Micronization of astaxanthin by the supercritical antisolvent

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1 Micronization of astaxanthin by the supercritical antisolvent process (SAS) Joana Jorge da Costa Dep. Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Portugal *Corresponding author: Abstract This aim of this work was the micronization of synthetic astaxanthin at 98.6% purity by supercritical antisolvent technique (SAS). The objectives were accomplished using CO2 as antisolvent and THF as solvent. Doe was applied in a fractional factorial design at 4 factors, pressure (100 to 150 bar), concentration (0.5-3 bar), temperature (40-60 C) and solution flow rate ( ml/min) and at 2 responses (yield of micronized product and mean particle size. Screening analysis showed higher significance to pressure, concentration, and temperature. 2 experiments were run to have a better understanding of the temperature influence, it showed that it influences morphologies of micronized particles, and that at increasing temperature, sphere like and smaller particles were obtained. Central Composite Design was studied for optimization process. Factors for this analyse was pressure ( bar) and concentration (1-3 mg/ml) and mean particle size for the response. Temperature and flow rate were maintained respectively at 60 C and1.5ml/min. Minimum mean particle size obtained was of nm during the screening process at 100 bar, 60 C, 0.5ml/min and 3 mg/ml. Central composite design predicted that a similar response could be obtained at flow rate of 1.5 ml/min but the mean particle size found in this conditions was a little higher, nm. Introduction Astaxanthin is a red carotenoid from the carotenoid family. The most common source of Astaxanthin is the microalgae Haematoccus pluvialis, which can synthetize large amounts of this carotenoid in order to protect itself from ultraviolet radiation and in response to nutrient and environmental stress. Astaxanthin started to be applied in coloring salmonid fish in the feed industry, but is currently used in health and well-being markets, due to its anti-oxidant properties, as well as in in cosmetics (Guedes et al., 2011). Studies have shown that this compound is not only a super antioxidant, but it possesses anti-inflammatory properties. This carotenoid appears to have potential benefits for acid reflux and macular degeneration, provides vascular benefits, and less oxidative stress and inflammation. Moreover, astaxanthin enhances and strengthens the immune system and decreases DNA damage (Anarjan and Tan, 2013, Guerin et al., 2003, Hussein et al., 2006, Vílchez et al., 2011). Fasset and Coombes, (2011) reported that the regular ingestion of astaxanthin may improve oxidative response and prevent tissue damage. Also, astaxanthin proved to be a very good protective agent to membranous phospholipids and other lipids against peroxidation (Naguib, 2000 and Guerin et al., 2003). In the pharmaceutical industry smaller particle sizes can increase the efficiency of drug uptake by cells, which can mean the need of lower doses and the consequent reduce cost of the medicine. The size of solid particles of an active pharmaceutical ingredient used in a pharmaceutical formulation can have a great impact in properties like solubility, dissolution rate, dosage levels and bioavailability. Particle design is an area of most importance since it can be determinant in the efficiency of absorption, solubility and diffusion of a solid compound. Particles can be designed using traditional micronization techniques or supercritical and compressed gas based micronization techniques. Micronization is the general term used to

2 describe numerous processes that aim to reduce the average diameter of solid material particles. Usually, micronization is referred to the creation of particles with diameters in the order of 10 µm, however, due the development of modern techniques as well as the demand of pharmaceutical industry, it is now also used to describe the formation of particles with nano- sized diameters. The most common micronization techniques are spray drying, mechanical comminution, solute recrystallization, freeze drying, and interfacial polymerization. Nevertheless, these techniques presented significant disadvantages, such as excessive use of solvent, thermal and chemical solute degradation, high residual solvent concentration, and difficulty in controlling the particle size, particles size distribution as well as, changing the crystal structure of the precipitated powder. To overcome those disadvantages micronization techniques relying on supercritical fluids technology were developed. In the SAS process (Figure1), the supercritical fluid acts as anti-solvent, and the substrate is dissolved into a liquid solvent (solution). The supercritical antisolvent is continuously fed to the precipitation vessel (PV), as well as the liquid solution, which is sprayed through a restrictor into the PV. Figure 1: Schematic representation of a SAS micronization apparatus. S1: CO2 supply; S2: liquid supply; RB: refrigerating bath; P1, P2: pumps; TC: thermocouple; M: manometer; PV: precipitation vessel; MV: micrometering valve; LS: liquid separator; BPV: back pressure valve; R: rotameter; DM: dry-test meter (De Marco and Reverchon 2011). The rapid contact between the two media causes the precipitation of the solute, which is mediated by solubility interactions between the supercritical fluid and the liquid solvent. After precipitation the fluid phase is expanded through a micrometric valve (MV), and the liquid solvent is recovered in the lowpressure liquid solvent recovery vessel (LS). Furthermore, the antisolvent is expanded to atmospheric pressure. During the supercritical antisolvent process the surface area will be increased, which leads to an improvement in bioavailability. This fact is of great importance in drug delivery since narrower particle size distribution means a better flexibility of administration. Moreover increasing the bioavailability the required drug dosage decreases and raises the control over a sustained period (Acosta, 2009). Materials and Methods Astaxanthin was obtained from Dr. Ehrenstorfer GmbH (98,6%). Tetrahydrofuran (p.a grade) was purchase from Sigma-Aldrich and CO2 was provided from air liquid (99.998%). Solubility tests were carried in ethanol, acetone, ethyl acetate, DMSO, tetrahydrophuran and dichloromethane. Experimental procedure consisted in dissolving 10mg of astaxanthin in a volume of 1 ml of organic solvent. Subsequent additions of 1ml were made until it was observed that no more solid could be dissolve. The suspension was then stirred for 30 minutes, to ensure that saturation of the solution was reached. Afterwards, samples of 2ml were taken from the solution, spectrophotometry (Hitachi-2000) to determine the concentration of the dissolved fraction. Absorption spectra were run between 380 and 700 nm and the concentration of astaxanthin in the solvent was determined using the Beer-Lambert law, considering the maximum absorbance of the solution and the specific optical coefficient at the wavelength of the maximum absorbance of astaxanthin in the solvent (Delia B. Rodriguez-Amaya, Ph.D;, 2001)

3 SAS experimental studies were conducted in apparatus constructed at IST, under orientation of Dra Beatriz Nobre and Professor António Palavra at IST (Instituto Superior Técnico). The experimental procedure was the following: after reaching the target pressure, by pumping CO2, a previous calculated amount of organic solvent is injected into this vessel to ensure that all the operation will be carried out in steady state. When the organic solvent concentration inside the vessel reaches the fed concentration, the micrometering valve, MV, is regulated to establish the flow rate at the exit (bottom) of the precipitation vessel and it is given some time for the system to stabilize. In that point, the solution is injected and the micronization takes place. At the end of the solution injection, SC-CO2 will pass through the precipitation vessel in order to remove all existing organic solvent. The washing time with pure SC-CO2 is approximately 75 min. The morphology of unprocessed and processed particles was assessed using SEM (CamScan MV 2300, England). Particles of the several samples were coated with gold palladium at room temperature before the examination. The accelerator voltage for scanning was 25.0 kv. ImageJ software was used to analyze SEM photomicrographs, considering the ferret diameter as the measure of the particle size. Malvern Mastersizer Hydro 2000, Beckman Coulter Multisizer 4 or Nano Particle Tracking Analysis (NTA, from Nanosight) were also used to determine the mean particle size and size distribution of the processed astaxanthin. HPLC analysis was used to evaluate the purity and presence of degradation compounds of the obtained micronized powder, as well as to determine the concentration of astaxanthin in the solution (organic solvent and supercritical CO2) leaving the precipitation vessel. Design of experiments has been employed in many areas of investigation in order to maximize the efficiency of scientific work and minimize waste and cost. It allows a smarter choice of experiments that give the most information possible with the fewest experiments (Hibbert, 2012). Fractional factorial design (FFD) is usually use as a screening method to determine the significant effects, since it allows obtain the main effects model with a minimum number of experiments. Using the responses obtained by the experimental work a factorial model is then constructed through a list of coefficients multiplied by associated factor levels. This model is in the form of presented by equation 1. Y = β 0 + β 1 A + β 2 B + β 3 C + β 4 D + β 12 AB + β 13 AC + (Eq 1) Where β, is the coefficient associated with factor n, and the letters, A, B, C, D, represent the factors in the model. Combinations of factors, such as AB, represent an interaction between the individual factors in the term. Anova tests are then run by Design-Expert The results given, allow to determine significance of the model, lack of fit and the weight that each factor has in the model construction. The first parameter is determined by R-squared value and the other two by p-value. After determined the factors with higher importance by FFD a central composite design (CCD) can be run. In this stage another matrix of experiments is generated, response values are introduced in the matrix and Anova results predict once again the new model, by a similar equation. Having a significant model and well-adjusted it is possible to run space design to find the response that meet our goal. CCD generates a series of new experiments to obtain similar responses at different factors levels. Those experiences can be run and new values can be introduced, so the model can be adjust or confirmed. Results and Discussion THF was the only solvent that returned a significant amount of micronized powder, and is a class 2 solvent, regulated to be used in food industry, it was chosen as the organic solvent for the astaxanthin SAS micronization experimental studies. FFD with 4 factors, and 2 responses was built. The range selected for each effective

4 Run Pressure (bar) Temperat ure ( C) Astaxanthin Concentration (mg/ml) Solution Flow (ml/min) Yield of micronization (%) Mean Particle Size (µm) Std. Dev Table 1: Matrix for FFD at 4 factors (Pressure, Temperature, Astaxanthin Concentration of organic solution and Solution Flow Rate) and 2 responses (Yield of Micronization and Mean Particle Size) created by Design- Expert Std. Dev. values stands for mean particle size analysis factor was carefully chosen: 40 to 60º C for temperature, 100 to 150 bar for pressure, 1.0 to 1.5 for CO2/organic solution flow rate ratio and 0.15 to 3 mg/ml for solution concentration. Two response factors were chosen as the most important criteria to optimize the SAS micronization of astaxanthin, and these were the mean particle size and the yield of the process (which was defined as the ratio of the amount of micronized astaxanthin collected in the precipitation vessel and the amount of astaxanthin in the organic solution). The total matrix design showed 12 runs and is described in table 1. Experiments were carried out by the order of table 1. The analysis of variance (ANOVA) results was carried out to assess the main effects. Table 2 summarizes Anova for mean particle size analysis, being considered that factors with p-value below 0.05 have significant effect. Negative values on Stdized effect means an inverse proportionality between factor and response. Thus the final Equation in Terms of Factors for mean particle size (MPS) analysis is the following (equation 2): MPS = Pressure Temperature Flow Concentration ^( 3) Pressure Temperature Pressure Flow Pressure Concentration (Eq.2). Sum of Term df Stdized Effect % Contribution F Value p-value Squares Model A-Pressure B-Temperature C-Flow D-Concentration AB AC AD Residual Lack of Fit Pure Error Cor Total Table 2: Anova results for mean particle size analysis.

5 A B Figure 2: SEM images of SAS at (A) 40ºC, 100 bar, 3mg/ml and 0.15 ml/min and experience (B) 60ºC, 100 bar 3mg7ml and 1.5 ml/min Considering now the yield analysis, the given model had a significant curvature (centre points in-formation) p-value, which means that the design should be augmented via Design Tools to add runs that can estimate quadratic terms. Problems with curvature result in different estimations for adjusted and not adjusted models and the model may not be appropriate for prediction. Standard deviation was and R- Squared was Even if the model obtained (Adjusted) is not appropriate for prediction it can be used to make good diagnostics. Through p-value observation it seems that the main effect that influences the response yield, in the micronization process, is the concentration followed by the pressure. Taking in account the results of FFD screening test study, to continue the optimization, two variables were fixed at suitable amounts (temperature of 60ºC and flow rate ratio of 10) and a central composite design with 2 factors (Pressure and organic solution concentration) and one response (Mean Particle Size) was created. The obtained matrix was generated and randomized by Design-Expert 9.0.3, and is presented in table 3 with the respective values of the obtained response and standard deviation of the particle size analyses. Note that value fixed for the temperature was chosen taking into account the morphology and mean particle size results obtained with FFD. In fact, it was possible to observe from SEM images that the morphology the micronized astaxanthin changed from long needles to small spheres (Figure 2) when the temperature rose from 40 to 60ºC. Also, the mean particle size and particle size distribution for the experiment carried out at 60ºC, 150 bar, 0.5 ml/min organic solution flow rate and 0.15 g/ml of organic solution concentration, was significantly smaller and narrow, respectively, than the results obtained at 40ºC. In order to confirm the selection of Run Pressure (bar) Astaxanthin Concentration (mg/ml) Mean Particle Size (µm) Std. Dev Table 3: CCD matrix obtained by Design- Expert at 2 factors ( Pressure and Astaxanthin Concentration) and one response ( Mean Particle Size). Std. Dev. stands for the Mean Particle Size determination.

6 temperature two experiments were carried out at the following conditions. 40ºC, 100 bar 3mg/ml, 0.15 ml/min and 60ºC, 100 bar, 3 mg/ml, 1.5 ml/min. SEM analysis of the micronized powder (figure 10) showed that the experiment carried out at 60ºC lead to small spheres with mean particle size of µm and particle size distribution of µm, in contrast with the results obtained at 40ºC which lead to long needles with larges particle size and particle size distribution. Therefore the temperature was fixed at 60ºC. In CCD particle size analysis required a natural log transformation so the model could be better adjusted. Quadratic order for Anova calculation was selected as suggested for Design-Expert software. Table 4 presents the results of ANOVA for chosen model in CCD. The Model F-value of implies the model is significant. There is only a 0.02% chance that an F-value this large could occur due to noise. In this case the effects A (pressure) and B (concentration) are significant model terms. The "Lack of Fit F- value" is of 67.91, which implies that the Lack of Fit is significant. Table 5 resumes Anova results with predicted and adjusted values. The "Pred R-Squared" of is not as close to the "Adj R-Squared" of as one might normally expect; i.e. the difference is more than 0.2. This may indicate a large block effect or a possible problem with the model and/or data. Things to consider are model reduction, response transformation, outliers, etc. All empirical Source models should be tested by doing confirmation runs. Std. Dev R-Squared Mean 2.96 Adj R-Squared C.V. % Pred R-Squared PRESS Adeq Precision "Adeq Precision" measures the signal to noise ratio. A ratio greater than 4 is desirable. The obtained ratio of indicates an adequate signal. The final model in terms of factors is presented in Equation 4, and can be used to estimate mean particle size progression in the design space. Ln(MPS) = Pressure Concentration Pressure Concentration Pressure Concentration 2 (Eq. 4) Optimization of the factors can be done numerically and graphically. Numeric optimization search in the design space, using the model created during analysis to find factor settings that meet the defined purposes, which in this case was to minimize mean particle size. A set of 21 solutions were given so the aim was fulfilled, being the first solution the one with lower prediction of mean particle size. Figure 2 represents the graphical optimization of the created model with the first solution optimization marked. Table 4 Anova results for choosen model in CCD calculated by Design-Expert Sum of Squares df Coefficient Estimate Standard Error F Value p-value Prob > F Model A-pressure B- concentration AB A^ B^ Residual Lack of Fit Pure Error Cor Total Table 5: Resume of Anova results with predicted and adjusted values.

7 predicted value predicted value p a r t ic le s iz e ( u m ) A: pressure (bar) B: concentration (mg/ml) Figure 2: Graphical representation of created CCD model at 2 factors (Pressure and Concentration) and 1 response (mean particle size). Thelabeled point (0.17) is an estimation of the minimum response that can be obtained from the given model. R A B C D E Figure 3: SEM images of: Raw Astaxanthin (R); A- Micronization CCD run 1 (1.0 mg/ml); B- CCD Run3 (2.0mg/ml); C- CCD Run7 (3.0mg/ml); D- CCD Run2 (125bar); E- CCD Run11 (150bar). Particles morphologies observed in SEM photomicrographs, were mainly two: long or middle needles and small spheres. Figure 3 shows particle SEM images of raw astaxanthin and processed by SAS. From the SEM images showed in figure 3 images A, B and C, it is possible to see the effect of concentration, particularly in particle morphology. When concentration of astaxanthin increased in the organic solution, particle morphology changed from long needles like to sphere like particles. Particle size is also affected due concentration. However, the significant effect is observed when comparing different working pressures. Images D and E shows the effect of pressure at astaxanthin concentration in the organ-ic solution of 3 Comparing image C with images D and E it

8 Figure 4: SEM images of a sample of micronized astaxanthin at solution flow rate of 0.15ml/min (Run 8 FFD) at the left, and flow rate of 1.5ml/min (Run 7 CCD) at the right.. Its possible to verify that pressure as a major effect on particle size, being verified that when pressure increased micronized astaxanthin presented larger particle sizemg/ml, 60ºC and 1.5 ml/min of organic solution flow-rate.particle size measurements were made by Image J software through analysis of SEM images, Mastersize and Nano Particle Analysis (NTA). These methods are complementary in terms of range of size and other constrains. HPLC analysis of the micronized astaxanthin at 60ºC, 100 bar, organic solution concentration of 3mg/ml and solution flow rate of 1.5 ml/min showed that the micronized powder presented a composition of around 100% astaxanthin (relative percentage of pigments obtained from HPLC). It was not observed the presence of other minor or degradation Analysis of the solution collected in the separation vessel, for the SAS experiments carried out in the previously mentioned experimental conditions,products. showed the presence of other pigment. Astaxathin corresponded only to 72% (relative percentage obtained from HPLC chromatograms) of the total pigments in the solutions. The other pigment present in the chromatogram, which presented a relative compostion of 28%, could be a degradation product or the impurities of the initial astaxanthin that were concentrated in the solution leaving the precipitation vessel. Possibly this impurities are more soluble in supercritical CO2 than the astaxanthin and so are dissolved in the flow leaving the high pressure vessel. FFD Run 8 and CCD Run 7 were performed at the same conditions of pressure, 100 bar, concentration of organic solution, 3.0 mg/ml, and temperature, 60 C. The only difference between the experimental conditions of both runs is the organic solution flow rate, which was of 0.5ml/min in Run 8 of FFD and 1.5ml/min in Run 7 of CCD. Experimental design analysis showed insignificant contribution of the solution flow rate factor in the astaxanthin micronization process. However, comparing the particle size distribution of both runs, it is possible to verify a small difference between them. Moreover, SEM images of both runs showed a slight difference in the morphology of the particles (Figure 3). From the Figure 4 it is possible to verify that for the lower organic solvent flow-rate particle size presents a narrow particle size distribution. The combination of these facts indicates that even if the solution flow rate has numerically low relevance in the FFD model, its contribution can be considered important to obtain a narrow particle size distribution. These results ilustrate an extra valorization of solution flow rate, which was discarded in experimental design. The images presented in Figure 17 are at the same amplification, and it can be seen that, although the morphology was the same for both organic solution flow rates, sphere-like particles, the run at the lower organic

9 Mean Particle Size (mm) MPS (µm) solvent flow rate showed smaller particles with very similar particle size. On the other hand, when a higher organic solution flowrate was used slightly larger particles were obtained. From Tables 3 and 6 it is possible to verif a difference of 20 nm between the two samples % o f P a r t i c l e s 0,008 0,006 0,004 0, Mean Particle Size (nm) CCD Run 7 FFD Run 8 Figure5: Particle size distribution of FFD Run8 and CCD Run7. Temperature effect, althought also not considered in CCD, had an expressive effect in the micronization process. It was found that a complete different morphology, as well as smaller particle size could be obtained at 60 C. A possible reason for this behaviour is the fact that the rise in temperature leads to an increase of astaxanthin solubility in THF and, since the concentration remains the same, a less saturated solution is obtained. Astaxanthin will be more disperse in the solvent and interaction of organic solvent/ supercritical anti-solvent occurs and astaxanthin will precipitate in smaller particles. To a better visualization of temperature effect, Figure 6 shows the evolution of mean particle size with this factor. As seen in this figure MPS decreases with the increase of temperature. In what concerns the effect of pressure, in the SAS micronization of astaxanthin, CCD analysis allowed to obtain the results shown on Table 6, as well as in the Figure 19. The point at 100 bar shows that at this pressure there is a higher probability to find particles with desirable properties, and increasing this factor will lead to a larger range of the particle size. Therefore, higher organic solution concentration and lower pressure proved to be the most favorable conditions for the SAS micronization of astaxanthin bar 150 bar Temperature ( C) Figure 6: Graphical representatio6n of mean particle size evolution with temperature change. Figure 7 represents the effect of pressure at 60ºC, 1,5 ml/min solvent flow-rate and 3 mg/ml of organic solution concentration (the same trend was observed for the other organic solution concentrations studied see Table 6). Mean particle size increases with pressure and particle size distribution becomes narrower for lower pressures. The increase of particle size with pressure has been notice by other authors for SAS of compounds like beta-carotene or lycopene (Cocero et al., 2006 and Cocero et al., 2008). A possible explanation can be the fact that the increase in pressure corresponds to a rise in the density of the supercritical fluid and consequently the solubility of astaxanthin increases in CO2 since the supersaturation decreases leading, to a decrease in the particle size Pressure (bar) Figure 7: Mean particle size of SAS micronized astaxanthin as a function of pressure, at 60ºC, 3 mg/ml and 1.5 ml/min organic solution flow rate. In Figure 8 is shown the influence of organic solution concentration in the mean particle size, at 100 bar, 60ºC and 1.5 ml/min of organic solution flow rate. It can be observed that the mean particle size decreased with the increase of astaxanthin concentration in

10 Mean Particle Size (mm) the organic solution. Larger particles with a different morphology, similar to needle-like, were obtained when using the lowest concentration. When using concentrations of 3 mg/ml, sphere-like particles and lower mean particle sizes were obtained. A narrower particle size distribution was also achieved using 3 mg/ml. This trend possibly occurs, because, the higher concentration allows to attain higher supersaturation, which tends to decrease the particle size. 6,0 5,0 4,0 3,0 2,0 1,0 0, Organic Solution concentration (mg/ml) Figure 8: Mean particle size of SAS micronized astaxanthin as a function of organic solution concentration Conclusion Yield of micronization obtained for the experiments with lower particle size was of approximately 67% for FFD and 50% for CCD. Conclusion Micronization of astaxanthin was successfully done by SAS process. The lowest particle size found was µm at 100 bar, 60 C, 3mg/ml and 0.5mg/ml. in this experiment (Run8 FFD) was found particle size with sphere morphology and narrow particle size distribution ( range of 1.0 µm). References Symposium series 406. American Chemical Society,Washington, DC, pp Guedes, A. C., Amaro, H., & Malcata, F. (2011). Microalgae as Sources of Carotenoids. Mar. Drugs. Guerin, M., Huntley, M., & Olaizola, M. (2003). Haematococcus astaxanthin:applications for human health and nutrition. TRENDS in Biotechnology. Hannay, J., & Hogarth, J. (1879). Proc. Roy. Soc. London. Hong, H. L., Suo, Q., Han, L., & Li, C. (2009). Study on Precipitation of Astaxanthin in Supercritical Fluid. Powder Technology. HUSSEIN, G., GOTO, H., ODA, S., SANKAWA, U., MATSUMOTO, K., & WATANABE, H. (2006). Antihypertensive Potential and Mechanism of Action of Astaxanthin:IIIAntioxidant and Histopathological Effects in Spontaneously. Biol. Pharm. Bull. Marco, I. D., Cardea, S., & Reverchon, E. (2013). Polymer Micronization using Batch Supercritical Antisolvent process. CHEMICAL ENGINEERING TRANSACTIONS. Marco, I. D., & Reverchon, E. (2011). Influence of pressure, temperature and concentration on the mechanisms of particle precipitation in supercritical antisolvent micronization. J. of Supercritical Fluids. Mattea, F., Martín, Á., & Cocero, M. (2009). Carotenoid processing with supercritical fluids. Journal of Food Engineering. Mendes, R. L., Nobre, B., Cardoso, M., Ana P. Pereira, A., & Palavra, A. (2003). Supercritical carbon dioxide extraction of compounds with pharmaceutical importance from microalgae. Inorganica Chimica Acta. Miguel, F., Martín, A., Gamse, T., & Cocero, M. (2006). Supercritical anti solvent precipitation of lycopene Effect of the operating parameters. J. of Supercritical Fluids. Miguel, F., Martín, A., Mattea, F., & Cocero, M. (2008). Precipitation of lutein and co-precipitation of lutein and poly-lactic acid with the supercritical anti-solvent process. Chemical Engineering and Processing. Montes, A., Gordillo, M., Pereyra, C., & de la Ossa, E. (2013). Supercritical CO2 precipitation of poly(l-lactic acid) in a wide range of miscibility. J. of Supercritical Fluids. D

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