Synthesis and Characterization of Phosphoric Silica Catalyst from Bamboo Leaves for Production of Triacetin

In this research, the bamboo leaf shows promise as an alternative raw material for silica production. This study investigated the performance of heterogeneous catalyst prepared from silica derived bamboo leaf ash after that impregnated with phosphoric acid at ratio various. The catalyst was characterized by X-Ray Diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscope Energy Dispersive X-Ray Spectroscopy (SEM-EDS), Brunauer Emmet Teller (BET) and Barrett, Joyner and Halenda (BJH) method and triacetin product analyzed by GC-MS. The optimum condition phosphoric silica catalyst was obtained at phosphoric silica molar ratio of 1:2 and employed in the acetylation of glycerol, respectively. As result, 24 % selectivity for triacetin was obtained in the presence of catalytic amount 5%, molar ratio 1:9 at 100 °C for 4 hours. Bamboo leaf derived phosphoric silica calcined showed high potential to be used as an easy to prepare and high-performance solid catalyst for industrial scale.

Dimensional Stability of Treated Sengon Wood by Nano-Silica of Betung Bamboo Leaves

Sengon (Falcataria moluccana Miq.) is one of the fastest growing wood that is broadly planted in Indonesia. Sengon wood has inferior wood properties, such as a low density and dimensional stability. Therefore, sengon wood requires a method to improve its wood quality through wood modification. One type of wood modification is wood impregnation. On the other hand, Betung Bamboo leaves are considered as waste. Betung Bamboo leaves contain silica. Based on several researches, nano-SiO2 could improve fast-growing wood qualities. According to its perfect solubility in water, monoethylene glycol (MEG) is used in the study. The objectives are to evaluate the impregnation treatment (MEG and nano-silica originated from betung bamboo leaves) in regard to the dimensional stability and density of 5-year-old sengon wood and to characterize the treated sengon wood. MEG, MNano-Silica 0.5%, MNano-Silica 0.75%, and MNano-Silica 1% were used as impregnation solutions. The impregnation method was started with 0.5 bar of vacuum for 60 min, followed by 2.5 bar of pressure for 120 min. The dimensional stability, density, and characterization of the samples were studied through the use of scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy, X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FTIR). The results show that the treatment had a significant effect on the dimensional stability and density of sengon wood. Alterations in the morphology of treated sengon wood were observed through the full coverage of the pits on the vessel walls (SEM analysis results) and the detection of ethylene (FTIR analysis results) and silica (XRD and FTIR analysis results). Overall, the 0.75% MNano-Silica treatment was the most optimal treatment for increasing the dimensional stability and density of 5-year-old sengon wood.

Characteristics of impregnated wood by nano silica from betung bamboo leaves

Abstract, Sengon (Falcataria moluccana Miq.) as a fast-growing wood species that has low quality. Therefore, wood modification is needed to improve its wood qualities. The objective of this study was to analyse the effect of monoethylene glycol (MEG) and nano silica of betung bamboo leaves impregnation treatment on physical, mechanical properties and durability of sengon wood. 5-years-old Sengon wood from community forest, MEG and nano silica (average size = 436.16 nm) from betung bamboo leaves were used. The impregnation solutions were consisted of water treated (untreated), MEG, MEGSilika 0.5% and MEGSilika 1%. Impregnation process with 0.5 bar (60 minutes) vacuum and 2.5 bar (120 minutes) pressure. Physical properties (density and colour alteration), mechanical properties (Modulus of Elasticity (MOE), Modulus of Rupture (MOR) and hardness) and durability against subterranean (Coptotermes curvignathus) attack. The results showed that the weight percent gain (WPG) and density of treated Sengon wood were increased as the nano silica concentration increased. While colour alteration (Δε) of treated samples were declining. Mechanical properties (MOE, MOR and hardness) were also improved. Durability based on laboratory tested against subterranean attack resulted that the percentage of termite mortality from the treated samples increased, while the percentage of weight loss decreased.

Different Agricultural Wastes as Substrates for Growth and Production of Pleurotus florida

The present study was done impact of agricultural wastes on growth and production by oyster mushrooms i.e., Pleurotus florida which have a rich source of protein and also has important medicinal properties. The yield and Biological efficiency of different lignocellulosic agricultural wastes viz. Wheat straw, Maize leaves, Cob leaves, Jawar residue, Bajra residue, Bamboo leaves, Paddy straw, Sugarcane, Cotton, Soyabean, Safflower (Pods residue), Pigeon pea (Pods residue), Mung bean (Pods residue), Cowpea (Pods residue). the paddy straw showed the highest yield 892.25 gm with the highest biological efficiency 89.09 % followed by Cowpea pods residue gives 879.65 gm yield of Pleurotus florida and shows biological efficiency of 87.72%, followed by Wheat straw substrate showed 871.18 gm yields with 87.27 % biological efficiency. The Pigeon pea pod residue showed 865.85 gm yield during three harvestings having 86.36% biological efficiency. Similar result was seen Bajra residue and soybean pod residue in bajra residue showed 792.45gm yield during three harvestings having 79.245% biological efficiency, in soybean pod residue showed 791.63 gm yield during three harvestings having 79.163 % biological efficiency. The lowest yield was seen in Bamboo leaves and cotton residues. In cotton residue substrate 433.22 gm total yield along with 43.32% biological efficiency, Bamboo leaves substrate total yield were seen 438.12gm with 43.81 % biological efficacy. Keywords: Pleurotus florida, Agricultural waste, Substrates, Biological efficiency

Monitoring the Severity of Pantana phyllostachysae Chao on Bamboo Using Leaf Hyperspectral Data

Effectively monitoring Pantana phyllostachysae Chao (PPC) is essential for the sustainable development of the bamboo industry. However, the morphological similarity between damaged and off-year bamboo imposes challenges in the monitoring. The knowledge on whether the severity of this pest could be effectively monitored by using remote sensing methods is very limited. To fill this gap, this study aimed to identify the PPC damage of moso bamboo leaves using hyperspectral data. Specifically, we investigated differences in relative chlorophyll content (RCC), leaf water content (LWC), leaf nitrogen content (LNC), and hyperspectral spectrum among healthy, damaged (mildly damage, moderately damage, severely damage), and off-year bamboo leaves. Then, the hyperspectral indices sensitive to pest damage were selected by recursive feature elimination (RFE). The PPC damage identification model was constructed using the light gradient boosting machine (LightGBM) algorithm. We designed two different scenarios, without (A) and with (B) off-year samples, to evaluate the impact of off-year leaves on identification results. The RCC, the LWC, and the LNC of damaged leaves generally showed clear declined trends with the deterioration of damaged severity. The RCC and the LNC of off-year leaves were significantly lower than those of healthy and damaged leaves, whereas the LWC of off-leaves was significantly different from that of damaged leaves. The pest infestation caused noticeable distortion of leaf spectrum, increases in red and shortwave infrared bands, and decreases in green and near-infrared bands. The magnitude of reflectance change increased with the pest severity. The reflectance of off-year leaves in visible and near-infrared regions was distinguishably higher than that of healthy and damaged leaves. The overall accuracy (OA) of the constructed model for the identification of leaves with different degrees of damage severity reached 81.51%. When off-year, healthy, and damaged leaves were lumped together, the OA of the constructed model decreased by 5%. About half of the off-year leaf samples were misclassified into the damaged group. The identification of off-year leaves is a challenge for monitoring PPC damage using hyperspectral data. These results can provide practical guidance for monitoring PPC using remote sensing methods.

Multi-omics reveals the positive leverage of plant secondary metabolites on the gut microbiota in a non-model mammal

Background Flavonoids are important plant secondary metabolites (PSMs) that have been widely used for their health-promoting effects. However, little is known about overall flavonoid metabolism and the interactive effects between flavonoids and the gut microbiota. The flavonoid-rich bamboo and the giant panda provide an ideal system to bridge this gap. Results Here, integrating metabolomic and metagenomic approaches, and in vitro culture experiment, we identified 97 flavonoids in bamboo and most of them have not been identified previously; the utilization of more than 70% flavonoid monomers was attributed to gut microbiota; the variation of flavonoid in bamboo leaves and shoots shaped the seasonal microbial fluctuation. The greater the flavonoid content in the diet was, the lower microbial diversity and virulence factor, but the more cellulose-degrading species. Conclusions Our study shows an unprecedented landscape of beneficial PSMs in a non-model mammal and reveals that PSMs remodel the gut microbiota conferring host adaptation to diet transition in an ecological context, providing a novel insight into host-microbe interaction.

Preparation of Cellulose Nanoparticles from Foliage by Bio-Enzyme Methods

There are vast reserves of foliage in nature, which is an inexhaustible precious resource. In this study, the chemical components of five foliage types (pine needles, black locust tree leaves, bamboo leaves, elm leaves and poplar leaves) were analyzed, including cellulose content, hemicellulose content, and lignin content. The bio-enzymatic method was then used to prepare cellulose nanoparticles (CNPs) from these five kinds of leaves, and the prepared CNPs were analyzed using TEM, FTIR, FESEM, and XRD. The results showed that the content of hemicellulose in bamboo leaves was the highest, and the lignin content in the other four leaves was the highest. The cellulose content in the five kinds of foliage was arranged from large to small as pine needles (20.5%), bamboo leaves (19.5%), black locust leaves (18.0%), elm leaves (17.6%), and poplar leaves (15.5%). TEM images showed that the CNPs prepared by the five kinds of foliage reached the nanometer level in width and the micrometer level in length; therefore, the CNPs prepared in this study belonged to cellulose nanofibers (CNFs). The results of FTIR and XRD showed that CNFs prepared by the enzyme treatment exhibited a typical crystalline structure of cellulose II. The degree of crystallinity (DOC) of CNFs prepared from pine needle, poplar leaves, and bamboo leaves are 78.46%, 77.39%, and 81.51%, respectively. FESEM results showed that the CNFs prepared from pine needles, poplar leaves and bamboo leaves by enzymatic method presents a three-dimensional (3D) network structure, and their widths are 31 nm, 36 nm, and 37 nm, respectively. This study provides a meaningful reference for broadening the use of foliage types and improving their added value.