Cellulose nanocrystals/graphene oxide composite for the adsorption and removal of levofloxacin hydrochloride antibiotic from aqueous solution — TEI-tagged view

Junhong TaopersName, Jie YangpersName, Chengxiao MapersName, Junfeng LipersName, Keqing DupersName, Zhen WeipersName, Cuizhong ChenpersName, Zhaoyang WangpersName, Chun ZhaopersName, Xiaoya DengpersName (2020date). “Cellulose nanocrystals/graphene oxide composite for the adsorption and removal of levofloxacin hydrochloride antibiotic from aqueous solution.” Royal Society Open Sciencetitle·j.

Object of study — adsorptionterm·key and removal of levofloxacin hydrochlorideterm·dom from water by a cellulose nanocrystals/graphene oxide compositeterm·dom

Research question — whether a new, low-cost, high-efficiency composite adsorbent can remove antibioticsterm·key from water

Contribution — a three-dimensional CNCs-GOterm·dom adsorbent characterised and optimised by response surface methodologyterm·mth

Full text — tagged

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Cellulose nanocrystals/graphene oxide compositeterm·dom for the adsorptionterm·key and removal of levofloxacin hydrochlorideterm·dom antibioticterm·key from aqueous solution Abstract Residual antibioticsterm·key in water are often persistent organic pollutants. The purpose of this study was to prepare a cellulose nanocrystals/graphene oxide compositeterm·dom (CNCs-GOterm·dom) with a three-dimensional structureterm·dom for the removal of the antibioticterm·key levofloxacin hydrochlorideterm·dom (Levo-HClterm·dom) in water by adsorptionterm·key. The scanning electron microscope, Fourier transform infrared (FT-IR), energy-dispersive spectroscopy, X-ray photoelectron spectroscopy and other characterization methods were used to study the physical structure and chemical properties of the CNCs-GOterm·dom. The three-dimensional structureterm·dom of the composite material rendered a high surface area and electrostatic attraction, resulting in increased adsorption capacityterm·dom of the CNCs-GOterm·dom for Levo-HClterm·dom. Based on the Box–Behnken design, the effects of different factors on the removal of Levo-HClterm·dom by the CNCs-GOterm·dom were explored. The composite material exhibited good antibioticterm·key adsorption capacityterm·dom, with a removal percentage exceeding 80.1% at an optimal pHterm·dom of 4, the adsorbent dosage of 1.0 g l −1 , initial pollutant concentration of 10.0 mg l −1 and contact time of 4 h. The adsorptionterm·key isotherm was well fitted by the Sips modelterm·mth, and kinetics studies demonstrated that the adsorptionterm·key process conformed to a quasi-second-order kinetics model. Consequently, the as-synthesized CNCs-GOterm·dom demonstrates good potential for the effective removal of antibioticsterm·key such as levofloxacin hydrochlorideterm·dom from aqueous media.

Introduction Water-soluble antibioticsterm·key constitute a serious type of organic environmental pollutants as the early lack of guidance and regulatory measures related to antibioticsterm·key has led to their overuse and abuse [ 1 ]. Once released into the environment, these substances spread through aquatic systems and the food chain where they persist and become enriched in the air, water and soil [ 2 , 3 ]. The heavy use of antibioticsterm·key has exerted a huge impact on ecosystems and human health, and the problem of antibioticterm·key removal urgently requires attention [ 4 , 5 ].

In recent years, a variety of methods have been explored to mitigate environmental antibioticterm·key contamination, such as adsorptionterm·key, catalytic degradation, biodegradation, photocatalytic degradation and advanced oxidation [ 1 , 6 – 8 ]. Among these methods, the widely used adsorptionterm·key technique offers the advantages of easy operation, flexibility, low energy consumption, high removal ratesterm·dom, low secondary pollution and low adsorbent regeneration cost [ 9 ]. Many absorbent materials have been explored for the removal of antibioticsterm·key from water, including activated carbon [ 10 ], anaerobic granular sludge [ 11 ], kaolin [ 12 ], graphene oxideterm·dom (GO) [ 13 ], multi-walled carbon nanotubes [ 14 ], fly ash [ 15 ], montmorillonite [ 16 ] and bamboo charcoal [ 17 ]. However, the inadequate adsorptionterm·key efficiencies, poor adsorptionterm·key capacities, unsatisfactory recyclability, secondary pollution problems and high costs of these adsorbent materials have greatly hampered their practical application. Therefore, the development of new, high-efficiency, low-cost adsorbents for the removal of antibioticsterm·key from water is a pressing need.

One material that meets some of the requirements for a good adsorbent is cellulose, an abundant natural resource. Its structural formula reveals a plethora of exposed hydroxyl and reduced and non-reduced end groups at which chemical reactions can occur. As a modified derivative of cellulose, nanocellulose [ 18 ] is a new biomass nanomaterial with attractive characteristics such as a large specific surface area, high mechanical strength, good thermal stability and low thermal expansion coefficient because of its highly reduced particle size [ 19 ]. Due to the large number of hydroxyl groups on the surface of nanocellulose ( table 1 ), it is easily modified by reactions such as carboxylation, oxidation, esterification, etherification, silanization, acetylation and graft polymerization. These modifications facilitate its addition and dispersal into different polymers [ 20 – 23 ]. Hence, nanocellulose nanocomposites have been used as sorbents for heavy metals and organic pollutants removal from aqueous solution [ 24 ]. It is known that graphene has many excellent characteristics such as high strength, high intrinsic mobility, high specific surface area, high light transmittance and high thermal conductivity [ 25 ]. Among graphene materialsterm·dom, graphene oxideterm·dom ( table 1 ) is a commonly used type. It is structurally similar to graphene. That is, the bottom surface of graphene contains epoxy groups and hydroxyl groups, and the edges of graphene have carboxyl groups and carbonyl groups [ 13 , 26 ]. These hydrophilic groups impart surface activity and wettability, and their electrostatic repulsion characteristics facilitate the stable dispersion of GO in water or alkaline solution [ 27 – 29 ]. This stability has an important influence on the mechanical strength and electrical properties of the GO, and also allows various functional groups to be grafted onto modified GO sheets, expanding their prospective range of applications. Recently, GO has attracted increasing attention as a new adsorbent owing to its distinguished properties of high surface area as well as easy to functionalize ability [ 30 ]. Response surface methodologyterm·mth (RSMterm·mth) is a mathematical statistical method for solving multi-variable problems by using reasonable experimental design methods and experimental data, fitting the functional relationship between various factors and response values with multiple quadratic regression equations and obtaining the optimal process parameters through regression analysis [ 31 , 32 ]. RSMterm·mth overcomes the disadvantage of orthogonal design, which can only deal with discrete level values. It reduces experimentterm·mth time, offers high precision and exhibits good predictive performance [ 33 ]. It is widely used in the experimental design of food, chemical, biological and other studies, but is rarely used in water pollution control and theoretical research. Table 1. The molecular structures of main medicine.

The purpose of the present work is the synthesis of novel adsorbent, cellulose nanocrystalsterm·key/graphene oxideterm·dom nanocomposite (CNCs-GOterm·dom), for the removal of antibioticterm·key levofloxacin hydrochlorideterm·dom (Levo-HClterm·dom, table 1 ), a pharmaceutical contaminant in waste water treatment. Adsorbent was characterized using Fourier transform infrared (FT-IR), X-ray diffraction (XRD), scanning electron microscope (SEM)–energy-dispersive spectroscopy (EDS), X-ray photoelectron spectroscopy (XPS) and Brunauer–Emmett–Teller (BET) analysis. The response surface method (RSMterm·mth) was used to optimize Levo-HClterm·dom adsorptionterm·key conditions. Finally, we investigated the adsorptionterm·key performance and mechanism for Levo-HClterm·dom removal process by CNCs-GOterm·dom.

Material and methods Materials Levofloxacin hydrochlorideterm·dom 98%, cellulose nanocrystalsterm·key and graphite powder 99.95% (for the preparation of GO) were purchased from Shanghai Macklin Biochemical Co., Ltd. Deionized water was used for all experimentsterm·mth. Otherwise specified chemicals were of reagent grade and used without further purification. Deionized water was used throughout the experimentsterm·mth.

Preparation of nanocrystalline cellulose/graphene oxideterm·dom composite GO was prepared from graphite powder according to the Hummers method [ 34 ]. To prepare the composite, the GO suspension (5 g l −1 , 120 ml) was accurately measured into a 500 ml round-bottomed flask, followed by the CNCs suspension (14.434 g l −1 , 100 ml). The brown mixture was continuously sonicated for 1 h, and then stirred at room temperature for 2 h to obtain the brown CNCs-GOterm·dom suspension. This suspension was freeze-dried (Christ Alpha 1–2 LDplus, Germany) for 48 h to produce the CNCs-GOterm·dom composite.

Characterization The FT-IR spectra of CNCs, GO and CNCs-GOterm·dom samples were recorded using a Nicolet 6700 FT-IR instrument (USA). The spectra were recorded over the wave number range of 400–4000 cm −1 with 10 scans at a resolution of 4 cm −1 . CNCs, GO and CNCs-GOterm·dom were performed to analyse the surface morphology using Quanta 650FEG Emission SEM (USA) and 10 kV accelerating voltage. The specific surface area and the pore volume of CNCs, GO and CNCs-GOterm·dom were measured using ASAP 2020 Surface and porosity analyser (USA) under N 2 analysis adsorptive at 77 K. Powered XRD patterns were obtained (Bruker D8 X-ray powder diffractrometer, Germany) using Cu-K α radiation ( λ = 0.15418 nm). XPS were performed with an Al K α mono and vacuum degree of the analysis room 10–8 mbar (ThermoScientific Escalab250Xi, USA). The contact angle tester (LSA100, LAUDA Scientific, Germany) was used to record the dynamic contact angles of CNCs, GO and CNCs-GOterm·dom within a certain time.

Adsorptionterm·key of antibioticterm·key Adsorptionterm·key of antibioticterm·key Levo-HClterm·dom solution with an initial concentration of 5–12 mg l −1 and CNCs-GOterm·dom composite adsorptionterm·key material (0.025–0.125 g) were introduced into a 50 ml reaction bottle for batch adsorptionterm·key experimentsterm·mth. The pHterm·dom was adjusted between 2 and 9 by adding different concentrations of NaOH and HCl solutions. The configured suspension was shaken for 6 h, with samples withdrawn at specific intervals and centrifuged at 10 000 r.p.m. for 15 min to separate the solid and liquid phases. The supernatant was removed and the concentration of Levo-HClterm·dom in the liquid phase was measured by HPLC (Agilent-1200, USA). The residual Levo-HClterm·dom concentration was obtained using a standard curve derived from a series of Levo-HClterm·dom solutions with known antibioticterm·key content. The adsorbed amount of Levo-HClterm·dom was calculated according to equation (2.1), 2.1 q t = ( C 0 − C t ) × V m , where q t is the amount adsorbed after time t , C 0 and C t are initial concentration and concentration of the adsorbate after time t , respectively (mg l −1 ); V is the volume of the solution (l) and m is the weight of the CNCs-GOterm·dom used (g).

The percentage removal ( R %) of the Levo-HClterm·dom was calculated using equation (2.2): 2.2 R % = ( C 0 − C t ) C 0 × 100 % .

Statistical analysis and response surface design Data analysis was performed using IBM SPSS Statistics 19.0, USA and Stat-Ease Design-Expert 10.0, USA. According to RSMterm·mth design principles, the Box–Behnken model was used to study four factors impacting Levo-HClterm·dom removal from water by the CNCs-GOterm·dom: initial pollutant concentration, initial pHterm·dom of the solution, contact time and amount of adsorbent. We predicted the optimal conditions in the experimental range; the centre point experimentterm·mth is three parallel experimentsterm·mth. The relationship between the four factor, three horizontal coding and experimental values is shown in table 2 . Table 2. Experimental independent variables and their levels. factor coding values for each level of coding −1 0 +1 initial pollutant concentration (mg l −1 ) A 9 10 11 pHterm·dom B 2 4 6 dosage of adsorbent (g l −1 ) C 0.5 1.0 1.5 time (h) D 3 4 5 Results and discussion Characterization of the adsorbent Scanning electron microscope–energy-dispersive spectroscopy and transmission electron microscopy analysis Figure 1 a , b presents SEM images of the CNCs at 5000× and 30 000× magnifications, respectively. Many rod-like cellulose crystals are observed on the CNCs surfaces, probably due to the large number of hydroxyl groups they contain. After hydrolysis treatment, the size of cellulose becomes smaller and the specific surface area increases. The hydroxyl groups on CNCs surface are more likely to interact with each other to form hydrogen bonds, and the irregular surface roughness further increases the surface area [ 35 , 36 ]. In figure 1 c,d are the 5000× and 30 000× SEM images for GO, respectively. Here, the GO sheet layers overlap to form a three-dimensional structureterm·dom. The GO sheet layers have many folds stacked on the surface, which account for its relatively large specific surface area. These folds are potential adsorptionterm·key sites. The analogous SEM images for the CNCs-GOterm·dom are presented in figure 1 e,f . In this case, the surface morphology of the composite material reveals the appearance of CNCs on the surface of the three-dimensional GO structure, resulting in an even larger specific surface area and a greater number of adsorptionterm·key sites. Figure 1. SEM images of CNCs, GO and CNCs-GOterm·dom: ( a ) SEM images of CNCs at 5000× magnification; ( b ) SEM images of CNCs at 30 000× magnification; ( c ) SEM images of GO at 5000× magnification; ( d ) SEM images of GO at 30 000× magnification; ( e ) SEM images of CNCs-GOterm·dom at 5000× magnification; ( f ) SEM images of CNCs-GOterm·dom at 30 000× magnification.

Figure 2 demonstrates the transmission electron microscopy (TEM) micrographs of CNCs-GOterm·dom. From the TEM micrograph, it can be seen that nano-sized CNCs and GOs CNCs-GOterm·dom composites are formed. The CNCs are evenly distributed between the GO slices, interlaced with each other. At the same time, there are fewer folds of GO sheets, which may be due to the interaction between CNCs and GO, which prevents the folding of GO sheets. These data provide a basis for the successful preparation of composite materials. Figure 2. TEM images of CNCs-GOterm·dom.

The EDS spectra of the CNCs, GO and CNCs-GOterm·dom, presented in figure 3 , show that the CNCs and GO contain mostly C and O atoms, and with a small number of S atoms. Unmarked peaks due to other elements may be due to adventitious impurities not removed during the preparation and washing processes. Comparing the mass and element ratios, the CNCs-GOterm·dom has both a larger C content than GO and a greater O content than the CNCs, indicating that the numbers of carbon and oxygen atoms in the composite material are increased (because the CNCs contain more carbon atoms and GO contains more oxygen atoms). Thus, the EDS analysis also provides clear support for successful composite formation. Figure 3. EDS spectra of CNCs, GO and CNCs-GOterm·dom.

Dynamic light scattering analysis Dynamic light scattering (DLS) measures particle size on the basis of fluctuations in scattered light intensity with time that may be due to the random Brownian motion of the sample particles present in suspension or polymers in a solution. Diffusion is directly related to the statistical nature of these fluctuations in scattered intensity [ 37 , 38 ]. In addition, dynamic light scattering can be used to help prove whether the prepared composite material is at the nano-scale level [ 39 ]. As shown in figure 4 , the CNCs-GOterm·dom were unimodal in distribution, all the particles are in a range between 300 and 1000 nm, and the mean average size of the resulting nanoparticles was found to be 842.3 (nm), showing highly stable without forming any aggregation. At the same time, a narrow size distribution indicates many particles that are homogeneous in size with small variations; this result proves that the composite particles have a nano-size. Figure 4. Spectroscopic measurement of particle size by dynamic light scattering of CNCs-GOterm·dom.

Fourier transform infrared analysis The FT-IR patterns of the CNCs, GO and CNCs-GOterm·dom are depicted in figure 5 . The GO spectrum reveals absorption peaks at 3340, 1732, 1621, 1218 and 1049 cm −1 , corresponding to –OH stretching motions, C=O stretching vibrations in –COOH groups, C=C stretching vibrations of sp 2 hybridized carbon chains, C–OH stretching vibrations in –COOH groups and C–O–C stretching vibrations. The absorption peak at 864 cm −1 is due to C–H bending vibrations. In the IR spectrum of the CNCs, absorption peaks are observed at 3340, 2900, 1649, 1428 and 1058 cm −1 , which correspond to –OH stretching vibrations, C–H stretching vibrations, OH bending vibrations and C-OH stretching vibration and C-O stretching vibration in carboxyl group. The FT-IR spectrum of the CNCs-GOterm·dom composite contains all the absorption peaks observed in GO, as well as peaks unique to nanocellulose, such as that at 2900 cm −1 , corresponding to the –C–H stretching vibrations of its methyl and methylene groups. These FT-IR results also affirm that the CNCs-GOterm·dom composite was successfully prepared. Figure 5. FT-IR spectra of GO, CNCs and CNCs-GOterm·dom.

Pore specifications and surface area analysis N 2 adsorptionterm·key–desorption isotherms and Barrett–Joyner–Halenda (BJH) pore volume distributions for the CNCs and CNCs-GOterm·dom are shown in figure 6 . The CNCs produce type II isothermsterm·dom, in contrast with the CNCs-GOterm·dom, which produce type IV isothermsterm·dom. In the low-pressure range of P/P 0 < 0.1, the N 2 adsorptionterm·key–desorption curves of the CNCs and CNCs-GOterm·dom rise sharply, indicating that both materials have a certain number of micropores. In the relative pressure range of 0.4–0.8, there is a significant hysteresis loop in the N 2 adsorptionterm·key–desorption curve of the CNCs-GOterm·dom, indicating the existence of mesoporous structure. In the relative pressure range of 0.8–1.0, the curve of the CNCs-GOterm·dom increases significantly, indicating that there are large cavities in the composite. The N 2 adsorptionterm·key–desorption curve of the CNCs has no obvious hysteresis loop in the 0.4–1.0 relative pressure range, indicating that this material has no mesoporous or hollow structures. Figure 6 b presents the pore size distribution curves for the CNCs and CNCs-GOterm·dom calculated using the BJH model. Detailed structural parameters for the different samples are shown in table 3 . As expected, the CNCs-GOterm·dom has the largest BET surface area and pore volume. These results indicate CNCs-GOterm·dom compound material with ultrahigh surface areas might be effective in adsorbing antibioticsterm·key. Figure 6. N 2 adsorptionterm·key–desorption isotherms and BJH pore volume distributions of CNCs and CNCs-GOterm·dom: ( a ) N 2 adsorptionterm·key–desorption isotherms of CNCs and CNCs-GOterm·dom, ( b ) Pore volume distribution of CNCs and CNCs-GOterm·dom. Table 3. Specific surface area and pore structure parameters of CNCs and CNCs-GOterm·dom. sample BET (m 2 g −1 ) BJH adsorptionterm·key cumulative volume of pores (cm 3 g −1 ) single point surface area (m 2 g −1 ) adsorptionterm·key average pore diameter (nm) CNCs 27.2370 0.043409 26.6827 6.37498 CNCs-GOterm·dom 32.7995 0.062377 32.0515 7.60709 X-ray diffraction analysis The XRD patterns for the CNCs, GO and CNCs-GOterm·dom are shown in figure 7 . The CNCs display three distinct characteristic peaks at 2 θ = 14.9°, 16.5° and 22.8°, indicating that they possess the typical monoclinic cellulose I lattice [ 40 ]. The concave area at 2 θ = 17.8° corresponds to an amorphous area of the CNCs. Compared to the crystalline regions, the amorphous areas of the CNCs afford higher chemical reactivity. In the CNCs-GOterm·dom, the peak at 22.5° confirms the amorphous nature of the composite. GO has a characteristic peak at 2 θ = 9.9° which corresponds to the (001) crystal plane of GO; this arises from the lattice distortion of the carbon structure during the transformation of graphite to GO, during which the oxidation process introduces a large number of functional groups and the interlayer spacing is enlarged, so that the graphite layers are separated and the specific surface area and the number of adsorptionterm·key sites are increased [ 41 , 42 ]. The characteristic peaks of the CNCs-GOterm·dom, observed at 2 θ = 15.1°, 16.2° and 22.6°, are similar to those of the CNCs, and show that the composite generates almost the same spectrum as the CNCs after slight modification. Further, the peak occurring at 2 θ = 10.21° indicates that the composite material also retains the characteristics of GO. Therefore, it can be proven that the CNCs-GOterm·dom composite is a substance comprising CNCs and GO. Figure 7. X-ray diffraction patterns of CNCs, GO and CNCs-GOterm·dom.

X-ray photoelectron spectroscopy The XPS analysis results for the CNCs are displayed in figure 8 a , d , g . The full spectrum scan shows that the main chemical components of the CNCs are C (C 1 s , 287 eV) and O (O 1 s , 533 eV). The C 1 s component peak scan shows that C is mainly present in three forms: C–C/C–H, C–O and O–C=O, with corresponding peaks at 284.8, 286.2 and 287.5 eV, respectively. The O 1 s spectrum reveals that O is mainly present in the forms C–O and C=O, with peaks at 532.5 and 531.7 eV, respectively. The XPS analysis results for GO are shown in figure 8 b , e , h . The full spectrum scan reveals that the main chemical components are C (C 1 s , 285 eV) and O (O 1 s , 533.6 eV). The C 1 s spectrum is decomposed into peaks for four carbon forms: C–C/C=C, C–O, C=O and O–C=O, corresponding to peaks at 284.8, 286.7, 287.2 and 288.5 eV, respectively, whereas the O 1 s deconvolution shows the presence of three types of O, C–O, C=O and O–C=O (at 532.6, 531.7, and 532.8 eV, respectively). The XPS analysis results for the CNCs-GOterm·dom composite are shown in figure 8 c , h , i . In the full spectrum scan, the main chemical components of the CNCs-GOterm·dom are also C (C 1 s , 287 eV) and O (O 1 s , 533 eV) [ 43 ]. The C 1 s peak decomposition reveals four peaks for C at 284.9, 285.4, 286.2 and 287.2 eV, corresponding to C–O, C–C/C=C, O–C=O and C=O functional groups. Compared with that in the scan for CNCs, the C–C/C=C peak intensity in the scan for CNCs-GOterm·dom increases significantly, indicating that the CNCs and GO are effectively combined. For the C 1 s spectrum of the CNCs, the addition of GO introduces −C=O groups in the composite [ 44 ]. This should be advantageous for the adsorptionterm·key of antibioticsterm·key by the new adsorbent [ 45 ]. In addition, here still remain a large number of oxygen-containing functional groups in the CNCs-GOterm·dom, mainly derived from the CNCs. The O 1 s peak separation results show three different peaks for O at 532.1, 532.6 and 533.1 eV, corresponding to the functional groups C=O, C−O and O−C=O, respectively, which are typically found in GO. Compared with that in the scan for CNCs, the O peak intensity in the scan for CNCs-GOterm·dom is significantly increased, indicating that GO effectively binds to the CNCs through hydrogen bonding. For the CNCs-GOterm·dom, both the O 1 s and C 1 s spectra exhibit changes consistent with the formation of the composite material, with respect to the corresponding spectra of the CNCs. Figure 8. X-ray photoelectron spectroscopy of CNCs, GO and CNCs-GOterm·dom.

Contact angle analysis The hydrophilicity characteristics of the CNCs, GO and CNCs-GOterm·dom composites were analysed by contact angle measurements. Figure 9 a shows a contact angle for GO of 33.93°, indicating good hydrophilicity [ 46 ]. In the nanocellulose ( figure 9 b ), the contact angle is 23.63°, also signalling good hydrophilicity. Finally, the 26.72° contact angle of the CNCs-GOterm·dom composite figure 9 c similarly suggests good hydrophilicity. With nanocellulose as the matrix and the GO as filler, the contact angle of the composite was reduced and the hydrophilicity was improved. The contact angle analysis indicates the successful preparation of the CNCs-GOterm·dom material. Figure 9. Contact angle diagrams of GO ( a ), CNCs ( b ) and CNCs-GOterm·dom ( c ).

Response surface analysis Response surface experimentterm·mth design and results The statistical software Design-Expert 10.0 was used to fit the multiple regression equations to the experimental data from the response surface in table 4 to obtain the initial pollutant concentration ( A ), pHterm·dom ( B ), amount of adsorbent added ( C ), contact time ( D ) and the quadratic polynomial equation for the Levo-HClterm·dom removal rateterm·dom ( η ), as shown in equation (3.1), 3.1 η = 82.64 + 14.64 A + 15.40 B + 5.09 C + 1.29 D + 10.68 A B − 1.68 A C + 0.82 A D − 2.35 B C − 3.96 B D − 0.47 C D − 19.07 A 2 − 15.66 B 2 − 6.87 C 2 − 2.72 D 2 . Table 4. Experimental design and results. numbering variable coding level actual value removal rateterm·dom (%) A B C D A B C D 1 0 −1 −1 0 10 2 0.5 4 38.22 2 0 0 1 −1 10 4 1.5 3 79.45 3 −1 0 −1 0 9 4 0.5 4 34.56 4 0 0 1 1 10 4 1.5 5 78.46 5 0 0 −1 1 10 4 0.5 5 61.56 6 1 0 −1 0 11 4 0.5 4 74.56 7 −1 −1 0 0 9 2 1.0 4 31.57 8 0 −1 0 1 10 2 1.0 5 60.57 9 0 0 0 0 10 4 1.0 4 80.57 10 0 0 0 0 10 4 1.0 4 80.93 11 0 −1 1 0 10 2 1.5 4 47.55 12 1 0 1 0 11 4 1.5 4 79.25 13 1 0 0 1 11 4 1.0 5 79.76 14 0 0 0 0 10 4 1.0 4 80.12 15 1 0 0 −1 11 4 1.0 3 77.17 16 −1 0 0 1 9 4 1.0 5 45.16 17 0 1 0 −1 10 6 1.0 3 79.63 18 −1 0 1 0 9 4 1.5 4 45.96 19 1 1 0 0 11 6 1.0 4 79.56 20 0 1 0 1 10 6 1.0 5 78.55 21 0 1 1 0 10 6 1.5 4 79.56 22 1 −1 0 0 11 2 1.0 4 28.42 23 0 0 0 0 10 4 1.0 4 85.35 24 0 1 −1 0 10 6 0.5 4 79.73 25 0 −1 0 −1 10 2 1.0 3 45.82 26 0 0 −1 −1 10 4 0.5 3 60.66 27 −1 0 0 −1 9 4 1.0 3 45.86 28 −1 1 0 0 9 6 1.0 4 39.98 29 0 0 0 0 10 4 1.0 4 86.22 Analysis of regression model variance and principal factors The results from the regression analysis of variance are shown in table 5 . Among the p -values, if an item is p < 0.05, the effect of the item on the response value is significant; if a certain item is p < 0.01, the effect of the item on the response value is very significant [ 32 ]. The smaller the lack of fit of the model, the better, and the larger the p -value corresponding to the lack of fit, the better. The larger the p -value, the better the p -value. From table 5 , the independent variables A, B, C, AB, A 2 , B 2 and C 2 respond significantly ( p < 0.05), that is, the initial pollutant concentration, the initial pHterm·dom, the amount of adsorbent added, the interaction between initial pHterm·dom and initial pollutant concentration, the square of pollutant concentration, the square of the initial pHterm·dom and the square of the amount of adsorbent added all have significant effects, and other factors have little effect on the response value η . The adaptability of the model is extremely significant ( p < 0.01), and the misfit term is not significant ( p = 0.0534), indicating that the model can well describe the nonlinear relationship between the influencing factors and the response value [ 47 ]. The model's coefficient of determination ( R 2 = 0.9494) can explain 94.94% of the experimental data [ 48 ]. The difference between the corrected complex correlation coefficient ( R Adj 2 ) and the predicted complex correlation coefficient ( R pred 2 ) is 0.1764, which is less than 0.2; the coefficient of variation CV = 9.46%, and the precision is 13.738, which is much greater than 4.0, indicating that the model has high precision and reliability [ 32 , 49 ]. It further shows that this experimental method is reliable. Therefore, the model can be used to optimize and predict the experimental conditions for the removal of Levo-HClterm·dom by the CNCs-GOterm·dom in water. Table 5. Variance analysis results of regression model. R 2 = 0.9494, R Adj 2 = 0.8989 , R pred 2 = 0.7225 , CV = 9.46%, adequate precision = 13.738. source sum of squares df mean square F value p -value Prob > F model 9714.15 14 693.87 18.77 <0.0001 A 2570.49 1 2570.49 69.55 <0.0001 B 2844.69 1 2844.69 76.97 <0.0001 C 310.49 1 310.49 8.40 0.0117 D 19.94 1 19.94 0.54 0.4747 AB 456.46 1 456.46 12.35 0.0034 AC 11.26 1 11.26 0.30 0.5897 AD 2.71 1 2.71 0.073 0.7907 BC 22.09 1 22.09 0.60 0.4523 BD 62.65 1 62.65 1.70 0.2140 CD 0.89 1 0.89 0.024 0.8787 A 2 2359.07 1 2359.07 63.83 <0.0001 B 2 1591.61 1 1591.61 43.06 <0.0001 C 2 306.09 1 306.09 8.28 0.0122 D 2 47.84 1 47.84 1.29 0.2744 residual 517.44 14 36.96 lack of fit 483.72 10 48.37 5.74 0.0534 pure error 33.72 4 8.43 corrected total 10 231.59 28 Response surface analysis of levofloxacin hydrochlorideterm·dom removal rateterm·dom By drawing three-dimensional images, each with two independent variables as coordinates, using Stat-Ease Design-Expert 10.0, we can more intuitively explain the effects of sorbent dosage, initial pollutant concentration, solution pHterm·dom and contact time on the removal rateterm·dom, and characterize the response surface function. The results are shown in figures 10 and 11 . Figure 10. Surface and contour plots for Levo-HClterm·dom removal (%) interaction between independent parameters. ( a ) Initial pHterm·dom of solution–Levo-HClterm·dom concentration. ( b ) the dosage of CNCs-GOterm·dom–Levo-HClterm·dom concentration. ( c ) Time–Levo-HClterm·dom concentration. Figure 11. Surface and contour plots for Levo-HClterm·dom removal (%) interaction between independent parameters. ( a ) Initial pHterm·dom of solution–the dosage of CNCs-GOterm·dom. ( b ) Time–initial pHterm·dom of solution. ( c ) Time–the dosage of CNCs-GOterm·dom.

Figure 10 a shows the influence of the solution pHterm·dom and initial pollutant concentration on the removal rateterm·dom under the centre point conditions of adsorbent dosage (1.0 g l −1 ) and contact time (4 h). Figure 10 a shows that, as the initial pHterm·dom and pollutant concentration increase, the removal rateterm·dom also gradually increases. With an initial pHterm·dom of 4 and initial pollutant concentration of 10.0 mg l −1 , the removal rateterm·dom reaches a maximum of 80.12%. This is due to the increase in the initial concentration of the pollutant: providing a sufficient number of antibioticterm·key molecules for the adsorbent's adsorptionterm·key sites effectively results in a higher pollutant concentration, and therefore, a higher removal rateterm·dom. From the contour map, the interaction between the initial concentration and pHterm·dom lies close to the ellipse, so there is an obvious interaction between the initial concentration and the initial pHterm·dom of the solution.

Figure 10 b shows the effects of the adsorbent dosage and initial pollutant concentration on the removal rateterm·dom for an initial pHterm·dom and contact time under the centre point conditions (pHterm·dom 4; contact time, 4 h). As the initial pollutant concentration and amount of adsorbent added increase, the removal rateterm·dom also gradually increases. When the initial concentration of the pollutant is 10.0 mg l −1 and the adsorbent dosage is 1.0 g l −1 , the removal rateterm·dom reaches a maximum of 80.12%. Here, as the amount of adsorbent added increases, sufficient adsorptionterm·key sites are provided to adsorb antibioticterm·key molecules, resulting in a higher removal rateterm·dom. When the initial concentration is greater than 10.0 mg l −1 and the adsorbent dose exceeds 1.0 g l −1 , the removal rateterm·dom tends to slow. In this case, when adsorptionterm·key reaches saturation, increasing the dosage will cause violent collisions between adsorbent particles, reduce the number of channels contacting with antibioticterm·key molecules and thus affect the removal rateterm·dom [ 50 ]. From the contour map, the shape of the interaction between the initial concentration and amount of adsorbent added is closer to an oval, indicating a clear interaction between the two conditions.

Figure 10 c maps the effects of contact time and initial pollutant concentration on the removal rateterm·dom based on the solution pHterm·dom and the amount of adsorbent added under the centre point conditions (pHterm·dom 4; adsorbent dose, 1.0 g l −1 ). When the contact time and initial pollutant concentration increase, the removal rateterm·dom also gradually increases. When the initial concentration of the pollutant is 10.0 mg l −1 and the contact time is 4 h, the removal rateterm·dom reaches a maximum of 80.12%, because when the contact time is increased, adsorptionterm·key sites have more time for contact with and adsorptionterm·key of the antibioticterm·key molecules, resulting in a higher removal rateterm·dom. When the maximum removal rateterm·dom is reached, as the influencing factors increase, the removal rateterm·dom begins to plateau, as adsorptionterm·key equilibrium is achieved. The contour map reveals that the interaction between the contact time and initial pollutant concentration is closer to an oval, so there is a clear interaction between the contact time and the initial concentration of the pollutant.

The influence of the adsorbent concentration and initial solution pHterm·dom on the removal rateterm·dom under the centre point conditions of the pollutant concentration (10.0 mg l −1 ) and contact time (4 h) is shown in figure 11 a . When the amount of adsorbent and initial pHterm·dom are increased, the removal rateterm·dom gradually increases. For an adsorbent dosage of 1.0 g l −1 and initial pHterm·dom of 4, the removal rateterm·dom reaches a maximum of 80.12%. In this case, the higher adsorbent dosage causes an increase in the number of adsorptionterm·key sites. At the same time, when the pHterm·dom is in the lower range, many positive charges are formed on the surface of the remaining adsorbent; the higher proportion of adsorptionterm·key sites with positive charges, which strongly and electrostatically attract the antibioticterm·key molecules, produces a higher removal rateterm·dom. As the pHterm·dom increases, the negative charge content on the surface of the adsorbent increases, decreasing its electrostatic attractions with the antibioticterm·key molecules and decreasing the removal rateterm·dom. The contour map reveals that the interaction between the dosage of the adsorbent and initial pHterm·dom is closer to an oval, indicating the clear interaction between the dosage of the adsorbent and initial pHterm·dom.

The influence of contact time and initial solution pHterm·dom on the removal rateterm·dom under the centre point conditions of pollutant concentration (10.0 mg l −1 ) and adsorbent dosage (1.0 g l −1 ) is depicted in figure 11 b . When the contact time and initial pHterm·dom increase, the removal rateterm·dom also gradually increases. After 4 h at an initial pHterm·dom of 4, the removal rateterm·dom reaches a maximum of 80.12%; the increase in contact time enables more adsorptionterm·key sites to contact the antibioticterm·key molecules and adsorb them. However, as the pHterm·dom rises, the removal rateterm·dom gradually declines because the number of positive charges distributed on the surface of the remaining adsorbent decreases, resulting in weaker electrostatic attractions with the antibioticterm·key molecules and a lower removal rateterm·dom. From the contour map, the interaction between the contact time and initial solution pHterm·dom is closer to an oval, denoting a clear interaction between these two parameters.

Finally, figure 11 c presents the effect of contact time and the amount of adsorbent on the removal rateterm·dom under the centre point conditions of the pollutant concentration (10.0 mg l −1 ) and initial solution pHterm·dom (pHterm·dom 4). Accordingly, as the contact time and the amount of adsorbent added are increased, the removal rateterm·dom is gradually increased. For a contact time of 4 h and adsorbent dosage of 1.0 g l −1 , the removal rateterm·dom reaches a maximum of 80.12%. Here, the increases in the contact time and adsorbent dosage result in more adsorptionterm·key sites having more time to contact and adsorb the antibioticterm·key molecules, and hence, a higher removal rateterm·dom. It can be seen from the contour map that the interaction between the contact time and adsorbent dosage is closer to an oval, indicating a clear interaction between the contact time and adsorbent loading.

The Design-Expert 10.0 program was used to predict the optimal experimental conditions for the adsorptionterm·key of Levo-HClterm·dom by the CNCs-GOterm·dom, as follows: adsorbent dosage, 1.0 g l −1 ; initial pollutant concentration, 10.0 mg l −1 ; solution pHterm·dom, 4; and contact time, 4 h. Under these conditions, the Levo-HClterm·dom removal rateterm·dom can reach 80.12%. To verify the accuracy of the response surface model, three parallel experimentsterm·mth were performed under the predicted optimal conditions; the average value of the measured removal rateterm·dom was 80.06%, indicating that the calculation model has good predictive ability.

The effect of the amount of adsorbent on the adsorptionterm·key of Levo-HClterm·dom by the prepared composite is shown in figure 12 a . The reaction reaches the adsorptionterm·key equilibrium state at 240 min, and the removal rateterm·dom reaches 77.01% at an adsorbent dosage of 1.0 g l −1 . The removal rateterm·dom gradually decreases to 62.43% with the increase of adsorbent dosage from 1.0 to 2.5 g l −1 , which may be due to partial aggregation of the CNCs-GOterm·dom at higher concentration and the concomitant reduction of effective adsorptionterm·key sites [ 51 ]. Figure 12. Adsorptionterm·key of levofloxacin hydrochlorideterm·dom by CNCs-GOterm·dom under different influence factors.

Figure 12 b shows the effect of the initial pollutant concentration on this adsorptionterm·key system. As the initial concentration of antibioticterm·key increases, its rate of removal by the CNCs-GOterm·dom gradually increases, and the reaction reaches the adsorptionterm·key equilibrium state at 240 min. For an initial concentration increase from 6.0 to 10.0 mg l −1 , the Levo-HClterm·dom removal rateterm·dom increases from 44.45% to 72.59%. However, when the initial concentration rises from 10.0 to 12.0 mg l −1 , the removal rateterm·dom decreases from 72.59% to 53.38%. Thus, within a certain concentration range, as the initial concentration of the pollutant increases, the probability of binding antibioticterm·key molecules to the adsorptionterm·key sites on the CNCs-GOterm·dom surface increases, and the removal rateterm·dom increases at a faster rate. However, when the initial concentration is higher, the limited adsorptionterm·key sites and active sites tend to saturate, leading to reduced material removal ratesterm·dom.

The effect of the initial pHterm·dom of the solution on the adsorptionterm·key of Levo-HClterm·dom by the CNCs-GOterm·dom is shown in figure 12 c . The presence of H + or OH − in the solution changes the surface charge of the adsorbent. The figure reveals that the reaction reaches the adsorptionterm·key equilibrium state at 240 min, and the removal rateterm·dom is at least 55.68% at an initial pHterm·dom of 2. As the pHterm·dom increases, the removal rateterm·dom gradually increases, reaching a maximum of 82.19% at pHterm·dom 4. In the pHterm·dom range 4–9, the removal rateterm·dom gradually decreases. The variation of pHterm·dom results in both the protonation–deprotonation of functional groups in the nanocellulose and graphene oxideterm·dom and changes in the chemical speciation of ionizable organic compounds [ 52 ]. Levo-HClterm·dom can be adsorbed on the CNCs-GOterm·dom by surface complexation or cation exchange. If surface complexation governs the sorption process, the adsorption capacityterm·dom should not greatly decrease with the increase in solution pHterm·dom. At lower pHterm·dom values, the adsorption capacityterm·dom is found to be reduced due to the decreased interaction between the antibioticterm·key molecules and H + ions. However, as the pHterm·dom increases, the adsorption capacityterm·dom also increases slowly, due to the ionization of –COOH groups on the surface of the CNCs-GOterm·dom. When the pHterm·dom is higher than 4, the removal rateterm·dom decreases. This enhances the formation of water clusters and lowers the formation of H-bonds [ 9 ]. However, H-bonds may contribute to the Levo-HClterm·dom/composite interaction in acidic medium. In addition, there are electrostatic interactions between the active material on the surface of the adsorbent and the lone pair electrons on the surface of Levo-HClterm·dom acid.

According to the response surface analysis of the second-order polynomial regression equation for the removal rateterm·dom of Levo-HClterm·dom, the optimal adsorptionterm·key conditions can be obtained. At the same time, single-factor experimentsterm·mth are performed to verify the results. The optimal conditions obtained by the two methods are the same, which proves that the model can better fit the actual values, and has certain guiding significance in practical application.

Adsorption kineticsterm·key To clarify the adsorptionterm·key mechanism and confirm which of three processes—chemical reaction, diffusion, or mass transfer—dominate in the rate-controlling step, the adsorption kineticsterm·key of the aqueous Levo-HClterm·dom/CNCs-GOterm·dom system was studied. The characteristics of the adsorption kineticsterm·key are shown in figure 13 . The adsorption capacityterm·dom gradually increases over time, exhibiting rapid adsorptionterm·key in the initial stage (0–60 min) which can reach 60% of the equilibrium adsorptionterm·key amount. The adsorptionterm·key amount changes linearly with time, during which the surface-active sites of the CNCs-GOterm·dom become occupied and the adsorptionterm·key rate gradually decreases, eventually reaching saturation. It may be that, during the rapid adsorptionterm·key phase, the dispersion of the CNCs-GOterm·dom and Levo-HClterm·dom in water is high, and antibioticterm·key molecules quickly diffuse to the surface-active sites of the adsorbent. During the slow adsorptionterm·key phase, with the progress of the reaction, the Levo-HClterm·dom migrates toward the composite's internal pores and diffuses. This increases the mass transfer resistance, and the adsorptionterm·key rate, affected by the nature of the composite material, solution pHterm·dom and other factors, gradually decreases and finally reaches equilibrium. Figure 13. Adsorption kineticsterm·key of levofloxacin hydrochlorideterm·dom on CNCs-GOterm·dom: ( a ) pseudo-first-order kinetic model, pseudo-second-order kinetic model; ( b ) intra-particle diffusion modelterm·mth.

To investigate the adsorptionterm·key process of Levo-HClterm·dom on the CNCs-GOterm·dom, pseudo-first- and pseudo-second-order kinetics models were used to fit the experimental results. The equations are expressed as follows equations (3.2) and (3.3): 3.2 first-order kinetics model: ln ( q e − q t ) = ln q e − k 1 t and 3.3 second-order kinetics model: t q t = 1 k 2 q e 2 + t q e , where q e and q t are the amounts of adsorbed Levo-HClterm·dom per unit mass of adsorbent (mg g −1 ) at equilibrium and time t , respectively; k 1 is the pseudo-first-order sorption rate constant (min −1 ); and k 2 is the rate constant of pseudo-second-order adsorptionterm·key (g mg −1 min −1 ).

The kinetics parameters were calculated accordingly and are listed in table 6 . Comparing the correlation coefficients of the two models, the fitting parameter R 2 of the pseudo-second-order kinetics equation is higher than that of the pseudo-first-order kinetics equation, indicating that the adsorptionterm·key of Levo-HClterm·dom onto CNCs-GOterm·dom does not follow pseudo-first-order kinetics. This may be due to control of the boundary layer in the initial stages of adsorptionterm·key [ 53 ]. Therefore, a pseudo-second-order kinetics model is more suitable for describing the adsorption kineticsterm·key of Levo-HClterm·dom on CNCs-GOterm·dom. Thus, the adsorptionterm·key of Levo-HClterm·dom may be a process of chemical reaction to control the adsorptionterm·key rate between Levo-HClterm·dom and CNCs-GOterm·dom through electron sharing and electron exchange between particles [ 54 ]. The pseudo-first-order kinetics equation more suitably describes the initial stages of adsorptionterm·key, with some limitations, while the pseudo-second-order kinetics equation reflects the complete adsorptionterm·key process—membrane diffusion, surface adsorptionterm·key and internal diffusion—and is more suitable for the current system [ 55 ]. The adsorptionterm·key rate is directly proportional to the square of the pollutant concentration. The chemical reaction may be an important limiting factor for the adsorptionterm·key of Levo-HClterm·dom by the CNCs-GOterm·dom. This process may be affected by an interaction between the adsorbate and adsorbent or controlled by the exchange of electrons. The adsorptionterm·key of Levo-HClterm·dom may be a process of chemical reaction to control the adsorptionterm·key rate between Levo-HClterm·dom and CNCs-GOterm·dom through electron sharing and electron exchange between particles. Table 6. Parameters of kinetic model for levofloxacin hydrochlorideterm·dom adsorptionterm·key onto CNCs-GOterm·dom. kinetic model parameters values pseudo-first-order q e (mg g −1 ) 8.696 K 1 (min −1 ) 0.0133 R 2 0.9843 pseudo-second-order q e (mg g −1 ) 9.7383 K 2 (g mg −1 min −1 ) 0.0017 R 2 0.9867 intra-particle diffusion stage I K d1 (mg (g min 1/2 ) −1 ) 0.6201 C 1 -0.5926 R 1 2 0.8149 stage II K d2 (mg (g min 1/2 ) −1 ) 0.3732 C 1 2.6141 R 2 2 0.9861 stage III K d3 (mg (g min 1/2 ) −1 ) 0.0636 C 1 7.1735 R 3 2 0.9534 To predict the actual rate-controlling step in Levo-HClterm·dom adsorptionterm·key and further explore the adsorptionterm·key mechanism, a third model was applied. The intra-particle diffusion modelterm·mth provides a more comprehensive approach to defining the adsorptionterm·key mechanism, including transport in the adsorbate, external mass transfer, diffusion in the pores and chemical reactions (adsorptionterm·key–desorption). The model equation is expressed in equation (3.4): 3.4 q t = k 3 t 1 / 2 + C , where q t is the adsorptionterm·key amount at time t (mg g −1 ); k 3 is the rate constant for diffusion in the particle (mg (g min 1/2) −1 ; its relationship with the internal diffusion coefficient D is k 3 = 6 q e / r D / Π , where r is the particle radius); and C is the thickness of the boundary layer, (mm).

The calculated kinetics parameters are shown in figure 13 b and table 6 . From the figure, the adsorptionterm·key process of Levo-HClterm·dom by the CNCs-GOterm·dom involves a multi-level linear relationship. Initially, the adsorption capacityterm·dom increases rapidly with time. Then, the adsorptionterm·key rate gradually decreases to a dynamic equilibrium, indicating that the adsorptionterm·key process is affected by multiple diffusion steps. Three stages can be used to describe the diffusion/adsorptionterm·key process for this system. The first stage is rapid adsorptionterm·key, in which the Levo-HClterm·dom in the system quickly accumulates on the surface of the composite through membrane diffusion with a high adsorptionterm·key rate. In the second stage, surface chemical adsorptionterm·key occurs and the antibioticterm·key molecules gradually enter into the adsorbent, which is a rapid diffusion process in the particles. As the reaction progresses, diffusion into the particles gradually slows due to the decreasing adsorbate concentration, an increasing mass transfer resistance and an increasing boundary layer effect. In the third stage, the Levo-HClterm·dom continues to interact with the adsorbent, diffusion in the particles decreases, the solid–liquid phase distribution gradually becomes balanced, and the adsorptionterm·key amount no longer increases but gradually reaches equilibrium. The intra-particle diffusion modelterm·mth posits that, if three straight lines in the graphed equation pass through the coordinate origin, the rate-controlling step is intra-particle diffusion; if this condition does not hold, intra-particle diffusion is not the only controlling step, and other processes control the reaction rate, these processes together form control steps [ 56 , 57 ]. The three phase-fitting equations for the adsorptionterm·key of Levo-HClterm·dom on the CNCs-GOterm·dom do not pass through the origin, indicating that intra-particle diffusion is not the only rate-limiting step for the process, which therefore may be the combined effect of internal diffusion and surface adsorptionterm·key.

Adsorptionterm·key isotherms The equilibrium adsorptionterm·key isotherm is also used to understand the mechanism of the adsorptionterm·key. Adsorptionterm·key isotherm models describe the interaction between the adsorbate and adsorbent. Thus, nonlinear regression of the equilibrium data by either theoretical or empirical isotherm equations is essential to optimize the design of an adsorptionterm·key system. In this paper, the isotherm parameters for the adsorptionterm·key of Levo-HClterm·dom on CNCs-GOterm·dom were obtained using three equations from classical isotherm models (Langmuir, Freundlich and Sips). The adsorptionterm·key of Levo-HClterm·dom on CNCs-GOterm·dom was analysed at different temperatures. The Langmuir isotherm modelterm·mth is based on single-layer adsorptionterm·key on a surface with a limited number of adsorptionterm·key points and uniform adsorptionterm·key energy [ 58 ]. The equation is expressed as follows in equation (3.5): 3.5 q e = K L q max C e 1 + K L C e , where q e is the equilibrium adsorption capacityterm·dom (mg g −1 ); C e is the equilibrium concentration of the adsorbate in the solution (mg l −1 ); q max is the maximum amount of Levo-HClterm·dom adsorbed per unit weight of the adsorbent (mg g −1 ); and K L is the Langmuir adsorptionterm·key equilibrium constant related to the affinity of the binding sites and indicates the bond energy of the adsorptionterm·key reaction between adsorbent and adsorbate (l g −1 ).

The Freundlich isotherm modelterm·mth is an empirical equation that is used to understand adsorptionterm·key on heterogeneous surfaces with multiple adsorptionterm·key layers [ 58 ]. The equation is expressed as follows in equation (3.6): 3.6 q e = K F C e 1 / n , where K F (mg g −1 ) and n are Freundlich constants related to the adsorption capacityterm·dom and adsorptionterm·key intensity and spontaneity, respectively. A value of n in the range of 1 < n < 10 indicates a favourable adsorptionterm·key process. The greater the value of n , the more favourable is the adsorptionterm·key.

The Sips modelterm·mth is a combination of the Langmuir and Freundlich isotherms [ 59 ] and is expressed in equation (3.7) as 3.7 q e = q max × ( K S C e ) γ 1 + ( K S C e ) γ , where q max is the specific adsorption capacityterm·dom at saturation (mg g −1 ); K S is the Sips isotherm constant related to the energy of adsorptionterm·key (ml mg −1 ) and γ is a heterogeneity factor. If the value of K S approaches zero, the Sips isotherm equation follows the Freundlich isotherm modelterm·mth, whereas if the value of γ is equal or close to 1, the Sips isotherm equation reduces to the Langmuir isotherm.

Plots of the adsorptionterm·key isotherms are illustrated in figure 14 and the calculated parameters are listed in table 7 . The adsorptionterm·key amount is found to increase rapidly when the equilibrium concentration is low. As the equilibrium concentration increases, the adsorptionterm·key rate gradually decreases, and the adsorptionterm·key amount gradually approaches a saturated state. This may be because, for a fixed CNCs-GOterm·dom dosage, when the antibioticterm·key concentration increases, more antibioticterm·key molecules gather around the active sites of the CNCs-GOterm·dom and the adsorption capacityterm·dom increases. When the concentration exceeds a certain level, the surface-active sites become fully occupied and the CNCs-GOterm·dom can no longer absorb additional antibioticterm·key molecules; thus, the adsorption capacityterm·dom is close to saturation and the removal rateterm·dom decreases accordingly. Compared with Langmuir and Freundlich models, the correlation coefficient ( R 2 ) of the Sips modelterm·mth fitting is closer to 1, indicating that the Sips isothermal model can describe the adsorptionterm·key process well. In addition, in the Sips modelterm·mth, the value of γ is often used to indicate the heterogeneity of adsorptionterm·key. The closer the value of γ is to 0, the more uneven the surface adsorptionterm·key of the adsorbent is. In this study, the γ of the Sips modelterm·mth is 0.3852, which is close to zero, indicating that the surface of CNCs-GOterm·dom is not uniform, that is, the adsorptionterm·key process is a multi-layer chemical adsorptionterm·key on a heterogeneous surface. Figure 14. Adsorptionterm·key isotherms of CNCs-GOterm·dom for levofloxacin hydrochlorideterm·dom at different temperatures and fitting of Langmuir, Freundlich and Sips modelsterm·mth. Table 7. Isotherm parameters for adsorptionterm·key of levofloxacin hydrochlorideterm·dom onto CNCs-GOterm·dom at various temperatures. isotherm model temperature 293.15 K 303.15 K 313.15 K Langmuir q m (mg g −1 ) 49.7225 41.4427 55.7460 K L (l mg −1 ) 0.0370 0.0633 0.0495 R 2 0.9203 0.9262 0.9291 Freundlich K F (mg 1−1/n l 1/n g −1 ) 2.0657 3.1220 3.2790 1/ n 0.8040 0.7016 0.7403 R 2 0.9030 0.8980 0.9049 Sips q m (mg g −1 ) 17.2922 19.3374 23.2898 K s (l mg −1 ) 0.1911 0.2200 0.1975 γ 0.3852 0.3918 0.3809 R 2 0.9739 0.9825 0.9882 Analysis of the interaction mechanism For an additional investigation of the possible potential adsorptionterm·key sites and bonding modes, XPS and SEM-EDS were used to detect the synthesized CNCs-GOterm·dom after Levo-HClterm·dom adsorptionterm·key. Figure 15 a illustrates the XPS wide-scan spectra of the CNCs-GOterm·dom composite after Levo-HClterm·dom adsorptionterm·key. A distinct peak appeared after adsorptionterm·key at a binding energy of approximately 685.1 eV and was assigned to Levo-HClterm·dom, suggesting that the Levo-HClterm·dom was removed entirely. In addition, the composition of the composites after Levo-HClterm·dom adsorptionterm·key was verified by SEM-EDS ( figure 15 b , c ). The ratio of Leco-HCl was estimated at about 1.90% and is evenly dispersed on the surface of the material. Figure 15. ( a ) XPS and ( b , c ) SEM-EDS spectra of CNCs-GOterm·dom after Levo-HClterm·dom adsorptionterm·key.

The possible adsorptionterm·key mechanism of the antibioticsterm·key by CNCs-GOterm·dom is based on the following properties. First, the composite material has a large surface area and many adsorptionterm·key sites due to its large pore area and large void space between the core and the shell, which is conducive to the free diffusion of antibioticsterm·key on the adsorbent, and fully exposed active sites would enhance the opportunity for antibioticsterm·key to contact with the CNCs-GOterm·dom via electrostatic attraction. Second, a lot of oxygen-containing functional groups both in CNCs-GOterm·dom composite material afford the formation of hydrogen bonds with antibioticsterm·key molecules and rely on hydrogen bonding to produce adsorptionterm·key. In addition, CNCs-GOterm·dom with primarily π–π stacking could act as electron acceptors and be advantageous for adsorbing the antibioticsterm·key with an unsaturated bond ( figure 16 ) [ 60 ]. Therefore, CNCs-GOterm·dom can rapidly and efficiently adsorb levofloxacin hydrochlorideterm·dom antibioticsterm·key from aqueous solution due to their large specific surface area and abundant active, through sites π-π bond stacking, hydrogen bonding and electrostatic attraction. Figure 16. Schematic diagram of the CNCs-GOterm·dom for antibioticsterm·key adsorptionterm·key.

Conclusion In conclusion, a CNCs-GOterm·dom was prepared ultrasonically and its adsorptionterm·key properties for the antibioticterm·key levofloxacin hydrochlorideterm·dom were studied. Based on single-factor tests, a multiple regression model was obtained through fitting with Design-Expert 10.0 software, and the reliability of the model was verified. According to the optimization results from response surface graphs and practical operation, the optimal conditions for adsorptionterm·key were determined as an initial pollutant concentration of 10.0 mg l −1 , an initial pHterm·dom of 4, an adsorbent dosage of 0.1 g l −1 and a 4 h contact time. Adsorption kineticsterm·key data were best fitted with a pseudo-second-order model, suggesting the interaction of Levo-HClterm·dom with the CNCs-GOterm·dom via hydrogen bonding as well as electronic interaction, and the adsorptionterm·key is mainly controlled by chemical adsorptionterm·key behaviour. The three-parameter Sips modelterm·mth well described the adsorptionterm·key isotherms of the CNCs-GOterm·dom. Finally, this research shows that the prepared CNCs-GOterm·dom composite can be a potentially effective absorbent for the removal of antibioticsterm·key such as levofloxacin hydrochlorideterm·dom from aqueous solution.

Supplementary Material