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Ketonization can efficiently convert carboxylic acids into ketones, promising in bio-oil upgrading. For economic-sustainable bio-refining, Ca/Zn/AlOx (CZA) metal oxides with various Ca/Zn/Al ratios have been prepared by the low-cost and natural abundant metals and are applied in the process here. Mechanistic study on the ketonization of acetic acid revealed the reaction pathways strongly depends on “composition-structure-function” correlation of the Ca/Zn/AlOx catalysts. The reaction is mainly performed on strong base sites (e.g. CaO and ZnO) via thermal decomposition of carboxylates at high temperature (≥375 °C), but depends on acid-base pairs (e.g. amorphous calcium aluminates) significantly at low temperature (≤350 °C). CZA(331) (Ca/Zn/Al = 3/3/1) obtained the highest acetone yield of 97.1% at 425 °C hitherto and retained for over 100 h with simple regeneration process. Adding major bio-oil model compounds (e.g. phenol) into the reaction mixture has minor effect on the CZA catalysts. Therefore, current highly active and stable CZA catalysts are promising in boosting the efficiency and economy of bio-oil upgrading process in future.

Biodegradable transient Mg/MgO/CaO/MgO/ Mg resistive random access memories (RRAMs) with CaO interlayer of different thicknesses have been successfully fabricated on polylactic acid (PLA) substrate at low processing temperature. Comparative analysis shows that the addition of an 8.7-nm CaO interlayer greatly increases the ON/OFF ratio to >105 (by more than ten times) and the retention time of the devices to >104 s. The current–voltage measurements indicate that the low-resistance state (LRS) current is attributed to the Ohmic conduction and the high-resistance state (HRS) current is governed by the Poole–Frenkel (P-F) emission. The CaO interlayer is revealed to elevate the trap energy levels for the P-F emission and work as a block for the mobile charges, thus greatly improving the performance of the memory devices. Moreover, under certain stimulus pulses, the devices can integrate the input pulses, and then “fire” with output current abruptly increased by several orders, bearing similarities to the integrate-and-fire functionality of neuron. The “firing” frequencies of devices with the stimulus pulse number are observed to be stochastic, which are able to be modified by the CaO interlayer. Furthermore, immersed in physiological saline the devices dissolve within 1 h except for PLA substrates that are able to completely degrade in proteinase K solution within 15 days.

Renewable arenes and aromatic alcohols can be derived from lignocellulose by biorefineries, which has been considered as a sustainable alternative to replace petrochemical feedstocks in the synthesis of monobenzylation products, key industrial intermediates, via benzylation reactions. Zeolites with micropores are the most widely used catalysts in the benzylation of arenes, however, their performance suffers from diffusion limitations in converting large arenes. In this work, mesoporous and microporous H–Beta zeolites were prepared and applied in the systematic study of benzylation of arenes (benzene, toluene, p-xylene and mesitylene) with benzyl alcohol (BA). The porous structure of these zeolites has been confirmed by XRD, BET and TEM techniques. The catalytically active Bronsted acid sites (BAS) were determined by quantitative 1H magic-angle spinning (MAS) nuclear magnetic resonance (NMR) experiments. The benzylation studies have shown that introducing mesopores into H–Beta zeolites can significantly increase the diffusion/access of arenes to surface sites, particularly for bulky arenes (e.g. mesitylene), while micropores are mainly selective for the conversion of small arenes (e.g. benzene). Increasing the nucleophilicity of arenes with more alkyl groups can enhance their catalytic performance in mesopores, however, the increase hinders their conversion in micropores because of the shape selectivity due to their increasing molecular size. Compared to mesopores, micropores promote the conversion of small arenes (e.g. benzene), which can be additionally enhanced by a high Bronsted acidity. Therefore, introducing a suitable porosity balanced with acidity are keys in the tailoring of the catalytic performance of H-Beta zeolites for target benzylation reactions.

The temporal and spatial accuracy of precipitation of ensemble numerical forecast systems is an important factor that affects the level of meteorological and hydrological coupled forecasting. This article focuses on the current research of verification of precipitation accuracy and statistical post-processing. The verification of forecast precipitation accuracy mainly focuses on the probabilistic characteristics such deterministic accuracy, the resolution, the forecasting skills and the degree of dispersion. Some mainstream statistical post-processing methods have strong performance of spatial downscaling and error correction, but they commonly have the defect of destroying the temporal and spatial dependent structure of precipitation. A comprehensive statistical post-processing method integrated the three functions is the development direction in the future. At the same time, statistical post-processing methods to improve the certainty and probabilistic accuracy of forecast precipitation need to be systematically identified. Its impact on the spatio-temporal dependence structure also needs to be improved.

In recent decades, strong human activities have not only brought about climate change including both global warming and shifts in the weather patterns but have also caused anomalous variations of hydrological elements in different basins all around the world. Studying the mechanisms and causes of these hydrological variations scientifically is the basis for the management of water resources and the implementation of ecological protection. Therefore, taking the Yongding River mountain area as a representative watershed in China, the changes of different observed and simulated hydro-meteorological variables and their possible causes are analyzed on an inter-annual scale based on ground based observations and remotely sensed data of hydrology, meteorology and underlying surface characteristics from 1956 to 2016. The results show that the annual natural runoff of Guanting hydrological station in the main stream of the Yongding River, Cetian hydrological station and Xiangshuibao hydrological station in the tributary of the Yongding River all have a significant decreasing trend and abrupt changes, and all the abrupt change points of the annual natural runoff series of the three hydrological stations appear in the early 1980s. On the inter-annual scale, the water balance model with double parameters is unable to effectively simulate the natural surface runoff after the abrupt change points. The annual average precipitation after the abrupt change points decreases by no more than 10%, compared with that before the abrupt change points. However, the precipitation from July to August, which is the main runoff-production period, decreases by more than 25%, besides the intra-annual temporal distribution of precipitation becoming uniform and a significant decrease in effective rainfall, which is the source of the runoff. Meanwhile, the NDVI in the basin show an increasing trend, while the groundwater level and land water storage decrease significantly. These factors do not lead only to the continuous reduction of the annual natural runoff in the Yongding River mountain area from 1956 to 2016, but also result in significant changes of the hydro-meteorological relationship in the basin.

Tree-based machine learning models based on environmental features offer low-cost and timely solutions for predicting microbial fecal contamination in beach water to inform the public of the health risk. However, many of these models are black boxes that are difficult for humans to understand, which may cause severe consequences such as unexplained decisions and failure in accountability. To develop interpretable predictive models for beach water quality, we evaluate five tree-based models, namely classification tree, random forest, CatBoost, XGBoost, and LightGBM, and employ a state-of-the-art explanation method SHAP to explain the models. When tested on the Escherichia coli (E. coli) concentration data collected from three beach sites along Lake Erie shores, LightGBM, followed by XGBoost, achieves the highest averaged precision and recall scores. For all three sites, both models suggest lake turbidity as the most important predictor, and elucidate the crucial role of accurate local data of wave height and rainfall in the model development. Local SHAP values further reveal the robustness of the importance of lake turbidity as its SHAP value increases nearly monotonically with its value and is minimally affected by other environmental factors. Moreover, we found an intriguing interaction between lake turbidity and day-of-year. This work suggests that the combination of LightGBM and SHAP has a promising potential to develop interpretable models for predicting microbial water quality in freshwater lakes.

Owing to the small energy differences between its polymorphs, MoTe

Microbial pollution of beach water can expose swimmers to harmful pathogens. Predictive modeling provides an alternative method for beach management that addresses several limitations associated with traditional culture-based methods of assessing water quality. Widely-used machine learning methods often suffer from high variability in performance from one year or beach to another. Therefore, the best machine learning method varies between beaches and years, making method selection difficult. This study proposes an ensemble machine learning approach referred to as model stacking that has a two-layered learning structure, where the outputs of five widely-used individual machine learning models (multiple linear regression, partial least square, sparse partial least square, random forest, and Bayesian network) are taken as input features for another model that produces the final prediction. Applying this approach to three beaches along eastern Lake Erie, New York, USA, we show that generally the model stacking approach was able to generate reliably good predictions compared to all of the five base models. The accuracy rankings of the stacking model consistently stayed 1st or 2nd every year, with yearly-average accuracy of 78%, 81%, and 82.3% at the three studied beaches, respectively. This study highlights the value of the model stacking approach in predicting beach water quality and solving other pressing environmental problems.

The heat transfer of a reactor with improved Intermig impellers was numerically investigated by the finite element method (FEM) simulation software Fluent (V.19). A turbulence model utilized the standard k-ε model, and the turbulent flows in two large vortexes between vertical tubes were collided to form a strong convection. The influence of heat and mass transfer developing from the impeller diameters, the distance between the two impellers (C1), the rotational speed and the installation height of the bottom impeller (C2) were studied. The reactor was equipped with special structure vertical tubes to increase the heat exchange areas. The rate of heat transfer, including criteria such as the convective heat transfer coefficient, the Nusselt number of outside vertical tubes, and the temperature boundary layer thickness, assured the accurate control of the heat exchange mixing state. The experimental testing platform was designed to validate the simulated results, which revealed the influence order of related factors. The Nusselt number Nu was affected by various related factors, resulting in the rotation and diameter of impellers extending far beyond the distance between the two impellers (C1) and the installation height of the impeller (C2). The average temperature boundary layer thicknesses of the symmetrical and middle sections were 3.24 mm and 3.48 mm, respectively. Adjusting the appropriate parameters can accurately control the heat exchange process in such a reactor, and the conclusions provide a significant reference for engineering applications.

Owing to the small energy differences between its polymorphs, MoTe2 can access a full spectrum of electronic states, from the 2H semiconducting state to the 1T semimetallic state, and from the Td Weyl semimetallic state to the superconducting state in the 1T and Td phase at low temperature. Thus, it is a model system for phase transformation studies as well as quantum phenomena such as the quantum spin Hall effect and topological superconductivity. Careful studies of MoTe2 and its potential applications require large area MoTe2 thin films with high crystallinity and thickness control. Here, we present cm2 scale synthesis of 2H MoTe2 thin films with layer control and large grains that span several microns. Layer control is achieved by controlling the initial thickness of the precursor MoOx thin films, which are deposited on sapphire substrates by atomic layer deposition and subsequently tellurized. Despite the van der Waals epitaxy, the precursor-substrate interface is found to critically determine the uniformity in thickness and grain size of the resulting MoTe2 films: MoTe2 grown on sapphire show uniform films while MoTe2 grown on amorphous SiO2 substrates form islands. This synthesis strategy decouples the layer control from the variabilities of growth conditions for robust growth results, and is applicable to grow other transition metal dichalcogenides with layer control., Comment: 6 figure

The heat transfer process in a stirred tank of diameter T=0.5m equipped with dual-layer improved Intermig impellers and helical coils was investigated by experiment and numerical simulation methods. The temperature field, the temperature boundary layer lateral to the coil and the heat transfer coefficient were measured at different rotational speeds. The standard k-e turbulence model and multiple reference frames combined with a sliding mesh method were adopted in the numerical simulation. The results show that the temperature errors between numerical simulation and experimental measurement were within 2K. The temperature in the stirred tank gradually rose from top to bottom and inside to outside, and the maximum temperature difference was within 2K. The average thickness of the temperature boundary layer outside the helical coil is 3.66 mm. According to the experiments and numerical simulations, the heat transfer coefficient correlations, including Nu and Re, Nu and e of the helical coil outer side, were obtained, and the trends of heat transfer coefficients are consistent and regular. The correlation of the heat transfer coefficient lateral to the coil was acquired from the experimentally measured data. The research results can serve as a guide for the design and engineering application of mass and heat transfer processes in stirred tanks with improved Intermig impellers.

Rainfall is one of the most basic meteorological and hydrological elements. Quantitative rainfall estimation has always been a common concern in many fields of research and practice, such as meteorology, hydrology, and environment, as well as being one of the most important research hotspots in various fields nowadays. Due to the development of space observation technology and statistics, progress has been made in rainfall quantitative spatial estimation, which has continuously deepened our understanding of the water cycle across different space-time scales. In light of the information sources used in rainfall spatial estimation, this paper summarized the research progress in traditional spatial interpolation, remote sensing retrieval, atmospheric reanalysis rainfall, and multi-source rainfall merging since 2000. However, because of the extremely complex spatiotemporal variability and physical mechanism of rainfall, it is still quite challenging to obtain rainfall spatial distribution with high quality and resolution. Therefore, we present existing problems that require further exploration, including the improvement of interpolation and merging methods, the comprehensive evaluation of remote sensing, and the reanalysis of rainfall data and in-depth application of non-gauge based rainfall data.

This paper investigates calibration of Hargreaves equation in Xiliaohe Basin. Twelve meteorologicalgauges located within Xiliaohe Basin in Northeast China were monitored during 1970 and 2014 providing continuous records of meteorological data. Taking daily ET0 calculated by Penman-Montieth equation as the benchmark, the error of Hargreaves equation for computing ET0 was evaluated and the investigation on regional calibration of Hargreaves equation was carried out. Results showed there was an obvious difference between the calculating results of Hargreaves and Penman-Monteith equation. The estimation of the former was obviously higher during June and September while lower during the rest time in a year. The three empirical parameters of the Hargreaves equation were calibrated using the SCE-UA (Shuffled Complex Evolution) method, and the calibrated Hargreaves equation showed an obvious promotion in the accuracy both during the calibration and verification period.

In the absence of long-series streamflow records in plain areas, design storm, which serves as the most important input in a hydrologic model, plays an important role in flood control and water resources management. For a large drainage basin, design storm may be estimated for sub-basins separately, thus the spatial distribution of design storm needs to be carefully treated. However, few studies have been carried out to evaluate the rationality of the spatial distribution in a design storm, which means the storm over space should be in accordance with actual needs or its distributing patterns. Taking the Tai Lake Basin (TLB), 3-d Copula-based models combining extreme rainfall of different sub-basins were built using long-term rainfall data, and conditional probabilities of sub-basins encountering certain amounts of rainfall were investigated to evaluate the rationality of the design storm. Results show that the spatial distribution of the design storm based on a typical year is hardly rational, in which rainfall of the northeastern part of the basin is suggested to be weakened while in the southwest to be strengthened, after the rainfall is redistributed based on long-term information, it shows a better rationality of spatial distribution. Such information provides valuable significance in guiding flood control of TLB, and the considered evaluating method can be used for similar basins in plain areas.

A lack of understanding of the quantum and even semiclassical interactions in two-dimensional electron gas (2DEG) systems continues to hold back many applications. This study of transport in high-electron-density semiconductor heterostructures reveals a complex interplay of mobility-limiting mechanisms. Scattering due to phonons is highly temperature-dependent, whereas charged impurities produce small-angle scattering that is important at low temperatures. Also observed is $w\phantom{\rule{0}{0ex}}e\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}k$ $l\phantom{\rule{0}{0ex}}o\phantom{\rule{0}{0ex}}c\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}l\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}z\phantom{\rule{0}{0ex}}a\phantom{\rule{0}{0ex}}t\phantom{\rule{0}{0ex}}i\phantom{\rule{0}{0ex}}o\phantom{\rule{0}{0ex}}n$, a counterintuitive effect of quantum interference. This insight will help in suppressing scattering and boosting the performance of GaN-based power electronics.

In view of the two key problems in hydrological mid-long term runoff forecasting-the selection of key forecasting factors and the construction of forecasting models, an analysis is made on, taking Danjiangkou Reservoir as an example, the basis of preliminarily identifying the sea-air physical factors such as atmospheric circulation, sea surface temperature and Southern Oscillation, et al. The rough set theory is used to establish the data decision table and reduce the factors, and the relevance vector machine method is adopted to establish the mid-long term runoff forecasting model based on reduced factor set. Meanwhile, this paper simulates and predicts the amount of runoff of the reservoir in September and October during the autumn floods from 1952 to 2008, and makes comparison with the model adopting support vector machine. The result shows that the relevance vector machine has better robustness and generalization performance. According to the standard of 20% annual variation, the simulation accuracy of September and October reaches 93.9% and 95.9%, respectively, and the accuracy of the trial forecasting is all up to standard. Moreover, this model better reflects the characteristics of ample flow period and low water period of the forecasting years.

Selective dehydration of glycerol, a byproduct from the biodiesel industry, on solid acids is an important reaction in the production of the value-added chemical acrolein for economic-sustainable biorefinery. Most efforts have been made on the development of strong Bronsted acid sites (BAS) to improve the production of acrolein, because the Lewis acid sites (LAS) generally promote the generation of the byproduct acetol. However, exclusively tuning the properties of BAS or LAS did not well-promote the acrolein production from glycerol as indicated in this work. We provide a new route for efficient and selective glycerol transformation to acrolein via the cooperative dehydration between the BAS and LAS. The role of LAS (extra-framework aluminum species on zeolites) was altered from competition with BAS to generate the byproduct acetol to cooperation with the neighboring BAS. It is very beneficial for the sequential two-step dehydration of the internal and terminal hydroxyl groups of glycerol to value-added ...

Quantum transport properties in monolayer graphene are sensitive to structural modifications. We find that the introduction of a hexagonal lattice of antidots has a wide impact on weak localization and Shubnikov-de Haas (SdH) oscillation of graphene. The antidot lattice reduces both phase coherence and intervalley scattering length. Remarkably, even with softened intervalley scattering, i.e., the phase-breaking time is shorter than intervalley scattering time, coherence between time reversed states remains adequate to retain weak localization, an offbeat and rarely reported occurrence. Whereas SdH oscillation is boosted by the antidot lattice, the amplitude of the SdH signal rises rapidly with the increasing antidot radius. But both effective mass and carrier density are reduced in a larger antidot lattice. A bandgap of ∼10 meV is opened. The antidot lattice is an effective dopant-free way to manipulate electronic properties in graphene.