Search

In this study, the flower-shaped Bi2S3/g-C3N4-2.6 heterojunction obtained by solvothermal method and its photocatalytic degradation efficiency of rhodamine B (RhB) and tetracycline (TC) in aqueous solution within 40 min is as high as 98.8% and 94.6%. For RhB degradation, the photocatalytic reaction rate constant (k) of Bi2S3/g-C3N4-2.6 is approximately 1.8 and 45.5 times that of Bi2S3 and g-C3N4. For TC, k is 3.1 and 2.4 times that of Bi2S3 and g-C3N4, respectively. The key to determining the high catalytic activity of Bi2S3/g-C3N4 lies in the formation of a good heterojunction between Bi2S3 and g-C3N4, which accelerates the electron transfer rate between the heterojunction interface and effectively avoids electron–hole recombination. The effects of catalyst dosage, different pH values, inorganic anions, and capture agents on the photodegradation performance of RhB were investigated. The results show that the catalyst dosage is 1.33 g/L, and the solution pH is in the range of 5–9, which has the best removal effect on pollutants, and the isolation of holes (h+) with strong oxidizing ability promotes the collapse of pollutant molecules. Combined with electrochemical tests, a possible degradation mechanism was advised. [ABSTRACT FROM AUTHOR]

Despite the many applications of time series interferometric synthetic aperture radar (TS-InSAR) techniques in geophysical problems, error analysis and assessment have been largely overlooked. Tropospheric propagation error is still the dominant error source of InSAR observations. However, the spatiotemporal variation of atmospheric effects is seldom considered in the present standard TS-InSAR techniques, such as persistent scatterer interferometry and small baseline subset interferometry. The failure to consider the stochastic properties of atmospheric effects not only affects the accuracy of the estimators, but also makes it difficult to assess the uncertainty of the final geophysical results. To address this issue, this paper proposes a network-based variance–covariance estimation method to model the spatiotemporal variation of tropospheric signals, and to estimate the temporal variance–covariance matrix of TS-InSAR observations. The constructed stochastic model is then incorporated into the TS-InSAR estimators both for parameters (e.g., deformation velocity, topography residual) estimation and uncertainty assessment. It is an incremental and positive improvement to the traditional weighted least squares methods to solve the multitemporal InSAR time series. The performance of the proposed method is validated by using both simulated and real datasets. [ABSTRACT FROM AUTHOR]