Fabrication of three-dimensional hierarchical porous 2D/0D/2D g-C3N4 modified MXene-derived TiO2@C: Synergy effect of photocatalysis and H2O2 oxidation in NO removal.

A novel three-dimensional multi-level porous g-C3N4 modified MXene-derived TiO2@C aerogel (g-C3N4/TiO2@C aerogel) was synthesized for NO removal. Through SEM analysis, 2D g-C3N4 and 2D Ti3C2 nanosheets were constructed into an interconnected macroscopic framework with continuous macropores via ice template. OD TiO2 nanoparticles uniformly covered 2D C nanosheets with irregular mesopores and macropores in in-situ oxidation of Ti3C2 nanosheets by calcination via TEM analysis. g-C3N4/TiO2@C aerogel for photocatalytic activation of hydrogen peroxide (H2O2) had an excellent efficiency of 90.7% for NO removal at parts per million level. This efficiency was 4.9 times and 7.8 times that of g-C3N4/TiO2@C aerogel and H2O2 individually, due to the synergy between photocatalysis and H2O2 oxidation. Meantime, g-C3N4/TiO2@C aerogel exhibited an enhanced performance compared with g-C3N4 nanosheet (55.7%) and TiO2@C aerogel (38.5%). It was attributed to the large specific surface area (93.82 m2/g) with hierarchical mesoporous and macroporous structure and the 2D/OD/2D heterojunction of g-C3N4/TiO2@C aerogel, further enhancing electron-hole separation. The mechanism was hypothesized that g-C3N4/TiO2@C aerogel activated H2O2 to generate hydroxyl radicals (·OH) and superoxide radicals (·O2-) for oxidation of NO.

An improved method for quantitative recovery of conductivity using tomographically measured thermoacoustic data.

Noninvasive extraction of tissue conductivity distribution is important in brain imaging and cancer detection. Here we present an improved method that can accurately image tissue conductivity using tomographically measured microwave-induced thermoacoustic data. Our reconstruction algorithm is first tested using simulations, and then validated using tissue phantom experiments where saline-containing tubes are used as target(s) with various target sizes, positions and conductivities. The average error of reconstruction for the simulations is reduced from 4.87% to 1.38% compared with the previous algorithm. The experimental results obtained suggest that accurate quantitative thermoacoustic imaging would provide a potential tool for precision medicine.

Risk assessment of water quality using Monte Carlo simulation and artificial neural network method.

There is always uncertainty in any water quality risk assessment. A Monte Carlo simulation (MCS) is regarded as a flexible, efficient method for characterizing such uncertainties. However, the required computational effort for MCS-based risk assessment is great, particularly when the number of random variables is large and the complicated water quality models have to be calculated by a computationally expensive numerical method, such as the finite element method (FEM). To address this issue, this paper presents an improved method that incorporates an artificial neural network (ANN) into the MCS to enhance the computational efficiency of conventional risk assessment. The conventional risk assessment uses the FEM to create multiple water quality models, which can be time consuming or cumbersome. In this paper, an ANN model was used as a substitute for the iterative FEM runs, and thus, the number of water quality models that must be calculated can be dramatically reduced. A case study on the chemical oxygen demand (COD) pollution risks in the Lanzhou section of the Yellow River in China was taken as a reference. Compared with the conventional risk assessment method, the ANN-MCS-based method can save much computational effort without a loss of accuracy. The results show that the proposed method in this paper is more applicable to assess water quality risks. Because the characteristics of this ANN-MCS-based technique are quite general, it is hoped that the technique can also be applied to other MCS-based uncertainty analysis in the environmental field.

[Dynamic changes of groundwater level and vegetation in water table fluctuant belt in lower reaches of Heihe River: coupling simulation].

Based on the 2006 investigation data in lower reaches of Heihe River, and by using logarithmic normal distribution model, the models about the vegetation cover of Populus euphratica and Tamarix ramossima and the groundwater level in study area were built, and the potential plant of the study area was simulated. The results showed that in the lower reaches of Heihe River, the optimal groundwater level and mean groundwater level for P. euphratica were 2.6 m and 3.6 m, and those for T. ramossima were 2.0 m and 3.0 m, respectively. The high cover P. euphratica distribution area was mainly concentrated in the near-banks of Donghe River and Xihe River, while higher cover T. ramossima was distributed in most parts of the study area. From the aspect of current groundwater level, T. ramossima should be the adaptive species in the study area. The similarities between the potential and actual spatial distribution of P. euphratica and T. ramossima were 43. 4% and 55. 6% , respectively, and the main reason for the lower similarity was that there existed a gypsum salt pan in soil, which blocked the vertical movement of soil water.