Search

Purpose The application of fungicides is one of the main strategies to prevent clubroot disease. Currently, numerous studies focus on changes in the soil microbial community at different levels of clubroot disease severity. However, the effects of fungicides on the soil microbial community and causative pathogen, Plasmodiophora brassicae, while preventing clubroot disease remain unclear. Methods In this study, we evaluated the control efficacy of three fungicides (fluazinam, metalaxyl-mancozeb, and carbendazim) on clubroot disease of tumorous stem mustard in greenhouse experiment. Uninoculated and Water treatments after inoculation were performed as controls. At three (3 W) and six weeks (6 W) post-inoculation of P. brassicae, soil properties, bacterial composition (sequencing of 16S rRNA genes), and effector gene expression of the pathogen were analyzed. The correlation of these factors with disease index (DI) was explored. Results Fluazinam was the most effective in controlling clubroot disease of tumorous stem mustard with a controlled efficacy of 59.81%, and the abundance of P. brassicae in the soil decreased 21.29% after 3 weeks of treatment. Compared with other treatments, twelve out of twenty effector genes showed higher expression in fluazinam 3 W samples. Different fungicides had different effects on soil properties. EC (electrical conductivity), the main factor that positively associated with DI, was significantly lower in fluazinam treatment than the other two fungicide treatments. The application of fungicides, especially carbendazim, significantly reduced bacterial α-diversity and the composition of soil bacteria. Pseudomonas, Microbacterium, and Sphingobacterium (positively correlated with DI) were enriched in Water, metalaxyl-mancozeb, and carbendazim treatments, but were less abundant in fluazinam treatment. Among the three fungicide treatments, DI was significantly negatively correlated with Shannon and Chao 1 indices. Soil properties and the top bacterial genera that positively correlated with DI were influenced to a lesser degree in the fluazinam treatment. Conclusion Among three fungicides, fluazinam was the most effective agent with the highest control effects against clubroot disease. The strong virulence of fluazinam against P. brassicae was one of the main reasons for the prevention of clubroot disease, and in addition the alteration of rhizosphere bacterial community by fluazinam to the detriment of P. brassicae infection. Based on our results, EC could be an indicator of the severity of clubroot disease.

Engineered nanoscale zero-valent metals (NZVMs) representing the forefront of technologies have been considered as promising materials for environmental remediation and antimicrobial effect, due to their high reducibility and strong adsorption capability. This review is focused on the methodology for synthesis of bare NZVMs, supported NZVMs, modified NZVMs, and bimetallic systems with both traditional and green methods. Recent studies have demonstrated that self-assembly methods can play an important role for obtaining ordered, controllable, and tunable NZVMs. In addition to common characterization methods, the state-of-the-art methods have been developed to obtain the properties of NZVMs (e.g., granularity, size distribution, specific surface area, shape, crystal form, and chemical bond) with the resolution down to subnanometer scale. These methods include spherical aberration corrected scanning transmission electron microscopy (Cs-corrected STEM), electron energy-loss spectroscopy (EELS), and near edge X-ray absorption fine structure (NEXAFS). A growing body of experimental data has proven that nanoscale zero-valent iron (NZVI) is highly effective and versatile. This article discusses the applications of NZVMs to treatment of heavy metals, halogenated organic compounds, polycyclic aromatic hydrocarbons, nutrients, radioelements, and microorganisms, using both ex situ and in situ methods. Furthermore, this paper briefly describes the ecotoxicological effects for NZVMs and the research prospects related to their synthesis, modification, characterization, and applications.

Reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites were synthesized in the present study by chemical deposition method and were then characterized by various methods, such as Fourier-transform infrared spectroscopy (FTIR) and X-ray photoelectron spectroscopy (XPS). The nZVI/rGO composites prepared were utilized for Cd(II) removal from aqueous solutions in batch mode at different initial Cd(II) concentrations, initial pH values, contact times, and operating temperatures. Response surface methodology (RSM) and artificial neural network hybridized with genetic algorithm (ANN-GA) were used for modeling the removal efficiency of Cd(II) and optimizing the four removal process variables. The average values of prediction errors for the RSM and ANN-GA models were 6.47% and 1.08%. Although both models were proven to be reliable in terms of predicting the removal efficiency of Cd(II), the ANN-GA model was found to be more accurate than the RSM model. In addition, experimental data were fitted to the Langmuir, Freundlich, and Dubinin-Radushkevich (D-R) isotherms. It was found that the Cd(II) adsorption was best fitted to the Langmuir isotherm. Examination on thermodynamic parameters revealed that the removal process was spontaneous and exothermic in nature. Furthermore, the pseudo-second-order model can better describe the kinetics of Cd(II) removal with a good R2 value than the pseudo-first-order model.

The commercially available nanoscale zerovalent zinc (nZVZ) was used as an adsorbent for the removal of malachite green (MG) from aqueous solutions. This material was characterized by X-ray diffraction and X-ray photoelectron spectroscopy. The advanced experimental design tools were adopted to study the effect of process parameters (viz. initial pH, temperature, contact time and initial concentration) and to reduce number of trials and cost. Response surface methodology and rapidly developing artificial intelligence technologies, i.e., artificial neural network coupled with particle swarm optimization (ANN-PSO) and artificial neural network coupled with genetic algorithm (ANN-GA) were employed for predicting the optimum process variables and obtaining the maximum removal efficiency of MG. The results showed that the removal efficiency predicted by ANN-GA (94.12%) was compatible with the experimental value (90.72%). Furthermore, the Langmuir isotherm was found to be the best model to describe the adsorption of MG onto nZVZ, while the maximum adsorption capacity was calculated to be 1000.00 mg/g. The kinetics for adsorption of MG onto nZVZ was found to follow the pseudo-second-order kinetic model. Thermodynamic parameters (ΔG0, ΔH0 and ΔS0) were calculated from the Van’t Hoff plot of lnKc vs. 1/T in order to discuss the removal mechanism of MG.

The authors wish to make the following addendum to their paper [1]:We need to add the following three references to Section 3.5 “Equilibrium Adsorption Isothermand Kinetics Studies” of our recently published paper [1]: “Zur Theorie Der Sogenannten AdsorptionGeloster Stoffe”, by Lagergren, published in Bihang till K. Svenska Vet-Akad. Handlingar, 1898 [2];“Adsorption of Heavy Metals from Waste Streams by Peat”, by Ho, Ph.D. Thesis, University ofBirmingham, 1995 [3]; “Sorption of Dye from Aqueous Solution by Peat”, by Ho and McKay, publishedin Chem. Eng. J., 1998 [4]. These three articles described the adsorption kinetics, i.e., the pseudo-firstorder kinetic model and the pseudo-second order kinetic model.The authors would like to apologize for any inconvenience caused. The change does not affectthe scientific results. The manuscript will be updated and the original will remain online on thearticle webpage.

Rhodamine B (Rh B) is a toxic dye that is harmful to the environment, humans, and animals, and thus the discharge of Rh B wastewater has become a critical concern. In the present study, reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) was used to treat Rh B aqueous solutions. The nZVI/rGO composites were synthesized with the chemical deposition method and were characterized using scanning electron microscopy (SEM), X-ray diffraction (XRD), Raman spectroscopy, N2-sorption, and X-ray photoelectron spectroscopy (XPS) analysis. The effects of several important parameters (initial pH, initial concentration, temperature, and contact time) on the removal of Rh B by nZVI/rGO were optimized by response surface methodology (RSM) and artificial neural network hybridized with genetic algorithm (ANN-GA). The results suggest that the ANN-GA model was more accurate than the RSM model. The predicted optimum value of Rh B removal efficiency (90.0%) was determined using the ANN-GA model, which was compatible with the experimental value (86.4%). Moreover, the Langmuir, Freundlich, and Temkin isotherm equations were applied to fit the adsorption equilibrium data, and the Freundlich isotherm was the most suitable model for describing the process for sorption of Rh B onto the nZVI/rGO composites. The maximum adsorption capacity based on the Langmuir isotherm was 87.72 mg/g. The removal process of Rh B could be completed within 20 min, which was well described by the pseudo-second order kinetic model.

Reduced graphene oxide-supported nanoscale zero-valent iron (nZVI/rGO) composites were prepared by chemical deposition method and were characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), Raman spectroscopy, N2-sorption and X-ray photoelectron spectroscopy (XPS). Operating parameters for the removal process of Pb(II) ions, such as temperature (20–40 °C), pH (3–5), initial concentration (400–600 mg/L) and contact time (20–60 min), were optimized using a quadratic model. The coefficient of determination (R2 > 0.99) obtained for the mathematical model indicates a high correlation between the experimental and predicted values. The optimal temperature, pH, initial concentration and contact time for Pb(II) ions removal in the present experiment were 21.30 °C, 5.00, 400.00 mg/L and 60.00 min, respectively. In addition, the Pb(II) removal by nZVI/rGO composites was quantitatively evaluated by using adsorption isotherms, such as Langmuir and Freundlich isotherm models, of which Langmuir isotherm gave a better correlation, and the calculated maximum adsorption capacity was 910 mg/g. The removal process of Pb(II) ions could be completed within 50 min, which was well described by the pseudo-second order kinetic model. Therefore, the nZVI/rGO composites are suitable as efficient materials for the advanced treatment of Pb(II)-containing wastewater.