Ni(II), Cr(VI), Cu(II) and nitrate removal by the co-system of Pseudomonas hibiscicola strain L1 immobilized on peanut shell biochar.

At present, the improvement of nitrate and mixed heavy metals removal in wastewater by microorganism are urgently needed. Previous studies have shown that Pseudomonas hibiscicola strain L1 exhibited Ni(II) removal ability under aerobic denitrification. In this study, the characteristics of the free strain L1, peanut shell biochar (PBC) and further the co-system of strain L1 immobilized on PBC were investigated for the removal of Ni(II), Cr(VI), Cu(II) and nitrate in mix-wastewater. The results illustrated that strain L1 could remove 15.51% - 32.55% of Ni(II) (20-100 mg·L-1), and removal ratios by co-system were ranked as Ni(II) (81.17%) > Cu(II) (45.84%) > Cr(VI) (38.21%). Scanning Electron Microscope (SEM), X-ray Diffractometer (XRD) and Fourier Transform Infrared Spectroscopy (FTIR) images indicated that the strain L1 immobilized well on PBC and had vigorous biological activity; the crystals of Ni(OH)2, Cu(OH)2 and CrO(OH) etc. were formed on surface of co-system with various functional groups participated in. In Sequential Batch Reactor (SBR), the pollutant removal ratios by co-system were higher than that by free strain L1. This study illustrated that the co-system of strain L1 immobilized on PBC was qualified to be applied for practical scenarios of effective heavy metal removal of electroplating mix-wastewater.

Study on the Automatic Identification of ABX3 Perovskite Crystal Structure Based on the Bond-Valence Vector Sum.

Perovskite materials have a variety of crystal structures, and the properties of crystalline materials are greatly influenced by geometric information such as the space group, crystal system, and lattice constant. It used to be mostly obtained using calculations based on density functional theory (DFT) and experimental data from X-ray diffraction (XRD) curve fitting. These two techniques cannot be utilized to identify materials on a wide scale in businesses since they require expensive equipment and take a lot of time. Machine learning (ML), which is based on big data statistics and nonlinear modeling, has advanced significantly in recent years and is now capable of swiftly and reliably predicting the structures of materials with known chemical ratios based on a few key material-specific factors. A dataset encompassing 1647 perovskite compounds in seven crystal systems was obtained from the Materials Project database for this study, which used the ABX3 perovskite system as its research object. A descriptor called the bond-valence vector sum (BVVS) is presented to describe the intricate geometry of perovskites in addition to information on the usual chemical composition of the elements. Additionally, a model for the automatic identification of perovskite structures was built through a comparison of various ML techniques. It is possible to identify the space group and crystal system using just a small dataset of 10 feature descriptors. The highest accuracy is 0.955 and 0.974, and the highest correlation coefficient (R2) value of the lattice constant can reach 0.887, making this a quick and efficient method for determining the crystal structure.