Amendment of organic compost proprieties through composting-vermicomposting integration

The aim of the current study is to evaluate the effects of integrating composting-vermicomposting to improve the organic compost as from the physicochemical analyses and by their application on Brachiaria decumbens growth. Experiments carried out in composting unit used 30:1 ratio of nitrogen-rich organic waste (raw vegetables, fruits and cooked food) and carbon (dry grass) in 2 pile configurations (with, or without passive aeration). After 60 days, product was subjected to vermicomposting for 45 days. Composts were analyzed to check their quality (temperature, pH, moisture, organic carbon, nitrogen and phosphorus levels), as well as compared to each other as biofertilizers (10% (w/v)) for B. decumbens growth. Data have suggested that the vermicomposting process improved the compost pile by increasing its nitrogen (1.26% to 1.95%), phosphorus (0.64% to 1.2%) and organic carbon contents (17.1% to 18.9%). B. decumbens growth showed no significant differences between those treatments, which indicates that organic fraction should be increased (>10%) to release their nutrients to plant.

Simulation of heat transfer processes during the growth of crystals of the NiFeGaCo alloy

Long crystals of NiFeGaCo alloy with shape memory effect, including magnetically controlled ones, were obtained by the methods of Czochralski and Stepanov. A strong influence on the properties of crystals of dendritic formations, especially noticeable in the initial part of the crystal, has been revealed. In order to optimize the growth experiments, the heat transfer process in the thermal growth zone was simulated. It is shown that the formation of dendrites is due to a change in heat transfer during growth, which leads to an increase in the axial temperature gradient near the crystallization front as the crystal grows. This fits into the framework of the classical concepts of the transition from dendritic growth to normal growth.

Advanced machine learning decision policies for diameter control of carbon nanotubes

The diameters of single-walled carbon nanotubes (SWCNTs) are directly related to their electronic properties, making diameter control highly desirable for a number of applications. Here we utilized a machine learning planner based on the Expected Improvement decision policy that mapped regions where growth was feasible vs. not feasible and further optimized synthesis conditions to selectively grow SWCNTs within a narrow diameter range. We maximized two ranges corresponding to Raman radial breathing mode frequencies around 265 and 225 cm−1 (SWCNT diameters around 0.92 and 1.06 nm, respectively), and our planner found optimal synthesis conditions within a hundred experiments. Extensive post-growth characterization showed high selectivity in the optimized growth experiments compared to the unoptimized growth experiments. Remarkably, our planner revealed significantly different synthesis conditions for maximizing the two diameter ranges in spite of their relative closeness. Our study shows the promise for machine learning-driven diameter optimization and paves the way towards chirality-controlled SWCNT growth.

THE EFFECT OF CHITOSAN HYDROLYSATE ON THE FUNGI ALTERNARIA SOLANI, FUSARIUM SOLANI AND RHIZOCTONIA SOLANI IN VITRO

Chitosan hydrolysate was obtained using nitric acid; the prevailing fraction had a molecular weight of 30 kDa and a deacetylation degree of 95%. The effect of chitosan hydrolysate when added to potato dextrose agar (PDA) in different concentrations (0.5, 1, 1.5, 2, 4, 6 and 8 mg/mL) was studied on the growth of the fungi Alternaria solani Sorauer, Fusarium solani (Mart.) Sacc. and Rhizoctonia solani J.G. Kühn. A. solani was the most sensitive to the addition of chitosan hydrolysate to PDA in radial growth experiments. On days 3 and 7 of incubation, the antifungal activity of the phytopathogen growth was 69%-92% and 69%-88%, respectively, in the concentration range of 0.5-2 mg/ml.