Pretreatment of cyanobacterial biomass for the production of biofuel in microbial fuel cells

Ultrasonication of microalgal biomass: Acoustics study and enhanced bioelectricity generation in microbial fuel cells

The present work involved the pretreatment of cultivated microalgal biomass by ultrasonication to improve its suitability as a substrate in microbial fuel cells (MFCs). Ultrasonication efficiently breaks down the cell walls of microalgae, therefore enhancing the accessibility of intracellular organic material by microbes and thussubsequently producing electrons and protons. In the present research work along with the ultrasonic pretreatment of our samples, non-destructive ultrasonic characterization using anultrasonic interferometer has been done, to study the molecular behavior of the microalgae. Scenedesmus sp. shows better molecular behavior and carbohydrate content than Chlorella sp. Around 47.9 % carbohydrate content estimated in Scenedesmus sp. Hence, Scenedesmus sp. was used as a substrate in the MFC for bioelectricity generation. Electrochemical techniques like electrochemical impedance spectroscopy (EIS), cyclic voltammetry (CV), and polarization studies were done to evaluate the performance of MFCs in the presence of electro-active bacteria Pseudomonas aeruginosa. A maximum power density of 11.64 W/m3 was achieved from the study. Further, the biofilm formation studies on the anode electrode surface were also done. The biofilm formation also increases with the increase in substrate concentration up to a certain threshold. Thus, this study reveals the molecular behavior of microalgae using the acoustics study and further ultrasonic pre-treatment of microalgae to use them as cost-effective and readily available substrates in MFCs.

Recent progress in the cyanobacterial products and applications of phycocyanins

Recent developments in the research on cyanobacterial products have drawn increasing attention, especially in the production and application of phycocyanin, which has shown great potential in various fields. Cyanobacteria are photosynthetic prokaryotes that live on Earth and are the ancestors of plant chloroplasts. They have a compact genome size compared to other eukaryotic photosynthesizing microorganisms; some species are genetically engineered and have high growth potential in indoor culture, and some strainscan maintain high growth potential even in outdoor culture. Cyanobacteria are valuable because they can selectively and effectively produce and recover useful substances that are poorly produced by other microalgae, although this depends on the algal species. However, the social implementation of biorefineries using cyanobacteria involves issues such as setting up useful products in addition to the culture methods and strains to be used. This review aims to present research trends over the last 20 years on the production of useful substances such as biodegradable plastics, lipids, polysaccharides, and pigment proteins (phycocyanins) from cyanobacteria. Phycocyanin is mainly recovered and purified by filamentous cyanobacteria and has contributed to the research field, especially in the food and beverage industry. Additionally, the production and functions of phycocyanin are summarized to provide a better understanding of these possibilities. Their potential applications as environmentally friendly materials are also described to further contribute to the research field and social implementation.

Fast analysis of straw proximates based on partial least squares using near-infrared spectroscopy

Near-infrared spectroscopy (NIRS) is a rapid measurement technique based on the spectroscopic absorption bands of specific functional groups within biomass. Its main advantages include simple preparation, precise analysis, and the ability to analyze multiple components simultaneously. Fast analysis of straw proximates (moisture, ash, and fixed carbon) has been investigated by means of NIRS. A total of 144 samples were collected, the spectral data were analyzed by partial least squares (PLS) regression and support vector regression (SVR) with four wavelength selection methods. PLS combined with competitive adaptive reweighted sampling (CARS) provided excellent predictive performance for moisture, ash, and fixed carbon. For moisture prediction, the values of RP2, RMSEP and RPD were 0.7202, 0.8196, and 2.11, respectively. For ash prediction, the values of RP2, RMSEP and RPD were 0.9307, 0.5901, and 3.69, respectively. For fixed carbon prediction, the values of RP2, RMSEP and RPD were 0.8504, 0.2735, and 2.76, respectively. Fast analysis of proximates of corn stover was possible using this NIRS system.