Surface Modification of Biomass and Enhancing Sorption Capacity of Azo-dyes

doi:10.21203/rs.3.rs-3022790/v1

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Surface Modification of Biomass and Enhancing Sorption Capacity of Azo-dyes

https://doi.org/10.21203/rs.3.rs-3022790/v1

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The effectiveness of citric acid modified biomasses Rice Husk (RH) and Water Hyacinth (WH) in removing Congo Red (CR) dye from its aqueous solution was studied. The modified biosorbents were characterized using FTIR and SEM. The optimum conditions of parameters- pH, adsorption time and adsorbent dosage, were evaluated using Box-Behnken Design (BBD) and Response Surface Methodology (RSM) to maximize dye removal. For WH the optimum removal was 91.4% in time 22.649 minutes, at pH of 2.0 and adsorbent dosage of 0.198 g for 20mL of dye, which was obtained with a regression coefficient R² value of 0. 9905. Whereas for RH the optimum was 88.204% removal in 34.048 minutes, at pH of 2.0 and adsorbent dosage of 0.189 g in 20mL of dye with R² value of 0.9828. The model of the mechanism of adsorption and the multi-factor interactions were arrived at through statistical modelling and testing of the adsorption equilibrium data. Overall, this research focuses on sustainable and effective methods for removing Congo Red dye from water using modified agricultural waste biomass.

Rice Husk

Water Hyacinth

Adsorption

Congo Red

Response surface methodology

A dye is an organic chemical or aromatic, polyaromatic, or heterocyclic compound used as a colorant to change the colour of a product [1]. Wastewater which containing these dyes are often effluents generated and discharged by the paper, printing, textile, leather, plastics, rubber, cosmetics and food industries. Effluents from the dye industry are considered highly toxic to aquatic organisms and they have been proven to disrupt the natural balance and affect symbiotic processes, by disrupting the photosynthesis activity and oxygen production due to water coloring [2]. Economically removing these dyes from wastewater remains a relevant problem for industries.

This study focuses on the CR azo-dye, named [1- naphthalenesulfonic acid, 3,30-(4,40-biphenylenebis(azo)) bis (4-amino)disodium salt). CR is a commonly used azo-dye and according to studies, between 5,000–10,000 tons of dyes are released into the waterways annually [3]. The degradability of CR in water-based environment is low. Additionally, it metabolizes to in water, to the carcinogen benzidine. Hence, the need of the hour is efficient systems of removal of dye from water before it is released into the environment.

The conventional methods of removing dyes from waste waters, which can are classified into- physical, chemical, and biological methods, are plenty [4]. Examples of them are biological degradation, membrane filtration, and ion exchange [5], electrochemical oxidation [6], reverse osmosis [7], photocatalytic degradation [8], and adsorption [9] and many more.

The dye removal effectiveness, the capital costs, and the operating costs for these methods vary.

Since adsorption process tends to be an inexpensive, effective and simple operation, this approach appears to an engaging, viable, and effective alternative to these strategies [10]. The adsorption process occurs when a substance in the gas or liquid phase comes into contact with a material, causing the substance to adhere to the phase of the material. A mass transfer process occurs when gases or solvents are absorbed from solids or liquids [11]. It very well may be utilized in a large number of enterprises. Importantly, it can be used to adsorb biomass as an adsorbent to remove certain colors from wastewater [12]. The primary objective of this study is to investigate the use of inexpensive and abundant agricultural waste biomass as an adsorbent, specifically water hyacinth and rice husk, and to determine the ideal conditions for adsorption-based removal of Congo red (CR) dye from wastewater [13].

The outer covering or shell of the rice grain is referred to as the "rice husk," which is a by-product of the rice milling process. It is one among the most prevalent agricultural waste products produced globally, especially in nations where rice is a main food. It ranks among the most significant agricultural residues in terms of volume. On a weight basis for the total rice, it makes up around 20% of the production [14]. Due to its granular nature, chemical stability, ease of availability, and affordable manufacture, RH is frequently used as a bio-adsorbent [15].

Water Hyacinth, or Eichhornia crassipes, is a free-floating aquatic plant native to South America, which has spread quickly to various parts of the world. Due to its rapid growth rate and ability to colonize freshwater bodies, it is considered an invasive species and poses significant environmental challenges in many areas.

Using it as an adsorbent could be a potential solution to these challenges. Water hyacinth possesses many plus points as an adsorbent for dye removal. The plant’s abundance makes it a low-cost and easily accessible option. Moreover, water hyacinth is a renewable and eco-friendly resource.

WH biomass possesses a high adsorption capacity due to its large area of surface, as well as its complex structure. Its biological composition, including cellulose, hemicellulose, and lignin, interacts effectively with dye molecules. These benefits collectively position water hyacinth as a promising adsorbent for efficient and environmentally friendly dye adsorption [16].

Many studies have reported the improvements in adsorption capacity of adsorbent materials after undergoing surface modification [17–20]. Hence, the biosorbents, i.e RH and WH are modified with citric acid with the aim of improving their dye adsorption ability. Citric acid activates the -OH groups from cellulose and carboxylic groups through esterification reactions. Additionally, citric acid-modification does not produce hazardous waste. Hence, this process was adopted [21].

Also, it is attempted to optimize the factors utilized in the CR adsorption process from recreated waste water on the surface of modified biomass and explore the impacts of changing adsorption factors like adsorption time, pH, and dose of biosorbent on CR color adsorption [22]. Since they don't consider the connection between the boundaries, conventional techniques that change just a single boundary at an at once for enhancement. In this manner, for multi-part improvement, the statistical techniques DoE and RSM were used [23].

RSM was used to plan, dissect, and upgrade the variables influencing the adsorption cycle [24]. The Box-Behnken Design (BBD) inside RSM was explicitly utilized for its adequacy in accomplishing better streamlining yield. The essential goal was to inspect the effect of functional circumstances on the adsorption productivity of the changed RH and WH utilizing BBD of RSM. The relapse model got from the BBD worked with the investigation of element connections through a three-layered reaction surface [25].

To find effectiveness of the adsorption, equilibrium data obtained from batch studies under optimized conditions were analysed, and their dye removal % and adsorption capacity. Additionally, the statistical models best fitted to the system were obtained using the DoE methodology. Overall, this study focused on surface modifying the biosorbents, optimizing operational conditions, exploring factor interactions, and understanding the adsorption mechanism of CR dye using modified RH and WH adsorbents.

1. Materials and Chemicals

Rice husk, Water hyacinth petiole, Congo red dye (Loba Chemie), Nitric acid (72%, Himedia), Citric acid (C6H8O7), Sodium dihydrogen phosphate (NaH2PO4), Sodium hydroxide (Himedia), filter paper, pH paper.

2. Equipment

Vacuum pump, Buchner funnel, grinder, analytical balance, sieve mesh and shaker, ultrasonicator, hot-air oven, rotary shaker, pH meter, UV/Vis Spectrophotometer-Double Beam, Hot water bath.

3. Physical treatment of biosorbents

RH obtained was washed with distilled water and then sun-dried for one day. This was then dried in Hot-air oven at 50oC for 6 hours. Harvested WH plants were washed and the WH petiole was obtained by cutting away the rest of the plant. The WH petiole was sun-dried for 3 days. This was then dried in Hot-air oven at 80oC for 6 hours. Both RH and WH are crushed in a grinder to a powder and then using mesh no. 36 (425 micron).

4. Surface Modification of biosorbents

RH and WH powder were soaked for three hours in a 1:3 S/L 0.016 M HNO3 solution as a pretreatment. After being rinsed with distilled water until it reached neutrality, the mixture was then passed through a vacuum pump filter. After that, the hot-air oven was used to dry this completely [26].

The surfaces of RH and WH were modified in a conical flask containing 6 grams of the adsorbent powder, 0.75 grams of sodium dihydrogen phosphate, and 0.9 grams of citric acid. After 30 and 100 milliliters of distilled water were added, an ultrasonicator was used for 15 minutes to homogenize the mixture. The subsequent step involved heating for two hours in a hot water bath at 81°C. To eliminate any remaining citric acid and sodium dihydrogen phosphate, the mixture was heated, cooled, and then washed with distilled water. Subsequent to being dried in a hot air stove, the example was separated utilizing a vacuum pump [27].

5. Characterization of biosorbents

FTIR was used to examine the biosorbent's characteristic groups, and SEM was used to examine the surface morphology. The RH and WH adsorbent powder's functional groups were identified using Fourier Transform Infrared Spectroscopy (Bruker Alpha 2 ATR, Germany), both before and after surface modification. From 400 cm^-1 to 4000 cm^-1, infrared scanning was carried out. A Hitachi S-3400N, Japan, scanning electron microscope was used to examine the morphology of the adsorbent powder's surface before, during, and after dye uptake.

6. Experimental Design

In our experimentation, three major parameters of adsorption were taken into consideration, namely- pH, shaking time and adsorbent dosage. For the software optimization, Design Expert v13 was applied to design appropriate adsorption experimental runs which would minimum number of trials while also giving a comprehensive analysis of the parameters. The removal % of the dye from solution was chosen as output response (Y). The adsorption followed the Box-Behnken experimental design for the response surface methodology (RSM), with 17 experiments and replicates (n = 5) in the central point. The parameters’ chemometric study was done in time 10 min (-1), 30 min (0) and 50 min (+ 1), pH 2.0 (-1); 6.0 (0) and 10.0 (+ 1) and the dosage of biosorbents in 0.1g (-1), 0.2 g (0) and 0.3 g (+ 1), in which the number in parenthesis refers to the central (0), upper (+ 1) and lower (-1) coded variables.

For the RSM, the second-order polynomial equation which was suggested to fit the data and to choose the appropriate model terms is as described by Eq. 1:

y = β₀ + Σβ_iX_i + Σβ_iiX_i² + Σβ_ijX_iX_j (1)

where y denotes the predicted response, β₀ is the constant coefficient, β_i is the linear coefficient of the input factor X_i, βii is the quadratic coefficient of input factor X_i and β_ij is the linear interaction coefficient between input factors X_i and X_j.

To analyse the adequacy of the proposed models, analysis of variance (ANOVA) was performed to check the suitability of the response model function and the level of correctness of independent variables, and their effects [28].

7. Adsorption Studies

Adsorption was done as per design developed using the DoE (Box-Behnken) methodology to study the effects of variation of parameters- pH (2–10), contact time (10–50 min) and adsorbent dosage (0.1 to 0.3 g) affected adsorption. Batch adsorption was done with a mechanical shaker (150 rpm), by taking 20ml of 1g/L dye solution in 100-mL Erlenmeyer flasks. As per each trial run, the pH of the dye solution was increased and decreased by adding 0.1 M NaOH or 0.1 M HNO₃. Then, the appropriate weight of surface modified adsorbent was placed in to the CR dye soln. and shaken according to contact time. The mixture was immediately filtered, and the absorbance value of the suspended fluid was measured using UV-Vis spectrophotometry at λ_max 497 nm.

The quantity of CR adsorbed on the adsorbent can be found by calculating the adsorption capacity (q) and removal efficiency (% R) from: [29]

$$q=\frac{\left(Co-Ce\right)\times V}{m}$$

$$\%R= \frac{(Co-Ce)}{Co}\times 100\%$$

where q (mg /g) is the adsorption capacity, C₀ and C_e (mg/L) is the starting conc and ending conc. of CR, m (g) represents adsorbent dosage, and V (L) represents the dye solution volume[30].

1. Biosorbent Characterization

1.1 Surface functional groups

The surface-active groups on the biosorbent were examined using Fourier Transform Infra-Red Spectroscopy (FTIR) and

SEM was made use of to examine the surface characterization of RH and WH pre and post the surface modification, using FTIR-ATR to evaluate functional groups involved in the adsorption process, represented by the variation in wave number and peak intensity in each infrared spectrum [31]. The outcome of the citrate-induced modification was obtained from this analysis. The figures show the variation of wave numbers in the 500–4000 cm^-1 range of spectra of the adsorbents.

The spectra (Fig. 2a, 3a) of the raw adsorbents display some characteristics showing similarity with those seen in most natural fibers, such as the O-H stretching (3334 to 3390 cm^–1), C-H stretching in hydrocarbon chains (2890 to 2930 cm^− 1) and strong signals of C-O stretching (1027 to 1037 cm^–1), characteristic to lignocellulosic materials [32].

Comparing the FTIR spectra of RH before (Fig. 2a) and after (Fig. 2b) surface modification, there is significant increase in the peak intensity at 1027 to 1037 cm^-1. This is attributed to stretching caused by Si-O-Si group, combined with the C-O stretch of the primary alcohol at 1040 cm^-1. Higher intensity of peak around 3400 and 2900 cm^-1 indicates increase in free hydroxyl groups [33].

Again comparing the FTIR spectra of WH before (Fig. 3a) and after (Fig. 3b) surface modification, the noticeable increase in the peak intensity at 1027 to 1037 cm^-1 is attributed to elongation of ν(C-O-C) of glycosidic bonds. The adsorption peak around 3290 cm^-1 indicates stretching vibration caused by -OH groups. The peak at 1730 cm^-1, represents the C = O stretching vibration of ester in the surfaces [34].

1.2 Structural Properties and Morphology

A powerful investigative tool, SEM analysis involves using a beam of focused electrons to obtain high-magnification images of the surface topography of a sample. The surface morphology of the biosorbents, including their roughness, porosity, and abundance of surface structures, was investigated using scanning electron microscopy (SEM). The adsorption process can be predicted by evaluating the change in the biosorbent surface prior to, during, and following the dye uptake procedure [35].

The raw WH surface morphology is depicted in Fig. 4(a). It appears to have some protrusions and an uneven surface. On the surface of the modified WH shown in Fig., a hydrolysis reaction is initiated by the concentrated acid. 4(b), which results in the WH's fiber surface becoming uneven and rough. It has extra edges and bulges, which further develops effectiveness. The morphology of WH following dye uptake shows the changes caused by the adsorption process (Fig.). 4(c). Modified WH's pores and voids are covered by the CR dye after the adsorption procedure, creating a smooth and uniform surface. These results demonstrate that CR molecule attachment to the adsorbent surface results in dye production.

Figure 5(a) portrays the raw RH's surface morphology. The surface appears to be uneven and has protrusions. Figure depicts the modified RH surface. 5(b) is found to have a lot of pores and the shape of a tube, which makes it good for adsorption. Adsorption efficiency is improved by the presence of more ridges and protrusions in the mesoporous structure. As shown in Figure, 5(c): The adsorption outcomes are seen in the shape of RH following dye uptake. The deposition of CR dye into the pores of the modified RH fills in and smooths out the cavities and pores, creating a level surface.

WH and (c) WH after adsorption

Factor	Coded Symbol	Level
Factor	Coded Symbol	-1	0	+ 1
Adsorption Time (min)	A	10	30	50
pH	B	2	6	10
Adsorbent Dosage (g)	C	0.1	0.2	0.3

Batch Adsorption Studies

For this study, the three parameters of adsorption were taken into consideration, namely- pH, shaking time and adsorbent dosage. The degree of adsorption of dye was measured by calculating the difference in absorbance at λ_max 497 nm after suitable contact between the biosorbents and dye [36, 37]. For this study, the Box-Behnken Design (BBD) was used to design appropriate adsorption trial runs which would minimum number of experiments while also giving a comprehensive analysis of the parameters- adsorption time (A), pH (B) and adsorbent dosage (C), and their interactions. The removal % of the dye from solution was chosen as output response (Y) [38].

Following this, the adsorption model was calculated and the appropriate equations to describe the equation were generated.

2.1 Experimental Design of Water Hyacinth

The Quadratic model appeared to have the best fit, with a regression coefficient (R²) of 0.9905, indicating that model is significant. The F test gives a value of 186.46 and the value of p less than 0.0001, with A, B, C, AB, B² and C² being the significant terms. Further, the Lack of Fit test value is 2.03 (not significant), which means the model fits. Altogether, they indicate the model’s correctness, and that this is the recommended model [39, 40].

The quadratic model equation (BBD) relating the removal % to the parameters was developed from the results of the experiment and is determined to be (in terms of uncoded factors)-

Y = 67.05 + 6.19 A − 27.06 B + 7.79 C + 3.58 AB -1.79 AC + 2.18 BC − 1.25A² + 2.94B² − 3.13 C² (4)

RSM was used in order to optimize the conditions of CR dye removal. Further analysis is done by using Student's ‘T’ and Fisher's ‘F test’ [40]. The purpose of T test is to determine the regression coefficients’ significance of parameters. The ‘P’ value is used to measure the significance of the interactions amongst the variables. Generally, larger T and P values with smaller magnitudes give more significant coefficient terms [41, 42, 43]. Table II provides the coefficient of regression value and the t and P for the linear, interaction and quadratic values. As we can see from the high t-value and the small P-value, the coefficients are significant for the linear and quadratic effects of each factor.

Table 2

Regression Coefficients, T and P values of WH
Term	Coefficient	Std Error	T	P
Intercept	67.05	0.9011	-1.899	0.067
A-Time	6.19	0.7123	-7.866	< 0.0001
B- pH	-27.06	0.7123	-20.594	< 0.0001
C-Dosage of Adsorbent	7.79	0.7123	25.056	< 0.0001
AB	3.58	1.01	1.71	0.097
AC	-1.79	1.01	0.54	0.593
BC	2.18	1.01	-1.515	0.14
A²	-1.25	0.9819	-47.954	0
B²	2.94	0.9819	2.113	0.043
C²	-3.13	0.9819	-2.62	0.014

Table 3

ANOVA of the model for WH
Source	Sum of Squares	df	Mean Square	F-Value
Model	6812.42	9	756.94	186.46
Residual Error	28.42	7	4.06
Lack of fit	17.15	3	5.72	2.03
Pure Error	11.27	4	2.82
Total	6840.84	16

R² = 0.9958 Adjusted R² = 0.9905

2.2 Experimental Design of Rice Husk

The Linear model, appeared to have the best fit, with a regression coefficient (R2) of 0.9828, which indicates the model is significant. The F test gives a value of 305.99 and the p value is less than 0.0001, with A, B and C being the significant terms. Further, the Lack of Fit test value is 3.45 (not significant), which means the model fits. Altogether, they indicate the model’s correctness, and that this is the recommended model.

The linear regression model equation (BBD) relating the removal % to the parameters was developed from the results of the experiment and is determined to be (in terms of uncoded factors)-

$$Y=59.41+5.71A-28.22B+5.49C$$

Table 4

Regression Coefficients, T and P values of RH
Term	Coefficient	Std Error	T	P
Intercept	59.41	0.6636	2.158	0.039
A-Time	5.71	0.9673	321.876	< 0.0001
B- pH	-28.22	0.9673	6.979	< 0.0001
C-Dosage of Adsorbent	5.49	0.9673	19.112	< 0.0001

Table 5

ANOVA of the model for RH
Source	Sum of Squares	df	Mean Square	F-Value
Model	6871.78	3	2290.59	305.99
Residual Error	97.32	13	7.49
Lack of fit	86.22	9	9.58	3.45
Pure Error	11.10	4	2.77
Total	6969.1	16
R² = 0.9860 Adjusted R² = 0.9828

2. Optimization of Adsorption Parameters

Design Expert software’s numerical optimisation section allows the setting of possible targets for factors and responses such as minimising, maximising, targeting a value, allowing any value in a range and setting to an exact value (factors only). Each parameter’s desirability is usually weighted from zero to one to indicate its specific need. A desirability value of 1 indicates an ideal situation, and zero indicates that one or more responses exceeded the expected limit. Numerical optimization determines what will maximize value. Starting at a few points in the design space usually results in finding the best local maximum [44].

In the conducted study, the necessary goal of each parameter and of the response function is selected. The adsorption time is to be minimised, pH is kept in range (2–10), adsorbent dosage is minimised and removal percentage is maximised. Maximisation of response (Dye Removal %) is given a slightly higher weightage than the constraints on the parameters.

Instead of using a full factorial design, the 17 adsorption trials that were designed using the three factor Box-Behnken Design were used to investigate the effect of parameters like pH, adsorbent dosage, and time on the percentage of dye that was removed. Investigation of the reaction surface system gave the model, showed that the pH was the most powerful factor, while time and adsorbent dose made a lesser difference. When compared to RH, water hyacinth had a higher percentage of dye removal and a greater capacity for adsorbing CR dye. Additionally, for the purpose of WH and RH adsorption, the ideal parameter values were determined.

Through this investigation, it us shown that minimal expense, successful biosorbents can be integrated from natural wastes like RH and from a lake weed like WH. The functionalization of biosorbent utilizing citrus extract brought about better properties like increased surface area and structure, and higher density of groups acting as active sites. The modified adsorbent's characteristics improve adsorption effectiveness. The method of preparation was simple and is possible to be applied in a bulk scale. The CR from the water solution was successfully removed with the produced adsorbent.

Instead of using a full factorial design, the 17 adsorption trials that were designed using the three factor Box-Behnken Design were used to investigate the effect of conditions like pH, adsorbent dosage, and time on the percentage of dye that was removed. RSM study stated that the pH was the most influential variable, whereas time and adsorbent dosage had a lesser effect. Of the two adsorbents, water hyacinth showed greater adsorption capacity with respect to CR dye as compared to RH, and it had a higher dye removal %. Optimal parameter values for adsorption of WH and RH were calculated.

ACKNOWLEDGEMENTS

We are very thankful to RV College of Engineering, Bengaluru for facilitating this research study.

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Surface Modification of Biomass and Enhancing Sorption Capacity of Azo-dyes

Status:

Version 1

Abstract

Figures

INTRODUCTION

MATERIALS AND METHODS

1. Materials and Chemicals

2. Equipment

3. Physical treatment of biosorbents

4. Surface Modification of biosorbents

5. Characterization of biosorbents

6. Experimental Design

7. Adsorption Studies

RESULTS AND DISCUSSION

1. Biosorbent Characterization

1.1 Surface functional groups

1.2 Structural Properties and Morphology

WH and (c) WH after adsorption

Batch Adsorption Studies

2.1 Experimental Design of Water Hyacinth

R² = 0.9958 Adjusted R² = 0.9905

2.2 Experimental Design of Rice Husk

2. Optimization of Adsorption Parameters

CONCLUSIONS

Declarations

ACKNOWLEDGEMENTS

References

Status:

Version 1

Surface Modification of Biomass and Enhancing Sorption Capacity of Azo-dyes

Status:

Version 1

Abstract

Figures

INTRODUCTION

MATERIALS AND METHODS

1. Materials and Chemicals

2. Equipment

3. Physical treatment of biosorbents

4. Surface Modification of biosorbents

5. Characterization of biosorbents

6. Experimental Design

7. Adsorption Studies

RESULTS AND DISCUSSION

1. Biosorbent Characterization

1.1 Surface functional groups

1.2 Structural Properties and Morphology

WH and (c) WH after adsorption

Batch Adsorption Studies

2.1 Experimental Design of Water Hyacinth

R2 = 0.9958 Adjusted R2 = 0.9905

2.2 Experimental Design of Rice Husk

2. Optimization of Adsorption Parameters

CONCLUSIONS

Declarations

ACKNOWLEDGEMENTS

References

Status:

Version 1

R² = 0.9958 Adjusted R² = 0.9905