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The existing automated wastewater treatment control systems encounter challenges such as the utilization of specialized testing instruments, equipment repair complications, high operational costs, substantial operational errors, and low detection accuracy. An effective soft measure model offers a viable approach for real-time monitoring and the development of automated control in the wastewater treatment process. Consequently, a novel hybrid deep learning CNN-BNLSTM-Attention (CBNLSMA) model, which incorporates convolutional neural networks (CNN), bidirectional nested long and short-term memory neural networks (BNLSTM), attention mechanisms (AM), and Tree-structure Parzen Estimators (TPE), has been developed for monitoring effluent water quality during the wastewater treatment process. The CBNLSMA model is divided into four stages: the CNN module for feature extraction and data filtering to expedite operations; the BNLSTM module for temporal data’s temporal information extraction; the AM module for model weight reassignment; and the TPE optimization algorithm for the CBNLSMA model’s hyperparameter search optimization. In comparison with other models (TPE-CNN-BNLSTM, TPE-BNLSTM-AM, TPE-CNN-AM, PSO-CBNLSTMA), the CBNLSMA model reduced the RMSE for effluent COD prediction by 25.4%, decreased the MAPE by 32.9%, and enhanced the R2 by 14.9%. For the effluent SS prediction, the CBNLSMA model reduced the RMSE by 26.4%, the MAPE by 21.0%, and improved the R2 by 35.7% compared to other models. The simulation results demonstrate that the proposed CBNLSMA model holds significant potential for real-time effluent quality monitoring, indicating its high potential for automated control in wastewater treatment processes. [ABSTRACT FROM AUTHOR]

Anaerobic process is regarded as a green and sustainable process due to low carbon emission and minimal energy consumption in wastewater treatment plants (WWTPs). However, some water quality metrics are not measurable in real time, thus influencing the judgment of the operators and may increase energy consumption and carbon emission. One of the solutions is using a soft-sensor prediction technique. This article introduces a water quality soft-sensor prediction method based on Bidirectional Gated Recurrent Unit (BiGRU) combined with Gaussian Progress Regression (GPR) optimized by Tree-structured Parzen Estimator (TPE). TPE automatically optimizes the hyperparameters of BiGRU, and BiGRU is trained to obtain the point prediction with GPR for the interval prediction. Then, a case study applying this prediction method for an actual anaerobic process (2500 m3/d) is carried out. Results show that TPE effectively optimizes the hyperparameters of BiGRU. For point prediction of CODeff and biogas yield, R2 values of BiGRU, which are 0.973 and 0.939, respectively, are increased by 1.03%–7.61% and 1.28%–10.33%, compared with those of other models, and the valid prediction interval can be obtained. Besides, the proposed model is assessed as a reliable model for anaerobic process through the probability prediction and reliable evaluation. It is expected to provide high accuracy and reliable water quality prediction to offer basis for operators in WWTPs to control the reactor and minimize carbon emission and energy consumption. [ABSTRACT FROM AUTHOR]

Nitenpyram (NIT) is the most water-soluble neonicotinoid (NEO). It has been shown to pose a serious threat to human health and the environment but was always ignored due to its limited market share. There were few experts who studied NIT's transport behavior on biochar. In this study, two types of biochar were co-activated separately using zinc chloride combined with phosphoric acid and potassium hydroxide combined with acetic acid, marked as ZBC and KBC. Characterizations suggested that hydrophilic ZBC and KBC had more surface functional groups than unmodified biochar (BC), and specific surface areas of ZBC (456.406 m2·g−1) and KBC (750.588 m2·g−1) were significantly higher than of BC (67.181 m2·g−1). The pore structures of KBC and ZBC were hierarchical porous structures with different pore sizes and typical microporous structure, respectively. The adsorption performance of either NIT or IMI on KBC was better than that on ZBC. Only 0.4 g·L−1 of KBC can absorb 89.62% of NIT in just 5 min. The equilibrium adsorption amounts of NIT on ZBC and KBC were 17.995 mg·g−1 and 82.910 mg·g−1. Elovich and Langmuir models were used to evaluate the whole adsorption process, which was attributed to the chemisorption mechanism. In addition, removal rates of NIT were negatively correlated to NIT's initial concentration and positively correlated to the dose of biochar. pH had almost no effect on adsorption, but the presence of salt ions can inhibit the removal of NIT. Long-term stabilities of biochars were also acceptable. These findings will promote the development in the preparation of biochar fields and provide a positive reference value for NEO removal. [ABSTRACT FROM AUTHOR]

Highlights: Poly(propylene glycol) bis(2-aminopropyl ether) (PEA) additive is introduced into the perovskite solar cells to passivate both surface and grain boundary defects, and hence improve the device efficiency and stability. MAPbI3 device with PEA exhibits significantly enhanced efficiency of 18.87%. (FAPbI3)1-x(MAPbBr3)x device with PEA exhibits enhanced efficiency of 21.60%. The unsealed passivated device degrades only by 5% in PCE after being exposed to air (30 ± 5% relative humidity) for 30 days.Perovskite solar cells (PSCs) are regarded as promising candidates for future renewable energy production. High-density defects in the perovskite films, however, lead to unsatisfactory device performances. Here, poly(propylene glycol) bis(2-aminopropyl ether) (PEA) additive is utilized to passivate the trap states in perovskite. The PEA molecules chemically interact with lead ions in perovskite, considerably passivate surface and bulk defects, which is in favor of charge transfer and extraction. Furthermore, the PEA additive can efficiently block moisture and oxygen to prolong the device lifetime. As a result, PEA-treated MAPbI3 (MA: CH3NH3) solar cells show increased power conversion efficiency (PCE) (from 17.18 to 18.87%) and good long-term stability. When PEA is introduced to (FAPbI3)1-x(MAPbBr3)x (FA: HC(NH2)2) solar cells, the PCE is enhanced from 19.66 to 21.60%. For both perovskites, their severe device hysteresis is efficiently relieved by PEA. [ABSTRACT FROM AUTHOR]

Nanoscale magnetization modulation by electric field enables the construction of low-power spintronic devices for information storage applications and, etc. Electric field-induced ion migration can introduce desired changes in the material's stoichiometry, defect profile, and lattice structure, which in turn provides a versatile and convenient means to modify the materials' chemical-physical properties at the nanoscale and in situ. In this review, we provide a brief overview on the recent study on nanoscale magnetization modulation driven by electric field-induced migration of ionic species either within the switching material or from external sources. The formation of magnetic conductive filaments that exhibit magnetoresistance behaviors in resistive switching memory via foreign metal ion migration and redox activities is also discussed. Combining the magnetoresistance and quantized conductance switching of the magnetic nanopoint contact structure may provide a future high-performance device for non-von Neumann computing architectures. [ABSTRACT FROM AUTHOR]

Structural regular polyaniline was synthesized via a modified-chemical oxidative polymerization reaction. Highly hydrophilic polyaniline (PANi) and polyaniline-poly(vinylidene fluoride) blend (PANi-PVDF) membranes were prepared by solution casting and phase inversion techniques. Both of the mechanical and filtration properties of the membranes depend on the polymer composition and doping level of the blends. The elasticity of the membrane is greatly improved upon introducing poly(vinylidene fluoride) into the blend. The water permeability of the blend membranes is further enhanced when the membranes are doped with hydrochloric acid. The PANi-PVDF blend membranes are capable of recovering metallic gold from the acid/halide leaching streams spontaneous and sustainably, and are promising candidates for wastewater treatments in electronic industries. [ABSTRACT FROM AUTHOR]

The impacts of excimer laser annealing on the crystallinity of Ge epilayers on Si substrate grown by low- and high-temperature two-step approach in an ultra-high vacuum chemical vapor deposition system were investigated. The samples were treated by excimer laser annealing (ELA) at various laser power densities with the temperature above the melting point of Ge, while below that of Si, resulting in effective reduction of point defects and dislocations in the Ge layer with smooth surface. The full-width at half-maximum (FWHM) of X-ray diffraction patterns of the low-temperature Ge epilayer decreases with the increase in laser power density, indicating the crystalline improvement and negligible effect of Ge-Si intermixing during ELA processes. The short laser pulse time and large cooling rate cause quick melting and recrystallization of Ge epilayer on Si in the non-thermal equilibrium process, rendering tensile strain in Ge epilayer as calculated quantitatively with thermal mismatch between Si and Ge. The FWHM of X-ray diffraction patterns is significantly reduced for the two-step grown samples after treated by a combination of ELA and conventional furnace thermal annealing, indicating that the crystalline of Ge epilayer is improved more effectively with pre- annealing by excimer laser. [ABSTRACT FROM AUTHOR]