Abstract: This work aims to enhance the efficiency of integrated poultry waste management in bio-circular-green economy by maximizing the co-digestion of chicken manure and its digestate-grown biomass. In a series of batch assays, Napier grass (NG) was mixed with chicken manure (CM) at various proportions (100:0, 80:20, 60:40, 50:50, 40:60, 20:80 and 0:100) to identify co-substrate synergism, followed by physiochemical conditioning (size reduction and ultrasonication) of NG before co-digestion. Results indicated that NG mix of at least 80% was required to gain a full methanation potential of the individual substrates; no synergistic ratio above unity was found. However, the combined effect of size reduction and sonication was found to markedly improve the cosubstrate's biodegradability by 88.7%. The findings were then used to run continuous co-digestion at various operating regimes. In optimal continuous co-digestion condition, NG particle size of 0.6-2.4 mm combined with sonication intensity at 1111 kJ/kgTS improved biomethanation yield as high as 106.3%. Sub-thermophilic digestion at 45 degrees C was shown to give a higher and more stable CH4 yield than at 55 degrees C. Finally, it was also found that recycling liquid effluent at 40% to replace freshwater in feed, although showed no significant difference in CH4 yield (& alpha; = 0.05), noticeably increased system buffer capacity. This optimized biodegradation regime could give co-digestion waste management a higher overall plant efficiency and economic return.

Abstract: Plasma medicine has attracted tremendous interest in a variety of medical conditions, ranging from wound healing to antimicrobial applications, even in cancer treatment, through the interactions of cold atmospheric plasma (CAP) and various biological tissues directly or indirectly. The underlying mechanisms of CAP treatment are still poorly understood although the oxidative effects of CAP with amino acids, peptides, and proteins have been explored experimentally. In this study, machine learning (ML) technology is introduced to efficiently unveil the interaction mechanisms of amino acids and reactive oxygen species (ROS) in seconds based on the data obtained from the reactive molecular dynamics (MD) simulations, which are performed to probe the interaction of five types of amino acids with various ROS on the timescale of hundreds of picoseconds but with the huge computational load of several days. The oxidative reactions typically start with H-abstraction, and the details of the breaking and formation of chemical bonds are revealed; the modification types, such as nitrosylation, hydroxylation, and carbonylation, can be observed. The dose effects of ROS are also investigated by varying the number of ROS in the simulation box, indicating agreement with the experimental observation. To overcome the limits of timescales and the size of molecular systems in reactive MD simulations, a deep neural network (DNN) with five hidden layers is constructed according to the reaction data and employed to predict the type of oxidative modification and the probability of occurrence only in seconds as the dose of ROS varies. The well-trained DNN can effectively and accurately predict the oxidative processes and productions, which greatly improves the computational efficiency by almost ten orders of magnitude compared with the reactive MD simulation. This study shows the great potential of ML technology to efficiently unveil the underpinning mechanisms in plasma medicine based on the data from reactive MD simulations or experimental measurements. In this study, since reactive molecular dynamics simulation can currently only describe interactions between a few hundred atoms in a few hundred picoseconds, deep neural networks (DNN) are introduced to enhance the simulation results by predicting more data efficiently. image