Bioleaching of Typical Electronic Waste-Printed Circuit Boards (WPCBs): A Short Review.

The rapid pace of innovations and the frequency of replacement of electrical and electronic equipment has made waste printed circuit boards (WPCB) one of the fastest growing waste streams. The frequency of replacement of equipment can be caused by a limited time of proper functioning and increasing malfunctions. Resource utilization of WPCBs have become some of the most profitable companies in the recycling industry. To facilitate WPCB recycling, several advanced technologies such as pyrometallurgy, hydrometallurgy and biometallurgy have been developed. Bioleaching uses naturally occurring microorganisms and their metabolic products to recover valuable metals, which is a promising technology due to its cost-effectiveness, environmental friendliness, and sustainability. However, there is sparse comprehensive research on WPCB bioleaching. Therefore, in this work, a short review was conducted from the perspective of potential microorganisms, bioleaching mechanisms and parameter optimization. Perspectives on future research directions are also discussed.

Liposomal Co-delivery of PD-L1 siRNA/Anemoside B4 for Enhanced Combinational Immunotherapeutic Effect.

Combination therapy has gained a lot of attention thanks to its superior activity against cancer. In the present study, we report a cRGD-targeted liposomal preparation for co-delivery of programmed cell death ligand 1 (PD-L1) small interfering RNA (siRNA) and anemoside B4 (AB4)─AB4/siP-c-L─and evaluate its anticancer efficiency in mouse models of LLC and 4T1 tumors. AB4/siP-c-L showed a particle size of (180.7 ± 7.3) nm and a ζ-potential of (32.8 ± 1.5) mV, with high drug encapsulation, pH-sensitive release properties, and good stability in serum. AB4/siP-c-L demonstrated prolonged blood circulation and increased tumor accumulation. Elevated cellular uptake was dependent on the targeting ligand cRGD. This combination induced significant tumor inhibition in LLC xenograft tumor-bearing mice by downregulating PD-L1 protein expression and modulating the immunosuppressive microenvironment. Liposomes favored the antitumor T-cell response with long-term memory, without obvious toxicity. A similar tumor growth inhibition was also demonstrated in the 4T1 tumor model. In summary, our results indicate that cRGD-modified and AB4- and PD-L1 siRNA-coloaded liposomes have potential as an antitumor preparation, and this approach may lay a foundation for the development of a new targeted drug delivery system.

Dynamic pollution emission prediction method of a combined heat and power system based on the hybrid CNN-LSTM model and attention mechanism.

Combined thermal power (CHP) production mode plays a more important role in energy production, but the impact of its pollutant emission on the natural environment is still difficult to eradicate. Traditional pollutant control adopts post-treatment process to degrade the generated pollutants, but there is little research on controlling the generation of pollutants from the source. Therefore, starting from the source, this paper predicts the pollutants through the prediction model, so as to provide countermeasures for production regulation and avoiding excessive emission. In this paper, a pollution emission prediction method of CHP systems based on feature engineering and a hybrid deep learning model is proposed. Feature engineering performs multi-step preprocessing on the original data, refines the correlation factors, and removes redundant variables. The hybrid deep learning model has a multi-variable input and is established by combining the convolutional neural network, long short-term memory network with the attention mechanism. The case study is conducted on the collected actual dataset. The influence of the prediction target periodicity on the prediction results is analyzed seasonally to verify the effectiveness of the hybrid model. The results show that the root mean square error of the proposed method is less than one, and the error is reduced compared to the other basic methods, which proves the superiority of the proposed pollution emission prediction method over the existing methods.