bioavailability

The aim of the present investigation was to enhance the oral bioavailability of olmesartan medoxomil by improving its solubility and dissolution rate by preparing nanosuspension (OM-NS), using the Box–Behnken design. In this, four factors were evaluated at three levels. Independent variables include: concentration of drug (X₁), concentration of surfactant (X₂), concentration of polymer (X₃) and number of homogenization cycles (X₄). Based on preliminary studies, the size (Y₁), zeta potential (ZP) (Y₂) and % drug release at 5?min (Y₃) were chosen as dependent responses. OM-NS was prepared by high pressure homogenization method. The size, PDI, ZP, assay, in vitro release and morphology of OM-NS were characterized. Further, the pharmacokinetic (PK) behavior of OM-NS was evaluated in male wistar rats. Statistically optimized OM-NS formulation exhibited mean particle size of 492?nm, ZP of –27.9?mV and 99.29% release in 5?min. OM-NS showed more than four times increase in its solubility than pure OM. DSC and XRD analyses indicated that the drug incorporated into OM-NS was in amorphous form. The morphology of OM-NS was found to be nearly spherical with high dispersity by scanning electron microscopic studies. The PK results showed that OM lyophilized nanosuspension (NS) exhibited improved PK properties compared to coarse powder suspension and marketed tablet powder suspension (TS). Oral bioavailability of lyophilized NS was increased by 2.45 and 2.25 folds when compared to marketed TS and coarse powder suspension, respectively. Results of this study lead to conclusion that NS approach was effective in preparing OM formulations with enhanced dissolution and improved oral bioavailability.     

Optimization of methotrexate loaded niosomes by Box–Behnken design: an understanding of solvent effect and formulation variability

Dermal drug delivery system which localizes methotrexate (MTX) in the skin is advantageous in topical treatment of psoriasis. The aim of the current study was to understand dilution effects and formulation variability for the potential formation of niosomes from proniosome gels of MTX. Box–Behnken’s design was employed to prepare a series of MTX proniosome gels of Span 40, cholesterol (Chol-X₁) and Tween 20 (T20-X₂). Short chain alcohols (X₃), namely ethanol (Et), propylene glycol (Pg) and glycerol (G) were evaluated for their dilution effects on proniosomes. The responses investigated were niosomal vesicles size (Y₁), MTX entrapment efficiency percent (EE%-Y₂) and zeta potential (Y₃). MTX loaded niosomes were formed immediately upon hydration of the proniosome gels with the employed solvents. Addition of Pg resulted in a decrease of vesicular size from 534?nm to 420?nm as Chol percentage increased from 10% to 30%, respectively. In addition, increasing the hydrophilicity of the employed solvents was enhancing the resultant zeta potential. On the other hand, using Et in proniosomal gels would abolish Chol action to increase the zeta potential value and hence less stable niosomal dispersion was formed. The optimized formula of MTX loaded niosomes showed vesicle size of 480?nm, high EE% (55%) and zeta potential of –25.5?mV, at Chol and T20 concentrations of 30% and 23.6%, respectively, when G was employed as the solvent. Hence, G was the solvent of choice to prepare MTX proniosomal gels with a maintained stability and highest entrapment.     

Box–Behnken Design optimization of malachite green dye biosortpion using nano zero valent iron Sargassum swartzii biocomposite

This study reports the biosorption of MG using a novel nano zero valent iron Sargassum swartzii (nZVI-SS) biocomposite. Response surface methodology derived from Box–Behnken design (BBD) was applied for optimizing the essential variables such as pH, initial MG concentration, and biosorbent dosage. The results obtained from BBD design showed the maximum biosorption capacity of 73.5?mg/g at pH 10, initial MG concentration of 100?mg/L, and biosorbent dosage of 0.55?g/L. The biosorption was highly dependent on pH and initial dye concentration. The nZVI-SS biocomposite was characterized using SEM, FTIR, TGA, and DSC analysis. The experimental and analytical result shows that nZVI-SS biocomposite can be effectively used as prominent biosorbent for the removal of dye bearing wastewater.     

Developing a fuzzy proportional–derivative controller optimization engine for engineering design optimization problems

In real world engineering design problems, decisions for design modifications are often based on engineering heuristics and knowledge. However, when solving an engineering design optimization problem using a numerical optimization algorithm, the engineering problem is basically viewed as purely mathematical. Design modifications in the iterative optimization process rely on numerical information. Engineering heuristics and knowledge are not utilized at all. In this article, the optimization process is analogous to a closed-loop control system, and a fuzzy proportional–derivative (PD) controller optimization engine is developed for engineering design optimization problems with monotonicity and implicit constraints. Monotonicity between design variables and the objective and constraint functions prevails in engineering design optimization problems. In this research, monotonicity of the design variables and activities of the constraints determined by the theory of monotonicity analysis are modelled in the fuzzy PD controller optimization engine using generic fuzzy rules. The designer only needs to define the initial values and move limits of the design variables to determine the parameters in the fuzzy PD controller optimization engine. In the optimization process using the fuzzy PD controller optimization engine, the function value of each constraint is evaluated once in each iteration. No sensitivity information is required. The fuzzy PD controller optimization engine appears to be robust in the various design examples tested.     

Box–Behnken优化马齿苋抗氧化澄清饮料制备工艺

目的 比较干、鲜马齿苋的活性成分及对1,1–二苯基–2–三硝基苯肼（DPPH）的清除率，选择抗氧化活性高的马齿苋酶解液为功能原料，优化马齿苋抗氧化饮料的配方。方法 以马齿苋酶解液、甜菊糖苷、柠檬酸为自变量，以饮料的感官评分为响应值，采用Box–Behnken响应面法优化马齿苋抗氧化澄清饮料的制备工艺。结果 鲜马齿苋酶解液中多糖和黄酮的含量高于干马齿苋，对DPPH的清除率也大于干马齿苋。在鲜马齿苋酶解液的体积分数为45.2%、甜菊糖苷的质量浓度为0.23g/L、柠檬酸的质量浓度为0.06 g/L等条件下，马齿苋饮料的理论感官评分值为33.2，实际评分值为33.3，表明其抗氧化能力较好（饮料的体积为0.60 mL时对DPPH的清除率为34.6%）。结论 回归模型的预测值与实测值接近，采用响应面法优化马齿苋抗氧化饮料的工艺合理可行，制备的马齿苋抗氧化饮料呈现酒红色、澄清透明、酸甜适宜。     

Development of a multidisciplinary–multiresponse robust design optimization model

Robust design is an efficient process improvement methodology that combines experimentation with optimization to create systems that are tolerant to uncontrollable variation. Most traditional robust design models, however, consider only a single quality characteristic, yet customers judge products simultaneously on a variety of scales. Additionally, it is often the case that these quality characteristics are not of the same type. To addresses these issues, a new robust design optimization model is proposed to solve design problems involving multiple responses of several different types. In this new approach, noise factors are incorporated into the robust design model using a combined array design, and the results of the experiment are optimized using a new approach that is formulated as a nonlinear goal programming problem. The results obtained from the proposed methodology are compared with those of other robust design methods in order to examine the trade-offs between meeting the objectives associated with different optimization approaches.     

Multiaxial fatigue assessment of welded joints – Recommendations for design codes

In the assessment of welded joints submitted to multiaxial loading the calculations method applied, independently of the concept (nominal, structural, hot-spot or local), must consider primarily the materials ductility. While proportional loading can be assessed by von Mises, the principal stress hypothesis, the Findley method or the Gough–Pollard relationship, using any of the mentioned concepts, difficulties occur when the loading is non-proportional, i.e. the principal stress (strain) direction changes. This causes a significant fatigue life reduction for ductile steel welds, but an indifferent behaviour for semi-ductile aluminium welds. This different response to non-proportional loading can be assessed when ductility related mechanisms of fatigue failures, i.e. the mean value of plane oriented shear stresses for ductile materials and a combination of shear and normal stresses for semi-ductile materials, are properly considered.However, as these methods require a good expertise in multiaxial fatigue, for design codes used by non-fatigue experts, simpler but sound calculation methodologies are required. The evaluation of known fatigue data obtained with multiaxial constant and variable amplitude (spectrum) loading in the range N > 10⁴ cycles suggests the application of the modified interaction algorithm of Gough–Pollard. In the case of variable amplitude loading, constant normal and shear stresses are replaced by modified reference normal and shear stresses of the particular spectrum. The modification of the reference stresses is based on the consideration of the real Palmgren–Miner damage sum of D_PM = 0.5 (for spectra with constant mean loads) and the modification of the Gough–Pollard algorithm by consideration of the multiaxial damage parameter D_MA = 1.0 or 0.5, which is dependent on the material’s ductility and on whether the multiaxial loading is proportional or non-proportional. This method is already part of the IIW-recommendations for the fatigue design of welded joints and can also be applied by using hot-spot or local stresses.     

Evolutionary methods for multidisciplinary optimization applied to the design of UAV systems†

The implementation and use of a framework in which engineering optimization problems can be analysed are described. In the first part, the foundations of the framework and the hierarchical asynchronous parallel multi-objective evolutionary algorithms (HAPMOEAs) are presented. These are based upon evolution strategies and incorporate the concepts of multi-objective optimization, hierarchical topology, asynchronous evaluation of candidate solutions, and parallel computing. The methodology is presented first and the potential of HAPMOEAs for solving multi-criteria optimization problems is demonstrated on test case problems of increasing difficulty. In the second part of the article several recent applications of multi-objective and multidisciplinary optimization (MO) are described. These illustrate the capabilities of the framework and methodology for the design of UAV and UCAV systems. The application presented deals with a two-objective (drag and weight) UAV wing plan-form optimization. The basic concepts are refined and more sophisticated software and design tools with low- and high-fidelity CFD and FEA models are introduced. Various features described in the text are used to meet the challenge in optimization presented by these test cases.     

A hybrid cellular automaton–bi-directional evolutionary optimization algorithm for topological optimization of crashworthiness

This article proposes a new algorithm for topological optimization under dynamic loading which combines cellular automata with bi-directional evolutionary structural optimization (BESO). The local rules of cellular automata are used to update the design variables, which avoids the difficulty of obtaining gradient information under nonlinear collision conditions. The intermediate-density design problem of hybrid cellular automata is solved using the BESO concept of 0–1 binary discrete variables. Some improvement strategies are also proposed for the hybrid algorithm to solve certain problems in nonlinear topological optimization, e.g. numerical oscillation. Some typical examples of crashworthiness problems are provided to illustrate the efficiency of the proposed method and its ability to find the final optimal solution. Finally, numerical results obtained using the proposed algorithms are compared with reference examples taken from the literature. The results show that the hybrid method is computationally efficient and stable.     

Cramér-Rao sensitivity limits for astronomical instruments: implications for interferometer design

Multiple-telescope interferometry for high-angular-resolution astronomical imaging in the optical-IR-far-IR bands is currently a topic of great scientific interest. The fundamentals that govern the sensitivity of direct-detection instruments and interferometers are reviewed, and the rigorous sensitivity limits imposed by the Cramér-Rao theorem are discussed. Numerical calculations of the Cramér-Rao limit are carried out for a simple example, and the results are used to support the argument that interferometers that have more compact instantaneous beam patterns are more sensitive, since they extract more spatial information from each detected photon. This argument favors arrays with a larger number of telescopes, and it favors all-on-one beam-combining methods as compared with pairwise combination.     

Teaching–learning-based optimization algorithm for unconstrained and constrained real-parameter optimization problems

An efficient optimization algorithm called teaching–learning-based optimization (TLBO) is proposed in this article to solve continuous unconstrained and constrained optimization problems. The proposed method is based on the effect of the influence of a teacher on the output of learners in a class. The basic philosophy of the method is explained in detail. The algorithm is tested on 25 different unconstrained benchmark functions and 35 constrained benchmark functions with different characteristics. For the constrained benchmark functions, TLBO is tested with different constraint handling techniques such as superiority of feasible solutions, self-adaptive penalty, ?-constraint, stochastic ranking and ensemble of constraints. The performance of the TLBO algorithm is compared with that of other optimization algorithms and the results show the better performance of the proposed algorithm.     

An approach for the aging process optimization of Al–Zn–Mg–Cu series alloys

A new model based on least square support vector machines (LSSVM) and capable of forecasting mechanical and electrical properties of Al–Zn–Mg–Cu series alloys has been proposed for the first time. Data mining and artificial intelligence techniques of aluminum alloys are used to examine the forecasting capability of the model. In order to improve predictive accuracy and generalization ability of LSSVM model, a grid algorithm and cross-validation technique has been adopted to determine the optimal hyper-parameters of LSSVM automatically. The forecasting performance of the LSSVM model and the artificial neural network (ANN) has been compared with the experimental values. The result shows that the LSSVM model provides slightly better capability of generalized prediction compared to back propagation network (BPN) in combination with the gradient descent training algorithm. Considering its advantages of the computation speed, unique optimal solution, and generalization performance, the LSSVM model is therefore considered to be used as an alternative powerful modeling tool for the aging process optimization of aluminum alloys. Furthermore, a novel methodology hybridizing nondominated sorting-based multi-objective genetic algorithm (MOGA) and LSSVM has been proposed to make tradeoffs between the mechanical and electrical properties. A desirable nondominated solution set has been obtained and reported.     

Investigation of the cyanide leaching optimization for ultrafine grinding gold–silver ore

ABSTRACT

In recent years, the ultrafine grinding (UFG) process has become increasingly important for the recovery of precious metals (especially for Au and Ag) from raw ores. It is well known that cyanide leaching of gold–silver ore has been an accepted process in the world. In the current study, UFG was proposed as a pretreatment method prior to cyanide leaching of a gold–silver ore. First, the ore was crushed and ground by jaw, cone crusher, and ring mill, respectively. Particle size of ground product was 75 µm based d₈₀. Second, the fine product was subjected to the grinding process by vertical wet stirred ball mill, and the final ultrafine product was used as leach feed material. The leach feed material had about d₈₀ = 20 µm and d₅₀ = 6.02 µm particle size. Some important leaching parameters were optimized by using the ultrafine powder in the study. The final leach experiments demonstrated that the recoveries of Au and Ag were 91.92% and 82.15% under optimum leaching conditions such as 150 kg/t quicklime dosage, 25% pulp density, 85°C leach temperature, 500 g/t cyanide concentration, and 90 h leaching time.