Composition Of Lignocellulosic Biomass Research Articles

The role of NMR spectroscopy in lignocellulosic biomass characterisation: A mini review

Lignocellulosic biomass (LB) is promising feedstock for the production of various bio-based products. However, due to its heterogenous character, complex chemical structure and recalcitrance, it is necessary to know its structural composition in order to optimize pretreatment process and further (bio)conversion into bio-based products. Nuclear Magnetic Resonance (NMR) spectroscopy is a fast and reliable method that can provide advanced data on the molecular architecture and composition of lignocellulosic biomass. In this brief overview, characteristic examples of the use of high-resolution NMR spectroscopy for the investigation of various types of LB and their structural units are given and the main drawbacks and future perspectives are outlined.

Enhancing monolignol ferulate conjugate levels in poplar lignin via OsFMT1

BackgroundThe phenolic polymer lignin is one of the primary chemical constituents of the plant secondary cell wall. Due to the inherent plasticity of lignin biosynthesis, several phenolic monomers have been shown to be incorporated into the polymer, as long as the monomer can undergo radicalization so it can participate in coupling reactions. In this study, we significantly enhance the level of incorporation of monolignol ferulate conjugates into the lignin polymer to improve the digestibility of lignocellulosic biomass.ResultsOverexpression of a rice Feruloyl-CoA Monolignol Transferase (FMT), OsFMT1, in hybrid poplar (Populus alba x grandidentata) produced transgenic trees clearly displaying increased cell wall-bound ester-linked ferulate, p-hydroxybenzoate, and p-coumarate, all of which are in the lignin cell wall fraction, as shown by NMR and DFRC. We also demonstrate the use of a novel UV–Vis spectroscopic technique to rapidly screen plants for the presence of both ferulate and p-hydroxybenzoate esters. Lastly we show, via saccharification assays, that the OsFMT1 transgenic p oplars have significantly improved processing efficiency compared to wild-type and Angelica sinensis-FMT-expressing poplars.ConclusionsThe findings demonstrate that OsFMT1 has a broad substrate specificity and a higher catalytic efficiency compared to the previously published FMT from Angelica sinensis (AsFMT). Importantly, enhanced wood processability makes OsFMT1 a promising gene to optimize the composition of lignocellulosic biomass.

Prediction of anaerobic degradation kinetics based on substrate composition of lignocellulosic biomass

This study presents a comprehensive biochemical predictive approach for assessing biogas production kinetics across ten lignocellulosic substrates in batch operation. The methodology employs a range of kinetic and regression models, all grounded in the substrates' chemical composition. Among the kinetic models, the cone model demonstrated superior performance, achieving an average error of 1.67 % in describing biogas production from all substrates. The quadratic Monod type model followed closely, with an error of 1.96 %. Among the regression models, on the other hand, the logistic function model exhibited enhanced predictive capabilities, yielding an average error of 6.02 %, while the Chen and Hashimoto one showed a higher error of 60.54 %. The findings underscore the potential of precise biogas production forecasting and tracking the daily rates of gas generation, rather than solely relying on cumulative gas yields at the end of the process.

Renewable Energy Potential: Second-Generation Biomass as Feedstock for Bioethanol Production.

Biofuels are clean and renewable energy resources gaining increased attention as a potential replacement for non-renewable petroleum-based fuels. They are derived from biomass that could either be animal-based or belong to any of the three generations of plant biomass (agricultural crops, lignocellulosic materials, or algae). Over 130 studies including experimental research, case studies, literature reviews, and website publications related to bioethanol production were evaluated; different methods and techniques have been tested by scientists and researchers in this field, and the most optimal conditions have been adopted for the generation of biofuels from biomass. This has ultimately led to a subsequent scale-up of procedures and the establishment of pilot, demo, and large-scale plants/biorefineries in some regions of the world. Nevertheless, there are still challenges associated with the production of bioethanol from lignocellulosic biomass, such as recalcitrance of the cell wall, multiple pretreatment steps, prolonged hydrolysis time, degradation product formation, cost, etc., which have impeded the implementation of its large-scale production, which needs to be addressed. This review gives an overview of biomass and bioenergy, the structure and composition of lignocellulosic biomass, biofuel classification, bioethanol as an energy source, bioethanol production processes, different pretreatment and hydrolysis techniques, inhibitory product formation, fermentation strategies/process, the microorganisms used for fermentation, distillation, legislation in support of advanced biofuel, and industrial projects on advanced bioethanol. The ultimate objective is still to find the best conditions and technology possible to sustainably and inexpensively produce a high bioethanol yield.

Role of Lignocellulosic Biomass Composition to Regulate Microbial Mutualism for Organic Mineralization and Humification during Digestate Composting

Role of Lignocellulosic Biomass Composition to Regulate Microbial Mutualism for Organic Mineralization and Humification during Digestate Composting

Can biomass structural composition be predicted from a small dataset using a hybrid deep learning approach?

The accurate determination of biomass composition can enhance the efficacy of biofuel yield evaluations and provide valuable estimates for process economic viability. This study proposes a generative adversarial algorithm (GAN)-based machine learning (ML) model (hybrid deep learning method) for predicting the composition of lignocellulosic biomass, which includes cellulose, hemicellulose, and lignin content. The model employs proximate and ultimate analyses of biomass as input features, while comparing the performance of three different ML models, with random forest (RF) outperforming all other models). The implementation of the GAN significantly enhanced the model's performance. For the test dataset, the R2 values experienced an increase across all biomass components, with cellulose demonstrating the most noteworthy improvement, elevating from 0.7233 to 0.854. To further enhance the predictive capabilities of the model, hyperparameter optimization was performed. Before optimization, the RF model displayed strong correlations between predicted and actual values. However, post-optimization improvements were relatively minimal, with Grid Search and Bayesian Optimization yielding only marginally better results than Randomized Search. Additionally, an interpretable analysis was conducted to provide a deeper understanding of the relationship between proximate and ultimate analysis, and the structural composition of biomass. The findings presented in this study could prove useful in assessing the potential of biomass for the production of bio-based chemicals and biofuels.

Application of DFT and Response Surface Models to analyze the adsorption process of basic blue 3 and reactive blue 19 dyes on sugarcane bagasse and coconut endocarp biomass

In this study, the biomass wastes of sugarcane bagasse and coconut endocarp were used for the adsorption of basic blue 3 and reactive blue 19 dyes from aqueous solutions. DFT calculations and Response Surface Models (RSM) were used to analyze the adsorbent-adsorbate interactions and to propose an adsorption mechanism. These adsorbents were characterized by FTIR, DRX, SEM and EDX. RSM allowed the evaluation of the effects of temperature and pH on the dye adsorption. DFT modeling was used to analyze the dye – biomass interactions using six representative model molecules for the composition of lignocellulosic biomass. The experimental and modeling results showed that the best experimental conditions to remove BB3 dye were 40 °C and pH 4, while the best conditions for RB19 dye adsorption were 40 °C and pH 6. RSM models satisfactorily correlated the experimental data with R2 values of 0.90 – 0.98 and modeling errors of 0.30 – 1.53%. The adsorption of these dye molecules was endothermic and governed by physical interaction forces. The adsorption capacity for BB3 increased with increasing pH, whereas that of RB19 showed the opposite trend. DFT calculations indicated that BB3 and RB19 molecules interact via the nitrogen located in the phenoxazine and the sulfonate group, respectively, with the hydroxyl and oxygenated groups of monomers that compose the natural polymers of these lignocellulosic materials.

Effect of lignocellulosic biomass composition on the performance of biochar for the activation of peroxymonosulfate to degrade diclofenac

In this study, poplar flour (PF) and walnut shell flour (WSF) with different proportions of lignocellulosic composition were selected as representative plant-based biomass precursors to prepare biochars (PB and WSB) for the activation of peroxymonosulfate (PMS). The structure of biochars and the removal performance of diclofenac (DCF) were examined in detail. Generally, the PB derived from PF that contained higher ratio of cellulose outperformed the WSB derived from WSF that contained higher ratio of lignin in the adsorption and degradation of DCF due to the more graphitized and aromatic structure of PB than that of WSB. Moreover, the adsorption and catalytic performance of two kinds of biochars were improved with the increase of pyrolysis temperature. The superior porosity property, abundant oxygen containing functional groups and good electrical conductivity of PB1000 led to the stronger capability than that of WSB1000. Nonradical oxidation mechanism including the singlet oxygen (1O2) and electron transfer was found to contribute to DCF degradation. Moreover, the PB1000 also demonstrated stronger adaptability to complex water environment than WSB1000. The findings of this work may provide enlightenment in the selection of lignocellulosic bio-wastes to prepare biochar for the degradation of organic pollutants.

Primary aerosol emissions from lignocellulosic biomass and major constituents under well-defined pyrolysis conditions

Near-source aerosol emissions from biomass burning are challenging to model and predict because of the complexity of the feedstocks and the evolved species, as well as the potential variability in local conditions. This study investigated and characterized near-source emissions from biomass and biomass constituent pyrolysis under well-defined conditions, providing data for validation of aerosol formation models and sub-models, and tested a hypothesis that near-source aerosol emissions from biomass can be predicted through a summative model approach based on constituent emissions and lignocellulosic biomass composition. Aerosol emission factors, concentrations, sizes, and volatility were assessed during highly repeatable lignocellulosic biomass and constituent pyrolysis experiments using a macro-TGA. Lignin and cellulose were found to produce significantly more and larger pyrolysis aerosol emissions than hemicellulose, and lignin produced aerosols with lower volatility than those from hemicellulose and cellulose. Lignocellulosic composition was investigated for its influence on primary aerosol emissions from biomass pyrolysis, including contributions to aerosol quantity, size, and volatility, through two approaches. First, the superposition model was tested using experimental aerosol measurements from individual constituents. Second, a simulated pine sample was produced by mixing constituents in the proportions in which they are present in pine, and aerosol emissions from pyrolysis of this sample were experimentally measured. Simulated aerosol emissions from superposition modeling and mixed-constituent-derived “pine” were compared to elucidate synergistic constituent influences on aerosol formation, and both results were found to predict pinewood aerosol emissions well. Conclusions regarding the successes and failures of the superposition model in predicting primary biomass aerosol emissions under similar conditions were drawn. Lastly, the influence of combustion on the biomass aerosol emissions under the conditions studied in this work was characterized to highlight the applicability and limitations of the pyrolysis results presented in this work to near-source aerosols formed under oxidative conditions.

Challenges of Biomass Utilization for Bioenergy in a Climate Change Scenario.

Simple SummaryThe most recent intergovernmental panel on climate change (IPCC 2021) has shown that the human influence on climate change has been unprecedented, predicting a global temperature increase of 1.5 °C in the earlies 2030s. The burning of fossil fuels has increased the emissions of nitrous oxide (N2O), methane (CH4), and carbon dioxide (CO2) to the atmosphere, amplifying the greenhouse effect in the last decades. In this scenario, the use of biorefineries, a renewable analog to petroleum refineries, has attracted a lot of attention since they use renewable sources as lignocellulosic feedstocks. However, climate change alters the temperature, rainfall patterns, drought, CO2 levels, and air moisture impacting biomass growth, productivity, chemical composition, and soil microbial community. Here, we discuss strategies to produce fuels and value-added products from biomass in a climate change scenario, potential feedstocks for bioenergy purposes, the chemical composition of lignocellulosic biomass, enzymes involved in biomass deconstruction, and other processes related to biomass production, processing, and conversion. Understanding these integrated factors involved in bioenergy production with plant responses to climate change shows that climate-smart agriculture is the only way to lower the negative impact of climate changes on crop adaptation and its use for bioenergy.The climate changes expected for the next decades will expose plants to increasing occurrences of combined abiotic stresses, including drought, higher temperatures, and elevated CO2 atmospheric concentrations. These abiotic stresses have significant consequences on photosynthesis and other plants’ physiological processes and can lead to tolerance mechanisms that impact metabolism dynamics and limit plant productivity. Furthermore, due to the high carbohydrate content on the cell wall, plants represent a an essential source of lignocellulosic biomass for biofuels production. Thus, it is necessary to estimate their potential as feedstock for renewable energy production in future climate conditions since the synthesis of cell wall components seems to be affected by abiotic stresses. This review provides a brief overview of plant responses and the tolerance mechanisms applied in climate change scenarios that could impact its use as lignocellulosic biomass for bioenergy purposes. Important steps of biofuel production, which might influence the effects of climate change, besides biomass pretreatments and enzymatic biochemical conversions, are also discussed. We believe that this study may improve our understanding of the plant biological adaptations to combined abiotic stress and assist in the decision-making for selecting key agronomic crops that can be efficiently adapted to climate changes and applied in bioenergy production.

Cell Wall Compositions of Sorghum bicolor Leaves and Roots Remain Relatively Constant Under Drought Conditions.

Renewable fuels are needed to replace fossil fuels in the immediate future. Lignocellulosic bioenergy crops provide a renewable alternative that sequesters atmospheric carbon. To prevent displacement of food crops, it would be advantageous to grow biofuel crops on marginal lands. These lands will likely face more frequent and extreme drought conditions than conventional agricultural land, so it is crucial to see how proposed bioenergy crops fare under these conditions and how that may affect lignocellulosic biomass composition and saccharification properties. We found that while drought impacts the plant cell wall of Sorghum bicolor differently according to tissue and timing of drought induction, drought-induced cell wall compositional modifications are relatively minor and produce no negative effect on biomass conversion. This contrasts with the cell wall-related transcriptome, which had a varied range of highly variable genes (HVGs) within four cell wall-related GO categories, depending on the tissues surveyed and time of drought induction. Further, many HVGs had expression changes in which putative impacts were not seen in the physical cell wall or which were in opposition to their putative impacts. Interestingly, most pre-flowering drought-induced cell wall changes occurred in the leaf, with matrix and lignin compositional changes that did not persist after recovery from drought. Most measurable physical post-flowering cell wall changes occurred in the root, affecting mainly polysaccharide composition and cross-linking. This study couples transcriptomics to cell wall chemical analyses of a C4 grass experiencing progressive and differing drought stresses in the field. As such, we can analyze the cell wall-specific response to agriculturally relevant drought stresses on the transcriptomic level and see whether those changes translate to compositional or biomass conversion differences. Our results bolster the conclusion that drought stress does not substantially affect the cell wall composition of specific aerial and subterranean biomass nor impede enzymatic hydrolysis of leaf biomass, a positive result for biorefinery processes. Coupled with previously reported results on the root microbiome and rhizosphere and whole transcriptome analyses of this study, we can formulate and test hypotheses on individual gene candidates’ function in mediating drought stress in the grass cell wall, as demonstrated in sorghum.

Enhancing in vitro ruminal digestibility of oil palm empty fruit bunch by biological pre-treatment with Ganoderma lucidum fungal culture.

The changes in lignocellulosic biomass composition and in vitro rumen digestibility of oil palm empty fruit bunch (OPEFB) after pre-treatment with the fungus Ganoderma lucidum were evaluated. The results demonstrated that the pre-treatment for 2-12 weeks has gradually degraded the OPEFB in a time-dependent manner; whereby lignin, cellulose, and hemicellulose were respectively degraded by 41.0, 20.5, and 26.7% at the end of the incubation period. The findings were corroborated using the physical examination of the OPEFB by scanning electron microscopy. Moreover, the OPEFB pre-treated for 12 weeks has shown the highest in vitro digestibility of dry (77.20%) and organic (69.78%) matter, where they were enhanced by 104.07 and 96.29%, respectively, as compared to the untreated control. The enhancement in the in vitro ruminal digestibility was negatively correlated with the lignin content in the OPEFB. Therefore, biologically delignified OPEFB with G. lucidum fungal culture pre-treatment have the potential to be utilized as one of the ingredients for the development of a novel ruminant forage.

Statistical prediction of biogas and methane yields during anaerobic digestion based on the composition of lignocellulosic biomass

In the described study, the relationships between the percentage and structure of selected lignocellulosic components and the efficiency of their anaerobic digestion and the quality of the produced biogas were analyzed. This research included various lignocellulosic raw materials. The biogas efficiency and quality tests were carried out according to DIN standard 38 414-8 (1985) and VDI standard 4630 (2016). Multiple TAPPI standards and the Seifert method were used to determine the chemical composition of the lignocellulose materials. Lignin structure analysis was performed using Fourier transform infrared spectroscopy. Wide-angle X-Ray scattering analysis was used to determine the degree of crystallinity of cellulose. The biogas was positively correlated with C=O and the syringyl to guaiacyl ratio, and negatively correlated with the crystalline structure of cellulose, lignin, cellulose, and extractives. In addition, methane was positively correlated with holocellulose and extractives and negatively correlated with the crystalline structure of cellulose, cellulose, substances soluble in NaOH, and the OH groups. The found independent features accounted for 86.0% of the biogas variability and 68.0% of the methane variability.

A comprehensive review of characterization, pretreatment and its applications on different lignocellulosic biomass for bioethanol production

With the draining of petroleum derivatives, expanding natural contamination issues, there has been rising enthusiasm for the examination of lignocellulosic biomass for an alternative fsource of energy. Characterization of various biomass, its intermediate, and products is a need for conversion of any biomass to biofuels. Chemical composition of lignocellulosic biomass is an essential point for developing potent pretreatment technologies to break its rigid structure, conversion of sugar by different enzymes mainly cellulose to glucose and even various microorganisms which can ferment sugars into bioethanol and other value-added green chemicals. In this present review work, the main focus is on the proximate and ultimate analysis of different feedstocks, and altered pretreatment techniques such as physical, chemical, physicochemical, and biological methods for bioethanol production have been addressed, which ultimately will help in overcoming the recalcitrance of lignocellulosic biomass by degrading the lignin fraction, breaking down of lignocellulose components, hydrolysis, and fermentation process. Recently, combined pretreatment is gaining popularity as it is more favorable and profitable for improving chemical yield and process of enzymatic hydrolysis of LBs, but it increases the cost of operation. Acid pretreatment, steam explosion, and hydrothermal processes all together show a comparatively high effect on degrading hemicelluloses fraction. Alkali, oxidative, and organosolv pretreatment are more efficient in removing and degrading of lignin portion. This present study will empower a better idea and knowledge of the available process with the upcoming advanced processes which would help to overcome the limitations and establish technology to facilitate the pretreatment methods to make an authentic concept of biorefinery.

Study of lignocellulosic biomass ignition properties estimation from thermogravimetric analysis

In the last decade, the use of renewable resources has increased significantly in order to reduce the energetic dependence on fossil fuels, as they have an important contribution to the global warning and greenhouse gasses effect. Because of that, research on biofuels has been increased in the last years as its characteristics of use match those of the conventional fuel's: solid biomass can be used instead of coals, and biodiesel could replace diesel. Research on solid biomass ignition properties has been considerably developed because of the amount of industrial accidents related to the treatment and use of solid biomass (self-ignition, dust explosions, etc.). On the other hand, thermogravimetric analysis (TGA) is becoming and important characterization technique as it can be used to determine a wide spectrum of properties, such as kinetics, composition, proximate analysis, etc. This research aims to combine thermal analysis and ignition properties, by using the TGA to obtain the elemental composition of lignocellulosic biomass and compare those results to Minimum Ignition Energy (MIE) values test output, so a relation between composition and MIE can be found.To achieve this aim, biomass samples from different origins have been used: oil palm wastes (empty fruit bunches, mesocarp fiber and palm kernel shell), agricultural wastes (straw chops) and forestry wastes (wood chips and wood powder). Also, raw materials and torrefied biomass were compared. The hemicellulose/cellulose ratio was calculated and compared to different flammability properties, finding out that the greater the ratio and the lower the onset temperature (temperature at which the pyrolysis reaction accelerates), the lower was the minimum ignition energy. From this basis it was possible to define “tendency areas” that grouped the samples whose MIE values were similar. Three tendency areas were found: high minimum ignition energy, medium minimum ignition energy, and low ignition energy.

Mechanism and Compatibility of Pretreated Lignocellulosic Biomass and Polymeric Mixed Matrix Membranes: A Review.

In this paper, a review of the compatibility of polymeric membranes with lignocellulosic biomass is presented. The structure and composition of lignocellulosic biomass which could enhance membrane fabrications are considered. However, strong cell walls and interchain hindrances have limited the commercial-scale applications of raw lignocellulosic biomasses. These shortcomings can be surpassed to improve lignocellulosic biomass applications by using the proposed pretreatment methods, including physical and chemical methods, before incorporation into a single-polymer or copolymer matrix. It is imperative to understand the characteristics of lignocellulosic biomass and polymeric membranes, as well as to investigate membrane materials and how the separation performance of polymeric membranes containing lignocellulosic biomass can be influenced. Hence, lignocellulosic biomass and polymer modification and interfacial morphology improvement become necessary in producing mixed matrix membranes (MMMs). In general, the present study has shown that future membrane generations could attain high performance, e.g., CO2 separation using MMMs containing pretreated lignocellulosic biomasses with reachable hydroxyl group radicals.

Potential and limitations of 13C CP/MAS NMR spectroscopy to determine the lignin content of lignocellulosic feedstock

Nuclear magnetic resonance (NMR) spectroscopy has been applied worldwide to study the chemical composition of lignocellulosic biomass and to analyse changes resulting from the chemical processing of these materials. In this work, solid-state 13C NMR was used to study the lignin contents of a large set of lignocellulosic materials derived from various Brazilian plant species. To determine the lignin content by NMR, the integrated intensity of the deconvoluted signal due to methoxyl groups in the 13C NMR spectra was used to obtain a calibration curve using mixtures of cellulose and lignin standards. The lignin contents obtained by NMR were, in general, in good agreement with the results obtained by methods based on acid hydrolysis (AH). For some samples, the NMR results showed that the AH methods were ineffective for the complete carbohydrate hydrolysis, indicating that the NMR-derived values represented more reliable estimates of the lignin contents in these cases.

Development of a non-linear model for prediction of higher heating value from the proximate composition of lignocellulosic biomass

ABSTRACT The determination of calorific value (in terms of higher heating value or HHV) is of much significance for the assessment of the energy recovery potential of biomass. The literature reported that HHV of the biomass can be computed using the proximate composition of the biomass. The prediction of HHV from the proximate composition of biomass is of great interest as it can save expensive and time-consuming laboratory trials. The present work evaluates the available prediction models for the estimation of HHV of the biomass from the literature. A thorough review showed that most literature reported models are limited in terms of applicability across different biomass categories. The present study proposed a non-linear prediction model that can be universally used to predict calorific value across biomass categories. Accordingly, the proposed model was validated using the proximate composition of various biomass specimens extracted from the literature to confirm the universal applicability of the model. The accuracy and precision of model prediction were assessed in terms of statistical indicators. The values of the statistical indicators showed that the proposed model had better prediction efficiency (in terms of accuracy and precision) and less bias compared to the other literature reported models for all biomass categories. The model predicted maximum energy recovery potential for biomass having high fixed carbon, high volatile matter, and low ash. The maximum energy recovery of 19.148 MJ/kg predicted by the model on validation was found to be associated with less than 2% (1.19%) percent error.

A Review of Potential of Lignocellulosic Biomass for Bioethanol Production in Kenya

Lignocellulosic biomass is the earth’s most abundant and renewable resource, and, lignin is its strongest component. The lignocellulosic biomass has a potential to produce bioethanol for both domestic and industrial use. The presence of lignin in the biomass, however, hinders the processing and production of bioethanol from the biomass. Hence, to enhance the chances of bioethanol production from the lignocellulosic biomass, lignin has to be pre-treated. The pre-treatment process efficiently separates the interlinked complex components. During the pre-treatment process, the strong lignin component that is highly resistant and a major barrier to solubilization is broken down by hydrolysis of cellulose and hemicellulose. Pre-treatment of lignocellulosic biomass is therefore, necessary to make it more susceptible to microorganisms, enzymes, and pathogens. The initial pre-treatment approaches include physical, physicochemical, and biological methods. The major drawback of this pre-treatment process is its cost implications, as it’s very costly. Studies suggest that even though it’s a costly affair, the pre-treatment methods, however, have a significant impact on the efficient production of ethanol from biomass.
 Situation Analysis: Bioethanol production from lignocellulosic biomass has mostly been undertaken in Brazil, USA, China, and India. In Kenya, however, little research on bioethanol production from lignocellulosic biomass has been done and adopted. The present review paper seeks to outlay the benefits of bioethanol production from lignocellulosic biomass, the composition of lignocellulosic biomass, its properties, different pre-treatment methods alongside advantages, and, disadvantages, and challenges encountered during bioethanol production. This review eventually will be of great assistance to researchers while developing bioethanol from different lignocellulosic biomass. Research, technology adaption/adaptation, and policy targeted at growing bioethanol industry, could enable Kenya to grow her bioethanol industry.

Microbial cellulolytic enzymes: diversity and biotechnology with reference to lignocellulosic biomass degradation

Lignocellulosic biomass is the earth’s most abundant renewable feedstock alternative that comprises of cellulose, hemi-cellulose and lignin. The synergistic action of cellulolytic/xylanolytic enzymes produced by lignocellulolytic micro-organisms such as bacteria, algae and fungi are capable of robust cellulosic biomass deconstruction. Most of the microorganisms dwelling in extreme environmental habitats such as rumen environment, hot/cold springs, deep ocean trenches, acidic/alkaline pH environment have been considered as an attractive producers of hemi/cellulolytic lignocellulolytic and other biotechnological enzymes with enhanced bio-chemical properties essential for industrial bioconversion processes. However, the potential microbial sources of cellulolytic enzymes and the underlying mechanism to achieve this is not fully elucidated. In this review article, first we detail the composition of lignocellulosic biomass. Next, we describe the structure and functions of divergent hydrolytic enzymes (cellulolytic and xylanolytic enzymes) involved in cellulosic biomass degradation. Third, we analyze, outline and unveil the prospective source of microbes encoding exceptionally diverse set of biotechnologically relevant cellulolytic enzymes which are critical to answer the specific ecological question of by whom, where and how cellulosic biomass is degraded in the environment. Finally, this review article features the snapshot about the future developments and perspectives on microbial enzymes, high-throughput techniques and molecular tools that could be exploited to derive those enzymes from the potential sources.