Moderate potassium ferrate dosage enhances methane production from the anaerobic digestion of waste activated sludge.

The annual increase of waste activated sludge (WAS) has become an urgent problem to be solved in sewage plants worldwide. Anaerobic digestion (AD) of WAS is an attractive choice to maximize the resource utilization rate. Nevertheless, the disintegration of sludge complex polymers is difficult, resulting in a low bioconversion rate. Potassium ferrate (PF), as a green oxidant with strong oxidizing property, has attracted great attention in WAS pretreatment recently. The effects of PF pretreatment on WAS hydrolysis and its dosage-response on methane production were investigated in the present study. Results show that as PF dosage raise from 0 to 50 g-K2FeO4/ kg-TS (total solids), the methane yield enhanced significantly by 40.3% from 0.083 to 0.12 L/g-VSadded (volatile solids). Nevertheless, the further increase in PF dosage resulted in decreased methane production. Especially with the PF dosage of 500 g-K2FeO4/ kg-TS, methane production is even slightly lower than the control reactor without PF oxidation. The mechanism analysis showed that although the dissolution of polysaccharides and proteins was enhanced with the high dosage of PF, the accompanying released humic-like substances and high concentration of ferric ions should be the main reasons inhibiting methane production.

Calcium nanoparticles (Ca-NPs) improve drought stress tolerance in Brassica napus by modulating the photosystem II, nutrient acquisition and antioxidant performance.

Foliar-application of nano-particles enhanced the foliar nutrient status and crop growth and yield. It is hypothesized that being second messenger molecule, supplementation of Ca2+ via calcium nanoparticles (Ca-NPs) can trigger various signaling pathways of physiological processes which can lead to alleviate the adverse effects of drought stress on the growth of canola (Brassica napus L.). Nano-enabled foliar-application could be an ideal strategy for advancing agricultural productivity. The present study explored the role of calcium nanoparticles (Ca-NPs) in alleviating drought stress in hydroponic Brassica napus (B. napus) plants. The foliar applied Ca-NPs were spherically shaped with an average size of 86 nm. Foliar application of 100 mg L-1 Ca-NPs enhanced biomass of canola plants and considered as optimal dose. Ca-NPs at 100 mg L-1 has a greater favorable impact on mesophyll ultrastructure, PSI and PSII efficacy, gas exchange parameters, chlorophyll content, and mineral absorption. The Ca-NPs treatment increased NPQ and Y(NPQ) under drought condition, indicating a higher PSII protective response to stressed conditions with better heat dissipation as a photoprotective component of NPQ. Ca-NPs application also reduced oxidative stress damage as measured by a reduction in reactive oxygen species (ROS) generation in terms of hydrogen peroxide and malondialdehyde (H2O2 and MDA). Furthermore, Ca-NPs induced drought tolerance response corresponded to an increased in key antioxidative defense enzymes (SOD, POD, CAT, APX), as well as non-enzymatic components (protease, lipoxygenase, proline, total soluble protein contents, endogenous hormonal biosynthesis), and secondary metabolite expression in B. napus plants. Taken together, the results of this study offer new insights into the physiological and molecular mechanisms by which B. napus responds to Ca-NPs exposure.

Application of Laser-Induced Breakdown Spectroscopy Coupled With Spectral Matrix and Convolutional Neural Network for Identifying Geographical Origins of Gentiana rigescens Franch.

Accurate geographical origin identification is of great significance to ensure the quality of traditional Chinese medicine (TCM). Laser-induced breakdown spectroscopy (LIBS) was applied to achieve the fast geographical origin identification of wild Gentiana rigescens Franch (G. rigescens Franch). However, LIBS spectra with too many variables could increase the training time of models and reduce the discrimination accuracy. In order to solve the problems, we proposed two methods. One was reducing the number of variables through two consecutive variable selections. The other was transforming the spectrum into spectral matrix by spectrum segmentation and recombination. Combined with convolutional neural network (CNN), both methods could improve the accuracy of discrimination. For the underground parts of G. rigescens Franch, the optimal accuracy in the prediction set for the two methods was 92.19 and 94.01%, respectively. For the aerial parts, the two corresponding accuracies were the same with the value of 94.01%. Saliency map was used to explain the rationality of discriminant analysis by CNN combined with spectral matrix. The first method could provide some support for LIBS portable instrument development. The second method could offer some reference for the discriminant analysis of LIBS spectra with too many variables by the end-to-end learning of CNN. The present results demonstrated that LIBS combined with CNN was an effective tool to quickly identify the geographical origin of G. rigescens Franch.