# Extraction of Cellulose Nano-Whiskers Using Ionic Liquid-Assisted Ultra-Sonication: Optimization and Mathematical Modelling Using Box–Behnken Design

^{1}

^{2}

^{3}

^{4}

^{5}

^{6}

^{7}

^{8}

^{*}

## Abstract

**:**

_{4}). Process parameters for ultra-sonication were optimized using a two-level factorial Box–Behnken design (BBD). Process variables such as ultra-sonication power (x

_{1}), hydrolysing time (x

_{2}) and temperature (x

_{3}) were varied. Responses selected were percentage crystallinity index, CrI% (y

_{1}) and yield% (y

_{1}) for the finally procured CNWs sample. Regression analysis was carried out to develop quadratic model to analyze the effect of process variables on IL-assisted ultra-sonication process. Analysis of variance (ANOVA) showed that ultra-sonication power was the most influential aspect for hydrolyzing the amorphous segments of crude cellulose extracted from baobab leaves. A relative study of the physio-chemical properties of the starting lignocellulosic substrate (AK), KOH pre-treated, bleached and IL-assisted ultra-sonicated CNWs was conducted. The synthesized samples were characterized using Fourier transform infrared spectroscopy, Scanning electron microscopy, atomic force microscopy, high resolution transmission electron microscopy, X-ray diffraction and thermo-gravimetric and zeta potential analysis. Under optimum condition, the extracted CNWs showed an average width of 15–20 nm; with high crystallinity index of 86.46%. This research provides an insight about the delignification of Adansonia kilima (AK) leaves and its effective conversion to CNWs having high crystallinity.

## 1. Introduction

_{2}SO

_{4}, HNO

_{3}, HCl) is carried out for selective hydrolysis of amorphous region of cellulose to yield nanocellulose. The cellulosic matrix in ligno-cellulosic biomass contain lignin polymer having complex structure in the primary cell wall which causes recalcitrance. This results in difficulties to yield nanocellulose [14,15]. Eliminating lignin using energy efficient, green technology to intensify the degree of nano-fibrillation is often critical. Commonly used chemical reagents, especially corrosive acids, sulphite treatment and TEMPO mediated oxidation have been widely used to delignify the lignocellulosic substrates. Although successful extraction has been carried out by following those methods, some practical obstacles remain for their large-scale production, such as prolonged reaction time with lower yield percentages, use of concentrated corrosive mineral acids and environmental pollution. In this regard, some novel protocols consisting of microwave-assisted heating in presence of alkali (NaOH, KOH, Ca(OH)

_{2}) and peroxide bleaching process with subsequent hydrolysis using ionic liquids (ILs)-as green solvent during the ultrasonication have been recognized as eco-friendly approaches [16,17,18]. Application of ionic liquids (ILs) can prevent the use of corrosive mineral acids and reduce environmental pollution [8,19,20].

_{2}SO

_{4}, H

_{3}PO

_{4}, HCl, etc. In this research, the acidic IL of [Bmim]HSO

_{4}was chosen to extract CNWs during ultrasonication process. [Bmim]HSO

_{4}is classified as Bronsted acidic ionic liquid having pKa values of 2 in presence of water [16,17,18]. It was expected that [Bmim]HSO

_{4}will play dual role for swelling and selective hydrolysis of crude cellulose obtained from pretreated and bleached AK leaves. The presence of shorter alkyl chain (Butyl) with alkylimidazolium cations of [Bmim]HSO

_{4}will have improved solvation capability with higher anion concentration. The presence of negative anions of [HSO

_{4}]—will make it acidic and enhance the interlayer distance between the cellulosic chain by swelling and will cause destruction of strong hydrogen bond [16,17,18].

_{4}IL using ultrasonication. The ultrasonication process was optimized and relevant mathematical polynomial models were developed for crystallinity index, CrI% (y

_{1}) and yield% (y

_{2}) of CNWs. Statistical analysis with model validation were carried out using RSM technique based on Box–Behnken (BBD) experimental design. Finally extracted CNWs samples were characterized in terms of FESEM, HRTEM, AFM, XRD, TGA, FTIR and Zeta potential analysis. The output of this research clearly shows the role of Bmim-HSO

_{4}IL in extracting highly purified crystals of CNWs having high crystallinity index (CrI%).

## 2. Materials and Methods

#### 2.1. Materials

_{4}), potassium hydroxide (KOH), hydrogen peroxide (30%, H

_{2}O

_{2}) were obtained from Merck (Selangor, Malaysia). Dried leaves of Adansonia kilima (AK) (AKL-1) was obtained from local farmhouses in Khartoum, Sudan. Analytical grade toluene (C

_{7}H

_{8}) and ethanol (C

_{2}H

_{5}OH) were purchased from Sigma-Aldrich (Petaling Jaya, Malaysia).

#### 2.2. Method

#### 2.2.1. Microwave Pre-Treatment Using KOH

#### 2.2.2. Peroxide Bleaching

_{2}O

_{2}for 6 h at 60 °C to bleach the sample completely. The sample was filtered and washed with DI water several times until the pH became 6.5–7.5. It was dried overnight in in vacuum oven at 55 °C and labeled as AKL-4 for subsequent characterizations.

#### 2.2.3. Extraction of CNWs Using Ultrasonication

_{1}), time (x

_{2}) and temperature (x

_{3}) were varied accordingly (Table 2). After the predetermined period (hydrolysis time) of ultrasonication at different conditions (Table 2), 30 mL cold deionized waster (DI) was added with the mixture and was stirred until the temperature reached room temperature. The milky white, colloidal suspension of cellulose thus obtained was centrifuged at 6500 rpm for 30 min. The white cellulosic precipitate obtained was separated from the mixture and washed with hot DI water several times until the pH became 5.5–6. The sample thus obtained was freeze dried at −4 °C for two days and labelled as AKL-5 which was the finally extracted CNWs sample.

#### 2.2.4. Experimental Design and Optimization

_{1}, time = x

_{2}and temperature = x

_{3}) were varied to observe its impact on preselected responses of crystallinity index, CrI% (y

_{1}) and yield% (y

_{2}) of finally extracted cellulose nano-whiskers (CNWs). The experimental data were analyzed using Design the Experiment (Design-Expert V9.0). A relevant mathematical model was developed and the accuracy of the model was validated by analysis of variance (ANOVA) test. The process optimization was carried out to get maximum crystallinity index, CrI% and yield% of CNWs (AKL-5).

#### 2.2.5. Characterizations

_{2}H

_{5}OH and sonicated for 10 min and then a drop of sample was placed over the copper grid and allowed to dry before taking the HRTEM images. Atomic force microscopic (AFM) analysis was carried out for finally extracted CNWs sample (AKL-5) to observe the shape and surface topography (Multimode Nano-Scope-IIIA, Bruker, Billerica, USA). The samples were sonicated for 15 min to prevent aggregation and a drop was placed over the glass slides. After it was dried at ambient temperature, AFM analysis was carried out. Thermogravimetric analysis with DTG

_{max}(TGA-Q0500, Shimadzu, New York, USA) was carried out to observe the thermal degradation profile of all the extracted samples where 5 mg of all the sample was heated from 30 °C to 1000 °C under the nitrogen flow (150 mL/min) at a heating rate of 5 °C per minute. The crystalline phase, crystallinity index (CrI%) including the d-spacing of the samples, was analyzed by X-ray diffraction technique using CuKα radiation at a voltage around 2.7 Kw (Shimadzu-XRD-6000, Kyoto, Japan). The crystallinity index was calculated using Segal’s method using Equation (1):

_{002}−I

_{am})/I

_{002}× 100%.

_{002}represents the intensity of both crystalline and amorphous regions of the sample at 2θ = 22–24° and I

_{am}represents the intensity of amorphous region of the sample [29]. The samples were ground to powder and mixed with dried KBr and pressed to ultrathin films (approximately 4 mm) and sent for FTIR analysis (Perkin Elmer, Tokyo, Japan).

## 3. Results and Discussion

#### 3.1. Mathematical Modeling and Statistical Analysis

_{1}) and yield% (y

_{2}) of CNWs, two polynomial equations were suggested after regression analysis based on the data obtained from the basic design matrix (Table 2). The design matrix provided the reactions conditions for each experimental run with the output responses of crystallinity index, CrI% (y

_{1}) and yield% (y

_{2}). The models were chosen based on their polynomial order which should be highest here [28]. Preselected input variables such as power (x

_{1}), time (x

_{2}) and temperature, (x

_{3}) were significant for developing the models. All these input variables had positive impact on crystallinity index, CrI% (y

_{1}) and yield% (y

_{2}) up to certain extent. After that range, enhancing power (x

_{1}), time (x

_{2}) and temperature (x

_{3}) would reduce the crystallinity index, CrI% (y

_{1}) and yield% (y

_{2}) both due to extensive hydrolysis of the amorphous and crystalline domain of cellulose to form other organic liquid fractions. Based on the magnitude of the sequential model sum of the squares, the models were not aliased [1,29].

_{1}) and yield% (y

_{2}), following models were proposed, and the empirical equations obtained for that is given bellow:

_{1}) and CNWs yield% (y

_{2}), respectively. The plots revealed that the experimental data obtained here for both the responses were closer to the predicted values exhibiting R

^{2}values for the Equations (2) and (3) around 0.987 and 0.986 for crystallinity index, CrI% (y

_{1}) (Figure 1a) and CNWs yield%, (y

_{2}) (Figure 1b); correspondingly. This evidences the outstanding adjustment of the proposed models with the experimental observation.

_{1}) and yield% (y

_{2}) of CNWs (AKL-5) throughout the 17 experimental runs (Table 2) are shown by Figure 2a,b. Residual error is the difference between experimental data and predicted data for modelling. Studentized residual is obtained by dividing the residual error with standard deviation. Figure 2a,b showed that the points are arbitrarily scattered but were within the range of ±3.00. Thus, transformation of responses is not required, and the models used here are suitable to analyze the overall extraction process using IL-assisted ultra-sonication [1,29].

#### 3.2. Analysis of Variance (ANOVA) Test and Statistical Analysis

^{2}values were in close agreement with the adjusted R

^{2}values. The magnitudes of standard deviation observed here for both the models were relatively smaller. Furthermore, the values for coefficient of variation (CV) were also only 1.22 and 1.06 for the empirical models Equations (2) and (3). This represents the reproducibility of the experimental data with the developed model. Adequate precision is defined as the ratio between signal to noise and for effective development of the polynomial model, the magnitude must be greater than 4. Here for both the responses, it was around 29.82 and 29.20, respectively, reflecting suitable navigation of the design [29].

_{1}) and yield%, (y

_{2}); respectively. It was observed based on the magnitude of the linear term that the ultrasonication power (x

_{1}) had a greater impact on CrI% rather than the time (x

_{2}) and temperature (x

_{3}) (Table 4). However, the yield% (y

_{2}) was mostly affected by temperature (x

_{3}) (Table 5).

_{1}), time (x

_{2}), temperature (x

_{3}), and their interaction terms x

_{1}x

_{2}, x

_{2}x

_{3}and x

_{1}x

_{3}together with the quadratic terms of (${x}_{1}^{2}$) and (${x}_{3}^{2}$) were significant model terms. Ultrasonication power (x

_{1}) had played most influential role over the CrI% (y

_{1}) by showing the highest F-value of 444.77. However, time (x

_{2}) had least impact on CrI% (y

_{1}) compared to the other two factors of time (x

_{2}) and temperature (x

_{3}), as observed from F-values of Table 4. As illustrated by Table 5, ultrasonication power (x

_{1}), time (x

_{2}), temperature (x

_{3}), and their interaction terms x

_{2}x

_{3}together with the quadratic terms of (${x}_{3}^{2}$) are significant model terms for CNWs yield% (y

_{2}). The F-values for temperature (x

_{3}) was 218.09 reflecting its prominent effect on yield% (y

_{2}) (Table 5). However, power (x

_{1}) and time (x

_{2}) had relatively less remarkable impact on yield% (y

_{2}) (Table 5) as observed from the magnitude of F-test. The interaction effect of power and time (x

_{1}x

_{2}= 0.43) had negligible impact whereas power and temperature (x

_{1}x

_{3}= 1.81) had reasonable impact on yield% of CNWs (y

_{2}). Time and temperature combined (x

_{2}x

_{3}) played a vital role for the yield% (y

_{2}) of CNWs.

#### 3.3. Process Variables Optimization

_{1}), time (x

_{2}) and temperature (x

_{3}) were kept within the range selected earlier (Table 1). The experiment was conducted under optimal condition. The analytical data obtained from experiments under optimum condition was compared with the predicted results as suggested by the software (Table 6). The percentage error between the predicted and actual/experimental results was determined. As observed from Table 6, the percentage error for both the responses obtained were negligible supporting suitability of the polynomial models developed earlier (Equations (2) and (3)) additionally.

#### 3.4. Effect of Process Variables for Synthesis of Cellulose Nano-Whiskers (CNWs)

_{1}) and time (x

_{2}) on CrI% (y

_{1}) of finally extracted CNWs is illustrated by a 3D surface mesh with contour plots where the temperature (x

_{3}) was kept at center point, in other words constant at 105 °C (Figure 4a). Similarly, Figure 4b was constructed to observe the combined effect of power (x

_{1}) and temperature over (x

_{3}) the CrI% (y

_{1}) where the ultrasonication time (x

_{2}) was fixed at 30 min. Overall, the three process parameters used here for ultrasonication have substantial effect on CrI% (y

_{1}) as observed from the basic design matrix of Table 2. CrI% of extracted CNWs increased with successive increase of ultrasonication power (x

_{1}) and time (x

_{2}) in presence of ILs as hydrolyzing solvent (Figure 2a). This was anticipated as the enhancement of power (x

_{1}) with hydrolyzing time (x

_{2}) would increase the kinetic force of ILs and improve its diffusion rate inside the amorphous segment of cellulose. Enhancing power (x

_{1}) during ultrasonication would produce cavitation bubbles which would travel fast towards the interior region of cellulose and collapse there suddenly to initiate the disruption of intra- and intermolecular hydrogen bonds of cellulose. Extended ultrasonication time (x

_{2}) would facilitate the physical swelling of crude cellulose (AKL-4) obtained from the previous pretreatment process. Thus, more surface area would be exposed for ILs to enter and hydrolyze the amorphous domain resulting in higher crystallinity index (CrI%) of the finally extracted CNWs sample (AKL-5). Increasing the temperature up to a certain level increased the CrI% of the CNWs. In that case, viscosity of the ILs will be reduced and its solvation properties will be improved [16,17]. Due to reduction of viscosity, significant amount of IL molecules will be dissociated to give [Bmim]

^{+}cations which can form complex with negative oxygen atom of –OH groups and form intermediate complexes [16]. Relatively higher temperature would reduce the viscosity of ILs which would subsequently increase the diffusion rate of them inside the compact cellulosic matrix [18]. The negative part of –HSO

_{4}would interact with positive H

^{+}ions. This would disintegrate the glycosidic bonds between the monosaccharide units of cellulose resulting in nano-dimensional cellulose having higher CrI% [16,17,18]. However, whenever the temperature was increased up to the maximum point of 120 °C at different power (x

_{1}) and time (x

_{2}); the crystallinity index dropped significantly. This happened due to excessive dissolution of cellulose and char formation [17,18].

_{2}). Figure 5a illustrates the collective effect of ultrasonication power (x

_{1}) and time (x

_{2}) on the yield% (y

_{2}) of the finally extracted CNWs where temperature (x

_{3}) was kept at fixed level of center point (105 °C). Figure 5b exhibits the effect of ultrasonication power (x

_{1}) and temperature (x

_{3}) over the yield% (y

_{2}), where ultrasonication time (x

_{2}) was fixed at center level (30 min). However, yield% (y

_{2}) of CNWs showed a decreasing trend with increasing power (x

_{1}), time (x

_{2}) and temperature (x

_{3}). Both the plots exhibited that (Figure 3a,b), temperature (x

_{3}) played most vital role for yield% (y

_{2}) whereas the other two variables of power (x

_{1}) and time (x

_{2}) had a relatively moderate impact on it. The yield% (y

_{2}) was lowest when power was maintained at 350 watts for time period of 45 min in presence of ILs at a maximum temperature of 120 °C (65.69%, Sample 7) as summarized by Table 2. This was evident due to dissolution of cellulose to other liquid fractions rather than forming solid fraction containing nanocellulose [1,29].

#### 3.5. Characterizations

#### 3.5.1. Surface Morphological Studies

_{4}would form [Bmim]

^{+}cations which would interact with the negative –OH groups of cellulose. The negative anions of [HSO

_{4}]

^{–}would interact β-1-4 glycosidic bonds. The positive cations of the ILs have electron rich ᴫ system which could attack the negative oxygen atoms of β-1-4 glycosidic bonds [34]. This would cause disintegration of intra-molecular hydrogen bonding between two cellulosic chains. It would further initiate the rupture of β-1-4 glycosidic linkages inside the single, long chain of cellulose resulting defragmented, smaller CNW crystals [35,36]. Most of the CNW crystals finally obtained had the length of 80–110 nm and width of 15–20 nm (Figure 7c). The AFM images (Figure 7b) of AKL-5 clearly showed the presence of needle-shaped cellulosic nano crystal (CNWs) samples finally after ultrasonication at optimal condition [37].

#### 3.5.2. XRD Analysis

_{4}ILs under optimum condition. The crystallinity index along with the crystallite size of the extracted sample after each treatment step was calculated and the results obtained are summarized in Table 7.

_{4}, ILs, the sample AKL-5 (CNWs) showed comparatively high crystallinity index with smaller crystallite size than the other samples. The cavitation effect of ultrasonication had initiated the removal of the intra-and intermolecular hydrogen bonds, releasing the micro fibrillated cellulosic fiber resulting in a higher crystallinity index than the untreated (AKL-1), solvent extracted (AKL-2), KOH treated (AKL-3) and bleached (AKL-4) samples.

#### 3.5.3. Thermogravimetric Analysis

_{max}), crystallinity index, amount of char and weight loss for all the samples obtained here.

#### 3.5.4. Surface Functional Groups Analysis

^{−1}represented the stretching of –OH groups [9]. The presences of –OH groups in cellulosic materials enable them to form different kinds of intra-and intermolecular hydrogen bonding between the different cellulosic strands and each cellulosic chain, respectively [45]. The stretching of –CH functional groups was visible in all the samples around 2900–2800 cm

^{−1}[46]. Peaks around 1620–1650 cm

^{−1}were related to O–H bending vibrations which showed the presence of absorbed moisture in all the samples [47]. The minor peaks obtained around 1420–1450 cm

^{−1}showed the intermolecular hydrogen bonding in C-6 groups of cellulosic substrates [44]. Peaks around 1510 to 1530 cm

^{−1}exhibited the presence of aromatic ring vibration in AKL-1, AKL-2 and AKL-3. In AKL-4 and AKL-5, the peak disappeared showing complete elimination of lignin from the synthesized sample. The bending vibration of –CH groups as well as presence of –CO groups were visible for all the samples in the range 1320–1380 cm

^{−1}[44]. All the samples contained the peaks around 1045 cm

^{−1}reflecting the presence of C–O–C stretching vibration of pyranose ring and glycosides linkages between two monosaccharaides units of cellulose [48]. The peaks around 800–900 cm

^{−1}typically represented the cellulosic chain which can be ascribed to β-glycosidic linkages between glucose units in cellulosic chain [44]. During microwave-induced heating, the alkali acts as microwave energy absorber and initiates easy penetration of solvent inside the cellulosic substrates. Thus, partial dissolution of hemicellulose and lignin takes place due to uniform heating process of microwaves. This makes the surface area of AKL-3 more accessible for the peroxide bleaching process which initiates the defibrillation process. Therefore, in AKL-3 and AKL-4, more micro- and nano-fibrillated fiber were released, which was supported earlier by our FESEM images. Table 9 summarizes the major peaks with their frequency regions usually observed for ligno-cellulosic biomass and the micro- and nano-dimensional cellulose extracted from them.

#### 3.5.5. Surface Charge Analysis

_{4}ILs had initiated the defibrillation as well as defragmentation of long cellulosic chain resulting CNWs [28,37]. Thus, more negative –OH groups were exposed resulting higher zeta potential values [49,50].

## 4. Conclusions

_{1}) was increasing with successive decrease in particle size of the synthesized sample. HRTEM and AFM images showed development of needle like CNWs. Highly negative zeta potential values of finally extracted sample (AKL-5) clearly reflected higher density of –OH groups over the defragmented nano-sized crystals (CNWs). The experimental design matrix was made based on Box–Behnken (BBD) design and ultrasonication variables were optimized. CrI% (y

_{1}) was influenced mostly by ultrasonication power (x

_{1}) and temperature (x

_{3}), whereas ultrasonication time (x

_{2}) had a moderate impact on it. On the other hand, yield% (y

_{2}) was affected more prominently by temperature (x

_{3}) and time (x

_{2}) rather than the power (x

_{1}) itself. Model validation with analysis of variance (ANOVA) test was carried out to check the adequacy of the proposed polynomial Equations (2) and (3). Theoretical optimization to obtain maximum CrI% (y

_{1}) and yield% (y

_{2}) was done. The suggested values by the software for CrI% (y

_{1}) and yield% (y

_{1}) were very close with the actual ones illustrating negligible error percentages of 1.61% and 1.30% for both the responses, respectively. The findings of this research revealed that the BBD design using mathematical modeling and statistical analysis concurrently is appropriate for detecting and optimizing the input variables influencing the IL-assisted ultrasonic extraction process of CNWs from the delignified, dried leaves of Adansonia kilima (AK) plants.

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Abd Hamid, S.B.; Chowdhury, Z.Z.; Karim, M.Z. Catalytic Extraction of Microcrystalline Cellulose (MCC) from Elaeis guineensis using Central Composite Design (CCD). BioResources
**2014**, 9, 7403–7426. [Google Scholar] [CrossRef] - Ma, Y.; Xia, Q.; Liu, Y.; Chen, Y.; Liu, S.; Wang, Q.; Liu, Y.; Li, J.; Yu, H. Production of Nanocellulose Using Hydrated Deep Eutectic Solvent Combined with Ultrasonic Treatment. ACS Omega
**2019**, 4, 8539–8547. [Google Scholar] [CrossRef] [PubMed] - Ravindran, L.; Sreekala, M.S.; Thomas, S. Novel processing parameters for the extraction of cellulose nanofibers (CNF) from environmentally benign pineapple leaf fibres (PALF): Structure-property relationships. Int. J. Biol. Macromol.
**2019**, 131, 858–870. [Google Scholar] [CrossRef] [PubMed] - Vanitjinda, G.; Nimchua, T.; Sukyai, P. Effect of xylanase-assisted pretreatment on the properties of cellulose and regenerated cellulose films from sugarcane bagasse. Int. J. Biol. Macromol.
**2019**, 122, 503–516. [Google Scholar] [CrossRef] [PubMed] - Mangalam, A.P.; Simonsen, J.; Benight, A.S. Cellulose/DNA hybrid nanomaterial. Biomacromolecules
**2009**, 10, 497–504. [Google Scholar] [CrossRef] [PubMed] - Mendez, J.D.; Weder, C. Synthesis, electrical properties, and nanocomposites of poly (3, 4-ethylenedioxythiophene) nanorods. Polym. Chem.
**2010**, 1, 137–1244. [Google Scholar] [CrossRef] - Zhou, C.; Wu, Q.; Yue, Y.; Zhang, Q. Application of rod-shaped cellulose nanocrystals in polyacrylamide hydrogels. J. Colloid Interface Sci.
**2011**, 353, 116–123. [Google Scholar] [CrossRef] [PubMed] - Chen, W.; Yu, H.; Liu, Y.; Chen, P.; Zhang, M.; Hai, Y. Individualization of Cellulose Nanofibers from Wood using High Intensity Ultrasonication Combined with Chemical Pretreatments. Carbohydr. Polym.
**2011**, 83, 1804–1811. [Google Scholar] [CrossRef] - Khalil, H.P.S.A.; Davoudpour, Y.; Nazrul Islam, M.; Mustapha, A.; Sudesh, K.; Dungani, R.; Jawaid, M. Production and Modification of Nanofibrillated Cellulose using Various Mechanical Processes: A Review. Carbohydr. Polym.
**2014**, 99, 649–665. [Google Scholar] [CrossRef] - Jiang, F.; Hsieh, Y.-L. Chemically and Mechanically Isolated Nanocellulose and Their Self-Assembled Structures. Carbohydr. Polym.
**2013**, 95, 32–40. [Google Scholar] [CrossRef] - Meng, X.; Pu, Y.; Yoo, C.G.; Li, M.; Bali, G.; Park, D.Y.; Gjersing, E.; Davis, M.F.; Muchero, W.; Tuskan, G.A.; et al. An In-Depth Understanding of Biomass Recalcitrance using Natural Poplar Variants as the Feedstock. ChemSusChem
**2017**, 10, 39–150. [Google Scholar] [CrossRef] [PubMed] - Li, N.; Li, Y.; Yoo, C.G.; Yang, X.; Lin, X.; Ralph, J.; Pan, X. An Uncondensed Lignin Depolymerized in the Solid State and Isolated from Lignocellulosic Biomass: A Mechanistic Study. Green Chem.
**2018**, 20, 4224–4235. [Google Scholar] [CrossRef] - Xu, J.; Hou, H.; Liu, B.; Hu, J. The integration of different pretreatments and ionic liquid processing of eucalyptus: hemicellulosic products and regenerated cellulose fibers. Ind. Crop. Prod.
**2017**, 101, 11–20. [Google Scholar] [CrossRef] - Liu, Z.; Sun, X.; Hao, M.; Huang, C.; Xue, Z.; Mu, T. Preparation and characterization of regenerated cellulose from ionic liquid using different methods. Carbohydr. Polym.
**2015**, 117, 99–105. [Google Scholar] [CrossRef] [PubMed] - Chen, J.; Xu, J.; Wang, K.; Qian, X.; Sun, R. Highly thermostable, flexible, and conductive films prepared from cellulose, graphite, and polypyrrole nanoparticles. ACS Appl. Mater. Interfaces
**2015**, 7, 15641–15648. [Google Scholar] [CrossRef] [PubMed] - Mao, J.; Heck, B.; Reiter, G.; Laborie, M.P. Cellulose nanocrystals’ production in near theoretical yields by 1-butyl-3-methylimidazolium hydrogen sulfate ([Bmim]HSO
_{4})–mediated hydrolysis. Carbohydr. Polym.**2015**, 117, 443–451. [Google Scholar] [CrossRef] [PubMed] - Tian, D.; Han, Y.; Lu, C.; Zhang, X.; Yuan, G. Acidic ionic liquid as “quasi-homogeneous” catalyst for controllable synthesis of cellulose acetate. Carbohydr. Polym.
**2014**, 113, 83–90. [Google Scholar] [CrossRef] - Jin, Z.; Wang, S.; Wang, J.; Zhao, M. Effects of plasticization conditions on the structures and properties of cellulose packaging films from ionic liquid [BMIM] Cl. J. Appl. Polym. Sci.
**2012**, 125, 704–709. [Google Scholar] [CrossRef] - Chen, W.S.; Yu, H.P.; Liu, Y.X. Preparation of millimetre- long cellulose I nanofibers with diameters of 30–80 nm from bamboo fibers. Carbohydr. Polym.
**2011**, 86, 453–461. [Google Scholar] [CrossRef] - Chen, W.S.; Yu, H.P.; Liu, Y.X.; Hai, Y.F.; Zhang, M.X.; Chen, P. Isolation and characterization of cellulose nanofibers from four plant cellulose fibers using a chemical-ultrasonic process. Cellulose
**2011**, 18, 433–442. [Google Scholar] [CrossRef] - Cheng, Q.Z.; Wang, S.Q.; Han, Q.Y. Novel process for isolating fibrils from cellulose fibres by high-intensity ultra-sonication. II. Fibril characterization. J. Appl. Polym. Sci.
**2010**, 115, 2756–2762. [Google Scholar] [CrossRef] - Cheng, Q.; Wang, S.Q.; Rials, T.G. Poly (vinyl alcohol) nanocomposites reinforced with cellulose fibrils isolated by high intensity ultrasonication. Compos Part A. Appl. Sci.
**2009**, 40, 218–224. [Google Scholar] [CrossRef] - Wang, S.Q.; Cheng, Q.Z. A novel process to isolate fibrils from cellulose fibers by high intensity ultrasonication part 1: process optimization. J. Appl. Polym. Sci.
**2011**, 113, 1270–1275. [Google Scholar] [CrossRef] - Sun, Y.; Cheng, J. Hydrolysis of Lignocellulosic materials for Ethanol Production: A Review. Bioresour. Technol.
**2002**, 83, 1–11. [Google Scholar] [CrossRef] - Chowdhury, Z.; Zain, S.; Khan, R.; Ahmad, A.; Islam, M.; Arami-Niya, A. Application of central composite design for preparation of Kenaf fiber based activated carbon for adsorption of manganese (II) ion. Int. J. Phys. Sci.
**2011**, 6, 7191–7202. [Google Scholar] [CrossRef] - Chowdhury, Z.Z.; Zain, S.M.; Khan, R.A.; Arami-Niya, A.; Khalid, K. Process variables optimization for preparation and characterization of novel adsorbent from Lignocellulosic Waste. Bioresources
**2012**, 7, 3732–3754. [Google Scholar] - Agbor, V.B.; Cicek, N.; Sparling, R.; Belin, A.; Levin, D.B. Biomass Pretreatment: Fundamental towards Application. Biotechnol. Adv.
**2011**, 29, 675–685. [Google Scholar] [CrossRef] - Mazlita, Y. Catalytic Synthesis of Nanocellulose from Oil Palm Empty Fruit Bunch Fiber. Master’s Thesis, University of Malaya, Kuala Lumpur, Malaysia, 2016. [Google Scholar]
- Karim, Md. Z.; Chowdhury, Z.Z.; Abd Hamid, S.B.; Ali, E.M. Statistical Optimization for Acid Hydrolysis of Microcrystalline Cellulose and Its Physiochemical Characterization by Using Metal Ion Catalyst. Materials
**2014**, 7, 6982–6999. [Google Scholar] [CrossRef] - Li, W.; Wang, R.; Liu, S. Nanocrystalliline cellulose prepared from softwood craft pulp via ultrasonic assisted acid hydrolysis. Bioresources
**2011**, 6, 4271–4281. [Google Scholar] - Abraham, E.; Deepa, B.; Pothan, L.A.; Jacob, M.; Thomas, S.; Cvelbar, U.; Anand, R. Extraction of Nanocellulosic fibers from lignocellulosic fibers: A novel approach. Carbohydr. Polym.
**2011**, 86, 1468–1475. [Google Scholar] [CrossRef] - Jiang, F.; Hsieh, Y.L. Controlled defibrillation of rice straw cellulose nanofibrills into highly crystalline fibrous materials. RSC Adv.
**2013**, 3, 12366–12375. [Google Scholar] [CrossRef] - Holmes, J.; Lassi, U. Ionic Liquids in Pretreatment of Lignocellulosic Biomass in Ionic Liquids Application and Perspectives; Intech Open: London, UK, 2011; pp. 545–560. ISBN 978-953-307-248-7. [Google Scholar]
- Han, J.; Zhou, C.; French, A.D.; Han, G.; Wu, Q. Characterizations of cellulose II nanoparticles regenerated from 1-butyl-3methylimidazolium chloride. Carbohydr. Polym.
**2013**, 94, 773–781. [Google Scholar] [CrossRef] [PubMed] - Moon, R.J.; Martini, A.; Narin, J.; Simonsen, J.; Youngblood, J. Cellulose nanoparticles review: Structure, Properties and Nanocomposites. Chem. Soc. Rev.
**2011**, 40, 3941–3994. [Google Scholar] [CrossRef] [PubMed] - Tischer, P.C.S.F.; Sierakowski, M.R.; Westfahl, H.; Tischer, C.A. Nanostructural Reorganization of Bacterial Cellulose by Ultrasonic Treatment. Biomacromolecules
**2010**, 11, 1217–1224. [Google Scholar] [CrossRef] [PubMed] - Abd Hamid, S.B.; Chowdhury, Z.Z.; Karim, M.Z.; Ali, M.E. Catalytic Isolation and Physicochemical Propertiesof Nanocrystalline Cellulose(NCC) using HCl-FeCl3System Combined with Ultrasonication. BioResources
**2016**, 11, 3840–3855. [Google Scholar] [CrossRef] - Tang, A.M.; Zhang, H.; Chen, G.; Xie, G.; Liang, W.Z. Influence of ultrasound treatment on accessibility and Region-selective oxidation reactivity of cellulose. Ultrason. Sonochem.
**2005**, 12, 467–472. [Google Scholar] - Chowdhury, Z.Z.; Abd Hamid, S.B. 'Preparation and Characterization of Nanocrystalline Cellulose Using Ultrasonication Combined with a Microwave-Assisted Pretreatment Process. BioResources
**2016**, 11, 3397–3415. [Google Scholar] [CrossRef] - Nishiyama, Y.; Sugiyama, J.; Chanzy, H.; Langan, P. Crystal structure and hydrogen bonding system in cellulose I from synchrotron X-ray and neuron fiber diffraction. J. Am. Chem. Soc.
**2003**, 125, 14300–14306. [Google Scholar] [CrossRef] - Lu, Q.; Lin, W.; Tang, L.; Wang, S.; Chen, X.; Huang, B. A mechanochemical approach to manufacturing bamboo cellulose nanocrystals. J. Mater. Sci.
**2015**, 50, 611–619. [Google Scholar] [CrossRef] - Deepa, B.; Eldho, A.; Nerieda, C.; Miran, M.; Mathew, A.P.; Oksman, K.; Faria, M.; Thomas, S.; Pothan, L.A. Utilization of various lignocellulosic biomass for the production of nanocellulose: A comparative study. Cellulose
**2015**, 22, 1075–1090. [Google Scholar] [CrossRef] - Li, M.; Wang, L.; Li, D.; Cheng, Y.; Adhikari, B. Preparation and characterizations of cellulose nanofiber from depectinized sugar beet pulp. Carbohydr. Polym.
**2014**, 102, 136–143. [Google Scholar] [CrossRef] [PubMed] - Johar, N.; Ahmad, I.; Dufresne, A. Extraction, preparation and characterization of cellulose fibres and nanocrystals from rice husk. Ind. Crops Prod.
**2012**, 37, 93–99. [Google Scholar] [CrossRef] - Habibi, Y.; Lucia, L.A.; Rojas, O.J. Cellulose nanocrystals: chemistry, self-assembly, and applications. Chem. Rev.
**2010**, 110, 3479–3500. [Google Scholar] [CrossRef] [PubMed] - Chirayil, C.J.; Mathew, L.; Thomas, S. Review of recent research in nano cellulose preparation from different lignocellulosic fibers. Rev. Adv. Mater. Sci.
**2014**, 37, 20–28. [Google Scholar] - Mandal, A.; Chakrabarty, D. Isolation of nanocellulose from waste sugarcane bagasse (SCB) and its characterization. Carbohydr. Polym.
**2011**, 86, 1291–1299. [Google Scholar] [CrossRef] - Maiti, S.; Jayaramudu, J.; Das, K.; Reddy, S.M.; Sadiku, R.; Ray, S.S.; Liu, D. Preparation and characterization of nano-cellulose with new shape from different precursor. Carbohydr. Polym.
**2013**, 98, 562–567. [Google Scholar] [CrossRef] [PubMed] - Zhao, D.; Li, H.; Zhang, J.; Fu, L.; Liu, M.; Fu, J.; Ren, P. Dissolution of cellulose in phosphate-based ionic liquids. Carbohydr. Polym.
**2012**, 87, 1490–1494. [Google Scholar] [CrossRef] - Guo, J.; Guo, X.; Wang, S.; Yin, Y. Effects of ultrasonic treatment during acid hydrolysis on the yield, particle size and structure of cellulose nanocrystals. Carbohydr. Polym.
**2016**, 135, 248–255. [Google Scholar] [CrossRef]

**Figure 1.**Predicted versus actual (

**a**) crystallinity Index, CrI% (y

_{1}); and (

**b**) yield% (y

_{2}) of CNWs (AKL-5).

**Figure 2.**Studentized residuals versus run number for (

**a**) crystallinity index, CrI% (y

_{1}); and (

**b**) yield% (y

_{2}) of CNWs (AKL-5).

**Figure 3.**Optimization ramp for crystallinity index, CrI% (y

_{1}) and yield% (y

_{2}) of CNWs (AKL-5) with desirability.

**Figure 4.**3D surface mesh and contour plots. (

**a**) Combined effects of power (x

_{1}) and time (x

_{2}); (

**b**) combined effect of power (x

_{1}) and temperature (x

_{3}) on percentage crystallinity, CrI% (y

_{1}) of CNWs when the other two variables were at center level.

**Figure 5.**3D surface mesh and contour plots. (

**a**) Combined effects of power (x

_{1}) and time (x

_{2}); (

**b**) combined effect of power (x

_{1}) and temperature (x

_{3}) on yield% (y

_{2}) of CNWs when the other two variables were at center level.

**Figure 6.**FESEM images of Adansonia kilima. (AK) (

**a**) untreated leaves, AKL-1 (

**b**) solvent extracted leaves, AKL-2; (

**c**) KOH treated leaves, AKL-3; (

**d**) bleached leaves, AKL-4; and (

**e**) IL-assisted ultrasonicated sample, AKL-5 (CNWs).

**Figure 7.**(

**a**) HRTEM image; (

**b**) AFM image; and (

**c**) size distribution (length) of Adansonia kilima (AK) leaves-based cellulose nano-whiskers (CNWs) from FESEM after IL-assisted ultrasonication process under optimum condition, AKL-5.

**Figure 8.**X-ray diffraction pattern of Adansonia kilima (AK): Untreated leaves, AKL-1; solvent extracted leaves, AKL-2; KOH treated leaves, AKL-3; bleached leaves AKL-4; and IL-assisted ultrasonicated sample, AKL-5 (CNWs).

**Figure 9.**Thermogravimetric analysis of Adansonia kilima (AK). Untreated leaves, AKL-1; solvent extracted leaves, AKL-2; KOH treated leaves, AKL-3; bleached leaves, AKL-4; and IL-assisted ultrasonicated sample, AKL-5 (CNWs).

**Table 1.**Input and output parameters with their levels using BBD design for extraction of cellulose nano-whiskers (CNWs) (AKL-5).

Factor | Input Parameters | Units | Low Actual | High Actual | Low Coded | High Coded | Output Responses |

x_{1} | Power | Watt | 250 | 350 | −1 | +1 | |

x_{2} | Time | Minutes | 15 | 45 | −1 | +1 | y_{1} = Crystallinity Index (%) |

x_{3} | Temperature | °C | 90 | 120 | −1 | +1 | y_{2} = Yield of CNWs (%) |

Std. Order | Run | Point Type | Power (watt) | Time (Minutes) | Temperature (°C) | Crystallinity Index (CrI) (%) | Yield (%) |
---|---|---|---|---|---|---|---|

14 | 3 | Center | 300.00 | 30.00 | 105.00 | 76.87 | 83.66 |

16 | 4 | Center | 300.00 | 30.00 | 105.00 | 76.11 | 83.77 |

15 | 5 | Center | 300.00 | 30.00 | 105.00 | 75.99 | 83.21 |

13 | 10 | Center | 300.00 | 30.00 | 105.00 | 76.43 | 83.99 |

17 | 16 | Center | 300.00 | 30.00 | 105.00 | 75.18 | 83.89 |

5 | 1 | IBFact | 250.00 | 30.00 | 90.00 | 78.88 | 86.66 |

4 | 2 | IBFact | 350.00 | 45.00 | 105.00 | 65.99 | 76.98 |

9 | 6 | IBFact | 250.00 | 15.00 | 90.00 | 64.97 | 80.99 |

12 | 7 | IBFact | 350.00 | 45.00 | 120.00 | 73.89 | 65.87 |

10 | 8 | IBFact | 350.00 | 45.00 | 90.00 | 76.99 | 80.77 |

1 | 9 | IBFact | 250.00 | 15.00 | 105.00 | 74.65 | 88.76 |

2 | 11 | IBFact | 350.00 | 15.00 | 105.00 | 67.11 | 82.65 |

6 | 12 | IBFact | 350.00 | 30.00 | 90.00 | 58.88 | 80.65 |

3 | 13 | IBFact | 250.00 | 45.00 | 105.00 | 87.88 | 81.78 |

8 | 14 | IBFact | 350.00 | 30.00 | 120.00 | 71.33 | 69.67 |

11 | 15 | IBFact | 250.00 | 15.00 | 120.00 | 82.08 | 78.77 |

7 | 17 | IBFact | 300.00 | 30.00 | 120.00 | 81.55 | 72.99 |

**Table 3.**Statistical accuracy test for proposed polynomial models (crystallinity index% and CNWs yield%).

Statistical Variables | Crystallinity Index (CrI%) | CNWs Yield% |
---|---|---|

y_{1} | y_{2} | |

Standard Deviation, SD% | 1.22 | 1.06 |

Correlation Coefficient, R^{2} | 0.987 | 0.986 |

Adjusted R^{2} | 0.970 | 0.968 |

Mean | 74.40 | 80.30 |

Coefficient of Variation, CV | 1.64 | 1.31 |

Adequate Precision | 29.82 | 29.20 |

**Table 4.**Analysis of variance test (ANOVA) test for regression polynomial equation for crystallinity index, CrI% (y

_{1}) of CNWs.

Source | Sum of Squares | Degree of Freedom | Mean Square | F-Value | Prob > F | Comments |
---|---|---|---|---|---|---|

Model | 787.93 | 9 | 87.55 | 59.15 | <0.0001 | Significant |

x_{1} | 444.77 | 1 | 444.77 | 300.49 | <0.0001 | |

x_{2} | 31.76 | 1 | 31.76 | 21.46 | 0.0024 | |

x_{3} | 106.07 | 1 | 106.07 | 71.66 | <0.0001 | |

x_{1}x_{2} | 51.48 | 1 | 51.48 | 34.78 | 0.0006 | |

x_{1}x_{3} | 23.91 | 1 | 23.91 | 16.16 | 0.0051 | |

x_{2}x_{3} | 102.11 | 1 | 102.11 | 68.99 | <0.0001 | |

x_{1}^{2} | 17.10 | 1 | 17.10 | 11.56 | 0.0115 | |

x_{2}^{2} | 0.16 | 1 | 0.16 | 0.11 | 0.7543 | |

x_{3}^{3} | 8.74 | 1 | 8.74 | 5.90 | 0.0454 | |

Residuals | 10.36 | 7 | 1.48 | |||

Lack of Fit | 8.80 | 3 | 2.93 | 7.53 | 0.0039 | Not Significant |

Pure Error | 1.56 | 4 | 0.39 |

**Table 5.**Analysis of variance test (ANOVA) test for regression polynomial equation for yield% (y

_{2}) of CNWs.

Source | Sum of Squares | Degree of Freedom | Mean Square | F-Value | Prob > F | Comments |
---|---|---|---|---|---|---|

Model | 559.08 | 9 | 62.12 | 55.79 | <0.0001 | Significant |

x_{1} | 51.21 | 1 | 51.21 | 45.99 | 0.0003 | |

x_{2} | 83.01 | 1 | 83.01 | 74.55 | <0.0001 | |

x_{3} | 218.09 | 1 | 218.09 | 195.87 | <0.0001 | |

x_{1}x_{2} | 0.43 | 1 | 0.43 | 0.39 | 0.5544 | |

x_{1}x_{3} | 1.81 | 1 | 1.81 | 1.62 | 0.2431 | |

x_{2}x_{3} | 40.20 | 1 | 40.20 | 36.10 | 0.0005 | |

x_{1}^{2} | 0.76 | 1 | 0.76 | 0.068 | 0.8012 | |

x_{2}^{2} | 4.44 | 1 | 4.44 | 3.99 | 0.0860 | |

x_{3}^{3} | 155.49 | 1 | 155.49 | 139.65 | <0.0001 | |

Residuals | 7.79 | 7 | 1.11 | |||

Lack of Fit | 7.43 | 3 | 2.48 | 27.00 | 0.0041 | Not Significant |

Pure Error | 0.37 | 4 | 0.09 |

Ultrasonication Power | Time | Temperature | Crystallinity Index of CNWs (%) (y_{1}) | Yield of CNWs (%) (y_{2}) | ||||
---|---|---|---|---|---|---|---|---|

(Watt) | (Minutes) | (°C) | Predicted | Experimental | Error | Predicted | Experimental | Error |

200 | 43.11 | 94 | 87.88 | 86.46 | 1.61 | 85.29 | 84.18 | 1.30 |

**Table 7.**X-ray crystallographic parameters for synthesized sample (AKL-1, AKL-2, AKL-3, AKl-4 and AKL-5).

Sample | Crystallinity Index (%) | Crystallites Sizes (nm) |
---|---|---|

AKL-1 | 51.43 | 12.34 |

AKL-2 | 52.22 | 10.34 |

AKL-3 | 64.77 | 7.54 |

AKL-4 | 71.32 | 4.24 |

AKL-5 | 86.46 | 3.43 |

**Table 8.**Thermogravimetric parameters for synthesized samples (AKL-1, AKL-2, AKL-3, AKl-4 and AKL-5).

Sample | Step-1 | Char Residues (%) | DTG_{max} (°C) | |
---|---|---|---|---|

Temperature (°C) | Weight Loss (%) | |||

AKL-1 | 100-120 | 3.5 | 19.76 | 362.01 |

AKL-2 | 100-120 | 3.1 | 17.22 | 360.56 |

AKL-3 | 100-120 | 2.8 | 15.89 | 359.70 |

AKL-4 | 100-120 | 2.3 | 14.44 | 356.87 |

AKL-5 | 100-120 | 1.9 | 12.99 | 351.09 |

**Table 9.**List of FTIR peaks with their frequency region for synthesized samples (AKL-1, AKL-2, AKL-3, AKl-4 and AKL-5).

Wave Number (cm^{−1})/Frequency Level | Peak Assignment |
---|---|

3200–3400 | –OH Stretching |

2800–2900 | –CH Stretching |

1640–1650 | –OH bending for moisture adsorbed |

1400–1450 | CH_{2} Bending |

1020–1054 | C–O–C pyranose ring stretching |

1150–1200 | Asymmetric vibration of C–O–C bond |

890–900 | Glycocidic β linkages of cellulosic chain |

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Chowdhury, Z.Z.; Chandran, R.R.R.; Jahan, A.; Khalid, K.; Rahman, M.M.; Al-Amin, M.; Akbarzadeh, O.; Badruddin, I.A.; Khan, T.M.Y.; Kamangar, S.;
et al. Extraction of Cellulose Nano-Whiskers Using Ionic Liquid-Assisted Ultra-Sonication: Optimization and Mathematical Modelling Using Box–Behnken Design. *Symmetry* **2019**, *11*, 1148.
https://doi.org/10.3390/sym11091148

**AMA Style**

Chowdhury ZZ, Chandran RRR, Jahan A, Khalid K, Rahman MM, Al-Amin M, Akbarzadeh O, Badruddin IA, Khan TMY, Kamangar S,
et al. Extraction of Cellulose Nano-Whiskers Using Ionic Liquid-Assisted Ultra-Sonication: Optimization and Mathematical Modelling Using Box–Behnken Design. *Symmetry*. 2019; 11(9):1148.
https://doi.org/10.3390/sym11091148

**Chicago/Turabian Style**

Chowdhury, Zaira Zaman, R. Reevenishaa Ravi Chandran, Afrin Jahan, Khalisanni Khalid, Md Mahfujur Rahman, Md Al-Amin, Omid Akbarzadeh, Irfan Anjum Badruddin, T. M. Yunus Khan, Sarfaraz Kamangar,
and et al. 2019. "Extraction of Cellulose Nano-Whiskers Using Ionic Liquid-Assisted Ultra-Sonication: Optimization and Mathematical Modelling Using Box–Behnken Design" *Symmetry* 11, no. 9: 1148.
https://doi.org/10.3390/sym11091148