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Microcrystalline cellulose production from sugarcane bagasse: Sustainable process development
Ranaprathap Katakojwala, S. Venkata Mohan
PII: S0959-6526(19)34212-X
DOI: https://doi.org/10.1016/j.jclepro.2019.119342 Reference: JCLP 119342
To appear in: Journal of Cleaner Production Received Date: 8 August 2019
Revised Date: 11 November 2019 Accepted Date: 15 November 2019
Please cite this article as: Katakojwala R, Mohan SV, Microcrystalline cellulose production from sugarcane bagasse: Sustainable process development, Journal of Cleaner Production (2019), doi:
https://doi.org/10.1016/j.jclepro.2019.119342.
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© 2019 Published by Elsevier Ltd.
Microcrystalline Cellulose Production from Sugarcane Bagasse: Sustainable Process Development
Ranaprathap Katakojwalaa,b and S Venkata Mohana,b*
aBioengineering and Environmental Sciences Lab, Department of Energy and Environmental Engineering,
CSIR-Indian Institute of Chemical Technology (CSIR-IICT), Hyderabad, 500 007, India
bAcademy of Scientific and Innovative Research (AcSIR), CSIR-Indian Institute of Chemical Technology (CSIR-IICT) campus, Hyderabad, India.
Graphical Abstract
Microcrystalline Cellulose Production from Sugarcane Bagasse: Sustainable Process 1
Development 2
Ranaprathap Katakojwalaa,b and S Venkata Mohana,b*
3
aBioengineering and Environmental Sciences Lab, 4
Department of Energy and Environmental Engineering, 5
CSIR-Indian Institute of Chemical Technology (CSIR-IICT), Hyderabad, 500 007, India 6
bAcademy of Scientific and Innovative Research (AcSIR), CSIR-Indian Institute of Chemical 7
Technology (CSIR-IICT) campus, Hyderabad, India.
8 9 10
Abstract 11
Sugarcane bagasse (SCB), a major and abundantly available byproduct of sugarcane processing 12
industries is presently being used for energy generation. However, its rich resource content 13
makes it a perfect feedstock for production of various value-added products. In this 14
communication, Microcrystalline Cellulose (MCC), a partially depolymerized cellulose is in 15
vogue due to its versatile physical properties which holds substantial applications in the 16
pharmaceutical industries was produced from the surplus available SCB. Experiments were 17
designed to optimize MCC production from SCB using three different chemical methods namely 18
MCC1, MCC2 and MCC3 by varying chemicals and reaction conditions. Relatively higher yield 19
of cellulose (0.34 g/g SCB) and MCC (0.32 g/g SCB) were observed with MCC2 method, 20
followed by MCC3 (cellulose 0.32 g/g SCB; MCC 0.31 g/g SCB) and MCC1 (cellulose 0.30 g/g 21
SCB; MCC 0.28 g/g SCB). Fourier transform infrared spectroscopy (FT-IR) showed good 22
correlation with commercial grade MCC for the characteristic functional groups. X-ray 23
diffractograms (XRD) showed 79.8%, 84.1% and 87.4% crystallinity for MCC1, MCC2 and 24
MCC3, respectively. The morphological analysis of all the three MCC samples correlated 25
effectively with the standard MCC. EcoScale analysis yielded score greater than 75, in which 26
MCC2 scored the maximum. However, Life cycle analysis depicted the impacts of MCC 27
production methods on various environmental impact categories, where MCC3 method showed 28
relatively less environmental impact in terms of global warming (27% less than MCC2). MCC3 29
further characterized with X-Ray Photoelectron Spectroscopy, Thermogravimetry analysis 30
(TGA), Differential thermal analysis (DTA) and Differential scanning calorimetry (DSC) 31
depicted its characteristic properties. The sustainability analysis functioned as a valuable tool to 32
assess the environmental impact of the process, prior to scaling up.
33
Keywords: Sustainability analysis; Agro-industrial waste; Resource Recovery; Life Cycle 34
Optimization; Bioeconomy; Global warming potential (GWP) 35
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1. Introduction 48
One of the major challenges in chemistry and engineering focused by the demand for climate 49
change mitigation is to develop green and sustainable technologies for the translation of waste 50
biomass to biofuels, commodity chemicals and novel bio-based materials (Sheldon, 2014). Agro- 51
industrial wastes are abundantly available and inexpensive source of lignocellulosic biomass, the 52
utilization and valorization of these wastes is considered to be a dynamic strategy that 53
contributes to the establishment of alternative, cleaner and sustainable processes to the 54
conventional processes (Çetinkaya and Yetilmezsoy, 2019). Sugarcane bagasse (SCB) is the 55
solid waste generated in huge quantities every year by the sugar processing and ethanol 56
production industries, and mainly used to generate electricity in the industry as it is having quite 57
good calorific value. Approximately 28-30 % of the processed sugarcane yields bagasse and only 58
half of it is used for co-generation (electricity) which is sufficient for the sugar processing unit, 59
and the rest is remains as waste (Soccol et al., 2010). Various SCB valorization processes and 60
bagasse derived products were reported in literature as they are applicable in paper production, 61
post fermentation products, additives, sugars (xylitol), cellulosic derivatives and as reinforced 62
fibers in composite materials (Katakojwala et al., 2019). Therefore, there is an ample scope for 63
the valorization of bagasse into value-added products with additional economic benefits apart 64
from its renewable characteristic (Ferriera 2019).
65
Typically, SCB is highly heterogeneous material consisting 40-45% of cellulose (mainly α- 66
Cellulose) which have more crystalline domains, 30-35% of hemicellulose, a heterogenous 67
polysaccharide consists of xylose (C5), arabinose (C5), galactose (C6) and mannose (C6) sugars.
68
The remaining fraction is generally lignin 18–25% with smaller amounts of minerals, wax and 69
ash content (Karp et al., 2013). Cellulose is the most abundantly available natural polymer with 70
long chains of D-glucose units connected through β-1, 4-glycosidic linkages and also it is the 71
most widely used organic material and acts as structural backbone in plants (Bochek et al., 72
2003). Cellulose holds substantial applications in the pharmaceutical industry as diluents, 73
lubricants, disintegrates, binders and coatings in the manufacture of tablets and capsule.
74
Production of eco-friendly and high-performance materials has been in the spotlight in recent 75
times, wherein microcrystalline cellulose (MCC) gained prominence for its application in various 76
industries due its rheological, extruding, physical and mechanical properties (Wan et al., 2018).
77
MCC is defined as “purified, partially depolymerized cellulose” produced by treating alpha 78
cellulose, derived from ligno-cellulosic biomass with mineral acids (Vora and Shah, 2015). The 79
particle size, thermal stability and tensile strength of MCC depends significantly on the 80
characteristics of the feedstock as well as manufacturing conditions such as acid concentration, 81
reaction conditions (El-Sakhwy and Hassan., 2007). MCC is used as excipient, binder and 82
adsorbent in pharmaceutical industry, as stabilizer, anti-caking agents, fat substitute, additive and 83
emulsifiers in food industry, as gelling agents, stabilizers and suspending agents in beverage 84
industry, and as fat substitutes, thickeners, stabilizer for emulsions, reinforcing agents for cement 85
based composite materials and binders in cosmetics, which is derived from hard wood (Hindi., 86
2017, Long 2019 and Ahsan et al., 2019). Several methods for the production of MCC from 87
biomass using chemical treatment like acids and alkalis (Trache et al., 2016), physical treatment 88
such as ultrasonic and extrusion methods (Abdullah et al 2016, Hanna et al., 2001), steam 89
explosion (Ha and Landi., 1998), enzymatic hydrolysis (Janardhan and Sain., 2006) and hybrid 90
processes like coupled acid-enzyme treatment (Agblevor et al., 2007) and radiative enzymatic 91
treatment (Henriksson et al., 2007) were reported. These cited methods are quite expensive as 92
the energy consumption for the treatment is very high in pressure intensive processes like steam 93
explosion and pressure extrusion. The enzymatic treatment is also not an economic strategy for 94
MCC production, due to the price of selective enzyme (Endogluacanase), however normal 95
cellulases may not selectively produce partially depolymerized cellulose, MCC and the 96
separation of cellulases in to Cellobiase, Exocellulase and Endocellulase is very difficult to act 97
selectively on the β-1, 4-glycosidic linkages to convert cellulose to MCC (Jørgensen et al., 98
2003). Although, these different methods reported the qualitative extraction of MCC from 99
waste, not much focus was given either on the quantitative production or on evaluating the 100
scalability of these processes. Therefore, the current study addresses these shortcomings by 101
providing a holistic view of the scalability of these methods without compromising the 102
sustainability at the industry level. Moreover, the quantitative production of MCC from SCB was 103
evaluated through characterization and sustainability analysis and that is the innovative approach 104
followed. In recent days, worldwide industrial attention is shifting towards the development of 105
efficient and more cleaner production practices that facilitates the product manufacturing in 106
sustainable manner eventually cutting down the environmental, economic and social impacts 107
(Gbededo 2018). In this study, three different methods were designed with different chemicals 108
and reaction conditions for the production of MCC and compared for their productivity and 109
sustainability. EcoScale was used as a tool to compare the processes based on safety, economics 110
and ecological features (Van Aken et al., 2006). Environmental sustainability analysis was 111
employed considering LCA tool to quantify and understand the definite impact of the method or 112
process on nature, society and economy which are considered as tangible pillars for 113
sustainability.
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2. Materials and Methods 117
2.1. Materials 118
Sugarcane bagasse (SCB) was collected from M/s. Gayatri Sugars Ltd. Kamareddy, India. SCB 119
was sorted and cleaned and dried in sunlight. The dried SCB cut into small pieces about 1-2 cm, 120
grinded and the fraction passing 40 meshes was selected as raw material for the process.
121
Technical grade Nitric acid (HNO3), Sulfuric acid (H2SO4), Hydrogen peroxide (H2O2), Sodium 122
hydroxide (NaOH), Acetic acid and Sodium hypochlorite (NaClO) were used. Cellulose 123
microcrystalline, analytical grade was purchased from Sigma-Aldrich as standard for 124
characterization analysis. Environmental sustainability analyses were performed with EcoScale 125
and LCA tools.
126
2.2. Compositional analysis of SCB 127
The compositional analysis of SCB was performed with NREL protocol for the determination of 128
Structural Carbohydrates and Lignin (Sluiter et al., 2008) 129
2.3. Experimental Methods 130
The processed SCB was used as substrate and production of MCC was achieved by extraction of 131
cellulose from SCB followed by conversion of cellulose to MCC. The pretreatments were 132
executed with different concentrations of Acids, alkali, reaction conditions. MCC production was 133
optimized with different methods as explained in the following sections and the schematic 134
representation of three methods was depicted in Fig. 1.
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Fig. 1 136
137
2.3.1. Method 1 (MCC1) 138
For extraction of cellulose from SCB, a three-step SCB delignification was implemented with 139
acid, alkaline pretreatment and oxidation process (Supranto et al., 2014). The grounded SCB was 140
first treated with 5% HNO3, at 80oC for 2 hours with stirring. The reaction was quenched with 141
distilled water and filtered. The solid residue was neutralized to pH 7 by washing with distilled 142
water. After drying, the residue was further treated with 2N NaOH at 80oC for 2 hours with 143
stirring. Lignin was removed as filtrate and the residue was neutralized with distilled water. The 144
delignified biomass was bleached with 10% CH3COOH/NaClO at 80oC for 2 hours with stirring.
145
The resulting solution was diluted with distilled water and filtered. Cream white pulpy cellulose 146
was extracted. The cellulose was subjected to depolymerization in presence of 10% H2O2 with 147
0.5 ml of H2SO4 at 80oC for 5 hours. A white turbid solution was resulted. After filtration, the 148
depolymerized cellulose was dried in oven overnight 60oC and grounded to produce a white, 149
crystalline powder.
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2.3.2. Method 2 (MCC2) 151
In this method, the grounded SCB was first treated with 2N NaOH at 70oC for 2 hours with 152
continuous stirring for the removal of lignin; the resulting solution was diluted with distilled 153
water to cease the reaction. The delignified solid residue dried and further treated with 1N H2SO4 154
at 70oC for 2 hours with stirring and the resulting solution is diluted with distilled water. The 155
residue was dried and bleached with 5% CH3COOH/NaClO at 80oC for an hour with stirring.
156
The bleached cellulose solution was diluted with distilled water and filtered. The extracted 157
cellulose is subjected to depolymerization in presence of 20% H2O2 and 0.5 ml of H2SO4 at 70oC 158
for 3 hours. A white turbid solution was resulted through oxidation process. As mentioned in 159
previous method (MCC1), the solid residue resulted in every step was neutralized to pH 7 with 160
water washing and the formed MCC was dried in oven at 60oC followed by grounding to 161
produce a white, crystalline powder.
162
2.3.3. Method 3 (MCC3) 163
In this method, for reducing the chemical load in extraction of cellulose from SCB, Autoclaving 164
was used as physical pretreatment followed by alkaline pretreatment and depolymerization.
165
Firstly, the grounded SCB was mixed with water in 1:10 ratio (W:V), autoclaved for 1 hour at 15 166
psi and 121oC for the removal of hemicellulose fraction. The solution was filtered and the 167
residue was treated with 10% NaOH at 75oC for an hour with stirring for the removal of lignin, 168
the solution with lignin was diluted with distilled water to stop the reaction and was filtered.
169
Then the delignified residue was dried and bleached with 5% CH3COOH/NaClO at 75oC for an 170
hour with continuous stirring. The solution thus obtained, was diluted with distilled water and 171
filtered to get cellulose as a residue. Then the cellulose was subjected to depolymerization in 172
presence of 30% H2O2 and 0.5 ml of H2SO4 at 75oC for an hour. A white turbid solution was 173
obtained through the oxidation process. The solid residue at every step was neutralized to pH 7 174
by washing with distilled water. The depolymerized cellulose was dried and ground to yield 175
white, crystalline powder.
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2.4. Microcrystalline Cellulose Characterization 177
2.4.1. Fourier-Transform Infrared Spectroscopy (FT-IR) Analysis 178
FT-IR spectra of the MCC samples were analyzed with Thermo Nicolet Nexus 670 179
spectrophotometer in the wave number range of 4000-400 cm-1 to identify the functional groups 180
present in them. The samples were grounded with potassium bromide and the pelletized. The 181
spectra resolution was maintained at 4 cm−1 for the analysis.
182
2.4.2. X-Ray Diffraction (XRD) Analysis 183
XRD was performed to the MCC samples using PANalytical Empyrean X-ray diffractometer 184
using Cu Ka (1.54178 Å) radiation with the X-ray generator operating at 45 kV and 40 mA.
185
Powder XRD was performed with slower scan rate and samples were loaded on to nickel coated 186
steel sample holders.
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2.4.3. Field Emission Scanning Electron Microscopy (FE-SEM) Analysis 188
Morphological studies of the MCC samples along with standard were examined through FE- 189
SEM (JOEL, JSM-7610F) with a voltage ranges from 1-15 kV. As the cellulosic samples were 190
non-conductive, the samples were coated with a thin layer of palladium prior to loading onto the 191
slide (Moubarik et al., 2013).
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2.4.4. X-Ray Photoelectron Spectroscopy (XPS) 193
XPS analysis of MCC sample was performed using Kratos Analytical (Shimadzu-Axis SUPRA) 194
Spectrometer equipped with Mg Kα line (hυ=1253.6 eV) at low pressure and operated at 10 eV 195
binding energy. The observation from the XPS analysis was used to understand the surface 196
characterization, empirical formula, chemical and electronic state of the elements within a 197
material based on the binding energies.
198
2.4.5. Thermal Stability Analysis 199
Thermal stability of MCC sample was evaluated with Thermogravimetry analysis (TGA), 200
Differential thermal analysis (DTA) and Differential scanning calorimetry (DSC). All the 201
thermal analyses were examined simultaneously using TA-SDT Q600 thermo gravimetric 202
analyzer. The analyses were carried in range of 25-600℃ with a ramp rate of 10°C per min under 203
high purity nitrogen.
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2.5. Environmental Sustainability Analysis 205
2.5.1. EcoScale Analysis 206
EcoScale is a tool that evaluates environmental feasibility of the synthesized product by 207
considering the crucial reaction parameters such as yield of the product, cost of the reactants and 208
reaction setup, safety pertaining to use of chemicals (reactants and products) and mode of 209
purification of the product (Van Aken et al., 2006). The Ecoscale was developed by assigning 210
penalty points to various parameters based on their property (S Table 4). It can act as potential 211
tool to correlate several production methods of a single product with respect to environmental 212
friendliness. The EcoScale was designed as a user-friendly tool for evaluating a process for its 213
ecological viability. Six typical parameters which can affect the quality of the product of a 214
reaction are taken in to consideration and the tool represents as a scale from 0 to 100, wherein 0 215
corresponds to a completely futile reaction in which the yield is 0 and 100 represents the ideal 216
reaction where the reaction efficiency is 100%. Penalty points were assigned to each parameter 217
and the EcoScale score was calculated by deducting the sum of all the penalty points from 100.
218
Higher the EcoScale value better the reaction efficiency. The EcoScale value more than 75 is 219
categorized as excellent process, the value in between 50 and 75 is acceptable and the process 220
with EcoScale value less than 50 is considered as inadequate process.
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2.5.2. Lifecycle Assessment (LCA) 222
LCA is considered as the most advanced tool for an integrated approach on the environmental 223
repercussions associated with raw material extraction from nature, processing, manufacture of a 224
product, distribution (transport), utility, repairs/modifications, maintenance, disposal of product 225
and recycling (ISO 2006). In this regard, LCA is widely used by environmental experts and 226
legislators for the organized evaluation of the environmental sustainability. For performing LCA 227
of a product or a process, the goal and scope of the study needs to be defined, in this study the 228
goal is to produce MCC from raw SCB. The functional unit of the study was set as one kilo gram 229
(kg) of MCC production in each described method it offers a reference to which the inputs and 230
outputs are associated. The boundary of the considered system and various stages involved in 231
MCC production were depicted in Fig. 2. SimaPro8.5.2.0 software was used to carry LCA for the 232
computational application of the inventory data of MCC production. The chemical processes 233
have been evaluated by considering the specifications as stated in international standards 234
organization (Kralisch et al., 2015). As a result of this analysis a complete impact of the 235
proposed process or product on various environmental impact categories would be assessed 236
thoroughly for its sustainability.
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Fig. 2 238
2.5.2.1. Inventory Data Acquisition 239
Inventory data for the direct inputs like raw materials, chemicals and energy along with outputs 240
such as the targeted products/by products and effluents to environment at each step of the 241
processes was taken from the Ecoinvent v3.4 database provided in SimaPro 8.5.2.0 LCA 242
software. The primary data pertaining to electricity requirements in different unit operations such 243
as mixing, filtration, autoclave, etc., as well as the chemicals, and water was considered from the 244
software. Secondary data for the processes such as production of electricity, chemicals, nutrients 245
and water was considered from ecoinvent database v3.4 (González-García et al., 2019). The data 246
quality requirements were addressed as per the guidelines of ISO 14044 to enable the goal and 247
scope of the study.
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2.5.2.2. Impact Assessment 249
The sustainability analysis was performed to understand the effects of different MCC production 250
processes on several impact categories viz., global warming, ozone depletion, human toxicity, 251
acidification, eutrophication, resource depletion and ecotoxicity. The impacts on those categories 252
were compared to evaluate the best sustainable process. The impact assessment analysis was 253
executed in SimaPro 8.5.2.0 software using CML-IA baseline V3.05/World 2000 and IPCC 254
GWP 100a methods to evaluate influence of a product/process on impact categories. The Centre 255
of Environmental Science, Leiden University, Netherlands have developed a method to assess 256
the impact of a product/process with respect to ten different impact categories called as CML-IA 257
baseline V3.05 / World 2000 method (Guinée., 2001). IPCC 2013 is the revised version of IPCC 258
2007 method, developed by the Intergovernmental Panel on Climate Change, Geneva, 259
Switzerland.
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3. Results and Discussion 261
3.1. Compositional analysis of SCB 262
The compositional analysis of SCB was depicted 42±1% of cellulose, 30±2% of hemicellulose, 263
lignin 20±2% with lesser quantities of ash (3–4%) and extractives.
264
3.2. MCC Production 265
3.2.1. Cellulose Extraction 266
Cellulose is the major fraction in the SCB residues and it can be utilized as a sustainable 267
(feedstock) for MCC production. Cellulose was initially extracted from SCB followed by the 268
production (de-polymerization of cellulose) of MCC from extracted cellulose. In MCC1 method, 269
the resulted cellulose content was 0.30±0.02 g/g SCB. MCC2 method yielded higher cellulose 270
(0.34±0.02 g/g) followed by MCC3 (0.32±0.02 g/g SCB). The variation in the yields resulted 271
from MCC1 and MCC2 methods might be due the acids utilized for the pretreatment of SCB and 272
the time of reactions. H2SO4 was used in MCC2 and it was considered to be best suitable for acid 273
pretreatment than HNO3 which was used in MCC1. In acid pretreatment the hemicellulose 274
content was removed effectively from SCB. Lignin removal plays a key role. Due to its complex 275
structure, strong alkali removes lignin content from SCB. The alkali pretreatment was carried 276
with NaOH in three designed methods for removing the lignin content from the bagasse, which 277
reacts with coniferyl, sinapyl and coumaryl groups of complex lignin structure and breaks the 278
cross linking between those groups results in dissolution of lignin. In the case of MCC3, the 279
process of physical pretreatment (autoclave) had significant influence on the cellulose 280
production. As hemicellulose is a not more complex material compared to cellulose and lignin, 281
physical pretreatment was sufficient without using acid in MCC3 method. Raw cellulose 282
produced in all the processes, contains color due the presence of some of the pigments, remove 283
by a combination of bleaching agents such as CH3COOH and NaClO resulted in the formation of 284
hypochlorous acid (HOCl) which is a highly reactive compound that acts as the best bleaching 285
compound.
286
3.2.2. Conversion of Cellulose to MCC 287
The highest yield of MCC from extracted cellulose was observed in MCC3 method. In MCC1 288
method, MCC produced was 0.93±0.01 g/g cellulose. In the second method (MCC2), production 289
of 0.92±0.01 g from gram of cellulose was noticed. Where in the third method, MCC produced 290
was 0.96±0.01 g/g cellulose. MCC production was also calculated with respect to the total 291
amount of SCB utilization and the productivity of MCC1, MCC2 and MCC3 were 0.28±0.02, 292
0.32±0.02 and 0.31±0.02 g/g SCB respectively. The important step in all the methods was 293
depolymerization of cellulose in presence of H2O2 in presence of H2SO4 which favors acid 294
hydrolysis where the long polymeric chain of cellulose will break to result in small chains of 295
glucose molecules, which was merely microcrystalline cellulose. The advantage of using 296
hydrogen peroxide was it can act as bleaching agent as well as delignifying agent and that result 297
in complete removal of lignin and color of produced MCC. The highest yield of cellulose and 298
MCC was observed in MCC2 method. The quantitative yields were listed in table 1.
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Table 1 300
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3.3. Structural and Functional Group Analysis 302
The FT-IR analysis was performed to study the chemical structures by dint of the functional 303
groups in each sample in comparison to the standard (Fig. 3). The comparative FT-IR spectra, 304
clearly indicating the absorption patterns of the samples at different frequency (wave number) 305
regions. The characteristic absorption at 3600-3200 cm−1, near 2900 cm−1, near 1635 cm−1,near 306
1430 cm−1, 1365 – 1375 cm−1, 1160 cm−1,1020 – 1060 cm−1 and near 898 cm−1 correlating with 307
the standard, authorized the presence of cellulosic functional groups in the MCC samples 308
(Kačuráková and Wilson., 2001). Absorbance peaks absorbed in the range of 3400-3200 cm−1 309
and 2900-2850 cm−1 are associated to stretching of OH groups (alcoholic groups) and aliphatic 310
C–H symmetrical stretching vibration respectively. The absorbance peaks located near 1635 311
cm−1, 1375 cm−1 and 1060 cm−1 are assigned to the O-H bending of absorbed water, in plane C-H 312
bending, and C-C, C-OH, C-H ring as well as side group vibrations respectively (Fan et al., 313
2012, Oun and Rhim., 2016). The absorption near 1160 cm−1 and 897 cm−1 were the 314
characteristic absorptions associated with ether functional groups of C-O-C asymmetrical 315
stretching and C– O–C stretching at β-(1→4) glycosidic linkages respectively. Additionally, the 316
spectra absorption peak at 1430 cm−1, which is attributed to a symmetric CH2 bending vibration 317
and is known as the “crystallinity band” confirms the presence of a greater number of crystalline 318
domains in the MCC samples (Kalita et al., 2013). The comparative study of functional group 319
vibrational modes of the samples and standard were depicted in Table 2. As a result of FT-IR 320
analysis, the structural and functional groups of the samples were observed as the pure cellulosic 321
materials. In FTIR analysis, the absorption peak of 1735 cm−1, which is specifically assigned to 322
the C=O stretching (Carbonyl group stretching) that represents aldehyde, ketone or carboxylic 323
acids in hemicellulose and the absorption peak near 1512 cm−1, which corresponds to the C=C 324
vibration of aromatic skeletal of lignin are not observed in any of the MCC samples. This 325
ensures the removal of hemicellulose and lignin effectively from bagasse during the pretreatment 326
steps and the cellulose only retained in the samples (Zhao et al., 2018).
327
Fig. 3 and Table 2 328
3.4. Crystallinity and Particle Size Analysis 329
X-ray diffraction spectra of MCC samples and control were shown in Fig. 4. The calculated 330
values for the percentage crystallinity of samples and the crystallite sizes pertaining to 1 0, 1 1 331
0 and 0 0 2 planes in each sample were summarized in Table 3. The crystalline index (CrI) of the 332
MCC samples were calculated from X-ray diffraction patterns using the following equation 333
(Segal et al., 1959).
334
(CrI) = [(I002 −Iam) / I002] ………. (1) 335
% Crystallinity = [(I002 −Iam) / I002] × 100 ……… (2) 336
Where, CrI is crystallinity index, I002 is the peak intensity of the 002-lattice plane at 2θ value 337
near 22.5°and Iam is the minimum intensity that lies between peaks at 2θ value 16o and 22°
338
respectively (near 2θ value 18°). The crystallite sizes were calculated from the following 339
Scherrer equation 340
L= kλ / βcosθ ………... (3) 341
Where, L is the size of crystallite (in nm), k is the Scherrer constant (0.94 for spherical crystals), 342
is the X-ray wavelength 0.154059 nm, β is the full width half maximum (FWHM) of the lattice 343
plane reflection in radian, and θ is the corresponds to Bragg reflection angle. The X-ray 344
diffraction demonstrated that all the samples hold the cellulose structure with the fingerprint 345
peaks near 15, 17, 22.5 and 35 2θ values, which was in harmony with the results of FT-IR. Four 346
crystalline peaks (101, 10 , 002 and 040) have been assumed in the X- ray diffractograms of the 347
current study (Park et al., 2010). The percentage crystallinity of MCC1, MCC2, MCC3 and 348
control was calculated as 79.8, 84.1, 87.4 and 89.8 respectively.
349
From the X-ray diffractograms, particle size of crystallites and percentage crystallinity were 350
calculated and the crystallinity of cellulose was improved with the pretreatment and 351
depolymerization. The increment in crystallite sizes in MCC 3 sample particles in 1 0, 1 1 0 352
and 0 0 2 crystallites strongly allied with the reduction in amorphous regions by the cleavage of 353
glycosidic bonds in cellulose. However, the crystalline regions were highly resistant to 354
hydrolysis (Wang et al., 2017). When the crystallinity size and index increase, the toughness of 355
cellulose structure improves that result in better mechanical properties.
356
Table 3 and Fig. 4 357
3.5. Morphological Analysis 358
The morphology of three MCC samples and standard were portrayed in Fig.5. The FE-SEM 359
images taken at similar resolution the particles’ size is larger in the case of MCC1 compared to 360
MCC2, MCC3 and standard MCC particles. This observation emphasizes that application of 361
higher concentrated chemicals facilitates the effective hydrolysis of glycosidic linkages in the 362
long cellulosic chains in MCC2 and MCC3. Heterogeneity is the specific characteristic of 363
cellulose materials (El-Sakhwy and Hassan., 2007). Due to the heterogeneity in the particle 364
morphologies, it is difficult to identify similar structures at a particular magnification. The 365
particles were clearly observed at lower voltage from 2-5 kV and at lower magnifications. MCC3 366
sample was comparatively identical in morphology with the standard.
367
Fig. 5 368
3.6. Process Sustainability Analysis 369
3.6.1. EcoScale Analysis 370
The EcoScale score of three MCC methods studied are coming to 78, 84 and 83 for MCC1, 371
MCC2 and MCC3 respectively (Table 4). The scores for all three methods are falling under 372
excellent category (EcoScale score > 75). The variation in penalty points was observed mainly 373
due to the yield of the product from the individual method. The yields from MCC1, MCC2 and 374
MCC3 process were 70%, 80% and 78% respectively. No penalty points were awarded in cost 375
and safety parameters because of inexpensive raw materials (waste/renewable feedstock) and 376
chemicals used in the process which are non-toxic were utilized in all the three methods. The 377
penalty points for temperature and reaction condition category were assigned based on the 378
operating temperatures and the time of reactions. In these parameters, MCC1 method scored 379
highest penalty points as the time of reactions was relatively more when compared to other two 380
methods. Simple reaction setup and purifications in all the methods were responsible for one 381
penalty point each under technical setup and work up purification respectively. MCC1 method 382
got least EcoScale value compared to MCC2 and MCC3 eventually due to lower yield and 383
additional heating operations in the process.
384
Table 4 385
3.6.2. Life Cycle Assessment 386
LCA is employed to study by considering critical issues such as data pertaining to chemicals 387
used, legal or intellectual property concerns and time constrains for pharmaceutical or 388
biochemical product. In this regard, the gate-to-gate life cycle assessment is considered wherein 389
the data of chemical substances incorporated and this would become a feasible approach 390
(Jiménez-González et al., 2000). In this study, the methodology considering selection of process, 391
definition of the process, mass balance and energy inputs was employed. The inventory data for 392
sustainability analysis of all the three MCC production methods was provided in table 5.
393
Table 5 394
3.6.2.1. CML-IA baseline V3.05 / World 2000 Method 395
CML-IA baseline V3.05 method was employed to study and compare the MCC production 396
methods as depicted in Fig. 6, in which impact categories such as Human toxicity, 397
Photochemical oxidation, Global warming (GWP100a), Ozone layer depletion, Eutrophication, 398
Abiotic depletion, Acidification, Terrestrial ecotoxicity, Fresh water aquatic ecotoxicity and 399
Marine aquatic ecotoxicity were considered for relative analysis. The contribution of each input 400
on the impact categories was provided in supplementary material (S Table 1-3). In comparison, 401
MCC1 has shown the highest impact on almost all the impact categories except on the abiotic 402
depletion, where MCC2 showed more impact due to the use of H2SO4 in the acid pretreatment 403
step as it has greater impact towards abiotic depletion than MCC1 method. The chemicals such 404
as NaOH, NaClO and H2O2 showed potential impact along with electricity on ozone depletion.
405
Among all the inputs, SCB and deionized water showed very less effect on all the impact 406
categories due to their amiability towards environment. A huge impact on fresh water, marine 407
aquatic ecotoxicity and terrestrial ecotoxicity was detected in all the three methods; however, 408
that was due to the electricity utilized in various unit operations. The usage of chemicals had less 409
influence than electricity. With reference to these observations, the prime untenable contributor 410
to environmental sustainability was identified as electricity and MCC3 method denoted minimal 411
impact on all the impact categories as the energy input in the form of electricity was lesser 412
compared to MCC1 and MCC2. As depicted in Table 6, influence of all the methods on marine 413
aquatic ecotoxicity and fresh water ecotoxicity due to the discharge of used water at all the stages 414
into the near water bodies. This accentuates the mandatory reuse, minimum water consumption 415
for processing and wastewater treatment (Sravan et al., 2016).
416
Table 6 and Fig. 6 417
3.6.2.2. Sankey Diagrams Using IPCC 2013 GWP 100a / V1.03 Method 418
Sankey diagrams prepared with IPCC GWP 100a method were provided in the supplementary 419
material (S Fig.1-3). Sankey are special type of flow diagrams, wherein the width of the arrow 420
represents to the quantity of the flow and that provides a proper understanding on the 421
contribution of the particular input on the environmental parameters. These diagrams establish a 422
pictorial significance on the major transfers or flows within the system. They are supportive in 423
detecting vital contributions to an overall energy flow in the system boundary. As depicted in 424
sankey diagrams of three MCC methods showed more than 95% of the global warming (CO2 kg 425
eq) contributed by electricity which was main energy input. Electricity utilized in various unit 426
operations was the major contributor towards environmental impact in the three methods. The 427
chemical inputs to the methods such as HNO3, H2SO4, NaOH, NaClO, CH3COOH and H2O2
428
were showed negligible contribution in global warming.
429
The performance of three methods were comparatively evaluated using IPCC GWP 100a method 430
(Table 7) illustrated the contribution of each input in terms of CO2 equivalents. The results 431
showed that MCC3 method had around 45% less impact than that of MCC1 in terms of global 432
warming. The variation in the concentrations of chemicals employed in pretreatment and 433
depolymerization of the methods had relatively minimum effect on the environmental impact 434
categories. In all the cases, the wastewater generated had a strong impact on the aquatic toxicity.
435
This impact could be reduced significantly by recovering the hemicellulose and lignin in the 436
respective step and convert them to value added products. The sustainability analysis with 437
various methods in SimaPro tool showed that MCC3 method for the production of MCC from 438
sugarcane bagasse was showing considerably lesser impact on the environmental impact 439
categories. Although pretreatment of SCB in MCC3 method was employed with more 440
concentrated chemicals than the other approaches but the time of reactions was reduced, this 441
observation confirmed that electricity had a potential impact on environment. Relatively, MCC3 442
method was found to be sustainable and ecofriendly process of producing MCC from SCB, when 443
compared to MCC1 and MCC2 approaches due to its less energy consumption along with less 444
global warming gases emissions to the environment. This approach will result in production of 445
biobased products with simultaneous high value carbon recycling and supports Bioeconomy 446
(Venkata Mohan et al., 2016). The data provided in ecoinvent database more often contains 447
information pertaining to specific geographical regions mainly the European Union. Therefore, 448
by expanding the database by incorporating various countries’ data, more accurate results could 449
be achieved with sustainability analysis 450
Table 7 451
3.7. Detailed Characterization of MCC3 452
The results derived from functional group, crystallinity, particle size and sustainability analyses 453
emphasize that MCC3 method is sustainable method adopted for production of MCC from SCB.
454
The further qualitative analyses for MCC3 were performed using XPS, TGA, DSC and DTA.
455
3.7.1. Elemental Analysis 456
The elemental analysis, chemical composition and empirical formula of MCC3 derived from 457
XPS analysis wide XPS, C(1s) and O(1s) spectra were depicted in Fig. 7. The wide XPS 458
spectrum confirmed that no impurities were present in the MCC3 sample other than Carbon and 459
Oxygen in the binding energy range of 280-290 eV and 525 to 535 eV respectively (Fig.7a). The 460
presence of hydrogen cannot analyze with XPS analysis as it in any compound does not have 461
core electron so that it will not produce photoelectron peak. Generally, the C(1s) signal of 462
polymers and polysaccharides perhaps deconvoluted in to four classes of carbons namely C1, 463
C2, C3 and C4 that corresponds to C-C/C-H, C-O/C-OH, C-O-C/C=O and O=C-O bonding 464
respectively. The C4 signal is not observed in MCC3 sample due to the absence of ester linkages 465
in the structure of cellulose (Pan et al., 2013). As described, the C(1s) spectrum of MCC3 sample 466
was deconvoluted into three peaks: C1 (∼285 eV), that corresponds to C-C and C-H linkages of 467
glucose molecules of cellulose. Further, C2 (∼286 eV) was associated with C-O linkage of 468
alcoholic groups and finally, C3 (∼288 eV) associated with C-O-C linkages ie., ether linkages 469
that connecting glucose molecules (β-1,4-glycosidic linkages) (Ferreira et al., 2018). The 470
deconvoluted carbon signals present in MCC3 were depicted in Fig.7b.
471
472
The quantitative contributions of C1, C2 and C3 were determined based on their respective peak 473
areas. The quantitative deconvolution of C(1s) signal of MCC3 sample resulted in 29.50% of C1 474
(C-C/C-H bonding), 59.25% of C2 (C-O of alcohol bonds) and 11.25% of C3 (C-O-C;
475
ether/glycosidic links).
476
Furthermore, the O(1s) spectrum deconvoluted to obtain two peaks namely O1 and O2, that 477
speaks for –OH...O (oxygen bonded to hydrogen through inter molecular hydrogen bonding) and 478
C-O-H (oxygen singly bind to carbon or hydrogen) respectively. The deconvoluted O(1s) 479
spectrum resulted in two peaks, namely O1 (∼532.8 eV) pertaining to C–OH...O and O2 (∼533.0 480
eV) of C–OH bonding (Yao et al., 2017). The deconvoluted oxygen signals present in the sample 481
were depicted in Fig.7c. The quantitative contribution of O1 peak was higher and it was around 482
79.20%, as microcrystalline cellulose sample contained numerous free –OH groups (hydroxyl 483
groups) and those are responsible for the inter hydrogen bonding between the oxygen atom of 484
one cellulosic chain and the hydrogen atom of another cellulose chain present in MCC. The 485
contribution of O2 peak was relatively lower and it was around 20.80%. The increment in the 486
intermolecular hydrogen bonding peak (O1) was witnessing the effective depolymerization of 487
cellulose to MCC, that resulted in the formation of a greater number of cellulosic fragments.
488
489
Fig. 7 490
3.7.2. Thermal Analysis 491
Thermal analysis techniques such as TGA, DSC and DTA were assayed to understand the 492
thermal stability parameter of MCC3 with respect to temperature change. The TGA curves 493
signified the loss in weight of the sample with the course of heating with a constant ramp rate.
494
The Thermo-gram curve showed two weight loss expressions in the region of 25-600˚C. The first 495
degradation pattern was noticed in the temperature range of 30-120˚C where the loss of weight 496
might be due to the evaporation of surface bound moisture (Fig 8). The inter-molecular hydrogen 497
bonded water was evaporated at near about 120°C for both samples (Kumar et al., 2014).
498
Maximum degradation was observed from 250°C to 350°C, which was about 80% of its total due 499
to the degradation of cellulose by means of decomposition of glycosyl units followed by its 500
transformation to char. The thermal degradation was observed at a lower temperature compared 501
to the cellulose sample within a broader range of temperature manifested the lower thermal 502
stability due to micro size, higher number of free ends in the chain, reduction in molecular 503
weight and degradation of amorphous domains of cellulose (Mandal and Chakrabarty., 2011).
504
Fig.8 505
DSC and DTA plot of MCC3 showed two distinct endothermic peaks (Fig. 8) which relates to 506
the composition of the material. The method of DSC assists the quantification of energy flow to 507
and from a sample during controlled increase in temperature. First endothermic peak was noticed 508
below 100°C due to the loss of moisture and second one was observed in between 250-400°C 509
region due to melting of crystallites depicted the nature of decomposition of cellulose (Varma 510
and Mondal., 2016). Higher onset temperatures that transmit to the higher thermal stabilities of 511
the sample, MCC3 sample holds high thermal stability due to its higher crystallinity which 512
makes it fit for the applications such as biocomposites, pharmaceutical compounds and food 513
stabilizers (Trache et al., 2014).
514
Therefore, the current study establishes an economically viable production of microcrystalline 515
cellulose from sugarcane bagasse. Sugarcane industry produces around 280-300 kg of bagasse 516
per ton sugarcane during processing. The present work was carried to produce 1 kg of MCC 517
from SCB and the optimized method, MCC3 consumes 2.225 kg of SCB which costs Indian 518
rupee (INR) 4.45 (INR 2/kg). The overall operating cost of physico-chemical pretreatment, alkali 519
pretreatment, bleaching, depolymerization and grinding would reach to INR 90 per kg MCC 520
production. The market value of MCC varies from INR 150 to 1000 per kg, based on its quality.
521
Moreover, the capital investment (CAPEX) and operating cost (OPEX) will be significantly 522
reduces with upscaling. The economics of the process further improves with simultaneous 523
recovery of hemicellulose and lignin fractions generated in the process.
524
4. Conclusion 525
The study depicted an approach to cleaner production of MCC from SCB sustainably, taking into 526
consideration its influence on the environmental impact categories through the lifecycle 527
optimization methodology. The developed process resulted in nearly 80% recovery of cellulose 528
from SCB as MCC (310 gm MCC per kg SCB). Although MCC production was observed to be 529
energy intensive, the incorporation of chemical catalyzed-physical pretreatment reduced the 530
duration of reaction time, in turn minimizing the power consumption without affecting quality 531
and yield of MCC. The structural and functional analysis of MCC correlated well with the 532
commercial grade MCC. Further, MCC analyzed with XPS, TGA, DSC and DTA depicted the 533
chemical composition and purity of the sample. The EcoScale values for all three methods 534
acquired excellent category, where MCC2 method scored the maximum. However, Lifecycle 535
assessment of process depicted that MCC3 method is environmentally more sustainable with 536
good resource recovery over MCC1 and MCC2 processes. IPCC GWP 100a method described 537
that around 98% total impact on global warming was due to electricity consumption than 538
remaining inputs in all three methods. MCC3 method showed less electricity consumption 539
making it more environmentally amiable. The present study developed an eco-friendly method of 540
MCC production from inexpensive, renewable and abundantly available feedstock (Bagasse), 541
which facilitates a sustainable valorization of lignocellulosic biomass. The study offers economic 542
and ecologically viable alternate to sugar processing industries to produce high value product, 543
MCC. Additionally, utilization of the hemicellulose and lignin streams from the process may be 544
incorporated in biorefinery approach and the sugarcane processing industries might capitalize 545
with this strategy. The major outcome of the study confers that the environmental sustainability 546
analysis of any process incorporating lifecycle optimization would promote in optimizing critical 547
factors, that favors resource utilization in process up-scaling with lower environmental impacts.
548
Acknowledgements 549
Department of Biotechnology (DBT), India supported the research through project grant 550
(BIOREVIEW; BT/Indo-UK/SVM/09/2018-19). Part of the research on sustainability analysis 551
was supported by CSIR-INPROTICS Project (HCP-0011; MLP-0033). M/s. Gayatri Sugars Ltd, 552
India provided SCB for the experimental work. The authors would like to thank the Director 553
CSIR-IICT for the support and encouragement. (Manuscript no. IICT/Pubs./2018/343).
554
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References 563
Abdullah, M.A., Nazir, M.S., Raza, M.R., Wahjoedi, B.A., Yussof, A.W., 2016. Autoclave and 564
ultra-sonication treatments of oil palm empty fruit bunch fibers for cellulose extraction and its 565
polypropylene composite properties. J Clean prod, 126, 686-697.
566
https://doi.org/10.1016/j.jclepro.2016.03.107 567
Agblevor, F.A., Ibrahim, M.M., El-Zawawy, W.K., 2007. Coupled acid and enzyme mediated 568
production of microcrystalline cellulose from corn cob and cotton gin waste. Cellulose, 14(3), 569
247-256. doi.org/10.1007/s10570-006-9103-y.
570
Ahsan, H.M., Zhang, X., Li, Y., Li, B., Liu, S., 2019. Surface modification of microcrystalline 571
cellulose: Physicochemical characterization and applications in the Stabilization of Pickering 572
emulsions. Int J Biol Macromol, 132, 1176-1184. doi.org/10.1016/j.ijbiomac.2019.04.051.
573
Bochek, A.M., 2003. Effect of hydrogen bonding on cellulose solubility in aqueous and non- 574
aqueous solvents. Russ J Appl Chem. 76(11), 1711-1719. https://doi.org/10.1023/
575
B:RJAC.0000018669.88546.56.
576
Çetinkaya, A.Y. and Yetilmezsoy, K., 2019. Evaluation of anaerobic biodegradability potential 577
and comparative kinetics of different agro-industrial substrates using a new hybrid computational 578
coding scheme. J Clean Prod. 238,117921. Doi.org/10.1016/j.jclepro.2019.117921.
579
El-Sakhawy, M. and Hassan, M.L., 2007. Physical and mechanical properties of microcrystalline 580
cellulose prepared from agricultural residues. Carbohyd Polym.67(1), 1-10, DOI:
581
10.1016/j.carbpol.2006.04.009.
582
Fan, M., Dai, D., Huang, B., 2012. Fourier transform infrared spectroscopy for natural fibres. In 583
Fourier transform-materials analysis. InTech. DOI: 10.5772/35482.
584
Ferreira, F.V., Mariano, M., Rabelo, S.C., Gouveia, R.F., Lona, L.M.F., 2018. Isolation and 585
surface modification of cellulose nanocrystals from sugarcane bagasse waste: from a micro-to a 586
nano-scale view. Appl Surf Sci. 436, 1113-1122.
587
Ferreira, F.V., Trindade, G.N., Lona, L.M.F., Bernardes, J.S., Gouveia, R.F., 2019. LDPE-based 588
composites reinforced with surface modified cellulose fibres: 3D morphological and 589
morphometrical analyses to understand the improved mechanical performance. Eur Polym J.
590
117, 105-113.
591
Gbededo, M.A., Liyanage, K., Garza-Reyes, J.A., 2018. Towards a life cycle sustainability 592
analysis: a systematic review of approaches to sustainable manufacturing. J Clean prod. 184, 593
1002-1015. Doi.org/10.1016/j.jclepro.2018.02.310.
594
González-García, Sara & GullónEstévez, Patricia & Gullón, Beatriz., 2019. Bio-compounds 595
Production from Agri-food Wastes Under a Biorefinery Approach: Exploring Environmental and 596
Social Sustainability. 25-53. http://dx.doi.org/10.1007/978-981-13-2408-6_2.Guinée, J., 2001.
597
Ha, E.Y. and Landi, C.D., FMC Corp, 1998. Method for producing microcrystalline cellulose.
598
U.S. Patent 5,769,934.
599
Guinée., 2001. Handbook on lifecycle assessment - operational guide to the ISO standards. Int J 600
Lifecycle Ass. 6, 255-255.
601
Hanna, M., Biby, G., Miladinov, V., University of Nebraska, 2001. Production of 602
microcrystalline cellulose by reactive extrusion. U.S. Patent 6,228,213.
603
Henriksson, M., Henriksson, G., Berglund, L.A., Lindström, T., 2007. An environmentally 604
friendly method for enzyme-assisted preparation of microfibrillated cellulose (MFC) nanofibers.
605
Eur Polym J, 43(8), 3434-3441. doi.org/10.1016/j.eurpolymj.2007.05.038.
606
Hindi, S.S., 2017. Microcrystalline cellulose: the inexhaustible treasure for pharmaceutical 607
industry. Nanosci Nanotech Res, 4(1), 17-24. DOI:10.12691/nnr-4-1-3.
608
International Organization for Standardization, 2006. Environmental Management: Life Cycle 609
Assessment; Principles and Framework (No. 2006). ISO.
610
Janardhnan, S. and Sain, M.M., 2006. Isolation of cellulose microfibrils–an enzymatic approach.
611
Bioresources, 1(2),176-188.
612
Jiménez-González, C., Kim, S. and Overcash, M.R., 2000. Methodology for developing gate-to- 613
gate life cycle inventory information. Int. J. LCA, 5(3), 153-159.
614
https://doi.org/10.1007/BF02978615.
615
Jørgensen, H., Kutter, J.P., Olsson, L., 2003. Separation and quantification of cellulases and 616
hemicellulases by capillary electrophoresis. Anal Biochem, 317(1), 85-93.
617
doi.org/10.1016/S0003-2697(03)00052-6.
618
Kačuráková, M. and Wilson, R.H., 2001. Developments in mid-infrared FT-IR spectroscopy of 619
selected carbohydrates. Carbohyd Polym, 44(4), 291-303. doi.org/10.1016/S0144- 620
8617(00)00245-9.
621
Kalita, R.D., Nath, Y., Ochubiojo, M.E., Buragohain, A.K., 2013. Extraction and 622
characterization of microcrystalline cellulose from fodder grass, Setariaglauca (L) P. Beauv, and 623
its potential as a drug delivery vehicle for isoniazid, a first line antituberculosis drug. Colloid 624
Surface B.108, 85-89. https://doi.org/10.1016/j.colsurfb.2013.02.016.
625
Karp, S.G., Woiciechowski, A.L., Soccol, V.T., Soccol, C.R., 2013. Pretreatment strategies for 626
delignification of sugarcane bagasse: a review. Braz Arch BiolTechn. 56,679-689.
627
http://dx.doi.org/10.1590/S1516-89132013000400019.
628
Katakojwala, R., Kumar, A.N., Chakraborty, D., Mohan, S.V., 2019. Valorization of sugarcane 629
waste: Prospects of a biorefinery. In Industrial and Municipal Sludge (47-60). Butterworth- 630
Heinemann. Doi.org/10.1016/B978-0-12-815907-1.00003-9.
631
Kralisch, D., Ott, D., Gericke, D., 2015. Rules and benefits of lifecycle assessment in green 632
chemical process and synthesis design: a tutorial review. Green Chem. 17, 123-145. DOI:
633
10.1039/C4GC01153H.
634
Kumar, A., Negi, Y.S., Choudhary, V., Bhardwaj, N.K., 2014. Characterization of cellulose 635
nanocrystals produced by acid-hydrolysis from sugarcane bagasse as agro-waste. J. Mater.
636
Chem. Phys., 2, 1-8.
637
Long, W.J., Tao, J.L., Lin, C., Gu, Y.C., Mei, L., Duan, H.B., Xing, F., 2019. Rheology and 638
buildability of sustainable cement-based composites containing micro-crystalline cellulose for 639
3D-printing. J Clean Prod. 239, 118054. Doi.org/10.1016/j.jclepro.2019.118054.
640
Mandal, A., Chakrabarty, D., 2011. Isolation of nanocellulose from waste sugarcane bagasse 641
(SCB) and its characterization. Carbohyd. Polym., 86, 1291-1299.
642
Mohan, S.V., Nikhil, G.N., Chiranjeevi, P., Reddy, C.N., Rohit, M.V., Kumar, A.N. and Sarkar, 643
O., 2016. Waste biorefinery models towards sustainable circular bioeconomy: critical review and 644
future perspectives. Bioresour. Technol. 215, 2-12.
645
https://doi.org/10.1016/j.biortech.2016.03.130 646
Moubarik, A., Grimi, N. and Boussetta, N., 2013. Structural and thermal characterization of 647
Moroccan sugar cane bagasse cellulose fibers and their applications as a reinforcing agent in low 648
density polyethylene. Compos Part B: Eng. 52, 233-238. DOI:
649
https://doi.org/10.1016/j.compositesb.2013.04.040.
650
Oun, A.A., Rhim, J.W., 2016. Isolation of cellulose nanocrystals from grain straws and their use 651
for the preparation of carboxymethyl cellulose-based nanocomposite films Carbohyd Polym.150, 652
187-200. https://doi.org/10.1016/j.carbpol.2016.05.020.
653
Pan, M., Zhou, X., Chen, M., 2013. Cellulose nanowhiskers isolation and properties from acid 654
hydrolysis combined with high pressure homogenization. BioResources, 8(1), 933-943.
655
Park, S., Baker, J.O., Himmel, M.E., Parilla, P.A., Johnson, D.K., 2010. Cellulose crystallinity 656
index: measurement techniques and their impact on interpreting cellulase performance.
657
Biotechnol Biofuels, 3, 10. DOI: 10.1186/1754-6834-3-10.
658
Segal, L.G.J.M.A., Creely, J.J., Martin Jr, A.E., Conrad, C.M., 1959. An empirical method for 659
estimating the degree of crystallinity of native cellulose using the X-ray diffractometer. Text Res 660
J. 29, 786-794. https://doi.org/10.1177%2F004051755902901003.
661
Sheldon, R.A., 2014. Green and sustainable manufacture of chemicals from biomass: state of the 662
art. Green Chem.16, 950-963. DOI: 10.1039/C3GC41935E.
663
Sluiter, A., Hames, B., Ruiz, R., Scarlata, C., Sluiter, J., Templeton, D. and Crocker, D., 2008.
664
Determination of structural carbohydrates and lignin in biomass. Laboratory analytical 665
procedure, 1617, 1-16.
666
Soccol, C.R., de Souza Vandenberghe, L.P., Medeiros, A.B.P., Karp, S.G., Buckeridge, M., 667
Ramos, L.P., Pitarelo, A.P., Ferreira-Leitão, V., Gottschalk, L.M.F., Ferrara, M.A., da Silva Bon, 668
E.P., 2010. Bioethanol from lignocelluloses: status and perspectives in Brazil.
669
Bioresour.Technol.101,4820-4825. https://doi.org/10.1016/j.biortech.2009.11.067.
670
Sravan, J.S., Kumar, A.N. and Mohan, S.V., 2016. Multi-pollutant treatment of crystalline 671
cellulosic effluent: Function of dissolved oxygen on process control. Bioresour.Technol.
672
217,245-251.
673
Supranto, S.U.P.R.A.N.T.O., Tawfiequrrahman, A.H.M.A.D., Yunanto, D.E., 2014. Sugarcane 674
Bagasse Conversion to High Refined Cellulose using Nitric Acid, Sodium Hydroxide and 675
Hydrogen Peroxide as the Delignificating agents. In Proceeding of the 27th Regional 676
Symposium on Chemical Engineering. 29-30.
677
Trache, D., Donnot, A., Khimeche, K., Benelmir, R., Brosse, N., 2014. Physico-chemical 678
properties and thermal stability of microcrystalline cellulose isolated from Alfa fibres. Carbohyd.
679
Polym., 104, 223-230.
680
Trache, D., Hussin, M.H., Chuin, C.T.H., Sabar, S., Fazita, M.N., Taiwo, O.F., Hassan, T.M., 681
Haafiz, M.M., 2016. Microcrystalline cellulose: Isolation, characterization and bio-composites 682
application—A review. Int J Biol Macromol. 93, 789-804.
683
https://doi.org/10.1016/j.ijbiomac.2016.09.056.
684
Van Aken, K., Strekowski, L., Patiny, L., 2006. EcoScale, a semi-quantitative tool to select an 685
organic preparation based on economical and ecological parameters. Beilstein J Org Chem. 2, 3.
686
DOI: 10.1186/1860-5397-2-3.
687
Varma A.K., Mondal, P., 2016. Physicochemical characterization and pyrolysis kinetic study of 688
sugarcane bagasse using thermogravimetric analysis. J. Energ. Resour., 138, 052205.
689
Vora, R.S., Shah, Y.D., 2015. Production of microcrystalline cellulose from cornhusk and its 690
evaluation as pharmaceutical excipient. IJRSI. 2, 69-74.
691
Wan, C., Jiao, Y., Wei, S., Zhang, L., Wu, Y. and Li, J., 2018. Functional nanocomposites from 692
sustainable regenerated cellulose aerogels: A review. Chem Eng J.
693
Wang, Z., Yao, Z., Zhou, J., Zhang, Y., 2017. Reuse of waste cotton cloth for the extraction of 694
cellulose nanocrystals. Carbohyd Polym. 157, 945-952.
695
https://doi.org/10.1016/j.carbpol.2016.10.044.
696
Yao, Q., Fan, B., Xiong, Y., Jin, C., Sun, Q., Sheng, C., 2017. 3D assembly based on 2D 697
structure of cellulose nanofibril/graphene oxide hybrid aerogel for adsorptive removal of 698
antibiotics in water. Sci Rep-Uk, 7, 45914.
699
Zhao, T., Chen, Z., Lin, X., Ren, Z., Li, B., Zhang, Y., 2018. Preparation and characterization of 700
microcrystalline cellulose (MCC) from tea waste. Carbohyd Polym. 184,164-170.
701
https://doi.org/10.1016/j.carbpol.2017.12.024.
702
703
704
705
Table 1: MCC productivity in three methods of MCC preparation from SCB Method Cellulose production
(g/g SCB)
MCC production (g/g Cellulose)
MCC production (g/g SCB)
MCC1 0.30±0.02 0.93±0.01 0.28±0.02
MCC2 0.34±0.02 0.92±0.01 0.32±0.02
MCC3 0.32±0.01 0.96±0.01 0.31±0.02
Table 2: FT-IR spectral data of MCC produced from SCB in comparison with standard
FT-IR peaks, wave number (cm-1)
Functional groups
MCC1 MCC2 MCC3 Standard
3448 3448 3382 3600-3100 OH-stretching
2913 2922-2854 2914 ~ 2900 C–H symmetrical stretching vibration 1636 1637 1632 ~ 1635 OH-bending of absorbed water 1430 1430 1429 ~ 1430 Symmetric CH2 bending vibration
(crystallinity band) 1374 1377 1374 1365 - 1375 In-the-plane CH bending 1161 1162 1162 ~ 1160 C-O-C asymmetrical stretching 1060 1025 1060 1020 - 1060 C-C, C-OH, C-H ring and side group
vibrations
897 898 897 ~ 898 C– O–C stretching at β-(1→4)-
glycosidic linkages
Table 3: Crystallite sizes and the percentage crystallinity of the MCC samples and standard
Sample L (nm) 101 L (nm) 10 L (nm) 002 CrI Crystallinity (%)
MCC1 0.37 0.60 1.84 0.80 79.8
MCC2 0.38 0.55 2.26 0.84 84.1
MCC3 0.41 0.65 4.46 0.87 87.4
Standard 0.45 0.68 5.12 0.90 89.8
Table 4:EcoScalescore of threeMCC production methods.
Parameters Penalty Points
MCC1 MCC2 MCC3
Yield 15 10 11
Cost Nil Nil Nil
Temperature/time 5 4 4
Safety Nil Nil Nil
Technical setup 1 1 1
Work up and Purification 1 1 1
EcoScale = [100-Sum of Penalty Points] 78 84 83
