Response surface methodology combined Box-Behnken design to optimize the extraction methodology to isolate and characterize microcrystalline cellulose from sawdust

Thamer Nasser Aldhafeeri

doi:10.25259/AJC_378_2025

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Original Article

:19;

3782025

doi:

10.25259/AJC_378_2025

Response surface methodology combined Box-Behnken design to optimize the extraction methodology to isolate and characterize microcrystalline cellulose from sawdust

Thamer Nasser Aldhafeeri^,*

Department of Chemical Engineering, Jubail Industrial College, Jubail, Eastern, Saudi Arabia

*Corresponding author: E-mail address: Thamer_na@rcjy.edu.sa (T.N. Aldhafeeri)

Received: 2025-04-13, Accepted: 2025-11-27, Epub ahead of print: 2026-02-06, Published: 2026-03-02

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This is an open-access article distributed under the terms of the Creative Commons Attribution-Non Commercial-Share Alike 4.0 License, which allows others to remix, transform, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.

Abstract

The current study reports the extraction and characterization of microcrystalline cellulose (MCC) from sawdust. The isolation procedures included several steps, such as dewaxing, delignification, bleaching, and hydrolysis. Response surface methodology combined with the Box-Behnken design (RSM-BBD) was applied to optimize the procedure, thereby reducing the number of experimental trials, consumption of reagents, and solvents. Fourier transform infrared spectroscopy (FTIR) revealed the primary functional group present in MCC, and the removal of amorphous components, findings supported by X-ray diffraction (XRD) analysis. The higher crystallinity (CrI = 74%) is associated with greater MCC stability, as confirmed by Thermogravimetric analysis (TGA) and Differential thermal analysis (DTA) analysis. Scanning electron microscopy (SEM) analysis showed that isolated MCCs have random, elongated, or semi-spherical shapes. The Brunauer-Emmett-Teller (BET) analysis revealed a specific surface area of 1.3311±0.9173 m² g⁻¹, which is higher than that of the raw sawdust, and a similar pattern was observed in the XRD. Therefore, this sustainable approach could help isolate valuable MCC from other waste materials, reducing environmental concerns and benefiting both humans and the community, while also making it useful in various industrial applications.

Keywords

Characterization

Isolation

MCC

RSM-BBD

Sawdust

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1. Introduction

Environmental concerns are increasing daily. They are linked to fossil fuel consumption, which supports the conception of the circular economy as an environmentally responsible advancement for restraining waste production in modern development. Therefore, it is necessary to move toward natural-based products, which could reduce reliance on fuel-based resources. Over time, non-renewable petrochemical resources will become scarce. Many researchers have sought to use renewable sources to design novel materials. The tremendous rise in agricultural waste has attracted the scientific community’s attention to the development of value-added products from these materials [1-5]. However, plastic waste is a major contributor to environmental pollution, and regulations implemented by countries have highlighted the importance of developing eco-friendly products [6-9]. Several investigations were conducted to develop biodegradable complexes for alternative synthetic constituents. Natural fibres, such as abaca-sisal, hemp-banana, and others, are commonly used to strengthen polymer matrices, enhancing their mechanical and thermal properties [10-13]. Cellulosic natural fibres play a crucial role in improving the functionality of polymeric composites due to their unique properties, including low density, reusability, biodegradability, and low-cost production [14,15].

Cellulose is the most plentiful natural biopolymer, resulting from renewable polysaccharides accumulated with repetitive links of β-D-glucopyranose [16]. Lignocellulose biomass produces approximately 181.5 billion tons annually, but only 8.2 billion tons are used for various purposes [17]. Its evaluability is enhanced by hydroxyl groups in its moiety, which exhibit strong hydrogen bonding, enabling them to form crystals. This distinctive cellulosic nature has led to numerous studies that have increased demand for the extraction of Microcrystalline Cellulose (MCC) from renewable resources, such as banana, olive, coffee husk, and bamboo, which are often used in combination with several polymers as reinforcing agents [18-21].

The management of waste, such as sawdust, has received greater attention for conversion into valuable products for specific applications and targets [22]. Sawdust is tiny wood particles or powder produced as byproducts or waste from woodworking processes such as cutting, polishing, grinding, and routing. It is an abundant, economical lignocellulosic compound produced by agriculture and industry as waste in significant quantities, posing disposal challenges. Therefore, converting them into valuable products that could benefit the community and control pollution is essential [23].

The literature provided limited work on sawdust. Cahyani et al. [24] extracted MCC from Sengon wood sawdust, which contains cellulose at a potential range of 41–49%. The extraction carried out using traditional methodology yields quite satisfactory [24]. Whereas, Chuayplod et al. used Parawood waste to extract MCC using a conventional acid treatment procedure [25]. Lignin is also an important candidate for this process. Magalhães et al. extracted it from pine sawdust applying a binary solvent system derived from biomass [26]. All reported procedures used orthodox methodology without any design of experiments (DoE), which not only consumed reagents/solvents but also time and energy, becoming unsustainable. Given its importance, the current work emphasizes preparing MCC from sawdust collected from the local market, sourced from a variety of wood types rather than a single type. The proposed procedure’s novelty lies in applying response surface methodology (RSM) combined with the Box-Behnken design (BBD) to reduce the number of trials, the amount of reagents, the solution volume, and costs. This RSM-BBD combination is applied across various areas for optimization, including drilling and completion operations [27-29], metal removal [30-32], food and pharmaceutical applications [33-41], and wastewater pretreatment [42]. However, RSM-BBD also has limitations, including the need for continuous prediction and the establishment of persuasive relationships between variables, as well as potential inaccuracies in complex systems. Therefore, high-quality experimental trial data are required to prevent errors. However, challenges arise in modelling with five or more variables. The current work is the first attempt to utilize RSM-BBD to prepare MCC from sawdust, thereby minimizing the number of optimization trials, reducing solution volume, and reducing extraction time and cost. This could help in the future by extracting valuable MCC from various sources and controlling pollution, allowing it to be reused.

2. Materials and Methods

2.1. Materials

Sodium hydroxide (NaOH), hydrochloric acid (HCl), sodium hypochlorite (NaClO), chloroform, ethanol, and a Soxhlet apparatus were purchased from Sigma-Aldrich. The sawdust was bought from the local sawdust supplier/shop, and fine powder was used as much as possible for the work.

2.2. Instrumentation

The Thermogravimetry differential thermal analysis System Model (DTG-60) from Shimadzu uses a parallel-guide differential top-pan balance mechanism that simultaneously measures temperature and mass changes between an inert reference and a sample. The DTG-60 model analyzes samples over a temperature range from ambient to 1100°C. Liquid samples (100-300 µL) were dropped onto carbon tape and left to air dry overnight. Samples were coated with platinum using the JEC-3000FC auto fine coater. Samples were scanned using a JSM-IT500HR Field emission scanning electron microscope. Fourier transform infrared (FTIR) analysis was performed by applying attenuated total reflectance (ATR) with a Thermo NICOLET 6700 FTIR spectrometer at a scan rate of 32 scans with a resolution of 4 cm^-1 from wavelength region 500 to 4000 cm^-1. The powder X-ray diffraction (XRD) patterns of the raw sawdust papers and MCCs were collected using a powder diffractometer (Rigaku Ultima IV, Cu Kα radiation, λ = 1.5406 Å) operated at 40 kV and 40 mA. The diffractions were measured from 5 to 60° with a step size of 0.02° in continuous-scan mode. The Brunauer-Emmett-Teller (BET) surface area was determined from nitrogen adsorption-desorption isotherms at 77 K using a volumetric gas sorption analyzer. Before analysis, samples were degassed under vacuum at 120°C for 9 h to remove physically adsorbed moisture. The specific surface area was calculated using the BET model.

2.3. RSM-BBD experimental design & optimization

The extraction procedure for MCC from sawdust was optimized using RSM with a BBD design, thereby reducing the time and number of experimental trials. The literature survey [43-48] was conducted thoroughly and based on the available parameters. An initial investigation identified the most critical parameters, and, after preliminary studies, specified the significant parameters involved in MCC extraction. The most important independent factor that requires optimization is A. volume (mL) of ethanol & chloroform (1:2) mixture, B. NaOH (%), C. volume (mL) of hypochlorite & water (1:1) mixture, D. HCl (mL) against the dependent variable % MCC yield. The independent variables were utilized three levels to optimize: A: 115 mL (-1), 117.5 mL (0), 120 mL (+1); B: 17 % (-1), 17.5 % (0), 18 % (+1); C: 45 mL (-1), 50 mL (0), 55 mL (+1); D: 2 M (-1), 2.5 M (0), 3 M (+1), that have been provided in Table 1.

Table 1. The low, medium and high levels of RSM-BBD optimization parameters.

Independent parameters	Symbol	Levels			Dependent parameter
		Low (-1)	Medium (0)	High (+1)
Ethanol & chloroform mixture (mL)	A	115	117.5	120	MCC yield (%)
NaOH (%)	B	17	17.5	18
Hypochlorite & water mixture (mL)	C	45	50	55
HCl (M)	D	2	2.5	3

2.4. MCC extraction procedure

The sawdust powder was collected, and 10 g was transferred into the Soxhlet apparatus connected to a 500 mL RBF, in which the volumes of ethanol & chloroform were added as per the design (Table 2) to remove the wax. It continued until the liquid changed from yellow to colorless, usually took 90–120 min, and the materials were collected and dried at 85°C for 1 h. The delignification steps were carried out by treating the materials with 200 mL NaOH (Table 2) at 85°C for 90 min with constant stirring, and by washing the materials well after filtration, until the solution maintained a pH of 7. The materials were later bleached with a water-hypochlorite mixture (Table 2) at 90°C for 30 min. The bleached product’s pH was also controlled at 7 by washing with distilled water. The materials were hydrolyzed by refluxing with 110 mL of HCl for 25 min (Table 2). The substance was then dried at 105°C. After that, the samples underwent a repeated whitening process using 100 mL of 30% H₂O₂ to partially hydrolyze the cellulose and eliminate unnecessary non-cellulosic components (Scheme 1). The product (MCC) was dried, and the measured weight was used to calculate the % yield. Then, the design (Table 2) was followed, and the material was characterized using analytical instruments such as SEM, TGA, and FTIR to identify its key functional groups and properties.

Isolation of MCC from sawdust, including dewaxing, delignification, bleaching, hydrolysis, and whitening steps.

Table 2. BBD experimental design for three factors with the experimental and predicted absorbance responses.

Runs	Factors				% MCC yield (Exp)	% MCC yield (Pred)
Runs	A: Ethanol & chloroform mixture (mL)	B: NaOH (%)	C: Hypochlorite & water mixture (mL)	HCl (M)	% MCC yield (Exp)	% MCC yield (Pred)
1	117.5	18	50	2	12.03	12.03
2	117.5	18	55	2.5	13.60	13.59
3	117.5	17.5	50	2.5	15.30	15.30
4	117.5	17	50	2	13.30	13.31
5	117.5	17.5	50	2.5	15.30	15.30
6	117.5	17.5	50	2.5	15.30	15.30
7	117.5	17	55	2.5	14.25	14.25
8	120	17.5	45	2.5	13.07	13.08
9	120	17.5	50	2	11.58	11.57
10	115	17.5	50	3	13.28	13.28
11	117.5	18	50	3	14.23	14.23
12	117.5	17.5	50	2.5	15.30	15.30
13	115	17	50	2.5	14.54	14.54
14	117.5	17.5	55	2	14.52	14.52
15	115	18	50	2.5	12.10	12.11
16	120	17	50	2.5	11.59	11.58
17	115	17.5	45	2.5	13.80	13.80
18	117.5	17	45	2.5	13.10	13.11
19	120	17.5	55	2.5	13.75	13.76
20	115	17.5	55	2.5	14.35	14.35
21	117.5	17.5	45	3	14.97	14.97
22	115	17.5	50	2	14.03	14.03
23	117.5	17	50	3	13.21	13.21
24	120	18	50	2.5	13.75	13.75
25	120	17.5	50	3	14.43	14.43
26	117.5	17.5	55	3	13.97	13.98
27	117.5	17.5	50	2.5	15.30	15.30
28	117.5	18	45	2.5	13.52	13.51
29	117.5	17.5	45	2	12.32	12.31

Variables contributing to optimizing the model to extract MCC from sawdust.

3. Results and Discussion

3.1. Optimization of variables using BBD

The RSM-BBD combination was used to study the effect of extraction setting as the independent variable at its optimum levels, with the interaction between independent variables used to produce a maximum response at a desirability function close to 1, resulting from the design with 29 experimental trials. The mixing of different significant parameters: volume of ethanol & chloroform mixture, % NaOH, volume of hypochlorite & water mixture, and HCl concentration was evaluated against MCC amount (%). The current investigation includes a summary and sequential model to accomplish the topmost experimental trial and predicted values (Table 3) with correlation coefficient value (R²)–1, adjusted R²–1, predicted R²–0.9999 with the prediction sum of squares (or PRESS) value 0.0046 that is the developed model’s validation help to assess the model’s analytical capability that can also be cast-off to equivalence regression models to show its significance and smaller the value model predictability would enhanced. In a model, R² represents the proportion of variance described by all predictor variables. At the same time, adjusted R² regulates this value based on the number of predictors. It corrects for the effects of irrelevant variables, delivering a more consistent measure of goodness-of-fit for intricate designs.

Table 3. A sequential fitting model isolates MCC (%) from sawdust using RSM-BBD.

Sequential sum of squares
Source	Sum of squares	df	Mean square	F-value	p-value
Mean vs. Total	5511.45	1	5511.45
Linear vs. Mean	5.77	4	1.44	1.19	0.3410
Linear vs. 2FI	12.69	6	2.12	2.32	0.0780
Quadratic vs. 2FI	16.43	4	4.11	72622.51	< 0.0001
Cubic vs. Quadratic	0.0006	8	0.0001	3.00	0.0989
Residual	0.0002	6	0.0000
Total	5546.34	29	191.25
Summary statistics
Source	Std. Dev.	R²	Adjusted R²	Predicted R²	Press
Linear	1.10	0.1654	0.0263	-0.1455	39.96
2FI	0.9553	0.5291	0.2676	0.1752	28.78
Quadratic	0.0075	1.0000	1.0000	0.9999	0.0046
Cubic	0.0051	1.0000	1.0000	0.9993	0.0228
2FI: Two-Factor interaction

The Analysis of Variance (ANOVA) results yielded an F value of 44068.44 and a p-value < 0.001, thereby strengthening the model’s significance (Table 4). The percent contribution (PC) of each variable was also presented (Figure 1). The second-order polynomial expression could help in calculating the predicted % yield in terms of the coded factors as:

$\begin{matrix} % Yield = 15.3 - 0. 3275A - 0.0 633 B + 0. 3050 C + 0. 5258D + 1 . 15AB \\ + 0.0 325 AC + 0. 9000 AD - 0. 2675 BC + 0. 5725 BD \\ - 0. 8000 CD - 1 .0 9 A^{2} - 1.22 B^{2} - 0. 4679 C^{2} - 0. 8867 D^{2} \end{matrix}$

Table 4. The quadratic model uses ANOVA results to extract MCC from sawdust.

Source	Sum of squares	df	Mean square	F-value	p-value	PC
Model	34.89	14	2.49	44068.44	< 0.0001
A-Ethanol+Chloroform	1.29	1	1.29	22760.91	< 0.0001	3.47
B-NaOH	0.0481	1	0.0481	851.20	< 0.0001	0.13
C-Hypochlorite+Water	1.12	1	1.12	19740.88	< 0.0001	3.01
D-HCl	3.32	1	3.32	58676.36	< 0.0001	8.93
AB	5.29	1	5.29	93549.47	< 0.0001	14.23
AC	0.0042	1	0.0042	74.72	< 0.0001	0.01
AD	3.24	1	3.24	57296.84	< 0.0001	8.72
BC	0.2862	1	0.2862	5061.66	< 0.0001	0.77
BD	1.31	1	1.31	23184.44	< 0.0001	3.53
CD	2.56	1	2.56	45271.58	< 0.0001	6.89
A²	7.66	1	7.66	1.355E+05	< 0.0001	20.61
B²	9.62	1	9.62	1.701E+05	< 0.0001	25.88
C²	1.42	1	1.42	25114.94	< 0.0001	3.82
Std. Dev.	0.0075		R²	1.0000
Mean	13.79		Adjusted R²	1.0000
C.V. %	0.0545		Predicted R²	0.9999
PRESS	0.0046		Adeq Precision	689.0754
CV: Coefficient of variation, Adeq: Adequate

With actual factor

$\begin{matrix} % Yield = - 1849 .0 4 + 22.6977 * (Ethanol + chloroform) + 61.9067 * NaOH \\ + 4.29967 * (Hypochlorite + water) - 89.89 * HCl + 0. 92 \\ * (Ethanol + chloroform) NaOH + 0.00 26 * (Ethanol + chloroform) \\ (Hypochlorite + water) + 0. 72 * (Ethanol + chloroform) HCl \\ - 0. 107 * NaOH (Hypochlorite + water) + 2.29 * (NaOH) (HCl) \\ - 0. 32 * (Hypochlorite + water) HCl - 0. 173867 * {(Ethanol + chloroform)}^{2} \\ - 4.87167 * {(NaOH)}^{2} - 0.0 187167 * {(Hypochlorite + water)}^{2} - 3.54667 * {(HCl)}^{2} \end{matrix}$

The graphical representation validates the significance of the developed model. Generally, the normal plot of residuals (Figure 2a) is used to assess whether the normal probability distribution provides a good fit to the data [49]. It aligns well with the trial and expected value (Figure 2b). The x-axis represents the predicted value. In contrast, the y-axis represents residuals, indicating a well-mannered plot that rebounds randomly to produce a nearly horizontal band available in the zero lines on which no data points are emitted from the residual’s basic random pattern (Figure 2c). It examines the variables that influenced prompted response throughout the trials during the experiment with a plot of residuals against the run order to exhibit a random scatter (Figure 2d). The perturbation plot discussed the most influenced parameters on the yield (Figure 2e) and those which has the highest impact on it. It was found that hydrochloric acid concentration had the most significant impact, followed by the ethanol & chloroform mixture, the hypochlorite & water mixture, and NaOH. The desirability function (Figure 2f) was chosen for the stability of the procedure, where one is managed with the independent parameter used to execute the experiments, based on the predicted optimal values [50–52].

Box-Behnken plot that included (a) normal plot of residuals, (b) predicted vs. actual residuals, (c) residuals vs. predicted, (d) residuals vs. run, (e) perturbation; (f) % yield cube for MCC extracted from sawdust.

The 3D RSM plot (Figure 3a) demonstrated the effect of ethanol-chloroform with NaOH on the % yield. The % yield initially increased with both parameters, reached its maximum, and then decreased with higher volumes. A similar scenario was observed with the effect of ethanol-chloroform in combination with hypochlorite and water. Still, this volume produced a higher yield, increasing until the optimized volume was reached (Figure 3b). The yield percentage rose from 115 to 118 mL of ethanol-chloroform, then decreased. In contrast, HCl enhanced it up to 2.75 mL (Figure 3c). The effect of NaOH in combination with hypochlorite and water (Figure 3d) demonstrated a plot almost identical to that in Figure 3(b). Still, the initial % yield increased slowly with volume, then changed rapidly. The effect of HCl (Figure 3e) is similar to that of Figure 3(c), and NaOH increased the increment until 18 mL, after which it decreased. The % yield is enhanced by the hypochlorite-water/HCl combination (Figure 3f) and is optimized with the corresponding volume. A 2D contour plot visualizes a 3D relationship among two independent factors and a dependent factor by showing lines connecting points with the same output value. It is therefore represented visually in a projecting model of the specific surface area, as shown in Figure 4, where elevated regions in solid color correspond to higher desirability. Analysis of the surface reveals that the specific surface area increases with the volume of NaOH and the volume of ethanol-chloroform. Still, later the color dispersed due to a less pronounced effect and a reduced % yield (Figure 4a). The hypochlorite-water had a higher impact and ethanol-chloroform initially enhanced, later reduced (Figure 4b). The HCl and ethanol-chloroform had the combined effect (Figure 4c), and hypochlorite-water had a higher influence from initial volume to the optimized one. Still, NaOH was initially elevated, then reduced (Figure 4d). The combination of NaOH and HCl had a similar influence (Figure 4e), but hypochlorite-water and HCl (Figure 4f) yielded a higher percentage compared to their effect. Similar results were also observed with the % NaOH, hypochlorite, and water mixture volume, as well as the HCl concentration, which enhanced the yield to 15.3% (Figure 5). These conditions were used as the optimized conditions for extracting MCC from sawdust.

3D response plot between (X-axis- Variables; Y axis: Percent contribution). (a) NaOH (%) and ethanol+chloroform (mL), (b) hypochlorite+water (mL) & ethanol+chloroform (mL), (c) HCl (M) & ethanol+chloroform (mL), (d) hypochlorite+water (mL) & NaOH (%), (e) HCl (M) & NaOH (%), (f) HCl (M) & hypochlorite+water (mL) for MCC extraction from sawdust.

2D contour plot between (a) NaOH (%) and ethanol+chloroform (mL), (b) hypochlorite+water (mL) & ethanol+chloroform (mL), (c) HCl (M) & ethanol+chloroform (mL), (d) hypochlorite+water (mL) & NaOH (%), (e) HCl (M) & NaOH (%), (f) HCl (M) & hypochlorite+water (mL) for MCC extraction from sawdust.

Maximum desirability 1 with optimized conditions to extract MCC from sawdust.

In a comparative study of reported methods with proposed MCC extraction methods (Table 5), there are many sources of the synthesis of the MCC; these precursor materials include canal weed biomass, bamboo fiber, Ficus benghalensis leaf, Different parts of durian rind, Teff straw, Lagenaria siceraria fruit pedicles and sawdust (variety of wood types). The highest yield of 34.39–72.59% was reported from the precursor material “Different parts of durian rind.” This precursor material has a crystallinity of around 71.43–78.30% with a degradation temperature of 410.12°C. [43]. Canal weed biomass has a yield of 50.26% a degradation temperature of 317.32°C, and a crystallinity of 50.32% [44]. When bamboo fiber is used to synthesize microcrystalline cellulose, the resulting material has a crystallinity of around 77% and a degradation temperature of 315°C. In comparison, 43.85–60.03% of the starting material is converted into MCC [45]. Ficus benghalensis leaf yields 39.68 g MCC out of the 100 g precursor material with a degradation temperature of 377.33°C and 66.43 % crystallinity [46]. Teff straw was another material used for the synthesis of MCC, although the yield was comparatively low (27.2%) with excellent crystallinity and a degradation temperature of 325°C [47]. Lagenaria siceraria fruit pedicles yield similarly to the previous one, with a 30% yield and a crystallinity of 64.53; their degradation temperature was 366.2°C [48]. When comparing the above results with those of the developed MCC using sawdust, we find that the yield was 11.57–15.30°C, with a crystallinity of 74% and a degradation temperature of 410°C. The lower yield in the current synthesis may be attributed to the use of strong acid during acid hydrolysis, as the table shows that using weak acid yields a comparatively higher yield. The use of a strong acid is justified, as with this combination, the MCC synthesis time is reduced. The current method of MCC synthesis requires comparatively shorter synthesis time, with a total time from dewaxing to whitening of 4 h and 15 min. It requires 5 days of soaking, with additional requirements after more than 9 h. [48]. Some procedures require 24 h each for drying and bleaching, along with other treatments, while others require around 30 h [46]. Yet some procedures have shorter synthesis times [47], but they also yield lower yields, lower degradation temperatures, and lower crystallinity. However, the proposed procedure applied the Box-Behnken design, which reduced the time and reagents required for the high-quality extraction of MCC for various industrial applications, such as deliquescent agents in the cosmetic industry or could be used as fillers and binders in pharmaceutical dosage forms, as well as for the fabrication of bio-composite materials as a reinforcing element are highly popular in building and automotive productions.

Table 5. Comparative studies of reported methods with proposed MCC extraction methods.

Source	Optimization/Extraction methodology	Yield (%)	Degradation temperature (°C)	Crystallinity (%)	Ref.
Different parts of durian rind	Alkaline treatment: 100 mL of 2% NaOH with a temperature between 90–100 °C for 3 h. Then dried in the oven at 60 °C for 24 h Bleaching: First bleaching agent–Glacial CH₃COOH (0.5%) and NaCl (1.5%). Second bleaching agent- NaOCl (5%) with constant stirring at 70°C for 2 h. Dried at 60°C in oven for 24 h Acid hydrolysis: At 90°C with C₂H₂O₄ (30–65%) for 3 h	34.39–72.59	410.12	71.43–78.30	[43]
Canal weed biomass	Pyrolysis: Overnight with KCl (5%) and HCl (5%) Alkaline treatment: At 300°C with 5% NaOH for 6 h Acid hydrolysis: CH₃COOH (5%) Bleaching: NaOCl (5%) Drying: Hot air–oven at 80°C for 2 h Ball milling: To micronize	50.26	317.32	50.32	[44]
Bamboo fibers	Dewaxing: 300 mL mixture of toluene and ethanol (2: 1, v/v) for 6 h Delignification: At 80°C with NaOCl₂ and CH₃COOH for 1 h (repeated 3 times) Alkaline treatment: At 80°C with 2% KOH for 2 h Acid hydrolysis: At 90°C with 2M HCl for 10 min Drying: Overnight at 50°C	43.85–60.03	315	77.2	[45]
Ficus benghalensis leaf	Hydrolysis: Distilled water for 12 h Alkalization: NaOH (10%) for 6 h Acid hydrolysis: CH₃COOH (5%) for 12 h at room temperature (RT) Demineralization: Using dimethyl formamide solvent Pyrolysis: Mixture of KCl (5%) and HCl (5%) Bleaching: NaOCl (5%), H₂O₂ (50%) Drying: Hot air–oven at 80°C for 2 h	39.68	377.33	66.43	[46]
Teff straw	Acid treatment: HCOOH (85%) and CH₃COOH (99.5%) with ratio (70:30) having fiber and liquid of 1:8 in H₂O at 90°C for 90 min Delignification: H₂O₂ (10%), HCOOH (85%), and CH₃COOH with a volume ratio of 2:1:1 for 90 min Bleaching: H₂O₂ (10%) in boiled NaOH (4%) for 60 min Drying: Oven dried at 50°C Acid hydrolysis: At 105°C with HCl (2.5 N) for 30 min	27.2	325	72.26	[47]
Lagenaria siceraria fruit pedicles	Water treatment: Soaked in H₂O for 5 days in the open air Alkaline treatment: 5% NaOH (w/v) for 3 h at 180°C. Then soaked in distilled water for 3 h at 25°C. The alkaline process repeated 4 times. Bleaching: NaOH and CH₃COOH that maintained buffer of pH-6. The 1.7% NaOCl₂ solution mixed with buffer and reflux for 2.5 h at 120–130 °C Acid hydrolysis: H₂SO₄ (40%) at 85–95 °C for 40–50 min	30	366.2	64.53	[48]
Sawdust (variety of wood types)	Dewaxing: 118 mL mixture of ethanol and chloroform (1: 2, v/v) for 90–120 minutes. Then dried in oven at 85°C for 1 h Delignification: 200 mL of 5.3 M NaOH at 85 °C for 90 min Bleaching: 50.2 mL of NaOCl and water (1:1, v/v) mixture at 90 °C for 30 min Acid hydrolysis: At 80°C with 110 mL of 2.83 M HCl for 15 min Whitening: 100 mL of 30% H₂O₂. Dried at 105°C for 1 h	11.57–15.30	410	74	Current study

3.2. MCC characterization

3.2.1. Thermal stability

A TGA graph typically illustrates a material’s thermal stability, moisture content, and other properties by measuring its mass as the temperature changes. It discloses details about a material’s breakdown, incineration, and the kinetics of these processes at specific temperatures by displaying diverse mass gain or loss measures. Whereas the differential thermogravimetric graph (DTG) is TGA’s first derivative curve. Its key determination is to find precisely the material’s rate of decomposition. The TGA curve confirms the change in total mass, and the DTG focuses on the specific temperatures at which the most substantial fluctuations occur. The TG/DTA curve (Figure 6) shows the material’s thermal stability, visualizing the thermal degradation rate and weight loss as temperature increases. Initially, the MCC sample showed a weight loss of almost 8% over the temperature range 60–120°C [53]. This breakdown process is due to the elimination of water and volatile solvents. The range 130–460°C, with a peak at 410°C, was attributed to the degradation of hemicellulose and cellulose bonds, including C–H, C–O, and C–C bonds, as well as to pyranose linkages [54]. Cellulose usually has a crystalline structure with ordered regions, which makes it more stable [55]. This stage resulted in a loss of almost 80% of the mass. At 900°C, the high char formation by nearly 12% could possibly be due to the presence of inorganic materials that are flame-retardant compounds or stable oxides [56], as well as the presence of hemicellulose, lignin and high Degree of Polymerization (DP) polymer might subsidize resistance at raised temperature [57] for higher temperature and the high residue perceived in case of sawdust MCC [58]. The isolated MCC could be used across various industries due to its valuable applications and properties similar to those of the available MCC. The procedure could also be used with different waste materials to isolate MCC.

TG-DTG spectra of MCC extracted from sawdust.

3.2.2. Morphology analysis

The SEM images in Figure 7 demonstrate the monograph based on the raw materials (Figure 7a and b) without any treatment. In contrast, Figure 7(c) and (d) showed the MCC after dewaxing, alkaline treatment, and acid hydrolysis. The fibers were compacted, which is basically due to the presence of lignin that adsorbed on the raw cellulose’s surface [59]. The microfibers in the pack form are densely packed with minimal visible separations and a rough surface (Figure 7a and b). In contrast, MCC has less dense microfibers with a smooth surface, and the fibers also isolate each other (Figure 7c and d). This indicates that the removal of surface impurity, hemicellulose, and lignin [60, 61]. The raw material had a lower surface area than MCC. It is also found that MCCs consist of smaller fragments with multiple pore layers that form a rod-and-bundle shape, due to acid hydrolysis. The acquired MCC had random and clustered stretched or semi-spherical assemblies, which were similar to the structures of [62, 63], the commercially accessible MCC used as an excipient in pharmaceutical applications.

SEM images of MCC at (a) 100 μm with magnification 150x; (b) 50 μm with magnification 400x; (c) 100 μm with magnification 140x and (d) 20 μm with magnification 650x.

3.2.3. FTIR analysis

The FTIR technique was applied to distinguish between the raw material, sawdust, and the MCC spectrum. Figure 8 shows the IR spectra of untreated raw materials and MCC after dewaxing, alkaline, bleaching, and acid hydrolysis treatments. The spectra at 1001 cm^-1 characterized the C–O stretching vibrations of the polysaccharide in cellulose. Whereas 1732 cm^-1 showed the carbonyl C=O stretching of polysaccharide uronic ester and acetyl groups [64]. The small spectra at 1218 cm^-1 showed the elimination of lignin from raw material to MCC, which is linked to the aryl group’s C–O stretching frequency in hemicellulose and lignin [65]. The O-H stretching vibration and the hydroxyl group’s hydrogen bonds absorption showed in the range of 3600–3100 cm^-1. The C–H stretching frequency of cellulose is represented by the absorption peak at 2930 cm^-1 [65]. The peak observed at 1540 cm^-1 is associated with lignin’s aromatic ring stretching frequency. This peak was removed from the raw material during alkali treatment. The –OH stretching vibration was correlated with the absorption band at 3456 cm^-1 [66]. The absorption intensity is lower in MCC than in raw sawdust due to hydrogen bonding during acid hydrolysis of the cellulosic chain [67]. The absorption at 1732 cm^-1, characteristic of the C=O vibration, is removed in MCC following alkali treatment [67-69]. The removal of the peak at 1540 cm^-1, related to lignin, and the peak band at 2930, 3456 cm^-1, related to purified cellulose, is sharp in the MCC sample, indicating a higher cellulose content and progressively showing cellulose content in the sample. The characteristic peaks at 1001, 1130, 1430, and 1630 cm^-1 are associated with cellulose structures [67, 70] and are highly correlated with those observed in commercial MCC.

FTIR spectra of MCC extracted from sawdust.

3.2.4. X-ray diffraction

Moreover, in the raw sawdust pattern, there is a broad hump between 2θ ≈ 15°–25°, which indicates the presence of amorphous lignin and scattering of hemicellulose that breaks the packing of cellulose chains, which hinders crystallization, and impedes crystallization [71,72]. After the treatment process (red color), the pattern exhibits sharper peaks and increased intensity, indicating the presence of ordered crystalline cellulose I. A similar observation was reported in the literature [73], where the post-treatment sample shows well-defined peaks corresponding to cellulose Iα at 2θ ≈ 15.9°–16.8° and 22.6°. Moreover, the degree of crystallinity (CrI) was calculated [74] at 43% for the untreated sample and 74% after treatment, with a range of 27 Å to 55–90 Å.

Subsequently, this enhancement indicates that all the amorphous components have been removed from the sample after treatment and that hydrogen bonding in the cellulose chains is well formed, producing MCC, with even greater crystallinity and lattice order, comparable to that of bamboo, pine, and Sengon wood [74,75]. In the literature, it is reported that increased crystallinity is also associated with greater thermal stability and a denser morphology [76]. Figure 9 presents the XRD patterns of raw sawdust and the obtained MCC.

Displays the XRD pattern for two samples. The first untreated sawdust sample (blue color) exhibited broad diffraction peaks at 16.1° and 22.7°, corresponding to planes of cellulose (101) and (002).

3.2.5. Surface area analysis BET

The nitrogen-adsorption BET analysis of the raw sawdust sample showed a specific surface area of 1.2092 ± 0.2865 m² g⁻¹, while the modified sawdust composite (MCC) exhibited a similarly low value of 1.3311 ± 0.9173 m² g⁻¹. The BET surface area reported in the literature was higher [77] for the raw sawdust with 1.384 m² g⁻¹. After pyrolysis, it increased to 2.577, 45.78, and 221.10 at 350, 450, and 550°C, respectively [77,78]. On the other hand, slight differences in the surface areas that obtained from the current research are consistent with the XRD patterns (Figure 9), where both samples display the typical cellulose I reflections at around 2θ ≈ 16° (101) and 22° (002), but with only moderate broadening, indicating that the treatment mainly increases the crystalline ordering of the cellulose domains rather than generating a highly porous structure.

In addition, the treated MCC in the current research shows a sharper, more intense (002) peak compared with the raw sawdust, confirming an increase in crystallinity after removal of part of the amorphous hemicellulose/lignin fraction. The correlation between XRD and current BET surface results is consistent with a largely non-porous, microcrystalline cellulose matrix, leading to the assumption that the chemical treatment primarily enhances crystallinity without creating significant N₂-accessible porosity. As evidence, it has been reported [77,78] that the adsorption performance of the current sawdust-based materials is mainly attributed to surface functional groups and chemical affinity, rather than to a highly developed specific surface area.

4. Conclusions

The proposed investigation showed that combined bleaching and alkaline and acid hydrolysis treatments can isolate MCC from sawdust. Usually, these steps require more reagents, and several trials are needed to isolate higher quantities of MCC, thereby increasing costs and generating more waste to the environment. The RSM–BBD combination addressed these issues and provided a sustainable methodology for extracting MCC from sawdust. Based on the TGA, it was realized that MCC exhibits higher thermal stability. However, FTIR spectra showed acid hydrolysis eliminated hemicellulose and lignin. Still, components of the cellulosic chemical structure have not been altered. SEM analysis revealed microcrystals with a random, clustered morphology, measuring 20–50 μm. The XRD results showed higher crystallinity, which is highly associated with greater thermal stability, and a denser morphology. Whereas the BET surface is uniform with a largely non-porous, microcrystalline cellulose matrix. As a result, sawdust MCC has demonstrated notable thermal stability, can serve as a filler material, and could be an appropriate option for food packaging and pharmaceutical applications at higher temperatures.

CRediT authorship contribution statement

Thamer Nasser Aldhafeeri: Conceptualization, Investigation, Resources, Data curation, Writing-review & editing and agreed to the published version of the manuscript.

Declaration of competing interest

There are no conflicts of interest.

Declaration of generative AI and AI-assisted technologies in the writing process

The author confirms that there was no use of artificial intelligence (AI)-assisted technology for assisting in the writing or editing of the manuscript and no images were manipulated using AI.

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1. Introduction

2. Materials and Methods

2.1. Materials
2.2. Instrumentation
2.3. RSM-BBD experimental design & optimization
2.4. MCC extraction procedure

3. Results and Discussion

3.1. Optimization of variables using BBD
3.2. MCC characterization

4. Conclusions

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Response surface methodology combined Box-Behnken design to optimize the extraction methodology to isolate and characterize microcrystalline cellulose from sawdust

Abstract

Keywords

Characterization

Isolation

MCC

RSM-BBD

Sawdust

1. Introduction

2. Materials and Methods

2.1. Materials

2.2. Instrumentation

2.3. RSM-BBD experimental design & optimization

2.4. MCC extraction procedure

3. Results and Discussion

3.1. Optimization of variables using BBD

With actual factor

3.2. MCC characterization

3.2.1. Thermal stability

3.2.2. Morphology analysis

3.2.3. FTIR analysis

3.2.4. X-ray diffraction

3.2.5. Surface area analysis BET

4. Conclusions

CRediT authorship contribution statement

Declaration of competing interest

Declaration of generative AI and AI-assisted technologies in the writing process

References

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