Copper–ruthenium bimetallic nanoparticles decorated on a laser-induced graphene for non-enzymatic hydrogen peroxide sensor.

No abstract

Our aim was to ferment soymilk using lactic acid bacteria that showed protease activity and to optimize the condition for fermentation. In total, 108 strains of protease-producing lactic acid bacteria were isolated from various fermented foods such as kimchi and jeotgal, and among them, 29 strains displaying the highest protease activity were selected for further study. From these 29 strains, strain MK1, whose protease activity was 126 mU/mL•min, was selected as the optimal fermentation strain owing to its high ability to digest soymilk protein. It was henceforth labeled as Lactobacillus paracasei MK1. The optimum conditions for the fermentation of soymilk by using L. paracasei MK1 were determined to be as follows: 30 h of fermentation time at a temperature of 30 o C, and at a pH of 6.0 in the initial growth medium.

No abstract

Agricultural activity greatly contributes to the secondary PM2.5 concentrations by releasing relatively large amounts of ammonia emissions. Nonetheless, studies and air quality policies have traditionally focused on industrial emissions such as NOx and SOx. To compare them, this study used a three-dimensional modeling system (e.g., WRF/CMAQ) to estimate the effects of emission control policies of agricultural and industrial emissions on PM2.5 pollution in Chungcheong, an agriculturally active region in Korea. Scenario 1 (S1) was designed to estimate the effect of a 30% reduction in NH3 emissions from the agro-livestock sector on air pollution. Scenario 2 (S2) was designed to show the air quality under a mitigation policy on NOx, SOx, VOCs, and primary PM2.5 from industrial sources, such as power plants and factories. The results revealed that monthly mean PM2.5 in Chungcheong could decrease by 3.6% (1.1 µg/m3) under S1 with agricultural emission control, whereas S2 with industrial emission control may result in only a 0.7~1.1% improvement. These results indicate the importance of identifying trends of multiple precursor emissions and the chemical environment in the target area to enable more efficient air quality management.