Research advances of biodegradable microplastics in wastewater treatment plant: Current knowledge and future directions.

Plastic and microplastic pollution in the environment has become a significant global concern. Biodegradable plastics (BPs), as environmentally friendly alternatives to conventional plastics, have also emerged as a crucial topic of global discussion. The successful application of BPs appears to offer a solution to the potential ecological risks posed by conventional plastics.

Are micro/nanorobots an effective solution to eliminate micro/nanoplastics in water/wastewater treatment plants?

The extensive production and widespread use of plastic products have resulted in the gradual escalation of plastic pollution. Micro/nano/plastic pollution has become a global issue, and addressing how to "green" remove them is a crucial topic that needs to be tackled at this stage. Recently, micro/nanorobots have offered a promising solution for improving water monitoring and remediation as an environmentally friendly remediation strategy.

Livestock and poultry breeding farms as a fixed and underestimated source of antibiotic resistance genes.

The excessive use of antibiotics, disinfectants, and drugs in livestock and poultry breeding has resulted in a rise in the presence of antibiotic resistance genes (ARGs). Antibiotic-resistant bacteria (ARB) and ARGs have been widely found in animal feces, farm wastewater, and farm air. ARGs can not only spread across media through adsorption and migration, but also transfer resistance across bacterial genera through horizontal gene transfer.

The longevity evaluation of multi-metal stabilization by MgO in Pb/Zn smelter-contaminated soils.

The re-mobilization risks of potentially toxic elements (PTEs) during stabilization deserve to be considered. In this study, artificial simulation evaluation methods based on the environmental stress of freeze-thaw (F-T), acidification and variable pH were conducted to assess the long-term effectiveness of PTEs stabilized by MgO in Pb/Zn smelter contaminated soils. Among common stabilizing materials, MgO was considered as the best remediation material, since PTEs bioavailability reduced by 55.

Visible light-induced switching of soft matter materials properties based on thioindigo photoswitches.

Thioindigos are visible light responsive photoswitches with excellent spatial control over the conformational change between their trans- and cis- isomers. However, they possess limited solubility in all conventional organic solvents and polymers, hindering their application in soft matter materials. Herein, we introduce a strategy for the covalent insertion of thioindigo units into polymer main chains, enabling thioindigos to function within crosslinked polymeric hydrogels.

The occurrence of "yellowing" phenomenon and its main driving factors after the remediation of chromium (Cr)-contaminated soils: A literature review.

Chromium (Cr) is a highly toxic element, which is widely present in environment due to industrial activities. One of most applicable technique to clean up Cr pollution is chemical reduction. However, the Cr(VI) concentration in soil increases again after remediation, and meanwhile the yellow soil would appear, which is commonly called as "yellowing" phenomenon.

A Novel Automatic Generation Control Method Based on the Large-Scale Electric Vehicles and Wind Power Integration Into the Grid.

In order to solve the problem of frequency instability of power system due to strong random disturbance caused by large-scale electric vehicles and wind power grid connection, an improved reinforcement learning algorithm, namely, optimistic initialized double Q, is proposed in this article from the perspective of automatic generation control. The proposed algorithm uses the optimistic initialization principle to expand the agent action exploration space, so as to prevent Q-learning from falling into local optimum by greedy strategy; meanwhile, it integrates double Q-learning to solve the problem of overestimation of action value in traditional reinforcement learning based on Q-learning. In the algorithm, the hyperparameter α is introduced to improve the learning efficiency, and the reward b based on exploration times is introduced to increase the Q value estimation to drive the exploration of the algorithm, so as to obtain the optimal solution.