The van der Waals epitaxy of single crystalline Bi 2 Se 3 film was achieved on hydrogen passivated Si(111) (H:Si) substrate by physical vapor deposition. Valence band structures of Bi 2 Se 3 /H:Si heterojunction were investigated by X-ray Photoemission Spectroscopy and Ultraviolet Photoemission Spectroscopy. The measured Schottky barrier height at the Bi 2 Se 3 -H:Si interface was 0.31 eV. The findings pave the way for economically preparing heterojunctions and multilayers of layered compound families of topological insulators.

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We have developed two statistical models for extended seasonal predictions of the Upper Colorado River Basin (UCRB) natural streamflow during April–July: a stepwise linear regression (reduced to a simple regression with one predictor) and a neural network model. Monthly, basin-averaged soil moisture, snow water equivalent (SWE), precipitation, and the Pacific sea surface temperature (SST) are selected as potential predictors. Pacific SST Predictors (PSPs) are derived from a dipole pattern over the Pacific (30°S–65°N) that is correlated with the lagging streamflow. For both models, the correlation between the hindcasted and observed streamflow exceeds 0.60 for lead times less than four months using soil moisture, SWE, and precipitation as predictors. This correlation is higher than that of an autoregression model (correlation ~0.50). Since these land-surface and atmospheric variables have no statistically significant correlations with the streamflow, PSPs are then incorporated into the models. The two models have a correlation of ~0.50 using PSPs alone for lead times from six to nine months, and such skills are probably associated with stronger correlation between SST and streamflow in recent decades. The similar prediction skills between the two models suggest a largely linear system between SST and streamflow. Four predictors together can further improve short-lead prediction skills (correlation ~0.80). Therefore, our results confirm the advantage of the Pacific SST information in predicting the UCRB streamflow with a long lead time, and can provide useful climate information for water supply planning and decisions.

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The catalytic activity of the alkali compounds in alkaline black liquor can be used in supercritical water gasification (SCWG) of coal to lower the reaction temperature and improve the hydrogen production, and the black liquor can be handled simultaneously. In the present study, the gasification features of coal/black liquor blends at high temperatures (600–750 °C) were studied through thermodynamic analysis and experimental study. A synergetic effect was found during co-gasification of coal and black liquor, where both the gasification efficiency and hydrogen production was improved by efficiently using the alkali catalyst in black liquor. The highest improvement of the gasification was found at the blending ratio of about 50:50. Both the thermodynamic analysis and experiments indicated that higher temperature favors the hydrogen production. The gasification efficiency was improved with temperature and the maximum carbon conversion of 79.46% was achieved at 750 °C. The dilution of the black liquor/coal blends favors the gasification by improving the gasification efficiency and H2 production. The prolongation of reaction time enhanced the gasification, but its influence was insignificant when it was above 10 min. The initial pressure of the reactor and the reactant amount impacted the gasification results in different ways. This study may fill the research gaps in SCWG of coal/black liquor blends at higher temperatures and assist in its further development.

Tropical overshooting convection has a strong impact on both heat budget and moisture distribution in the upper troposphere and lower stratosphere, and it can pose a great risk to aviation safety. Cloud-top height is one of the essential concerns of overshooting convection for both the climate system and the aviation weather forecast. The main purpose of our work is to verify the application of the machine learning method, taking the random forest (RF) model as an instance, in overshooting cloud-top height retrieval from Himawari-8 data. By using collocated CloudSat observations as a reference, we utilize several infrared indicators of Himawari-8 that are commonly recognized to relate to cloud-top height, along with some temporal and geographical parameters (latitude, month, satellite zenith angle, etc.), as predictors to construct and validate the model. Analysis of variable importance shows that the brightness temperature of 6.2 um acts as the dominant predictor, followed by satellite zenith angle, brightness temperature of 13.3 um, latitude, and month. In the comparison between the RF model and the traditional single-channel interpolation method, retrievals from the RF model agree well with observation with a high correlation coefficient (0.92), small RMSE (222 m), and small MAE (164 m), while these metrics from traditional single-channel interpolation method shows lower skills (0.70, 1305 m, and 1179 m). This work presents a new sight of overshooting cloud-top height retrieval based on the machine learning method.

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