Overexploitation of natural resources to meet human needs has considerably impacted CO2 emissions, contributing to global warming and severe climatic change. This review furnishes an understanding of the sources, brutality, and effects of CO2 emissions and compelling requirements for metamorphosis from a linear to a circular bioeconomy. A detailed emphasis on microalgae, its types, properties, and cultivation are explained with significance in attaining a zero-carbon circular bioeconomy. Microalgal treatment of a variety of wastewaters with the conversion of generated biomass into value-added products such as bio-energy and pharmaceuticals, along with agricultural products is elaborated. Challenges encountered in large-scale implementation of microalgal technologies for low-carbon circular bioeconomy are discussed along with solutions and future perceptions. Emphasis on the suitability of microalgae in wastewater treatment and its conversion into alternate low-carbon footprint bio-energies and value-added products enforcing a zero-carbon circular bioeconomy is the major focus of this review.
This work aimed to study an newly isolated microalgal strain, Chlamydomonas sp. QWY37, that can achieve a maximum carbohydrate production of 944 mg/L·d, along with high pollutant removal efficiencies (chemical oxygen demand: 81%, total nitrogen: 96%, total phosphate: nearly 100%) by optimizing culture conditions and using an appropriate operation strategy. Through a cell-displayed technology that utilizes combined engineered system, a maximum microalgal bioethanol yield of 61 g/L was achieved. This is the first report demonstrating the highest microalgal carbohydrate productivity using swine wastewater without any pretreatments associated with direct high-density bioethanol production from the subsequent microalgal biomass. This work may represent a breakthrough in achieving feasible microalgal bioethanol conversion from real swine wastewater.
Pyrolysis of agricultural biomass is a promising technique for producing renewable energy and effectively managing solid waste. In this study, groundnut shell (GNS) was processed at 500 °C in an inert gas atmosphere with a gas flow rate and a heating rate of 10 mL/min and 10 °C/min, respectively, in a custom-designed fluidized bed pyrolytic-reactor. Under optimal operating conditions, the GNS-derived pyrolytic-oil yield was 62.8 wt.%, with the corresponding biochar (19.5 wt.%) and biogas yields (17.7 wt.%). The GC-MS analysis of the GNS-based bio-oil confirmed the presence of (trifluoromethyl)pyridin-2-amine (18.814%), 2-Fluoroformyl-3,3,4,4-tetrafluoro-1,2-oxazetidine (16.23%), 5,7-dimethyl-1H-Indazole (11.613%), N-methyl-N-nitropropan-2-amine (6.5%) and butyl piperidino sulfone (5.668%) as major components, which are used as building blocks in the biofuel, pharmaceutical, and food industries. Furthermore, a 2 × 5 × 1 artificial neural network (ANN) architecture was developed to predict the decomposition behavior of GNS at heating rates of 5, 10, and 20 °C/min, while the thermodynamic and kinetic parameters were estimated using a non-isothermal model-free method. The Popescu method predicted activation energy (Ea) of GNS biomass ranging from 111 kJ/mol to 260 kJ/mol, with changes in enthalpy (ΔH), Gibbs-free energy (ΔG), and entropy (ΔS) ranging from 106 to 254 kJ/mol, 162-241 kJ/mol, and -0.0937 to 0.0598 kJ/mol/K, respectively. The extraction of high-quality precursors from GNS pyrolysis was demonstrated in this study, as well as the usefulness of the ANN technique for thermogravimetric analysis of biomass.
The complex structure of lignocellulosic biomass forms the recalcitrance to prevent the embedded holo-cellulosic sugars from undergoing the biodegradation. Therefore, a pretreatment is often required for an efficient enzymatic lignocellulosic hydrolysis. Recently, glycerol organosolv (GO) pretreatment is revealed potent in selective deconstruction of various lignocellulosic biomass and effective improvement of enzymatic hydrolysis. Evidently, the GO pretreatment is capable to modify the structure of dissolved components by glycerolysis, i.e., by trans-glycosylation onto glyceryl glycosides and by hydroxylation grafting onto glyceryl lignin. Such modifications tend to protect these main components against excessive degradation, which can be mainly responsible for the obviously less fermentation inhibitors arising in the GO pretreatment. This pretreatment can provide opportunities for valorization of emerging lignocellulosic biorefinery with production of value-added biochemicals. Recent advances in GO pretreatment of lignocellulosic biomass followed by enzymatic hydrolysis are reviewed, and perspectives are made for addressing remaining challenges.
Green biomass is a renewable and biodegradable material that has the potential use to trap urea to develop a high-efficiency urea fertilizer for crops' better performance. Current work examined the morphology, chemical composition, biodegradability, urea release, soil health, and plant growth effects of the SRF films subjected to changes in the thickness of 0.27, 0.54, and 1.03 mm. The morphology was examined by Scanning Electron Microscopy, chemical composition was analyzed by Infrared Spectroscopy, and biodegradability was assessed through evolved CO2 and CH4 quantified through Gas Chromatography. The chloroform fumigation technique was used for microbial growth assessment in the soil. The soil pH and redox potential were also measured using a specific probe. CHNS analyzer was used to calculate the total carbon and total nitrogen of the soil. A plant growth experiment was conducted on the Wheat plant (Triticum sativum). The thinner the films, the more they supported the growth and penetration of the soil's microorganisms mainly the species of fungus possibly due to the presence of lignin in films. The fingerprint regions of the infrared spectrum of SRF films showed all films in soil changed in their chemical composition due to biodegradation but the increase in the thickness possibly provides resistance to the films' losses. The higher thickness of the film delayed the rate and time for biodegradation and the release of methane gas in the soil. The 1.03 mm film (47% in 56 days) and 0.54 mm film (35% in 91 days) showed the slowest biodegradability as compared to the 0.27 mm film with the highest losses (60% in 35 days). The slow urea release is more affected by the increase in thickness. The Korsymer Pappas model with release exponent value of < 0.5 explained the release from the SRF films followed the quasi-fickian diffusion and also reduced the diffusion coefficient for urea. An increase in the pH and decrease in the redox potential of the soil is correlated with higher total organic content and total nitrogen in the soil in response to amending SRF films with variable thickness. Growth of the wheat plant showed the highest average plant length, leaf area index and grain per plant in response to the increase in the film's thickness. This work developed an important knowledge to enhance the efficiency of film encapsulated urea that can better slow the urea release if the thickness is optimized.
Recently, chitin and chitosan are widely investigated for food preservation and active packaging applications. Chemical, as well as biological methods, are usually adopted for the production of these biopolymers. In this study, modification to a chemical method of chitin synthesis from shrimp shells has been proposed through the application of high-frequency ultrasound. The impact of sonication time on the deproteinization step of chitin and chitosan preparation was examined. The chemical identities of chitin and chitosan were verified using infrared spectroscopy. The influence of ultrasound on the deacetylation degree, molecular weight and particle size of the biopolymer products was analysed. The microscopic characteristics, crystallinity and the colour characteristics of the as-obtained biopolymers were investigated. Application of ultrasound for the production of biopolymers reduced the protein content as well as the particle size of chitin. Chitosan of high deacetylation degree and medium molecular weight was produced through ultrasound assistance. Finally, the as-derived chitosan was applied for beef preservation. High values of luminosity, chromatid and chrome were noted for the beef samples preserved using chitosan films, which were obtained by employing biopolymer subjected to sonication for 15, 25 and 40 min. Notably; these characteristics were maintained even after ten days of packaging. The molecular weight of these samples are 73.61 KDa, 86.82 KDa and 55.66 KDa, while the deacetylation degree are 80.60%, 92.86% and 94.03%, respectively; in the same order, the particle size of chitosan are 35.70 μm, 25.51 μm and 20.10 μm.
The potential of new trimetallic (Ce, Cu, La) loaded montmorillonite clay catalyst for synthesizing biodiesel using novel non-edible Celastrus paniculatus Willd seed oil via two-step transesterification reaction has been reported along with catalyst characterization. Transesterification reaction was optimized and maximum biodiesel yield of 89.42% achieved under optimal operating reaction states like; 1:12 oil to methanol ratio, 3.5% of catalyst amount, 120 °C of reaction temperature for 3 h. The predicted and experimental biodiesel yields under these reaction conditions were 89.42 and 89.40%, which showing less than 0.05% variation. Additionally, optimum biodiesel yield can be predicted by drawing 3D surface plots and 2D contour plots using MINITAB 17 software. For the characterization of the obtained biodiesel, analysis including the GC/MS, FT-IR, 1H NMR and 13C NMR were applied. The fuel properties of obtained biodiesel agrees well with the different European Union (EU-14214), China (GB/T 20828), and American (ASTM-951, 6751) standards.
This study presented a novel methodology to predict microalgae chlorophyll content from colour models using linear regression and artificial neural network. The analysis was performed using SPSS software. Type of extractant solvents and image indexes were used as the input data for the artificial neural network calculation. The findings revealed that the regression model was highly significant, with high R2 of 0.58 and RSME of 3.16, making it a useful tool for predicting the chlorophyll concentration. Simultaneously, artificial neural network model with R2 of 0.66 and low RMSE of 2.36 proved to be more accurate than regression model. The model which fitted to the experimental data indicated that acetone was a suitable extraction solvent. In comparison to the cyan-magenta-yellow-black model in image analysis, the red-greenblue model offered a better correlation. In short, the estimation of chlorophyll concentration using prediction models are rapid, more efficient, and less expensive.