Synthesis and Characterization of Cellulose Nanocrystals from Alkali- Pretreated Parthenium hysterophorus with Adsorption Kinetic Studies

Synthesis and Characterization of Cellulose Nanocrystals from Alkali- Pretreated Parthenium hysterophorus with Adsorption Kinetic Studies | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Synthesis and Characterization of Cellulose Nanocrystals from Alkali- Pretreated Parthenium hysterophorus with Adsorption Kinetic Studies Divya Gautam, Yogesh Kumar Walia, Vishal Rana This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4767300/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study investigates the synthesis and comprehensive characterization of cellulose nanocrystals (CNC) derived from Parthenium hysterophorus biomass through acid hydrolysis. Nanocellulose, a versatile nanomaterial sourced from renewable biomass, exhibits exceptional properties suitable for various applications. Acid hydrolysis of cellulose extracted from Parthenium resulted in a significant increase in crystallinity, as confirmed by X-ray diffraction (XRD), with the nanocellulose exhibiting a crystallinity index of 77%. The scanning electron microscopy (SEM) images revealed that acid hydrolysis resulted in the alteration of the fibrous and coiled structure of cellulose, resulting in the formation of spherical CNCs. Transmission electron microscopy (TEM) analysis of CNC revealed an average diameter of approximately 36-79 nm, demonstrating the impact of acid hydrolysis on morphology. Further characterization using Fourier-transform infrared spectroscopy (FTIR) indicated the preservation of cellulose chemical structure, while thermogravimetric analysis (TGA) showed improved thermal stability of the nanocellulose compared to raw biomass post-processing. Zeta potential analysis highlighted strong colloidal stability with a highly negative surface charge (-28.9 ± 6.18 mV), essential for applications requiring dispersion stability. In adsorption studies, the synthesized nanocellulose effectively removed 75% of MG dye from aqueous solutions at room temperature, following pseudo 2nd order kinetics. This research underscores the potential of Parthenium -derived nanocellulose in sustainable materials applications, leveraging invasive weed biomass for eco-friendly nanomaterial production. The findings contribute to advancing sustainable materials research by demonstrating the utility of Parthenium biomass for value-added nanomaterial production, specifically highlighting the enhanced properties and effective adsorption capabilities of the synthesized nanocellulose. Biomass cellulose nanocrystals adsorption capabilities morphology sustainable Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction Nanocellulose, derived from cellulose fibers, has emerged as a pivotal nanomaterial due to its sustainable nature and versatile properties, fostering innovation across diverse industries such as biomedicine, packaging, and renewable energy (Yang et al. 2019 ). Renowned for its mechanical strength, biocompatibility, and large surface area, nanocellulose offers a promising platform for developing advanced materials tailored to specific applications (Moon et al. 2011 ; Nehra and Chauhan 2022 ). The synthesis of cellulose nanocrystals (CNCs) is a focal point of current research, aiming to develop efficient methods for producing nanocellulose with desired characteristics. Various approaches including mechanical processing, chemical hydrolysis, and biological methods have been explored to influence CNC properties such as size, shape, and crystallinity (Wu ae al. 2017; Kaur et al. 2021 ). Acid hydrolysis, in particular, utilizing strong acids like sulfuric acid, stands out as a prevalent method for generating CNCs from cellulose sources due to its high yield and tunable properties (Song et al. 2019 ; Kumar et al. 2022 ). Cellulose nanocrystals can be sourced from diverse materials including wood, non-wood fibers, and agricultural residues such as rice straw and sugarcane bagasse (Chen et al. 2013 ; Hsieh 2013 ; dos santos et al. 2013 ; Tiwari and Sanjog 2023 ). Parthenium hysterophorus , an invasive weed abundant in many regions, represents an intriguing biomass source for nanocellulose production. Despite its ecological impact, Parthenium biomass offers substantial cellulose content, making it an attractive feedstock for sustainable materials (Nigam et al. 2021 ). In this study, cellulose nanocrystals were synthesized via acid hydrolysis using cellulose extracted from Parthenium hysterophorus biomass. This research explores the potential of Parthenium -derived nanocellulose as a sustainable biomaterial, leveraging the abundance of Parthenium biomass and the efficiency of acid hydrolysis. The structural properties, morphology, crystallinity, and thermal stability of the resulting CNCs were comprehensively characterized, contributing to advancements in sustainable materials research and biomass utilization strategies. Moreover, this study investigates the application of Parthenium -derived CNCs in wastewater treatment, specifically focusing on their effectiveness as adsorbents for MG dye removal. Cellulose nanocrystals have shown promise in water purification applications due to their high surface area and surface chemistry, which facilitate efficient adsorption of pollutants (Aggarwal et al. 2023 ). By elucidating the adsorption kinetics and mechanisms, this research aims to demonstrate the feasibility of utilizing Parthenium -derived CNCs as eco-friendly solutions for wastewater treatment, thereby addressing environmental challenges associated with dye pollution. Materials and Methods Material The plant material of Parthenium hysterophorus L. as cellulose source was collected from Kangra (middle himalayas), India. All the chemicals and reagents employed in this study were of analytical grade and used in their original reagent-grade form without further purification. These included NaOH, NaClO 2 , H 2 SO 4 , isopropyl alcohol, ethylene glycol, ethanol, 100% acetic acid, malachite green dye (HiMedia, India). Additionally, double distilled water was utilized exclusively in all experimental procedures. Pretreatment of Plant Biomass The plant biomass pretreatment involved alkali treatment, delignification, and bleaching. Parthenium plant material was washed, dried, and finely ground into powder. It underwent a 24-hour alkali treatment using 4% NaOH to remove impurities, followed by thorough washing until neutral. Then, it was bleached with a 1.7% sodium chlorite solution in a pH 4 acetic acid buffer for 4 hours (Kumari et al. 2016). After filtration, neutral pH washing, and air-drying, white cellulose was obtained. Cellulose content was also determined using equation (Xu and Hanna 2010): $$\:Cellulose\:\%=\frac{m}{M}\:\times\:100$$ 1 Where 'm' represented the mass of the white powder obtained, and 'M' stood for the original mass of the sample. Synthesis of Cellulose Nanocrystals Cellulose nanocrystals (CNC) were produced via sulfuric acid hydrolysis (Song et al. 2019). The cellulose powder was added gradually to 50 mL of 60 wt% H 2 SO 4 and stirred for 1 hour at room temperature. Hydrolysis was halted by adding 500 mL of iced water. After centrifugation, the sediment containing CNC was washed until the pH became neutral, then sonicated for 30 minutes. The resulting suspension was centrifuged, and the supernatant containing CNC was collected. This process of sonication and centrifugation was repeated followed by freeze-drying to get CNC. Characterization of CNC The synthesized CNC underwent examination through FTIR spectroscopy, SEM, and XRD techniques to discern the structural characteristics of the precursor involved in subsequent reactions. FTIR spectra were acquired using a PerkinElmer FTIR spectrophotometer, covering the 400 to 4000 cm⁻¹ range and employing the KBr pellet method. XRD patterns were obtained with a Philips PANAnalytica XPERT-PRO X-ray diffractometer using Cu Ka radiation (λ 1.54060 Å), with the diffraction angle 2θ ranging from 10° to 70°. The morphologies of the resulting CNC were evaluated by analyzing SEM images captured with an SEM Quanta 250 D9393, and TEM analysis was also performed for morphology measurement using Philips CM 200. Thermogravimetric analysis was conducted using a Perkin Elmer STA 6000 at a heating rate of 10°C min⁻¹ under a nitrogen atmosphere. Particle size distribution was determined using a Malvern nanosizer (Nano S90 version 7.02). Zeta potential measurements were carried out utilizing a Zeta – 90Plus particle size analyzer from Brookhaven Instruments Corporation, employing suspensions of variously prepared samples in water. Adsorption studies CNC, was evaluated as an adsorbent for Malachite green (MG) dye. Dye concentrations were measured using a PhotoLab 6600 UV-Vis spectrophotometer at λmax = 617 nm, the wavelength of maximum absorbance for MG dye. The MG stock solution was prepared at a concentration of 1000 mg/L using distilled water. Working solutions ranging from 10 mg/L to 150 mg/L were then prepared by diluting this stock solution prior to conducting batch adsorption experiments. The impact of contact time was investigated through adsorption studies conducted on CNC with MG dye across various initial dye concentrations. Initially, a 10.0 mg sample was immersed in dye solutions with concentrations of 30 ppm, 50 ppm, 70 ppm, 100 ppm and 120 ppm at a temperature of 25°C, neutral pH for durations ranging from 10 to 120 minutes. After adsorption, Ce (mg/L) represented the new dyestuff concentration, used to calculate qe (mg/g), the equilibrium adsorption concentration of the dyestuff. The equations used to evaluate the adsorption capacity at any specific time (qt) and the removal efficiency (% R) are as follows (Kumar et al. 2022) $$\:qt=\frac{\:({C}_{o}-{C}_{e})\:V}{w}$$ 2 $$\:\%\:R=\frac{{C}_{o}-{C}_{e}}{{C}_{o}}\times\:100$$ 3 Here, qt represents the quantity of dye adsorbed per unit mass of the adsorbent when dry (mg/g), Co stands for the initial dye concentration (mg/L), Ce denotes the equilibrium dye concentration at time t (mg/L), V indicates the volume of dye solution used (L), and w signifies the mass of the adsorbent (g). Results and Discussion Pretreatment of plant biomass and synthesis of CNC The preparation process to isolate cellulose and synthesize CNC (Cellulose Nanocrystals) from Parthenium hysterophorus involved several sequential steps (Fig. 1 ). Initially, cellulose was extracted from the natural biomass by eliminating lignin, hemicellulose, and other impurities. The biomass's high cellulose content (43.28%) underscored Parthenium hysterophorus as a viable and abundant source for nanocellulose production. Subsequent alkali treatment effectively removed extractives such as lignin, hemicellulose, pectin, and wax, crucially without significantly affecting the cellulose content. This chemical pretreatment was fundamental in eliminating non-cellulosic constituents from the Parthenium fiber, thereby facilitating the isolation of cellulose nanocrystals (CNC) using acid hydrolysis, essential for advanced applications in various fields. Fourier Transform Infrared (FTIR) Analysis In the FTIR spectra, the peaks observed around 3416 cm⁻¹ and 2903 cm⁻¹ were attributed to the stretching vibrations of O-H bonds and C-H bonds, respectively, characteristic of aliphatic moieties present in polysaccharides (Beltramino et al. 2016 ). In the spectra of plant biomass, the band observed at 1734 cm⁻¹ was assigned to the ester linkage of carboxylic groups of ferulic and p-coumaric acids found in lignin, while the peak at 1510 cm⁻¹ was associated with the stretching vibrations of C = C bonds within the aromatic rings of lignin. In contrast to the spectrum of plant biomass, cellulose exhibited an absence of peaks at 1734 cm⁻¹, indicating the removal of amorphous components (such as hemicellulose, lignin, pectin, and wax) during the alkali pretreatment and bleaching processes (Song et al. 2019 ). The appearance of a peak at 1216 cm⁻¹ in spectra of CNC was due to the addition of sulfate groups during the sulfuric acid hydrolysis process (Kazachenko et al. 2021 ). The results of FTIR characterizations are presented in Fig. 2 . X-ray Diffraction (XRD) Analysis XRD patterns were used to investigate the crystallite size ( D ) by using the Debye-Scherrer Eq. 4 (Holzwarth et al. 2011) $$\:D=\frac{0.94\lambda\:}{\beta\:Cos\theta\:}$$ 4 Where, λ = 1.5406 Å is the wavelength used during XRD analysis, β is full width at half maximum (FWHM) for the most intense peak, θ is Bragg’s angle, and (h, k, l) are Miller indices. The crystallinity index (CI) was derived using the formula derived by Eq. 5 (Mariano et al. 2016 ): $$\:CI=\frac{{I}_{002}-{I}_{am}}{{I}_{002}}\times\:100$$ 5 Where, I 002 ​ is the maximum intensity of diffraction of the (002) lattice peak at a 2θ angle typically between 22° and 23°, Iam ​ is the intensity of diffraction of the amorphous material, usually measured at a 2θ angle between 18° and 19° where the intensity is at its minimum. The results derived from XRD pattern suggested that the acid treatment digested the amorphous regions effectively (Fig. 3 ).For comparison purposes, XRD results of both plant biomass and cellulose were examined. The XRD pattern of plant biomass exhibited a broad and diffused spectrum, indicating predominantly amorphous material. The observed peaks at 2θ = 15.9°, 22.4°, and 34.4° were assigned to the (1 1 0), (2 0 0), and (0 0 4) planes, respectively. The intensity of these peaks increased, and the peaks became sharper in the XRD pattern of the obtained CNC, signifying a significant enhancement compared to cellulose (Zhao et al. 2015 ; Pandi et al. 2021 ). The crystallite size was determined from the prominent (200) peak, calculating a size of 4.979 nm. The d-spacing of the crystals at this peak was measured as 3.93 Å. The synthesized CNC exhibited a crystallinity of 77.32% in comparison to cellulose and plant biomass having CI 53.46 & 23.38% respectively. The results indicated marked rise in CI of CNC as compared to plant biomass and cellulose, suggesting that the acid treatment effectively removed amorphous hemicelluloses and defective regions from the cellulose (Ibrahim et al. 2015 ). Structural parameters &the Crystallinity index (CI) values obtained from XRD are presented in Table 1 . Also the obtained results are compared with literature presented in Table 2 . Table 1 The crystallite size (D) and inter-planer spacing (d) of synthesized Cellulose and CNC by using plant material of Parthenium hysterophorus. Sample D (nm) d (nm) CI (%) Plant Biomass 31.145 4.872 23.38 Cellulose 25.199 4.349 53.46 CNC 4.979 3.915 77.32 Table 2 The crystallinity index (CI) of the obtained CNC in this study and comparing with the reported literature by acid hydrolysis method Material d (nm) CI (%) Reference CNC from Garlic straw 480 68.8 Kallel et al. 2016 CNC from Groundnut shell 111 74 Bano et al. 2017 CNC from Calotropis procera 250 68.7 Song et al. 2019 CNC from waste cellulose 50 81.23 Pandi et al. 2021 Modified CNC 7.06 74.90 Whba et al. 2023 CNC from Rice Straw 28–82 58.43 Romreun et al. 2022 CNC from tea stalk 4–8 - Guo et al. 2020 CNC ( Eragrostis teff straw) 101 77.1 Getacho et al. 2021 CNC from sugarcane bagasse 3.5 72.5 Kumar et al. 2014 CNC from Parthenium 4.97 77.32 This study Morphological investigations The initial plant biomass exhibited a compact, rough surface morphology. However, following the delignification and bleaching processes, the structure loosened, revealing a fibrous morphology where cellulose fibers were coiled within long tubes (Fig. 4 ). This change can be attributed to the removal of lignin and hemicellulose from the plant biomass. The SEM images of CNC revealed a notable alteration in the shape of the nanoparticles due to acid hydrolysis of cellulose (Fig. 5 ). With an acid concentration of 60%, complete removal of the amorphous phase from cellulose was sufficient, resulting in the formation of larger clusters (Pan et al. 2013 ). The images showed that acid hydrolysis caused deformation of the fibrous, coiled structure of cellulose, leading to the formation of spherical shaped CNCs (Meyabadi et al. 2014 ; Liu et al. 2023 ). The overall particle size of the CNCs ranged from 10 to 100 nm. The CNC morphology exhibits clumping from the drying process, indicating strong cohesion within the nanomaterials (Wang et al. 2007 ). TEM images of the CNC obtained are depicted in Fig. 6 , revealing consistently spherical shapes with a diameter of 36–79 nm, consistent with previous findings (Mehanny et al. 2021 ; Xu et al. 2021; Romruen et al. 2022 ). Table 3 presents comparative morphological characteristics of CNC obtained from various raw materials and extraction methods. Table 3 Comparison of Cellulose nanocrystals (CNCs) characteristics obtained from various raw materials with acid hydrolysis methods. Source Size (nm) Morphology Reference Palm waste 42–82 Spherical Mehanny et al. 2021 Pine apple peel 11–36 Spherical Romruen et al. 2022 Rice straw 44–50 Needle Like Perumal et al. 2018 CNC from waste cellulose 50 Rod Shape Pandi et al. 2021 Sisal fibre 10–40 Needle Like Agarwal et al. 2022 Gai bamboo 2–10 Spherical Do et al. 2023 Cotton Pulp 40 Spherical Liu et al. 2023 CNC from Parthenium 20–80 Spherical This study Thermal Stability Analysis : TGA analysis was employed to carry out thermal stability investigations of plant biomass and the resulting CNC. Thermal stability of polymers hinges on both sample intrinsic properties and molecular interactions among macromolecules. TGA thermograms were recorded from 25°C to 750°C under nitrogen at a heating rate of 10°C/min, and the results obtained are depicted in Fig. 7 . Plant biomass exhibited weight loss between 300°C and 400°C, likely due to glycosyl unit decomposition in cellulose fibers (Vinayaka et al. 2017 ). Extracted cellulose and CNC displayed lower degradation temperatures. Cellulose degradation occurred in two stages: 150°C to 300°C and 350°C to 450°C, with weight losses of 56% and 27%, respectively, attributed to cellulose depolymerisation (Bano et al. 2017). In contrast, CNC showed initial degradation between 130°C and 350°C, and a second stage from 350°C to 550°C, with weight losses of 52% and 46%, respectively. Introduction of sulfate groups in cellulose nanocrystals accelerated cellulose depolymerization. These findings underscore that ultrasound-assisted acid hydrolysis enhanced the thermal stability of CNCs compared to raw materials, consistent with literature comparisons (Neto et al. 2013 ; Wang et al. 2018 ; Sasikala and Umapathy 2018 ). Particle size distribution of the Cellulose nanocrystals (CNCs) Figure 8 displays the particle size distribution (PSD) of CNCs produced from biomass via acid hydrolysis, measured using light scattering. The analysis revealed that most particles fell within the size range of 50 to 700 nm, with smaller particles ranging from 50 to 100 nm. The average size of the CNCs, as determined by PSD analysis, was 217 nm (An et al. 2016 ; Mehanny et al. 2021 ; Pandi et al. 2021 ) Zeta potential analysis Zeta potential analysis revealed that the CNC exhibited excellent colloidal stability with a highly negative surface charge, approximately − 28.9 ± 6.18 mV. This stability in water dispersion was attributed to the sulfate groups grafted onto the CNCs during sulfuric acid hydrolysis, which enhanced their negative zeta potential (Faradilla et al. 2016 ). Particles with zeta potentials below − 30 mV are known for their stability in solution, preventing aggregation and ensuring a uniform distribution (Morais et al. 2013 ; De Castro et al. 2016 ). Adsorption Studies CNC (cellulose nanocrystals) was employed as an effective adsorbent for the removal of MG dye from aqueous solutions. The zero point charge (ZPC) of CNC was 6–7, indicating a negative surface charge, is particularly advantageous for adsorbing cationic dyes like MG. This negative charge is primarily attributed to sulfate groups on the CNC surface, which electrostatically attract and bind with the positively charged nitrogen atoms of MG dye molecules. This interaction is pivotal in the adsorption process, facilitating the removal of MG from water. For visual clarity, the proposed adsorption mechanism is depicted in Fig. 9 , illustrating the electrostatic binding between CNC and MG dye molecules. The effect of contact time was systematically investigated over a range of 0 to 120 minutes across various concentrations, as depicted in Fig. 10 . The findings indicated that CNC effectively removed 70–75% of the dye across all tested concentrations. This demonstrates the consistent and reliable adsorption capability of CNC for MG dye removal under varying contact times and concentrations. The rate of adsorption progressively increased with time, average ranging from 20–75% during the experimental observations for all concentrations. At lower concentrations, CNC adsorption sites are quickly occupied because there are fewer dye molecules in the solution competing for these sites. Consequently, saturation occurs sooner (at 60 minutes) compared to higher concentrations. With increasing concentration, more dye molecules are available to adsorb onto CNC, requiring longer times to reach equilibrium (around 80 minutes). The percentage of dye uptake also tends to rise with concentration, reflecting the greater availability of adsorbate molecules. At lower concentrations, diffusion of adsorbate molecules to the adsorbent surface may occur more rapidly due to the lower molecule density in the solution, contributing to faster adsorption kinetics and quicker attainment of equilibrium. The experimental data collected in this study were analyzed using nonlinear pseudo-first-order and pseudo-second-order kinetic models. This analytical approach was employed to gain a deeper understanding of the adsorption kinetics involved. The non-linear forms of the pseudo-first-order (Eq. 6 ) and pseudo-second-order (Eq. 7 ) kinetic models are given as follows (Gautam et al. 2018 ): $$\:{q}_{t}={q}_{e}(1-{e}^{-{k}_{1}t\:})$$ 6 $$\:{q}_{t=}\frac{{k}_{2}t{q}_{e}^{2}}{1+{k}_{2}{q}_{e}}$$ 7 In these equations, q e and q t ​ represent the amounts of dye adsorbed on the adsorbent at equilibrium (mg/g) and at time t (min) (mg/g), respectively. The parameter k 1 ​ (min⁻¹) denotes the pseudo-first-order rate constant, which characterizes the rate of adsorption. On the other hand, k 2 (g/mg·min) signifies the pseudo-second-order rate constant, providing a measure of the adsorption rate and capacity of the adsorbent material. The parameters for the pseudo-first-order and pseudo-second-order kinetic models, respectively, were determined using nonlinear regression in this study. This approach was chosen to accurately model the complex adsorption kinetics observed, ensuring precise estimation of adsorption parameters from experimental data (Moussout et al. 2018 ). Figure 11 and 12 illustrate the results of nonlinear regression analysis comparing calculated qe values with experimental data. A closer match between experimental and predicted qe values across different models indicates better agreement. In this study, experimental values closely aligned with predictions from the nonlinear pseudo-second-order (NLPSO) model, as depicted in Fig. 12 (Mita et al. 2017 ; Nicola et al. 2020). The coefficient of determination (R²) values, which gauge model fit, were notably high (approaching 1.0) for the PSO equation compared to the nonlinear pseudo-first-order (NLPFO) equation across all tested concentrations, indicating superior performance (Figs. 11 and 12 ). The obtained results show a strong correlation with previous studies (Batmaz et al. 2014 ; Raj et al. 2022 ; Almuslem et al. 2023 ). Kinetic parameters for each model (PFO and PSO) were determined using nonlinear regression and are summarized in Table 4 . Table 4 Parameters of different kinetic models for adsorption of MG on CNC. Kinetic Model Parameter Concentration (ppm) 30 50 70 100 120 PFO k 1 (min -1 ) 5.58 ×10 − 2 2.8×10 − 2 2.5×10 − 2 2.1×10 − 2 2.4×10 − 2 q e (mg/g) 23.25 46.60 70.2 103.79 128.59 R 2 0.988 0.984 0.982 0.986 0.991 PSO k 2 (g/mg/min) 3.0 ×10 − 3 5.7 2.95 1.51 1.57 q e (mg/g) 20.08 35.71 54.27 85.21 99.63 R 2 0.994 0.993 0.991 0.992 0.997 Conclusion In conclusion, this study successfully synthesized nanocellulose crystals from Parthenium hysterophorus biomass through acid hydrolysis, showcasing its potential as a sustainable nanomaterial. With Parthenium biomass boasting a high cellulose content (43.28%), this invasive weed proves to be a viable and abundant source for nanocellulose production. The synthesized nanocellulose was meticulously characterized using advanced techniques such as SEM, TEM, XRD, FTIR, TGA, particle size distribution analysis, and zeta potential measurement. Key findings included the significant enhancement of crystallinity through combined pretreatment and acid hydrolysis processes. SEM and TEM images confirmed morphological changes, with an observed average particle size of approximately 217 nm. Moreover, nanocellulose exhibited improved thermal stability compared to raw biomass following alkali pretreatment, bleaching, and acid hydrolysis. Zeta potential analysis revealed strong colloidal stability with a highly negative surface charge (-28.9 ± 6.18 mV). In practical applications, the synthesized nanocellulose demonstrated small particle size (50–100 nm), high crystallinity (77%), and stable thermal properties, highlighting its potential in effective dye adsorption. Specifically, nanocellulose removed 75% of MG dye from aqueous solutions at room temperature, with adsorption kinetics following pseudo-second-order behavior supported by high R² values. This research significantly advances sustainable materials science by harnessing invasive weed biomass for the production of valuable nanomaterials. The findings underscore the promising applications of Parthenium -derived nanocellulose in environmental remediation and other industrial sectors, paving the way for further exploration and optimization in future studies. Declarations Ethical Statement: This research does not involve any human or animal participation and whole research will comply with standard ethical guidelines. Funding Support: Authors declare that no funds, grants, or other support was received from any organization for the submitted work. Financial interests: The authors declare that they have no financial interests. Non-financial interests: None Conflict of interest: The authors declare no competing interests. Availability of Data and Materials: The data and materials generated and analyzed during the current study are included within the article. 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(2019). https://doi.org/10.1186/s40643-019-0279-z Tiwari, A., Sanjog, J.: Nanocellulose from Agricultural Waste – A Concise Insight into Extraction and Applications. Orient J. Chem. 39 , (2023). https://bit.ly/3EUa2fW Vinayaka, D.L., Guna, V., Madhavi, D., Arpitha, M., Reddy, N.: Ricinus communis plant residues as a source for natural cellulose fibers potentially exploitable in polymer composites. Ind. Crops Prod. 100 ,126–131 (2017). https://doi.org/10.1016/j.indcrop.2017.02.019 Wang, Z., Qiao, X., Sun, K.: Rice straw cellulose nanofibrils reinforced poly(vinyl alcohol) composite films. Carbohydr. Polym. 197 , 442–450 (2018) https://doi.org/10.1016/j.matchemphys.2022.126879 Wang. N., Ding, E., Cheng, R. Thermal degradation behaviors of spherical cellulose nanocrystals with sulfate groups. Polymer 48 , 3486–3493 (2007). https://doi.org/10.1016/j.polymer.2007.03.062 Whba, F., Mohamed, F., Idris, M.I., Yahya, M.S.: Surface Modification of Cellulose Nanocrystals (CNCs) to Form a Biocompatible, Stable, and Hydrophilic Substrate for MRI. Appl. Sci. 13 , 6316 (2023). https://doi.org/10.3390/app13106316 Wu, X., Li, S., Fu, Q., Li, S., Wang, S.: Estimation of aspect ratio of cellulose nanocrystals by viscosity measurement: influence of surface charge density and NaCl concentration. Cellulose 24 , 3255-3264 (2017). https://doi.org/10.1007/s10570-017-1352-0 Xu, J.T., Chen, X.Q., Shen, W.H., Li, Z.: Spherical vs rod-like cellulose nanocrystals from enzymolysis: A comparative study as reinforcing agents on polyvinyl alcohol. Carbohydr. Polym. 256 , 117493. https://doi.org/10.1016/j.carbpol.2020.117493 Xu, Y., Hanna, M. A.: Optimum conditions for dilute acid hydrolysis of hemicellulose in dried distillers grains with solubles. Ind. Crops Prod. 32 , 511–517(2010). https://doi.org/10.1016/j.indcrop.2010.06.024 Yang, Y., Chen, Z., Zhang, J., Wang, G., Zhang, R., Suo, D.: Preparation and applications of the cellulose nanocrystal. Int. J. Polym. Sci. 39 , 417-427 (2019). https://doi.org/10.1155/2019/417427 Zhao, Y., Zhao, Y., Yang, Y.: Modified soy protein to substitute non-degradable petrochemicals for slashing industry. Ind. Crops Prod. 67 , 466–474 (2015). https://doi.org/10.1016/j.indcrop.2015.01.058 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4767300","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":334786678,"identity":"bfb3d5ce-fd78-429b-9e35-c8e6c774bc02","order_by":0,"name":"Divya Gautam","email":"","orcid":"","institution":"Career Point University Hamirpur","correspondingAuthor":false,"prefix":"","firstName":"Divya","middleName":"","lastName":"Gautam","suffix":""},{"id":334786679,"identity":"7971858e-eb5c-4b1f-ab51-e2a78b2b7d0c","order_by":1,"name":"Yogesh Kumar Walia","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYLACxgYGBgMG5gNA5gEGNhK0sCWQrIXHAKyFIDA43n7x4c8dDPLm7D0fPxe23WHgk24goOXMmWJj3jMMhjt7zm6Wntn2jIFNhoBNBjdy0qQZ2xgSDG7kbmPmbTvMwCaRQFBL+s+fIC333zwjVkv6MQZesC08bMRpkTxzhlmat03CcMOZNGPpGecO8xDUwne8/eHHn2028gbHDz/8XFB2WE5+BgEtCgfA0SEB5jADMQ9+9UAg38D+AM5hJqh8FIyCUTAKRiQAAODVRV7vbBiJAAAAAElFTkSuQmCC","orcid":"","institution":"Career Point University Hamirpur","correspondingAuthor":true,"prefix":"","firstName":"Yogesh","middleName":"Kumar","lastName":"Walia","suffix":""},{"id":334786680,"identity":"6b642432-a512-4a2d-846b-cdbbcd604e23","order_by":2,"name":"Vishal Rana","email":"","orcid":"","institution":"Thakur P. G. College of Education","correspondingAuthor":false,"prefix":"","firstName":"Vishal","middleName":"","lastName":"Rana","suffix":""}],"badges":[],"createdAt":"2024-07-19 09:38:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4767300/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4767300/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63279300,"identity":"66b60677-1ed9-4945-81f8-6f060671b232","added_by":"auto","created_at":"2024-08-26 12:42:08","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":159101,"visible":true,"origin":"","legend":"\u003cp\u003eSchematics for pretreatment of plant biomass and synthesis of CNC.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4767300/v1/a565878a68201de8aa599e55.png"},{"id":63279290,"identity":"9149add6-d779-4365-8547-2f38b8118cb8","added_by":"auto","created_at":"2024-08-26 12:42:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":8787,"visible":true,"origin":"","legend":"\u003cp\u003eFTIR spectra of (a). Plant biomass, (b). Cellulose, and (c).CNC.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4767300/v1/f425cf2ec9c66b6cb56fff8e.png"},{"id":63280262,"identity":"66f98e06-8ab4-4c31-a4d8-ec909ccb7249","added_by":"auto","created_at":"2024-08-26 12:50:08","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":10173,"visible":true,"origin":"","legend":"\u003cp\u003eXRD pattern of (a). Plant biomass, (b). Cellulose, and (c).CNC.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4767300/v1/b4721a337ae79e45b3b3a898.png"},{"id":63279292,"identity":"c79d3893-41ba-4718-b309-406b5d790a40","added_by":"auto","created_at":"2024-08-26 12:42:08","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":257423,"visible":true,"origin":"","legend":"\u003cp\u003eSEM images of (a) Plant biomass (b) Cellulose.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4767300/v1/6bb1bd9a756c9e8f193fb716.png"},{"id":63280261,"identity":"c2cbe9bd-9973-442c-a6c0-34f968894763","added_by":"auto","created_at":"2024-08-26 12:50:08","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":272983,"visible":true,"origin":"","legend":"\u003cp\u003eSEM images of CNC (a) at 5 µm, (b) 10 µm.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-4767300/v1/08d3ae9a37c0c85aaf0d4110.png"},{"id":63280264,"identity":"1e7d7156-7aa7-4c1b-9a07-c67aa7f9a105","added_by":"auto","created_at":"2024-08-26 12:50:08","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":269653,"visible":true,"origin":"","legend":"\u003cp\u003eTEM images of CNC (a) at 50 nm, (b) 100 nm.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-4767300/v1/c759dbafc8a174b68ff7bc16.png"},{"id":63279293,"identity":"5d2445c1-dd2c-4995-a481-6397e8de7bb1","added_by":"auto","created_at":"2024-08-26 12:42:08","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":13397,"visible":true,"origin":"","legend":"\u003cp\u003eTGA curves of (a). Plant biomass, (b). Cellulose, and (c). CNC.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-4767300/v1/2d931155216a093efd40cf3f.png"},{"id":63280263,"identity":"d181a75a-0c28-4612-97b8-4b568d065d86","added_by":"auto","created_at":"2024-08-26 12:50:08","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":6382,"visible":true,"origin":"","legend":"\u003cp\u003eParticle size distribution of CNC.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-4767300/v1/d333c282a8d60c6beb663682.png"},{"id":63279302,"identity":"5d2bf316-edb2-4f20-8dcf-ad96c967b434","added_by":"auto","created_at":"2024-08-26 12:42:09","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":29388,"visible":true,"origin":"","legend":"\u003cp\u003eProposed mechanism for adsorption of MG dye with CNC as adsorbent.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-4767300/v1/ac2e5e86aa789da0e56e0885.png"},{"id":63279299,"identity":"a73c9210-f1b5-40d3-822b-12fd6e880d1d","added_by":"auto","created_at":"2024-08-26 12:42:08","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":10466,"visible":true,"origin":"","legend":"\u003cp\u003eEffect of contact time on adsorption of MG dye on CNC at (A) 120 ppm (B) 100 ppm (C) 70 ppm (D) 50 ppm (E) 30 ppm.\u003c/p\u003e","description":"","filename":"10.png","url":"https://assets-eu.researchsquare.com/files/rs-4767300/v1/60e6320b2264e66a5d17b8b7.png"},{"id":63279298,"identity":"5d1d344a-14d5-48b2-bcff-7d84e8387c52","added_by":"auto","created_at":"2024-08-26 12:42:08","extension":"png","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":13053,"visible":true,"origin":"","legend":"\u003cp\u003eNon- linear pseudo first order kinetic plot.\u003c/p\u003e","description":"","filename":"11.png","url":"https://assets-eu.researchsquare.com/files/rs-4767300/v1/94c1c14f9c855e1eef284b49.png"},{"id":63279295,"identity":"ccda7efc-0bcf-4a96-80f3-4bf9019ebf84","added_by":"auto","created_at":"2024-08-26 12:42:08","extension":"png","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":12076,"visible":true,"origin":"","legend":"\u003cp\u003eNon- linear pseudo second order kinetic plot.\u003c/p\u003e","description":"","filename":"12.png","url":"https://assets-eu.researchsquare.com/files/rs-4767300/v1/e4af8954168c804c602d19bf.png"},{"id":65420142,"identity":"1f660524-1c34-4c00-94ae-6ec9d26fe1a6","added_by":"auto","created_at":"2024-09-27 08:02:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1927212,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4767300/v1/7eb536f0-e14a-45ab-af93-97258586906e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Synthesis and Characterization of Cellulose Nanocrystals from Alkali- Pretreated Parthenium hysterophorus with Adsorption Kinetic Studies","fulltext":[{"header":"Introduction","content":"\u003cp\u003eNanocellulose, derived from cellulose fibers, has emerged as a pivotal nanomaterial due to its sustainable nature and versatile properties, fostering innovation across diverse industries such as biomedicine, packaging, and renewable energy (Yang et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Renowned for its mechanical strength, biocompatibility, and large surface area, nanocellulose offers a promising platform for developing advanced materials tailored to specific applications (Moon et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Nehra and Chauhan \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe synthesis of cellulose nanocrystals (CNCs) is a focal point of current research, aiming to develop efficient methods for producing nanocellulose with desired characteristics. Various approaches including mechanical processing, chemical hydrolysis, and biological methods have been explored to influence CNC properties such as size, shape, and crystallinity (Wu ae al. 2017; Kaur et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Acid hydrolysis, in particular, utilizing strong acids like sulfuric acid, stands out as a prevalent method for generating CNCs from cellulose sources due to its high yield and tunable properties (Song et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kumar et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCellulose nanocrystals can be sourced from diverse materials including wood, non-wood fibers, and agricultural residues such as rice straw and sugarcane bagasse (Chen et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Hsieh \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; dos santos et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Tiwari and Sanjog \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). \u003cem\u003eParthenium hysterophorus\u003c/em\u003e, an invasive weed abundant in many regions, represents an intriguing biomass source for nanocellulose production. Despite its ecological impact, Parthenium biomass offers substantial cellulose content, making it an attractive feedstock for sustainable materials (Nigam et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn this study, cellulose nanocrystals were synthesized via acid hydrolysis using cellulose extracted from \u003cem\u003eParthenium hysterophorus\u003c/em\u003e biomass. This research explores the potential of \u003cem\u003eParthenium\u003c/em\u003e-derived nanocellulose as a sustainable biomaterial, leveraging the abundance of \u003cem\u003eParthenium\u003c/em\u003e biomass and the efficiency of acid hydrolysis. The structural properties, morphology, crystallinity, and thermal stability of the resulting CNCs were comprehensively characterized, contributing to advancements in sustainable materials research and biomass utilization strategies. Moreover, this study investigates the application of \u003cem\u003eParthenium\u003c/em\u003e-derived CNCs in wastewater treatment, specifically focusing on their effectiveness as adsorbents for MG dye removal. Cellulose nanocrystals have shown promise in water purification applications due to their high surface area and surface chemistry, which facilitate efficient adsorption of pollutants (Aggarwal et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). By elucidating the adsorption kinetics and mechanisms, this research aims to demonstrate the feasibility of utilizing \u003cem\u003eParthenium\u003c/em\u003e-derived CNCs as eco-friendly solutions for wastewater treatment, thereby addressing environmental challenges associated with dye pollution.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eMaterial\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe plant material of \u003cem\u003eParthenium hysterophorus\u003c/em\u003e L. as cellulose source was collected from Kangra (middle himalayas), India. All the chemicals and reagents employed in this study were of analytical grade and used in their original reagent-grade form without further purification. These included NaOH, NaClO\u003csub\u003e2\u003c/sub\u003e, H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e, isopropyl alcohol, ethylene glycol, ethanol, 100% acetic acid, malachite green dye (HiMedia, India). Additionally, double distilled water was utilized exclusively in all experimental procedures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePretreatment of Plant Biomass\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe plant biomass pretreatment involved alkali treatment, delignification, and bleaching. \u003cem\u003eParthenium\u003c/em\u003e plant material was washed, dried, and finely ground into powder. It underwent a 24-hour alkali treatment using 4% NaOH to remove impurities, followed by thorough washing until neutral. Then, it was bleached with a 1.7% sodium chlorite solution in a pH 4 acetic acid buffer for 4 hours (Kumari et al. 2016). After filtration, neutral pH washing, and air-drying, white cellulose was obtained.\u003c/p\u003e\n\u003cp\u003eCellulose content was also determined using equation (Xu and Hanna 2010):\u003c/p\u003e\n\u003cdiv id=\"Equ1\"\u003e\n \u003cdiv id=\"FileID_Equ1\" name=\"EquationSource\"\u003e$$\\:Cellulose\\:\\%=\\frac{m}{M}\\:\\times\\:100$$\u003c/div\u003e\n \u003cdiv\u003e1\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eWhere \u0026apos;m\u0026apos; represented the mass of the white powder obtained, and \u0026apos;M\u0026apos; stood for the original mass of the sample.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSynthesis of Cellulose Nanocrystals\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCellulose nanocrystals (CNC) were produced via sulfuric acid hydrolysis (Song et al. 2019). The cellulose powder was added gradually to 50 mL of 60 wt% H\u003csub\u003e2\u003c/sub\u003eSO\u003csub\u003e4\u003c/sub\u003e and stirred for 1 hour at room temperature. Hydrolysis was halted by adding 500 mL of iced water. After centrifugation, the sediment containing CNC was washed until the pH became neutral, then sonicated for 30 minutes. The resulting suspension was centrifuged, and the supernatant containing CNC was collected. This process of sonication and centrifugation was repeated followed by freeze-drying to get CNC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacterization of CNC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe synthesized CNC underwent examination through FTIR spectroscopy, SEM, and XRD techniques to discern the structural characteristics of the precursor involved in subsequent reactions. FTIR spectra were acquired using a PerkinElmer FTIR spectrophotometer, covering the 400 to 4000 cm⁻\u0026sup1; range and employing the KBr pellet method. XRD patterns were obtained with a Philips PANAnalytica XPERT-PRO X-ray diffractometer using Cu Ka radiation (\u0026lambda; 1.54060 \u0026Aring;), with the diffraction angle 2\u0026theta; ranging from 10\u0026deg; to 70\u0026deg;. The morphologies of the resulting CNC were evaluated by analyzing SEM images captured with an SEM Quanta 250 D9393, and TEM analysis was also performed for morphology measurement using Philips CM 200. Thermogravimetric analysis was conducted using a Perkin Elmer STA 6000 at a heating rate of 10\u0026deg;C min⁻\u0026sup1; under a nitrogen atmosphere. Particle size distribution was determined using a Malvern nanosizer (Nano S90 version 7.02). Zeta potential measurements were carried out utilizing a Zeta \u0026ndash; 90Plus particle size analyzer from Brookhaven Instruments Corporation, employing suspensions of variously prepared samples in water.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAdsorption studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCNC, was evaluated as an adsorbent for Malachite green (MG) dye. Dye concentrations were measured using a PhotoLab 6600 UV-Vis spectrophotometer at \u0026lambda;max\u0026thinsp;=\u0026thinsp;617 nm, the wavelength of maximum absorbance for MG dye. The MG stock solution was prepared at a concentration of 1000 mg/L using distilled water. Working solutions ranging from 10 mg/L to 150 mg/L were then prepared by diluting this stock solution prior to conducting batch adsorption experiments. The impact of contact time was investigated through adsorption studies conducted on CNC with MG dye across various initial dye concentrations. Initially, a 10.0 mg sample was immersed in dye solutions with concentrations of 30 ppm, 50 ppm, 70 ppm, 100 ppm and 120 ppm at a temperature of 25\u0026deg;C, neutral pH for durations ranging from 10 to 120 minutes. After adsorption, Ce (mg/L) represented the new dyestuff concentration, used to calculate qe (mg/g), the equilibrium adsorption concentration of the dyestuff. The equations used to evaluate the adsorption capacity at any specific time (qt) and the removal efficiency (% R) are as follows (Kumar et al. 2022)\u003c/p\u003e\n\u003cdiv id=\"Equ2\"\u003e\n \u003cdiv id=\"FileID_Equ2\" name=\"EquationSource\"\u003e$$\\:qt=\\frac{\\:({C}_{o}-{C}_{e})\\:V}{w}$$\u003c/div\u003e\n \u003cdiv\u003e2\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Equ3\"\u003e\n \u003cdiv id=\"FileID_Equ3\" name=\"EquationSource\"\u003e$$\\:\\%\\:R=\\frac{{C}_{o}-{C}_{e}}{{C}_{o}}\\times\\:100$$\u003c/div\u003e\n \u003cdiv\u003e3\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eHere, qt represents the quantity of dye adsorbed per unit mass of the adsorbent when dry (mg/g), Co stands for the initial dye concentration (mg/L), Ce denotes the equilibrium dye concentration at time t (mg/L), V indicates the volume of dye solution used (L), and w signifies the mass of the adsorbent (g).\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003e \u003cstrong\u003ePretreatment of plant biomass and synthesis of CNC\u003c/strong\u003e \u003cp\u003eThe preparation process to isolate cellulose and synthesize CNC (Cellulose Nanocrystals) from \u003cem\u003eParthenium hysterophorus\u003c/em\u003e involved several sequential steps (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Initially, cellulose was extracted from the natural biomass by eliminating lignin, hemicellulose, and other impurities. The biomass's high cellulose content (43.28%) underscored \u003cem\u003eParthenium hysterophorus\u003c/em\u003e as a viable and abundant source for nanocellulose production. Subsequent alkali treatment effectively removed extractives such as lignin, hemicellulose, pectin, and wax, crucially without significantly affecting the cellulose content. This chemical pretreatment was fundamental in eliminating non-cellulosic constituents from the \u003cem\u003eParthenium\u003c/em\u003e fiber, thereby facilitating the isolation of cellulose nanocrystals (CNC) using acid hydrolysis, essential for advanced applications in various fields.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFourier Transform Infrared (FTIR) Analysis\u003c/strong\u003e \u003cp\u003eIn the FTIR spectra, the peaks observed around 3416 cm⁻\u0026sup1; and 2903 cm⁻\u0026sup1; were attributed to the stretching vibrations of O-H bonds and C-H bonds, respectively, characteristic of aliphatic moieties present in polysaccharides (Beltramino et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In the spectra of plant biomass, the band observed at 1734 cm⁻\u0026sup1; was assigned to the ester linkage of carboxylic groups of ferulic and p-coumaric acids found in lignin, while the peak at 1510 cm⁻\u0026sup1; was associated with the stretching vibrations of C\u0026thinsp;=\u0026thinsp;C bonds within the aromatic rings of lignin. In contrast to the spectrum of plant biomass, cellulose exhibited an absence of peaks at 1734 cm⁻\u0026sup1;, indicating the removal of amorphous components (such as hemicellulose, lignin, pectin, and wax) during the alkali pretreatment and bleaching processes (Song et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The appearance of a peak at 1216 cm⁻\u0026sup1; in spectra of CNC was due to the addition of sulfate groups during the sulfuric acid hydrolysis process (Kazachenko et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The results of FTIR characterizations are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eX-ray Diffraction (XRD) Analysis\u003c/strong\u003e \u003cp\u003eXRD patterns were used to investigate the crystallite size (\u003cem\u003eD\u003c/em\u003e) by using the Debye-Scherrer Eq.\u0026nbsp;\u003cspan refid=\"Equ4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (Holzwarth et al. 2011)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv id=\"Equ4\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$$\\:D=\\frac{0.94\\lambda\\:}{\\beta\\:Cos\\theta\\:}$$\u003c/div\u003e \u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWhere, λ\u0026thinsp;=\u0026thinsp;1.5406 \u0026Aring; is the wavelength used during XRD analysis, β is full width at half maximum (FWHM) for the most intense peak, θ is Bragg\u0026rsquo;s angle, and (h, k, l) are Miller indices.\u003c/p\u003e \u003cp\u003eThe crystallinity index (CI) was derived using the formula derived by Eq.\u0026nbsp;\u003cspan refid=\"Equ5\" class=\"InternalRef\"\u003e5\u003c/span\u003e (Mariano et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2016\u003c/span\u003e):\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$$\\:CI=\\frac{{I}_{002}-{I}_{am}}{{I}_{002}}\\times\\:100$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere, \u003cem\u003eI\u003c/em\u003e\u003csub\u003e\u003cem\u003e002\u003c/em\u003e\u003c/sub\u003e​ is the maximum intensity of diffraction of the (002) lattice peak at a 2θ angle typically between 22\u0026deg; and 23\u0026deg;, \u003cem\u003eIam\u003c/em\u003e​ is the intensity of diffraction of the amorphous material, usually measured at a 2θ angle between 18\u0026deg; and 19\u0026deg; where the intensity is at its minimum.\u003c/p\u003e \u003cp\u003eThe results derived from XRD pattern suggested that the acid treatment digested the amorphous regions effectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).For comparison purposes, XRD results of both plant biomass and cellulose were examined. The XRD pattern of plant biomass exhibited a broad and diffused spectrum, indicating predominantly amorphous material. The observed peaks at 2θ\u0026thinsp;=\u0026thinsp;15.9\u0026deg;, 22.4\u0026deg;, and 34.4\u0026deg; were assigned to the (1 1 0), (2 0 0), and (0 0 4) planes, respectively. The intensity of these peaks increased, and the peaks became sharper in the XRD pattern of the obtained CNC, signifying a significant enhancement compared to cellulose (Zhao et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Pandi et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe crystallite size was determined from the prominent (200) peak, calculating a size of 4.979 nm. The d-spacing of the crystals at this peak was measured as 3.93 \u0026Aring;. The synthesized CNC exhibited a crystallinity of 77.32% in comparison to cellulose and plant biomass having \u003cem\u003eCI\u003c/em\u003e 53.46 \u0026amp; 23.38% respectively. The results indicated marked rise in \u003cem\u003eCI\u003c/em\u003e of CNC as compared to plant biomass and cellulose, suggesting that the acid treatment effectively removed amorphous hemicelluloses and defective regions from the cellulose (Ibrahim et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eStructural parameters \u0026amp;the Crystallinity index (CI) values obtained from XRD are presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Also the obtained results are compared with literature presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe crystallite size (D) and inter-planer spacing (d) of synthesized Cellulose and CNC by using plant material of \u003cem\u003eParthenium hysterophorus.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eD (nm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ed (nm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eCI\u003c/em\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlant Biomass\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCellulose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25.199\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e53.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.915\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e77.32\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe crystallinity index (CI) of the obtained CNC in this study and comparing with the reported literature by acid hydrolysis method\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMaterial\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ed (nm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eCI\u003c/em\u003e (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNC from Garlic straw\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e480\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKallel et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNC from Groundnut shell\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBano et al. 2017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNC from Calotropis procera\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e68.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSong et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2019\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNC from waste cellulose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e81.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePandi et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModified CNC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e74.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWhba et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNC from Rice Straw\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u0026ndash;82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRomreun et al. 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNC from tea stalk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u0026ndash;8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGuo et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2020\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNC (\u003cem\u003eEragrostis teff\u003c/em\u003e straw)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGetacho et al. 2021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNC from sugarcane bagasse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e72.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eKumar et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2014\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNC from \u003cem\u003eParthenium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMorphological investigations\u003c/strong\u003e \u003cp\u003eThe initial plant biomass exhibited a compact, rough surface morphology. However, following the delignification and bleaching processes, the structure loosened, revealing a fibrous morphology where cellulose fibers were coiled within long tubes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). This change can be attributed to the removal of lignin and hemicellulose from the plant biomass. The SEM images of CNC revealed a notable alteration in the shape of the nanoparticles due to acid hydrolysis of cellulose (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). With an acid concentration of 60%, complete removal of the amorphous phase from cellulose was sufficient, resulting in the formation of larger clusters (Pan et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The images showed that acid hydrolysis caused deformation of the fibrous, coiled structure of cellulose, leading to the formation of spherical shaped CNCs (Meyabadi et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Liu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The overall particle size of the CNCs ranged from 10 to 100 nm. The CNC morphology exhibits clumping from the drying process, indicating strong cohesion within the nanomaterials (Wang et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). TEM images of the CNC obtained are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, revealing consistently spherical shapes with a diameter of 36\u0026ndash;79 nm, consistent with previous findings (Mehanny et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Xu et al. 2021; Romruen et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents comparative morphological characteristics of CNC obtained from various raw materials and extraction methods.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of Cellulose nanocrystals (CNCs) characteristics obtained from various raw materials with acid hydrolysis methods.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSize (nm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMorphology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePalm waste\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42\u0026ndash;82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpherical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMehanny et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePine apple peel\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u0026ndash;36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpherical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRomruen et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRice straw\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e44\u0026ndash;50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeedle Like\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePerumal et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNC from waste cellulose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRod Shape\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePandi et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSisal fibre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeedle Like\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAgarwal et al. 2022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGai bamboo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u0026ndash;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpherical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDo et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCotton Pulp\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpherical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLiu et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCNC from \u003cem\u003eParthenium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSpherical\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThis study\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eThermal Stability Analysis\u003c/b\u003e: TGA analysis was employed to carry out thermal stability investigations of plant biomass and the resulting CNC. Thermal stability of polymers hinges on both sample intrinsic properties and molecular interactions among macromolecules. TGA thermograms were recorded from 25\u0026deg;C to 750\u0026deg;C under nitrogen at a heating rate of 10\u0026deg;C/min, and the results obtained are depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. Plant biomass exhibited weight loss between 300\u0026deg;C and 400\u0026deg;C, likely due to glycosyl unit decomposition in cellulose fibers (Vinayaka et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Extracted cellulose and CNC displayed lower degradation temperatures. Cellulose degradation occurred in two stages: 150\u0026deg;C to 300\u0026deg;C and 350\u0026deg;C to 450\u0026deg;C, with weight losses of 56% and 27%, respectively, attributed to cellulose depolymerisation (Bano et al. 2017). In contrast, CNC showed initial degradation between 130\u0026deg;C and 350\u0026deg;C, and a second stage from 350\u0026deg;C to 550\u0026deg;C, with weight losses of 52% and 46%, respectively. Introduction of sulfate groups in cellulose nanocrystals accelerated cellulose depolymerization. These findings underscore that ultrasound-assisted acid hydrolysis enhanced the thermal stability of CNCs compared to raw materials, consistent with literature comparisons (Neto et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Wang et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Sasikala and Umapathy \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eParticle size distribution of the Cellulose nanocrystals (CNCs)\u003c/strong\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e displays the particle size distribution (PSD) of CNCs produced from biomass via acid hydrolysis, measured using light scattering. The analysis revealed that most particles fell within the size range of 50 to 700 nm, with smaller particles ranging from 50 to 100 nm. The average size of the CNCs, as determined by PSD analysis, was 217 nm (An et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Mehanny et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Pandi et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e)\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eZeta potential analysis\u003c/strong\u003e \u003cp\u003eZeta potential analysis revealed that the CNC exhibited excellent colloidal stability with a highly negative surface charge, approximately \u0026minus;\u0026thinsp;28.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.18 mV. This stability in water dispersion was attributed to the sulfate groups grafted onto the CNCs during sulfuric acid hydrolysis, which enhanced their negative zeta potential (Faradilla et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Particles with zeta potentials below \u0026minus;\u0026thinsp;30 mV are known for their stability in solution, preventing aggregation and ensuring a uniform distribution (Morais et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; De Castro et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAdsorption Studies\u003c/strong\u003e \u003cp\u003eCNC (cellulose nanocrystals) was employed as an effective adsorbent for the removal of MG dye from aqueous solutions. The zero point charge (ZPC) of CNC was 6\u0026ndash;7, indicating a negative surface charge, is particularly advantageous for adsorbing cationic dyes like MG. This negative charge is primarily attributed to sulfate groups on the CNC surface, which electrostatically attract and bind with the positively charged nitrogen atoms of MG dye molecules. This interaction is pivotal in the adsorption process, facilitating the removal of MG from water. For visual clarity, the proposed adsorption mechanism is depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003e, illustrating the electrostatic binding between CNC and MG dye molecules.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe effect of contact time was systematically investigated over a range of 0 to 120 minutes across various concentrations, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e. The findings indicated that CNC effectively removed 70\u0026ndash;75% of the dye across all tested concentrations. This demonstrates the consistent and reliable adsorption capability of CNC for MG dye removal under varying contact times and concentrations. The rate of adsorption progressively increased with time, average ranging from 20\u0026ndash;75% during the experimental observations for all concentrations. At lower concentrations, CNC adsorption sites are quickly occupied because there are fewer dye molecules in the solution competing for these sites. Consequently, saturation occurs sooner (at 60 minutes) compared to higher concentrations. With increasing concentration, more dye molecules are available to adsorb onto CNC, requiring longer times to reach equilibrium (around 80 minutes). The percentage of dye uptake also tends to rise with concentration, reflecting the greater availability of adsorbate molecules. At lower concentrations, diffusion of adsorbate molecules to the adsorbent surface may occur more rapidly due to the lower molecule density in the solution, contributing to faster adsorption kinetics and quicker attainment of equilibrium.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe experimental data collected in this study were analyzed using nonlinear pseudo-first-order and pseudo-second-order kinetic models. This analytical approach was employed to gain a deeper understanding of the adsorption kinetics involved. The non-linear forms of the pseudo-first-order (Eq.\u0026nbsp;\u003cspan refid=\"Equ6\" class=\"InternalRef\"\u003e6\u003c/span\u003e) and pseudo-second-order (Eq.\u0026nbsp;\u003cspan refid=\"Equ7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) kinetic models are given as follows (Gautam et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2018\u003c/span\u003e):\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$$\\:{q}_{t}={q}_{e}(1-{e}^{-{k}_{1}t\\:})$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\:{q}_{t=}\\frac{{k}_{2}t{q}_{e}^{2}}{1+{k}_{2}{q}_{e}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn these equations, q\u003csub\u003ee\u003c/sub\u003e and q\u003csub\u003et\u003c/sub\u003e​ represent the amounts of dye adsorbed on the adsorbent at equilibrium (mg/g) and at time t (min) (mg/g), respectively. The parameter k\u003csub\u003e1\u003c/sub\u003e​ (min⁻\u0026sup1;) denotes the pseudo-first-order rate constant, which characterizes the rate of adsorption. On the other hand, k\u003csub\u003e2\u003c/sub\u003e (g/mg\u0026middot;min) signifies the pseudo-second-order rate constant, providing a measure of the adsorption rate and capacity of the adsorbent material.\u003c/p\u003e \u003cp\u003eThe parameters for the pseudo-first-order and pseudo-second-order kinetic models, respectively, were determined using nonlinear regression in this study. This approach was chosen to accurately model the complex adsorption kinetics observed, ensuring precise estimation of adsorption parameters from experimental data (Moussout et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e and \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e illustrate the results of nonlinear regression analysis comparing calculated qe values with experimental data. A closer match between experimental and predicted qe values across different models indicates better agreement. In this study, experimental values closely aligned with predictions from the nonlinear pseudo-second-order (NLPSO) model, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e (Mita et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Nicola et al. 2020). The coefficient of determination (R\u0026sup2;) values, which gauge model fit, were notably high (approaching 1.0) for the PSO equation compared to the nonlinear pseudo-first-order (NLPFO) equation across all tested concentrations, indicating superior performance (Figs.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e and \u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003e). The obtained results show a strong correlation with previous studies (Batmaz et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Raj et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Almuslem et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Kinetic parameters for each model (PFO and PSO) were determined using nonlinear regression and are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eParameters of different kinetic models for adsorption of MG on CNC.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eKinetic Model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c7\" namest=\"c3\"\u003e \u003cp\u003eConcentration (ppm)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003ePFO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003e1\u003c/em\u003e\u003c/sub\u003e (min\u003csup\u003e-1\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.58 \u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.8\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.5\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.1\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.4\u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eq\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e (mg/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e103.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e128.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.982\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.991\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003ePSO\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003ek\u003c/em\u003e\u003csub\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sub\u003e (g/mg/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.0 \u0026times;10\u003csup\u003e\u0026minus;\u0026thinsp;3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eq\u003c/em\u003e\u003csub\u003e\u003cem\u003ee\u003c/em\u003e\u003c/sub\u003e (mg/g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e54.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e85.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e99.63\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.994\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.993\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.991\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.992\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.997\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study successfully synthesized nanocellulose crystals from \u003cem\u003eParthenium hysterophorus\u003c/em\u003e biomass through acid hydrolysis, showcasing its potential as a sustainable nanomaterial. With \u003cem\u003eParthenium\u003c/em\u003e biomass boasting a high cellulose content (43.28%), this invasive weed proves to be a viable and abundant source for nanocellulose production. The synthesized nanocellulose was meticulously characterized using advanced techniques such as SEM, TEM, XRD, FTIR, TGA, particle size distribution analysis, and zeta potential measurement. Key findings included the significant enhancement of crystallinity through combined pretreatment and acid hydrolysis processes. SEM and TEM images confirmed morphological changes, with an observed average particle size of approximately 217 nm. Moreover, nanocellulose exhibited improved thermal stability compared to raw biomass following alkali pretreatment, bleaching, and acid hydrolysis. Zeta potential analysis revealed strong colloidal stability with a highly negative surface charge (-28.9\u0026thinsp;\u0026plusmn;\u0026thinsp;6.18 mV). In practical applications, the synthesized nanocellulose demonstrated small particle size (50\u0026ndash;100 nm), high crystallinity (77%), and stable thermal properties, highlighting its potential in effective dye adsorption. Specifically, nanocellulose removed 75% of MG dye from aqueous solutions at room temperature, with adsorption kinetics following pseudo-second-order behavior supported by high R\u0026sup2; values. This research significantly advances sustainable materials science by harnessing invasive weed biomass for the production of valuable nanomaterials. The findings underscore the promising applications of \u003cem\u003eParthenium\u003c/em\u003e-derived nanocellulose in environmental remediation and other industrial sectors, paving the way for further exploration and optimization in future studies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical Statement:\u0026nbsp;\u003c/strong\u003eThis research does not involve any human or animal participation and whole research will comply with standard ethical guidelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding Support:\u003c/strong\u003e Authors declare that no funds, grants, or other support was received from any organization for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFinancial interests:\u003c/strong\u003e The authors declare that they have no financial interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNon-financial interests:\u003c/strong\u003e None\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest:\u003c/strong\u003e The authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials:\u003c/strong\u003e The data and materials generated and analyzed during the current study are included within the article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution:\u003c/strong\u003e D.G.: Methodology, Investigation, Formal analysis, Writing- Original Draft. 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Crops Prod. \u003cstrong\u003e67\u003c/strong\u003e, 466\u0026ndash;474 (2015). https://doi.org/10.1016/j.indcrop.2015.01.058\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Biomass, cellulose nanocrystals, adsorption capabilities, morphology, sustainable","lastPublishedDoi":"10.21203/rs.3.rs-4767300/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4767300/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the synthesis and comprehensive characterization of cellulose nanocrystals (CNC) derived from \u003cem\u003eParthenium hysterophorus\u003c/em\u003e biomass through acid hydrolysis. Nanocellulose, a versatile nanomaterial sourced from renewable biomass, exhibits exceptional properties suitable for various applications. Acid hydrolysis of cellulose extracted from \u003cem\u003eParthenium \u003c/em\u003eresulted in a significant increase in crystallinity, as confirmed by X-ray diffraction (XRD), with the nanocellulose exhibiting a crystallinity index of 77%. The scanning electron microscopy (SEM) images revealed that acid hydrolysis resulted in the alteration of the fibrous and coiled structure of cellulose, resulting in the formation of spherical CNCs. Transmission electron microscopy (TEM) analysis of CNC revealed an average diameter of approximately 36-79 nm, demonstrating the impact of acid hydrolysis on morphology. Further characterization using Fourier-transform infrared spectroscopy (FTIR) indicated the preservation of cellulose chemical structure, while thermogravimetric analysis (TGA) showed improved thermal stability of the nanocellulose compared to raw biomass post-processing. Zeta potential analysis highlighted strong colloidal stability with a highly negative surface charge (-28.9 ± 6.18 mV), essential for applications requiring dispersion stability. In adsorption studies, the synthesized nanocellulose effectively removed 75% of MG dye from aqueous solutions at room temperature, following pseudo 2nd order kinetics. This research underscores the potential of \u003cem\u003eParthenium\u003c/em\u003e-derived nanocellulose in sustainable materials applications, leveraging invasive weed biomass for eco-friendly nanomaterial production. The findings contribute to advancing sustainable materials research by demonstrating the utility of \u003cem\u003eParthenium \u003c/em\u003ebiomass for value-added nanomaterial production, specifically highlighting the enhanced properties and effective adsorption capabilities of the synthesized nanocellulose.\u003c/p\u003e","manuscriptTitle":"Synthesis and Characterization of Cellulose Nanocrystals from Alkali- Pretreated Parthenium hysterophorus with Adsorption Kinetic Studies","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-26 12:42:02","doi":"10.21203/rs.3.rs-4767300/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0636294f-90e6-4801-b818-b1186ef3205c","owner":[],"postedDate":"August 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-09-28T04:38:18+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-26 12:42:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4767300","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4767300","identity":"rs-4767300","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}