Optimization of enzymatic hydrolysis of wool fibers for nanoparticles production using response surface methodology

Advanced Powder Technology 24 (2013) 416–426

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Advanced Powder Technology journal homepage: www.elsevier.com/locate/apt

Original Research Paper

Optimization of enzymatic hydrolysis of wool fibers for nanoparticles production using response surface methodology Niloofar Eslahi a, Fatemeh Dadashian a,b,⇑, Nahid Hemmati Nejad a a b

Department of Textile Engineering, Amirkabir University of Technology, Tehran, Iran Center of Excellence on Functional Fibrous Structures and Environmental Enhancement, Amirkabir University of Technology, Tehran, Iran

a r t i c l e

i n f o

Article history: Received 24 June 2012 Received in revised form 9 September 2012 Accepted 24 September 2012 Available online 17 October 2012 Keywords: Wool fibers Enzymatic hydrolysis Ultrasonic treatment Nanoparticles Response surface methodology

a b s t r a c t Optimization of enzymatic hydrolysis of wool fiber was carried out using a Central Composite Design (CCD) in order to produce wool nanoparticles. The effects of three important determinants, i.e. enzyme loading, substrate concentration and hydrolysis time on enzymatic efficiency were investigated. Polynomial regression model was fitted to the experimental data to generate predicted response such as particle size. The results were subjected to analysis of variance (ANOVA) to determine significant parameters used for optimization. Wool nanoparticles was produced under the attained optimal condition (enzyme loading: 3.3%, substrate concentration: 5 g/l and hydrolysis time: 214 h), followed by ultrasonic treatment. SEM micrographs indicated wool fiber degradation in which the outer cuticle layer was removed and the inner cortical cells were isolated. The results of particle size analysis indicated the positive effect of sonication on reducing particles size further. FTIR spectra denoted no evident changes in the composition of the chemical groups in the macromolecular structure of wool fiber. Besides, the enzymatic hydrolysis and ultrasonic treatment led to an increase in crystallinity, solubility in caustic solution and thermal stability of wool nanoparticles, but caused a decrease in moisture regain comparing to the raw wool fiber. Ó 2012 The Society of Powder Technology Japan. Published by Elsevier B.V. and The Society of Powder Technology Japan. All rights reserved.

1. Introduction Wool is a natural protein fiber that has been widely used as a high quality textile material. The excellent intrinsic properties of this fiber could bring about great marketing potential not only for textile industry but other fields as well [1,2]. As a result, there has been considerable interest in finding new applications of wool through development of new products such as films, gels and powders [3–5]. Since the protein powder could keep the original properties of the material without destroying the microstructure, it has been widely applied in modern industries due to its unique characteristics [6,7]. Many researchers have tried to produce wool powder by different methods such as regeneration from keratin solution, mechanical attrition and chemical–mechanical techniques [4,8–11]. Solution routes have inherent limitations in the preparation process, i.e. long time of dialysis, high production costs, safety and environmental constraints. Although mechanical attrition which involves chopping and crushing the fibers with suitable milling machines can avoid these problems, it has high energy consumption [12]. Furthermore, this process is relatively dif⇑ Corresponding author at: Center of Excellence on Functional Fibrous Structures and Environmental Enhancement, Amirkabir University of Technology, Tehran, Iran. E-mail address: [email protected] (F. Dadashian).

ficult due to the softness and elasticity of the fibers. Thus, special chemical pretreatments are usually employed to weaken the fiber’s structure and improve powder productivity [13]. This study investigates production of wool nanoparticles by enzymatic hydrolysis, as an environmentally friendly technique, which is believed to be the most promising approach due to the milder process conditions leaving no harmful byproducts. Intensive research demonstrates that the efficiency of the enzymatic hydrolysis depends on several parameters such as enzyme loading, substrate concentration, reaction time, and addition of surfactant [14–18]. These factors often interact with one another; therefore, optimization of the enzymatic hydrolysis process plays an important role in improving the performance of the procedure. Unlike conventional optimization, statistical optimization methods can take into account the interactions of variables in generating process responses [19–22]. Response surface methodology (RSM) is a powerful statistical technique for testing multiple variables because fewer experimental trials are needed as compared to the ‘‘one-factor-at-a-time’’ method [20,23]. In addition, it is an efficient mathematical approach for optimizing complex processes which can generate an empirical model for evaluation of the relationship of a set of controlled experimental factors and the observed results. This technique has been widely applied in different chemical and biochemical processes to analyze the effect of independent

0921-8831/$ - see front matter Ó 2012 The Society of Powder Technology Japan. Published by Elsevier B.V. and The Society of Powder Technology Japan. All rights reserved. http://dx.doi.org/10.1016/j.apt.2012.09.004

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to separate the particles from the remaining solution. Each supernatant was then decanted; the particle fractions were repeatedly washed with deionized water and centrifuged at 8000 rpm.

variables and optimize the process responses using appropriate values of the factors [24–26]. A general factorial design was first applied to optimize the best condition for enzymatic treatment of wool fibers [27]. In the present study, production of wool nanoparticles has been attempted. The main objective of our research is to examine the influence of enzyme loading, substrate concentration and hydrolysis time, on wool particle size. Accordingly, response surface methodology based on a five-level, three-factor Central Composite Design (CCD) was implemented to determine the significant factors that affect the response and to develop mathematical equation for the optimization. The suspension prepared under optimum condition was then subjected to ultrasonic treatment which might enhance the yield of polypeptides separation from wool by mechanical vibration and heating. Consequently, the obtained wool nanoparticles were characterized by means of scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction analyzer (XRD), differential scanning calorimeter (DSC), thermogravimetric analyzer (TGA), and Laser diffraction particle size analyzer. The moisture regain and solubility of the samples in caustic solution were determined as well.

2.2.2. Experimental design Central Composite Design (CCD), one of the most important experimental designs in the optimization process, has been extensively applied to develop quadratic response surface models [29]. In this study, the effects of enzyme loading (%), hydrolysis time (h) and substrate concentration (g/l) were investigated and optimized using a full-factorial rotatable CCD. The factors and their levels given in Table 1 were selected based on the preliminary experiments and previous studies. For statistical calculations, the variables were coded to lie ±1 for factorial points, 0 for the center points and ±2 for axial points. A three-factor CCD design with a total of 20 runs, including 8 factorial points, 6 axial points and 6 replicates at the center points, was used to fit a second-order response surface in order to optimize the process factors affecting the particle size of the hydrolyzed wool. The response (particle mean size of each suspension) could be related to the selected variables by a second-order polynomial regression model as given by the following equation:

2. Experimental

Y ¼ b0 þ

k k k X X X bi xi þ bii x2i þ bij xi xj þ e i¼1

2.1. Materials The experiments were conducted on wool fiber waste originating from New Zealand Merino wool with a mean diameter of 21 lm. The proteolytic enzyme used in this study, was the alkaline serine endoprotease, Savinase 16.0LEX (EC.3.4.21.14), supplied by Novozymes A/S (Denmark). The reducing agent, sodium bisulfite, was provided by Fluka Chemicals Inc. All other chemicals such as sodium dodecyl sulfate (SDS), borax, boric acid, and acetic acid were of analytical grade and purchased from Merck Co. (Germany). 2.2. Methods 2.2.1. Enzymatic hydrolysis of wool fibers Enzymatic treatment was carried out after washing the wool fiber and removing the fatty matters based on ASTM D584 [28]. Cleaned chopped wool fiber was incubated with Savinase in 20 ml of sodium borate buffer solution (50 mM, pH = 8.5) containing 12 g/l sodium bisulfite as well as 1 g/l sodium dodecyl sulfate (SDS) at 55 °C according to the experimental design. The treatment condition for enzymatic hydrolysis was chosen based on our previous research. It was found that the highest enzymatic efficiency, which was evaluated by measuring the enzyme activity, soluble protein concentration, and released sulfhydryl groups concentration, was achieved by making use of SDS (an anionic surfactant) together with sodium bisulfite (a reducing agent). Sodium hydrogen sulfite was used to break down the disulfide bonds in combination with the protease to catalyze the hydrolytic cleavage of wool protein into smaller peptide chains. In other words, cleavage of cystine cross-links makes wool more susceptible to enzymatic hydrolysis. The effect of incubation time on fiber degradation was also investigated and a decrease in wool fiber diameter was observed with increasing hydrolysis time [27]. After the enzymatic treatment, samples were scooped at different processing time and the enzyme in the mixtures was deactivated by adding a solution of acetic acid (10 mM) to lower the pH of the treatment baths to 4.5 while raising the temperature up to 75 °C for 20 min with an agitation of 300 rpm. Successively, the mixtures were individually centrifuged at 8000 rpm for 5 min

i¼1

ð1Þ

16i6j

where Y represents the predicted response, k is the number of the factors, xi and xj are the coded values of independent variables, b0, bi, bii, bij are the intercept, linear, squared and interaction coefficients, respectively and e is the random error or residual associated to the experiments which is assumed to follow a normal distribution with mean zero and variance (r2) across all values of Y [30]. This equation can be used to locate the optimum for the set of independent variables by the partial derivatives of the model response with respect to the individual independent variables is equal to zero [31]. Data was analyzed using Design-Expert software (Version 7.1.5, 2008; Stat-Ease, Minneapolis, MN) to yield regression equation and determine the optimum parameter combinations. Statistical significance of the model and the regression coefficients were estimated by analysis of variance (ANOVA) combined with the application of Fisher’s F-test as well as Student’s T-test at a probability P value of 0.05. The accuracy of the model was also checked by the coefficient of determination R2 as the measure of goodness of fit of the model. When R2 approaches unity, the empirical model fits the actual data. The relationships between the response and the variables were also visualized by three-dimensional response surfaces or twodimensional contour plots to see the relative influence of the parameters and predict experimental results for other combinations as well [29]. The optimal values of the independent variables were obtained by solving the regression equation together with analyzing the response surfaces and contour plots. Subsequently, the experimental acquired response under the optimum condition was compared with the predicted result in order to confirm the validity of the model.

Table 1 Coded and actual levels of the design factors. Independent factors

A: enzyme loading (%) B: hydrolysis time (h) C: substrate concentration (g/l)

Levels 2

1

0

+1

+2

2.5 96 5

3 144 10

3.5 192 15

4 240 20

4.5 288 25

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2.2.3. Characterization of wool nanoparticles Each centrifuged sample was immediately suspended in distilled water and the particle size distribution was determined by particle size analyzer (Zetasizer, ZEN3600, Malvern Instruments Ltd, Malvern, UK). The sample prepared under the optimum condition was subjected to ultrasonic treatment (Heilscher Ultrasonics UP200S, 200 W, 24 kHz) for 30 min at 80% amplitude. Formation of a colloidal suspension is an indication of the presence and release of wool nanoparticles. The possibility of the fragmentation of hydrolyzed fibers into nanoparticles by sonication energy was investigated. The hydrolyzed optimal sample and the collected turbid suspension (sonicated sample) were freeze-dried afterwards for further analysis. The morphology of the wool particles was examined with scanning electron microscope (SEM, Hitachi S4160, Japan), at 15 kV acceleration voltage after gold coating. To study the chemical changes during the processing of wool fiber, the Fourier transform infrared (FTIR) analysis was also performed by Thermo Nicolet Nexus 670 Spectrophotometer. The characteristic spectra were scanned in the wave number range of 4000–400 cm1 at a resolution of 4 cm1 using KBr pellets. Besides, the crystallinity of the powder was determined by X-ray diffraction technique which was conducted with Equinox 3000, INEL, France. Thermogravimetric analysis (TGA) was performed on TGA50 (Shimadzu, Japan) and the testing was carried out in flowing nitrogen atmosphere (30 ml/ min) at a heating rate of 10 °C/min in the range of 25–600 °C. Differential scanning calorimetry (DSC) was carried out with METTLER TOLEDO (Germany) at a heating rate of 10 °C/min within the range of 25–400 °C in flowing nitrogen atmosphere. The moisture regain of the samples was also measured under standard conditions (RH 65% and 25 °C) by gravimetric method. The moisture regain was defined as follows:

Moisture regainð%Þ ¼ ðW 2  W 1 Þ=W 1  100 where W1 and W2 stand for the dry and conditioned weight of the samples, respectively. To measure the solubility of the treated wool fiber, the samples were dissolved in an aqueous solution of 2.5% NaOH at 80 °C, because the disulfide bonds in the wool macromolecular are very sensitive to the caustic solution. Dissolving time of the samples was determined accordingly [32].

3. Results and discussion 3.1. Model building and statistical analysis The experimental design chosen for this study was a 20-run CCD with three factors and five levels in order to fit second-order response surfaces. The statistical treatment combinations of the test variables (i.e. enzyme loading, hydrolysis time and substrate concentration) along with the measured and predicted response values, expressed as mean size of the particles, are summarized in Table 2. The experiments, which were subjected to regression analysis, were run in random order to give randomly distributed variables and minimize the effects of unexplained variability in the observed results.

3.1.1. Effect of parameters on particles size To evaluate the influences of three factors including enzyme loading, hydrolysis time and substrate concentration on particle mean size, the design matrix of experimental conditions with the corresponding response values in Table 2 was fitted to a polynomial model. Based on the results of the sequential model sum of squares and the calculated statistics for all model terms, a quadratic model was suggested. Therefore, the mathematical equation proposed for this response (in terms of coded values) is:

Y 1 ¼ 279:41  38:06 A  122:81 B þ 26:06 C þ 22:63 AB  3:37 AC  4:37B C þ 32:95 A2 þ 37:70 B2 þ 15:83 C 2

ð2Þ

The ANOVA results of the regression model are shown in Table 3. The significance of each coefficient was checked using F-test and its associated probability, P value. Values of ‘‘Prob > F’’ less than 0.050 indicate model terms are significant. Thus, the model F-value of 21.73 implies it is significant and there is only a 0.01% chance that a ‘‘Model F-value’’ this large could occur due to noise. The significance of the model was also determined using the ‘‘Lack of fit’’ test to measure the failure of the model at data points that were not included in the regression analysis. The model shows statistically insignificant lack of fit, as is evident from the P value of 0.0887. The lack of fit F-value of 3.70 shows the validity of the predictive model which can be used to calculate particle size from Eq. (2).

Table 2 Experimental design layout and the obtained results of central composite design with the independent variables, A: enzyme loading (%), B: hydrolysis time (h) and C: substrate concentration (g/l). No.

Independent variables

Coded values

Particles mean size (nm)

A

B

C

A

B

C

Experimental

Predicted

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20

3.0 3.5 3.0 4.0 3.5 2.5 3.0 3.5 3.5 3.5 3.5 4.0 4.0 3.5 3.5 4.5 3.0 3.5 4.0 3.5

144 192 240 240 192 192 240 288 192 192 192 144 144 192 96 192 144 192 240 192

20 15 20 10 15 15 10 15 5 15 25 10 20 15 15 15 10 15 20 15

1 0 1 1 0 2 1 0 0 0 0 1 1 0 0 2 1 0 1 0

1 0 1 1 0 0 1 2 0 0 0 1 1 0 2 0 1 0 1 0

1 0 1 1 0 0 1 0 2 0 2 1 1 0 0 0 1 0 1 0

598 264 251 213 291 489 226 223 256 297 411 452 481 228 619 315 556 280 224 298

548 298 303 174 298 492 251 189 245 298 350 420 472 298 680 340 496 298 227 298

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N. Eslahi et al. / Advanced Powder Technology 24 (2013) 416–426 Table 3 Analysis of variance for particles size.a Source

a b

Sum of squares 5

Model Coefficient of determination (R2) = 0.9513 Adjusted (R2) = 0.9076 Predicted (R2) = 0.6659 Adeq. precision = 16.86

3.321  10

A: enzyme loading B: hydrolysis time C: substrate concentration AB AC BC A2 B2 C2 Residual Lack of fit Pure error Correlation total

23180.06 2.413  105 10868.06 4095.13 91.13 153.13 27305.19 35743.91 6300.16 16983.40 13370.07 3613.33 3.491  105

DFb

Mean square

F ratio

P value

9

36896.82

21.73

<0.0001

1 1 1 1 1 1 1 1 1 10 5 5 19

23180.06 2.413  105 10868.06 4095.13 91.13 153.13 27305.19 35743.91 6300.16 1698.34 2674.01 722.67

13.65 142.10 6.40 2.41 0.054 0.090 16.08 21.05 3.71

0.0041 <0.0001 0.0299 0.1515 0.8215 0.7701 0.0025 0.0010 0.0830

3.70

0.0887

Means significance (values of ‘‘Prob. > F’’ less than 0.05). DF = degrees of freedom.

Adequate precision measures the signal to noise ratio. The ratio of 16.86 indicates an adequate signal (a ratio greater than 4 is desirable). A coefficient of determination (R2) value of 0.9513 shows that the model is highly reliable; the regression model could explain 95.13% of the variability in the response and only 4.87% of the total variation could not be attributed to the variables. However, the predicted R2 of 0.6659 is not as close to the adjusted R2 of 0.9076 as one might normally expect and thus model reduction should be considered. In order to improve the model, the insignificant coefficients were eliminated and the final model was refined as follows:

Y 1 ¼ 297:5  38:06 A  122:81 B þ 26:06 C þ 29:56 A2 þ 34:31 B2

ð3Þ

Analyzing the ANOVA results after model reduction for this response given in Table 4, it can be seen that the coefficient of determination (R2) of 0.9209 with an adjusted R2 (0.8926) is in reasonable agreement with the predicted R2 (0.7379). Comparison between the statistical data of Tables 3 and 4 shows that the model F ratio and adequate precision have been improved due to the model adjustment. The significance of regression coefficients was also determined on the basis of their P value. The smaller the P value, the bigger the significance of the corresponding coefficient [24]. It can be seen in the results given in Table 4 that all coefficients are significant. However, the effect of hydrolysis time on particles size (P value

<0.0001) is higher than the other factors. The importance of the variables and their effects can be also explained by the magnitude and sign of the coefficients, accordingly [29]. Positive coefficient for C (substrate concentration) indicates a linear effect on the particle size, while negative coefficients of A (enzyme loading) and B (hydrolysis time) reveal the opposite influence. The model coefficient of factor B (122.81) is bigger than the coefficient of factor C (26.06) which represents the importance of hydrolysis time rather than substrate concentration in enzymatic process. Therefore, hydrolysis time (B) is the major factor affecting the particle size (lower P value and higher coefficient) followed by enzyme loading and substrate concentration, respectively. This can be observed in Fig. 1a which shows the effect of enzyme loading and hydrolysis time on particles mean size at constant substrate concentration of 15 g/l. As can be seen, increasing hydrolysis time in all levels of enzyme loading leads to a decrease in particles size, especially at high level of enzyme loading. The quadratic effects of hydrolysis time as well as enzyme loading can be clearly visualized in the response surface, while the former has more significant influence on the particles size. It is observed that from 2.5% to 3.8% of enzyme loading, the particles size declines from 492 to 283 nm at center point of hydrolysis time (192 h). In reverse, adding more enzyme up to 4.5% causes an increase in size. Fig. 1b demonstrates the surface and contour plots at center point of hydrolysis time (192 h) with varying enzyme loading and substrate concentration. The 3D response plot shows a reduction in particles size with decreasing substrate concentration

Table 4 Analysis of variance for the adjusted model of particles size. Source Model Coefficient of determination (R2) = 0.9209 Adjusted (R2) = 0.8926 Predicted (R2) = 0.7379 Adeq. precision = 20.80 A: enzyme loading B: hydrolysis time C: substrate concentration A2 B2 Residual Lack of fit Pure error Correlation total

Sum of squares

DF

Mean square

F ratio

P value

3.214  10

5

64286.37

32.58

<0.0001

23180.06 2.413  105 10868.06 23030.93 31026.57 27622.94 24009.60 3613.33 3.491  105

1 1 1 1 1 14 9 5 19

23180.06 2.413  105 10868.06 23030.93 31026.57 1973.07 2667.73 722.67

11.75 122.31 5.51 11.67 15.73

0.0041 <0.0001 0.0342 0.0042 0.0014

3.69

0.0821

5

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Fig. 1. Response surface and contour plots of the combined effects of (a) hydrolysis time and enzyme loading, (b) substrate concentration and enzyme loading and (c) hydrolysis time and substrate concentration on particles mean size.

particularly at high enzyme loading. According to the previous researches, high substrate concentration resulted in low hydrolysis yield due to product/substrate inhibition, enzyme inactivation and a decrease in substrate reactivity with prolonging hydrolysis time [33,34]. Besides, the quadratic effect of enzyme loading over the linear effect of substrate concentration can be vividly seen. In other words, the particles size is increasing after reaching a minimum at 3.8% of enzyme loading in all substrate concentrations. A fixed substrate concentration requires a certain amount of enzyme to

reach adsorption saturation for wool hydrolysis and further increase in enzyme loading would result in more free protease in the reaction mixture which might cause a hinderance for proteolytic attack. Thus, there is an optimum limit for enzyme loading to attain the minimum particle size. The interaction effects of hydrolysis time and substrate concentration on particles size when the other factor (enzyme loading) is at its center point are shown in Fig. 1c. The plots show that the particle size declines rapidly with an increase in hydrolysis time from 96 h to 288 h in all substrate concentrations. In addition, the higher

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range of input parameters and the constraints imposed together with the attained solution. The model was tested for validity and adequacy by conducting an additional confirmation experiment under the predicted optimum conditions i.e. 5 g/l wool and 3.3% enzyme at hydrolysis time of 214 h. The validation assay confirmed that the experimentally obtained particle size (215 nm) was in good correspondence with the predicted one (213 nm), verifying the accuracy of the proposed model for optimization. 3.3. Characterization of wool nanoparticles The attained optimal conditions were applied to produce wool nanoparticles followed by ultrasonic treatment. Thus, three samples including untreated wool fiber, hydrolyzed wool and sonicated wool nanoparticles, were characterized by SEM, FTIR spectroscopy, XRD, DSC, TGA and Laser diffraction particle size analyzer. Besides, the moisture regain and solubility of the samples in caustic solution were measured as well.

Fig. 2. Normal probability of internally studentized residuals for particle size.

impact of hydrolysis time in comparison with substrate concentration supports the results of Table 4. Consequently, graphical representations of regression equation confirmed the significance of each coefficient in the proposed model and minimum particle size can be obtained at high level of hydrolysis time, low substrate concentration and optimum amount of enzyme loading. It is important to check the accuracy of the model to ensure that it provides maximum approximation on the relationship between independent factors and the response. A normal probability plot of studentized residuals from the least squares is a good tool to find experiments that deviate significantly from the norm, otherwise known as outliers. A studentized residual is the sample residual divided by the square root of its estimated variance [35]. The normality assumption is satisfactory as normal residuals fall along a straight line as shown in Fig. 2. 3.2. Optimization of the enzymatic process and confirmation experiment The polynomial regression equation obtained from the experimental data (Eq. (3)) can be used to predict the particle size at any enzyme loading, hydrolysis time and substrate concentration within the range of the experimental design. The optimization was carried out to obtain the best combination of the factors to achieve the minimum particles size using the numerical optimization option of the Design Expert software. The optimum conditions were estimated by solving the regression equation and analyzing the response surface and contour plots. Table 5 demonstrates the

3.3.1. Surface morphology Fig. 3 shows SEM micrographs of the samples. The surface of the untreated wool fiber in Fig. 3a consists of a fine network of small, overlapping cuticle plate-shaped cells. However, upon enzymatic treatment the fiber surface has been smoothed and fibrillation has been occurred as shown in Fig. 3b. It is evident that fiber destruction is caused by the diffusion and the hydrolytic attack of the protease on the protein fiber. Enzymatic hydrolysis can degrade the cuticle scales of wool fiber, which are responsible for the wool textiles tendency to undergo felting and shrinkage [36,37]. However, the protease hydrolysis is not only limited to the fiber surface, since proteases can easily penetrate into the wool fiber and hydrolyses the non-keratinous parts such as parts of the endocuticle, intermacrofibrillar material and proteins in the cell membrane complex (CMC). The uncontrolled degradation of the CMC, a very labile and low cross-linked material between the cuticle and the cortex, leads to a complete disintegration of wool structure resulting in fiber fibrillation [18,38]. Proteases can catalyze the degradation of different components of wool, making reaction control difficult. The applied proteolytic enzyme degrades preferentially the intercellular cement, penetrating under favorable conditions relatively quickly into the fiber cortex. This fact is due to the high hydrophilicity of the inner parts of the wool fiber containing a large number of polar groups in the polypeptide chains [39]. Therefore, it is supposed that degradation of CMC, leads to the disruption of wool fiber into its histological components [40]. In other words, microfibrils (composed of highmolecular weight and low-sulfur keratins) are presumably separated from the surrounding matrix protein (composed of lowmolecular weight and high-sulfur keratins) through enzymatic hydrolysis. Fig. 3c depicts that wool fiber has been crushed into a combination of irregular, fibrous, sheet form, rod like and a large number of semispherical particles, from which the outer cuticle layer has been removed. In fact, the proteolytic attack is not uniform due to the complexity of wool fiber structure [39]. Besides, enzyme

Table 5 Optimum treatment conditions and the obtained solution. Variables/responses

Condition

Lower limit

Upper limit

Importance

Solution

Enzyme loading (%) Hydrolysis time (h) Substrate conc. (g/l) Particles mean size (nm)

Minimize Minimize In range Minimize

2.5 96 5 213

4.5 288 25 619

1 1  3

3.3 214 5 213

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Fig. 3. SEM images of wool samples: (a) untreated wool fiber, (b) fiber fibrillation during enzymatic treatment, (c) hydrolyzed wool particles, (d) and (e) hydrolyzed and sonicated wool particles and (f) conglomeration in wool nanoparticles.

diffusion in the heterogeneous system involving solid substrate requires the adsorption of the enzyme prior to hydrolysis limiting the reaction rate [38]. Application of ultrasonic energy resulted in more fiber degradation as shown in Fig. 3d and e. The hydrolyzed fibers fragmented into nanoscale particles by sonication which was evidenced by formation of a colloidal suspension. In fact, sonication causes two primary effects, namely, cavitation and heating. When microscopic cavitation bubbles collapse at the surface of the solid substrate (wool), powerful shock waves as well as enormous shear forces are generated that stimulate effective erosion on the solid’s surface and help disintegrate possible aggregates having high molecular weight in the solution. Besides, due to the high temperature and pressure inside the bubbles in the strong collapse, water vapor inside the bubbles is probably dissociated and chemical products such as hydrogen and hydroxyl radicals are formed. These molecules have high reaction ability and might combine to form hydrogen peroxide [41–43]. It should be noted that the irregular particles are very difficult to be dispersed into individuals during ultrasonic treatment and the particles are prone to form some agglomerates as can be visualized in Fig. 3f [44]. 3.3.2. Particle size analysis Fig. 4 demonstrates the number-based particle size distributions of the hydrolyzed and sonicated samples. Most of the hydrolyzed particles are in the range of 122–342 nm. The results show a second weak broad peak within 550–1250 nm; however the number of particles lying in this range is negligible. It is evident that sonication shifts the particle size distribution towards smaller particles. Indeed, large particles of around 800 nm have been broken into finer ones with narrower distribution by ultrasonic energy. Moreover, the mean size of hydrolyzed wool particles has decreased after ultrasonic treatment from 215 nm to 137 nm. It should be noted that 34.5% of the sonicated particles are less than 100 nm; 35.5% in the range of 100–122 nm, and 23.3% within 122– 164 nm. The results of dynamic light scattering are consistent with those of scanning electron microscopy as well.

Fig. 4. Particle size distributions of wool particles by number.

3.3.3. FTIR spectroscopy The FTIR spectra of untreated and treated samples are shown in Fig. 5 indicating characteristic absorption bands assigned mainly to the peptide bonds (CONH) which represent the fundamental structural unit of the polypeptide chain [45]. The results confirm that no new chemical bonds are produced in wool particles. However, the absorbing peak in the range of 3200–3500 cm1, which is connected with the stretching vibration of NAH and OAH bonds, has become narrow and weak in hydrolyzed and sonicated wool particles that could be related to the reduction of hydrogen bonds during enzymatic treatment. The applied proteolytic enzyme, Savinase, is an alkaline serine endopeptidase whose catalytic function is to hydrolyze peptide bonds in the inner regions of the polypeptide chain. During catalysis, there is nucleophilic attack of the hydroxyl group of the serine residue of the protease on the carbonyl group of the peptide bond that is to be cleaved. An acyl-enzyme intermediate is transiently

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cystine disulfide bonds are broken by sulfites to give cysteine thiol and Bunte salt (S-sulfo group), according to following reaction:

where W–CH2–S–S–CH2–W is the cross-linked keratin in the wool fiber, HSO 3 is the bisulfite, W–CH2–SH is the reduced keratin, and W–CH2–S–SO 3 is the Bunte salt [50]. The cleavage of disulfide bonds between polypeptide keratin chains by means of a suitable redox leads to protein denaturation facilitating the attack of proteases during proteolysis [51].

Fig. 5. FTIR spectra of untreated wool, enzyme treated, and enzyme + sonication treated wool particles.

formed and hydrolysis of the ester linkage yields the second peptide product. The released active enzyme can repeat the hydrolytic attack on the remained peptide bonds. Schematic representation of the reaction mechanism is illustrated by [46]:

As the protease hydrolyzes wool fiber, the intermolecular hydrogen bonds, which can occur between different aminoacids in protein side chains or between amide groups of separate polypeptide chains, may be broken in the aqueous enzymatic medium. Within the range of 1200–1700 cm1, the hydrolyzed and sonicated wool particles have stronger absorbance bands indicating structural changes in the amide groups. The position and intensity variability of amide bands is attributed to the change of conformation of the keratin molecule [47]. The heights of absorbing peaks in 1650 cm1 (which is assigned to Amide I, C@O stretching), 1540 cm1 (which is assigned to Amide II, secondary NAH bending) and 1232 cm1 (which is assigned to Amide III, CAN stretching and NAH in-plane bending) have increased as compared with untreated sample due to the presence of the greater number of amine groups. The amide I absorption, at 1600–1700 cm1, is known to be sensitive specially to the secondary structure of proteins. On the basis of the literature data, the absorption at 1650 cm1 indicates the presence of crystalline a-helix structure, while the band between 1610 and 1633 cm1 and 1675–1695 cm1 are typically found for b-sheet assembling and disordered conformation, respectively [48]. The absorbing peaks in this region imply that hydrolyzed and sonicated samples are composed of low sulfur protein with dominant crystalline a-helix microstructure. Moreover, the appearance of a new band at 1024 cm1 in hydrolyzed and sonicated samples illustrates the formation of Bunte salt (S-sulphunate) which is dependent on the cleavage of disulfide linkages by the reducing agent. Kunert et al. proposed that the mechanism of keratin biodegradation involves a two-stage process: sulfitolysis and proteolysis [49]. During sulfitolysis,

3.3.4. XRD X-ray diffraction graphs of the samples are given in Fig. 6 for evaluating the physical changes and crystallinity of wool. The graphs display the typical diffraction pattern of a-keratins with a prominent 2h peak at 20.2° and a minor peak at about 10°, corresponding to the crystalline spacing of 4.39 and 9.82 Å, respectively [52]. The peak around 10° is characteristic of the hydrated crystalline structure of wool fiber. During enzymatic hydrolysis, intensity of this peak lowered which might be due to the decrease in the amount of absorbed water in wool particles. However, there is no change in the X-ray pattern of the nanoparticles. In other words, the applied procedures caused no alterations in the macromolecular conformation and the crystal configuration of wool. The degree of crystallinity is determined based on the estimation ratio of the crystalline to amorphous material in the sample. The results showed that the crystallinity of the wool nanoparticles increased from 39.71% for untreated sample to 48.60% and 46.29% for the hydrolyzed and sonicated samples, respectively. This might be caused by hydrogen bonding reduction and destruction of some amorphous regions in the wool particles. Removal of cuticles, CMC and non-keratinous matrix protein surrounding the crystalline ahelical microfibrils during enzymatic process led to an increase in the proportion of crystallinity of the wool nanoparticles which was consistent with the results of FTIR spectroscopy. Furthermore, the average crystalline size calculated using Scherrer’s Equation (Eq. (4)) showed a decrease in size from raw wool fiber (120.4 nm) to wool nanoparticles (101.6 nm).

D¼

kk B cos h

ð4Þ

where D represents the average crystalline size; h Bragg’s angle; k = 1.54 Å the wavelength of X-ray applied; B full width at half

Fig. 6. X-ray diffraction curves of the samples.

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maximum of the diffraction peak (FWHM); and k = 0.94, a constant factor [53]. 3.3.5. Thermal analysis Fig. 7 illustrates the TG curves of the samples. There are two evident gravity losses in thermo gravimetric graphs. The percentage of weight loss in the first step is due to the vaporization of water and the second step corresponds to the weight loss due to the decomposition/denaturation of the protein fiber structure as reported by Martí et al. [54]. The second step is ascribed to crystal cleavage, breakdown of crosslinks, hydrogen bonds, salt links, peptide bonds, and some changes in the microfibrillar and matrix regions [55]. According to the thermo gravimetric results, the higher weight retained in the treated samples around 600 °C indicates an improvement in the thermal stability of wool particles. Perhaps this is mostly due to the increase in the degree of crystallinity. The raw wool fiber with higher amount of amorphous region with respect to the treated samples underwent faster degradation indicating its lower thermal stability. The major weight loss of each sample in the temperature range from 200 to 400 °C together with the corresponding thermal degradation temperature (Tonset) is reported in Table 6. As can be seen, untreated wool lost more weight and degraded at lower temperature comparing to the treated ones. The low temperature endothermic peak in DSC curves (Fig. 8) is consistent with the first weight loss in TG curves which shows the vaporization of absorbed water in the samples [1]. The peak temperature of 75 °C in untreated wool decreased to 62 °C and 56 °C in hydrolyzed and sonicated wool particles, respectively, indicating that the water maintaining abilities of the wool particles changed after enzymatic hydrolysis and ultrasonic treatment. Usually as the particle size decreased, its affinity to water enhanced. However, the obtained results show the opposite trend, i.e. the energy required for removing water from the samples reduced which might be because of the increase in crystallinity. Another reason for the peak transferring from high temperature to low temperature is associated with the change of the glass transition temperature of the particles [55]. As the particle size as well as amorphous

Fig. 7. The TG curves of the samples.

Fig. 8. The DSC curves of the samples.

regions decreased, the absorbed heat for the glass transition reduced. The second endothermic peak around 230–240 °C is associated with the melting of a-keratin crystallites in the intermediate filaments protein of wool. The bimodal endothermic melting of keratin crystalline in raw wool fiber is attributed to two interpretations. According to Cao [56], the origin of the bimodal endotherm has been considered to correspond to the differential melting of the a-form crystallites in the domains of ortho- and para-cortical cells. In other words, there is a difference in the transition enthalpy of the a-helical material in these domains. An alternative interpretation has been arisen from the overlapping of the melting endotherm of a-keratin with helicoidal structure and the thermal degradation of other wool histological components such as the matrix [57]. Enzymatic and ultrasonic treatments result in a slight shift of the a-helix melting peak to a higher temperature from 234 °C for untreated sample to 240 °C and 238 °C for hydrolyzed and sonicated particles, respectively, which could be owing to the increase in the crystallinity of a-keratin. The increase in the underlying area also supports the results of FTIR spectroscopy regarding the higher a-helix content in the treated samples. The baseline of the untreated sample began to drift with increasing the temperature from 240 °C to 321 °C corresponding to the thermal degradation of keratin. Visual observation confirmed the degradation since the raw wool fiber changed color to black and emitted an odor at these high temperatures. The third endothermic peak about 275 °C which is evident in the treated samples is ascribed to the melting/decomposition of highly cross-linked (disulfide bonds) inter-macrofibrillar matrix keratins [58]. The high temperature peak of 335 °C in raw wool corresponds to the rupture of peptide bonds, leading to the liquefaction [1]. The absence of this peak till 400 °C in the treated samples shows that decomposition of the wool particles has become more difficult implicating higher thermal stability of these samples in comparison with the untreated one. These findings are consistent with the results of thermogravimetric analysis. 3.3.6. Moisture regain and solubility Wool has high moisture regain owing to the presence of amorphous region and the amide and carboxyl groups [32]. As can be

Table 6 The weight loss with corresponding thermal degradation temperature in the TG curves of different samples.

Weight loss (%) Tonset (°C)

Untreated wool

Hydrolyzed wool

Hydrolyzed + sonicated wool

82.98 203.5

44.84 228.6

34.73 217.1

N. Eslahi et al. / Advanced Powder Technology 24 (2013) 416–426 Table 7 Moisture regain and dissolving time of samples. Untreated wool Moisture regain (%) 14.6 ± 0.5 Dissolving time 30 ± 4 (min)

Hydrolyzed wool

Hydrolyzed + sonicated wool

10 ± 0.2 9±3

8 ± 0.4 7±2

seen in Table 7, moisture regain of hydrolyzed and sonicated wool particles decreased comparing to the untreated wool fiber which might be because of an increase in crystallinity. The obtained results corresponds well with those of thermal analysis indicating lower temperature of water evaporation in wool nanoparticles. Enzymatic hydrolysis also brought about a significant change in wool solubility in caustic solution of 2.5% sodium hydroxide. It was evident that the dissolving time of the wool particles declined after hydrolysis and ultrasonic treatment owing to size reduction. Breakage of the disulfide bonds in the wool fiber by the reducing agent and the decrease in total number of hydrogen bonds in wool nanoparticles might lead to an enhancement in dissolving ability of the samples. 4. Conclusion A Central Composite Design (CCD) was applied to optimize the enzymatic hydrolysis of wool fiber in order to produce nanoparticles. The effects of enzyme loading, hydrolysis time and substrate concentration on particle mean size were investigated. After analyzing the experimental data by statistical tests (ANOVA) to yield polynomial regression model, response surfaces were drawn to determine the optimal conditions for enzymatic process. Accordingly, minimum particle size was achieved by making use of 5 g/l wool and 3.3% enzyme at hydrolysis time of 214 h. A validation assay confirmed the predictive response value under the aforementioned conditions. Therefore, the response model based on CCD was adequate for reflecting the expected optimization. The attained optimal conditions were applied to produce wool nanoparticles followed by ultrasonic treatment. The morphological investigation by SEM indicated gradual breakdown of the fiber as it was progressively converted into powder form. Based on the measurement of particle size distribution, ultrasonic treatment was an effective method to reduce the particle size further. FTIR spectroscopy results showed no remarkable changes in the chemical composition and macromolecular conformation of the wool fiber after enzymatic hydrolysis and sonication. X-ray diffraction analysis denoted that the applied procedures had no significant effect on the X-ray pattern and crystalline structure of the fiber, however, the degree of crystallinity increased due to the destruction of the amorphous regions. According to the obtained results of thermal analysis, wool nanoparticles had higher thermal stability than that of raw wool fiber. Moreover, dissolving ability of the nanoparticles in caustic solution enhanced; but moisture regain declined. As a consequence, through enzymatic treatment, an environmentally friendly process, wool nanoparticles with improved characteristics was produced which has promising potential for a variety of applications in different fields such as nanocomposites which is introduced as the main goal of a future research. References [1] W. Xu, W. Guo, W. Li, Thermal analysis of ultrafine wool powder, J. Appl. Polym. Sci. 87 (2003) 2372–2376. [2] Y.F. Cheng, C.W.M. Yuen, Y. Li, S.K.A. Ku, C.W. Kan, J.Y. Hu, Characterization of nanoscale wool particles, J. Appl. Polym. Sci. 104 (2007) 803–808. [3] A.E. Pavlath, C. Houssard, W. Camirand, G.H. Robertson, Clarity of films from wool keratin, Text. Res. J. 69 (1999) 539–541.

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